Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Xiang, Yingjiao | Sun, Baishun | Wang, Zhiqin | Taher, Fatma

Article Type: Research Article

Abstract: Long-distance running is an advantage of Chinese sports, but compared with the world level, there is still a big gap. Therefore, an advanced long-distance running training system is urgently needed to scientifically train our long-distance runners to change this situation. The purpose of this article is to study the long-distance running training system under inertial sensor network. According to the actual situation at home and abroad, a human gait analysis system based on inertial sensors is designed. Gait parameters are transformed into clinical medicine through related algorithms and software platforms. Experimental results show that although the step length calculated by …the gait analysis system is different from the actual step length, the error value is small, kept below 3 cm, and the error percentage is less than 2%, which meets the accuracy requirements of gait analysis. This fully proves the feasibility of the zero-speed correction method in gait analysis. Show more

Keywords: Inertial sensor, long-distance running, gait analysis, system design

DOI: 10.3233/JIFS-189832

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Chen, Yang | Yang, Jiaxiu

Article Type: Research Article

Abstract: In recent years, interval type-2 fuzzy logic systems (IT2 FLSs) have become a hot topic for the capability of coping with uncertainties. Compared with the centroid type-reduction (TR), investigating the center-of-sets (COS) TR of IT2 FLSs is more favorable for applying IT2 FLSs. Actually, it is still an open question for comparing Karnik-Mendel (KM) types of algorithms and other types of alternative algorithms for COS TR. This paper gives the block of fuzzy reasoning, COS TR, and defuzzification of IT2 FLSs based on Nagar-Bardini (NB), Nie-Tan (NT) and Begian-Melek-Mendel (BMM) noniterative algorithms. Six simulation experiments are used to show the …performances of three types of noniterative algorithms. The proposed noniterative algorithms can obtain much higher computational efficiencies compared with the KM algorithms, which give the potential value for designing T2 FLSs. Show more

Keywords: Interval type-2 fuzzy logic systems, center-of-sets type-reduction, Nagar-Bardini algorithms, computational efficiency, Nie-Tan algorithms

DOI: 10.3233/JIFS-202264

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Mama, Rachid | Machkour, Mustapha

Article Type: Research Article

Abstract:  Nowadays several works have been proposed that allow users to perform fuzzy queries on relational databases. But most of these systems based on an additional software layer to translate a fuzzy query and a supplementary layer of a classic database management system (DBMS) to evaluate fuzzy predicates, which induces an important overhead. They are not also easy to implement by a non-expert user. Here we have proposed a simple and intelligent approach to extend the SQL language to allow us to write flexible conditions in our queries without the need for translation. The main idea is to use a view …to manipulate the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating them at runtime employing user functions embedded in the query. Consequently, the response time of executing a fuzzy query statement will be reduced. This approach allows us to easily integrate most fuzzy request characters such as fuzzy modifiers, fuzzy quantifiers, fuzzy joins, etc. Moreover, we present a user-friendly interface to make it easy to use fuzzy linguistic values in all clauses of a select statement. The main contribution of this paper is to accelerate the execution of fuzzy query statements. Show more

Keywords: Fuzzy query, fuzzy logic, fuzzy SQL, relational database, user interface

DOI: 10.3233/JIFS-202551

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Huang, Jui-Chan | Shu, Ming-Hung | Hsu, Bi-Min | Hu, Chien-Ming | Kao, Meng-Chun | Selim, Mahmoud M.

Article Type: Research Article

Abstract: The remanufacturing industry is one of the important means to achieve sustainable development and resource recycling. It is of great significance to study the remanufacturing production system. This paper mainly studies the reliability of remanufacturing production system based on the uncertainty of part quality. In order to rationally arrange workshop production, minimize the maximum completion time and the cost of electricity in the production process, this study established a mixed integer linear programming model for the remanufacturing of flexible workshop based on batch processing of partial stations. In order to solve this mathematical model, the traditional genetic on the basis …of the algorithm, the crossover and mutation operators of the genetic algorithm conforming to the model are designed, and finally combined with actual examples, compared with traditional batch scheduling to verify the effectiveness of the system. This research takes the remanufacturing of the Steyr engine crankshaft as the research object. Based on the uncertainty of crankshaft wear, the uncertainty of the crankshaft remanufacturing process is investigated and discussed. From the three dimensions of environment, economy and technology, from the remanufacturing process. The evaluation was carried out at the level of the process chain and the modeling process and method were verified, and the sustainability value of the worn crankshaft remanufacturing process was obtained. The remanufacturing production system experiment can show that the average sustainability values of the three batches of used crankshafts are SR1 = 0.9082, SR2 = 0.8669, SR3 = 0.7803. The system reliability analysis can provide a theoretical basis for the reliability of enterprise remanufacturing systems, and has important application and research value. Show more

Keywords: Parts remanufacturing, part quality, system reliability, production system

DOI: 10.3233/JIFS-189837

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Fan, Yun | Fang, Zhigeng | Liu, Sifeng | Liu, Jun

Article Type: Research Article

Abstract: The construction of more nursing homes has become one of the most needed pension services in China, and the issue of site selection is one of the most important steps in their construction. The problem of site selection for nursing homes is a complex system engineering problem that involves not only economic interests but also social interests. Due to the limitations of human thinking in the evaluation process, the evaluation value of a nursing home site might be an interval grey number. Moreover, the evaluation indicator system for nursing home locations is a two-layer system that has been neglected in …the literature. Therefore, the fuzzy analytical hierarchy process is extended to a new grey approach, i.e., the grey analytic hierarchy process, which can solve the evaluation problems for a two-layer indicator system under an interval grey environment. By constructing a three-point interval grey number, grey evaluation criteria are given to obtain a judgment matrix for interval grey numbers. Definitions of the initial weights, nongreyness weights and integrated weights are proposed to find the best evaluation object. Finally, the effectiveness of the method proposed by this paper is verified by comparative analyses of other grey methods. Show more

Keywords: Nursing home site, site selection, grey analytic hierarchy process, fuzzy analytic hierarchy process, interval grey number

DOI: 10.3233/JIFS-200480

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Deng, Huali | Zhang, Aoduo

Article Type: Research Article

Abstract: Supply chain finance refers to one or more companies upstream and downstream of the industrial supply chain. According to the core company of each node, based on actual transactions, use customized and standardized financing transaction methods to control capital flows or control assets. The right to provide comprehensive financial products and services between supply chain nodes. This article only needs to introduce the financial risk analysis of the enterprise supply chain based on the fuzzy analytic hierarchy process. This paper proposes a fuzzy analytic hierarchy process, which uses a combination of qualitative and quantitative risk assessment methods to assess financial …risks, and designs a financial risk assessment system by constructing a fuzzy judgment matrix. It also proposes a comprehensive judgment of the financial risk assessment method. The impact of various risk factors on financial services provides a basis for risk prevention. The experimental results of this paper show that the fuzzy analytic hierarchy process evaluation method is relatively objective and can effectively evaluate the financial risks of the enterprise supply chain. From the weight analysis, it can be concluded that the technical risk weight value is 0.47, which accounts for the largest proportion and is the most important risk. Show more

Keywords: Fuzzy system, fuzzy analytic hierarchy process, enterprise supply chain, financial risk, hierarchical model

DOI: 10.3233/JIFS-189840

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Xu, Junxiang | Guo, Jingni | Sun, Yongdong | Tang, Qiuyu | Zhang, Jin

Article Type: Research Article

Abstract: We not only firstly applied the theory of hub-and-spoke network to the field of integrated transportation network planning, but also combined our proposed method with Sichuan-Tibet railway, one of super large projects in China, to discuss the optimization and the layout of hub-and-spoke integrated transportation network along the Sichuan-Tibet railway after it is put into operation in the future and put forward some directional policy recommendations. In our study, we have made clear the topological structure of the multi hub and single allocation hybrid hub-and-spoke integrated transportation network in the passenger transportation corridors, established the integer programming model aiming at …the minimum generalized travel cost in the network, and we designed the simulated annealing algorithm to solve this problem. In the empirical study, we find that if 5 nodes are selected as hub nodes in hub-and-spoke integrated transportation network, the generalized cost of network travel will be minimized and these specific location of 5 hub nodes can be determined by the selecting principle of hub nodes location, which we proposed in our study. The simulated annealing algorithm can help us to find the connection relationship between nodes. Then we can achieve three types of hub-and-spoke integrated transportation network layout patterns with railway, highway and aviation as the hub nodes. Though further comparative analysis, we find that it is more feasible to choose the integrated transportation network with railway nodes as the hub in transportation organization. Based on this understanding, we put forward policy recommendations on transportation organization to support high-quality planning and operation of integrated transportation network to Sichuan Tibet region in China in the future. Show more

Keywords: Sichuan-Tibet railway, comprehensive transportation network, multiple hubs and single allocation, simulated annealing algorithm

DOI: 10.3233/JIFS-202276

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2021

Authors: Akram, Muhammad | Shumaiza,

Article Type: Research Article

Abstract:  The q -rung picture fuzzy sets serve the fuzzy set theory as a competent, broader and accomplished extension of q -rung orthopair fuzzy sets and picture fuzzy sets which exhibit excellent performance in modeling the obscure data beyond the limits of existing approaches owing to the parameter q and three real valued membership functions. The accomplished strategy of VIKOR method is established on the major concepts of regret measure and group utility measure to specify the compromise solution. Further, TOPSIS method is another well established multi-criteria decision-making strategy that finds out the best solution with reference to the distances …from ideal solutions. In this research study, we propose the innovative and modified versions of VIKOR and TOPSIS techniques using the numerous advantages of q -rung picture fuzzy information for obtaining the compromise results and rankings of alternatives in decision-making problems with the help of two different point-scales of linguistic variables. The procedure for the entropy weighting information is adopted to compute the normal weights of attributes. The q -rung picture fuzzy VIKOR (q -RPF VIKOR) method utilizes ascending order to rank the alternatives on the basis of maximum group utility and minimum individual regret of opponent. Moreover, a compromise solution is established by scrutinizing the acceptable advantage and the stability of decision. In the case of TOPSIS technique, the distances of alternatives to ideal solutions are determined by employing the Euclidean distance between q -rung picture fuzzy numbers. The TOPSIS method provides the ranking of alternatives by considering the descending order of closeness coefficients. For explanation, the presented methodologies are practiced to select the right housing society and the suitable industrial robot. The comparative results of the proposed techniques with four existing approaches are also presented to validate their accuracy and effectiveness. Show more

Keywords: q-Rung picture fuzzy numbers, VIKOR, TOPSIS, entropy weight information, decision-making

DOI: 10.3233/JIFS-202646

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-26, 2021

Authors: Chang, Ying | Zhu, Qinghua

Article Type: Research Article

Abstract: With the rapid development of many storage devices and other science and technology, continuous discussion on the role of video target tracking technology in the practical application of photoelectric weapons, guidance systems and security tracking systems has become the current research direction of computer vision and artificial intelligence. The purpose of this study is to explore the differences and characteristics of different algorithms, and provide theoretical and methodological support for the realization of video echo signal tracking in complex environment. For echo signal tracking algorithm only uses a single feature to track, it is particularly easy to cause tracking failure. …Therefore, this study uses a method of multi feature fusion to establish the observation model. From the four aspects of gray, color, shape and texture, these four visual characteristics are very representative. In order to study the tracking accuracy, stability and real-time performance of the algorithm, pedestrian, vehicle and face are used as tracking targets to verify the tracking performance of the algorithm in different environments. Using the technical analysis of big data to find the target data file can improve the search speed of the target data and the operation speed of the tracking algorithm. The experimental results show that, in terms of accuracy, the simplest gray feature is only 0.42, and CN feature is improved by about 14% compared with the gray feature. It takes less time to find the target data file by index file method than by traversing the file name method. Show more

Keywords: Big data technology, echo signal, tracking algorithm, target tracking

DOI: 10.3233/JIFS-189831

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Wang, Jin | Wang, Jianxiong | Tan, Sijie

Article Type: Research Article

Abstract: Football is a popular sport all over the world, and it is also an important part to show the comprehensive strength of the country. With the development of football in China, how to effectively carry out football teaching and training has become a hot topic. At present, the international mainstream method is to use robot soccer simulation training to simulate the real game scene, analyze and study the game tactics. However, due to the late start of artificial intelligence in China, there are still technical problems in the field of robot soccer, such as insufficient recognition and unreasonable task allocation. …In order to solve these problems, this paper proposes an intelligent soccer teaching and training system based on fuzzy theory. In this paper, the control mode of the intelligent system is further optimized by combining the classical algorithm of fuzzy mathematics with PID control. In the design of football training model, this paper puts forward an innovative game situation assessment system, which can better analyze the impact of environmental factors. In the aspect of control circuit, this paper adopts the mainstream LM2678 step-down circuit, which has good stability and has been widely used in intelligent control system. In the final experimental analysis, this paper uses the current representative basic decision-making system as the comparative object, through a number of experiments including technical indicators, system characteristics, etc. Analysis of the data shows that the intelligent system based on fuzzy theory has better task allocation ability than the traditional way, and obviously improves the comprehensive performance of the system. In the simulation competition, the system in this paper has made outstanding achievements, which further shows the superiority of the system. Show more

Keywords: Simulation experiments, football training, robot soccer, fuzzy theory

DOI: 10.3233/JIFS-189830

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Gao, Yuxuan | Liang, Haiming | Sun, Bingzhen

Article Type: Research Article

Abstract: With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers …for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform. Show more

Keywords: DIHR, recommendation algorithm, network intelligent recommendation system, online shopping platform

DOI: 10.3233/JIFS-201579

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Wang, Peng | Wu, Jiao | Li, Xiaoyan | Cai, Mengyao | Qiao, Mengyu | Fu, Huitong

Article Type: Research Article

Abstract: Fuzzy target detection as an important task to reflect the detection ability of underwater robot, the artificial target recognition based on the image taken by underwater robot has been widely concerned. However, there is no open standard fuzzy underwater image data set, and in the harsh deep-water fuzzy environment, it is difficult to collect large-scale marked underwater fuzzy optical images. At the same time, it is also hoped that the detection model has the ability to learn quickly from small samples in the case of as few samples as possible. Therefore, combining depth learning and transfer learning, a new method …based on improved SSD and transfer learning is proposed. Firstly, we design a more accurate SSD network (underwater SSD) which is suitable for fuzzy underwater target detection. The features extracted from the detection network are highly representative. Secondly, we use the Transfer learning method to train the underwater SSD network, which can only use the tags in the air to identify fuzzy underwater objects, and have strong robustness in both the air and fuzzy underwater imaging modes. Finally, soft NMS is used to detect the target. The experimental results of the simulation data show that the algorithm not only overcomes the difficulties of the known data set of underwater target, but also effectively improves the accuracy of underwater target detection compared with the traditional deep learning method, reaching 82.31%, showing better detection performance. Show more

Keywords: Deep learning, neural network, underwater target detection, single point multi box detection algorithm (SSD), transfer learning

DOI: 10.3233/JIFS-189821

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Priambodo, Bagus | Ahmad, Azlina | Kadir, Rabiah Abdul

Article Type: Research Article

Abstract: Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic condition which can capture the nonlinear characteristics of traffic flow. An earlier study …has been done to predict traffic flow using K-NN based on connected roads (both downstream and upstream). However, impact of road congestion is not only to connected roads, but also to roads surrounding it. Surrounding roads that are impacted by road congestion are those having ‘high relationship’ with neighbouring roads. Thus, this study aims to predict traffic state using K-NN by determining high relationship roads within neighbouring roads. We determine the highest relationship neighbouring roads by clustering the surrounding roads by combining grey level co-occurrence matrix (GLCM) with k-means. Our experiments showed that prediction of traffic state using K-NN based on high relationship roads using both GLCM and k-means produced better accuracy than using k-means only. Show more

Keywords: Classification algorithm, clustering algorithm, machine learning algorithm, nearest neighbour search, intelligent transportation system

DOI: 10.3233/JIFS-201493

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Fezai, Radhia | Mansouri, Majdi | Abodayeh, Kamaleldin | Nounou, Hazem | Nounou, Mohamed | Puig, Vicenç | Bouzrara, Kais

Article Type: Research Article

Abstract: This paper aims at improving the operation of the water distribution networks (WDN) by developing a leak monitoring framework. To do that, an online statistical hypothesis test based on leak detection is proposed. The developed technique, the so-called exponentially weighted online reduced kernel generalized likelihood ratio test (EW-ORKGLRT), is addressed so that the modeling phase is performed using the reduced kernel principal component analysis (KPCA) model, which is capable of dealing with the higher computational cost. Then the computed model is fed to EW-ORKGLRT chart for leak detection purposes. The proposed approach extends the ORKGLRT method to the one that …uses exponential weights for the residuals in the moving window. It might be able to further enhance leak detection performance by detecting small and moderate leaks. The developed method’s main advantages are first dealing with the higher required computational time for detecting leaks and then updating the KPCA model according to the dynamic change of the process. The developed method’s performance is evaluated and compared to the conventional techniques using simulated WDN data. The selected performance criteria are the excellent detection rate, false alarm rate, and CPU time. Show more

Keywords: Leak detection, water distribution networks, kernel principal component analysis, online reduced kernel generalized likelihood ratio test, exponentially weighted moving average

DOI: 10.3233/JIFS-191524

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Guang Hui, Wei

Article Type: Research Article

Abstract: In cloud computing environment, how to reasonably carry out the resource scheduling and reduce the task execution time and task execution energy consumption has become a hot issue in the researches. In this paper, the task processing time and energy consumption were used as the optimization objectives, and a Double-Objective Particle Swarm Optimization (DOPSO) was used to perform the optimal scheduling of task completion time and energy consumption. Finally, the results obtained in DOPSO were simulated in CloudSim, and results showed that compared with existing scheduling algorithms, the proposed algorithm (DOPSO) had obvious advantages in the integrated scheduling performance.

Keywords: Cloud computing, double-objective optimization, PSO, cloud sim

DOI: 10.3233/JIFS-189818

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Gautam, Abhinav K. | Tariq, Mohd | Pandey, Jai Prakash | Verma, Kripa Shankar

Article Type: Research Article

Abstract: In this paper, the authors have addressed the modeling and design of the BLDC Motor-Driven E-Rickshaw based on hybrid energy storage system for optimum power management using fuzzy logic. In Hybrid energy sources, solar power is used to charge a battery (primary source) that is effectively coupled to an ultra-capacitor (ancillary source) for peak demand supplies. A power-split control strategy is proposed to control the power supply by using the HESS Fuzzy Logic in different engine operating modes. Projected power layering improves the battery life cycle with the proper use of the Ultra-capacitor. A new renewable braking system (RBS) is …proposed for HESS EV’s powered by a brushless DC (BLDC) engine. The electrical energy available during regenerative braking is stored in a supercapacitor battery. By providing a new switching algorithm, the DC link voltage is boosted to effectively transfer power to the HESS unit. The fuzzy logic technique is used as a braking force distribution system to ensure effective and smooth braking operations. Fuzzy logic-based HESS provides better performance in electric vehicles, such as highly efficient regenerative braking, deep discharge protection of the battery, and faster acceleration. Also, there is a quick comparison of E-rickshaw solar power with traditional E-rickshaw. The planned design model was simulated by MATLAB® /Simulink environment. Show more

Keywords: Solar power, battery, optimal power management (OPM), BLDC, E-Rickshaw, fuzzy logic controller (FLC), ultra capacitor

DOI: 10.3233/JIFS-189774

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Lai, Xiaocong | Li, Hua | Pan, Ying

Article Type: Research Article

Abstract: With the increasing attention to the environment and air quality, PM2.5 has been paid more and more attention. It is expected to excavate useful information in meteorological data to predict air pollution, however, the air quality is greatly affected by meteorological factors, and how to establish an effective air quality prediction model has always been a problem that people urgently need to solve. This paper proposed a combined model based on feature selection and Support Vector Machine (SVM) for PM2.5 prediction. Firstly, aiming at the influence of meteorological factors on PM2.5, a feature selection method based on linear causality is …proposed to find out the causality between features and select the features with strong causality, so as to remove the redundant features in air pollution data and reduce the workload of data analysis. Then, a method based on SVM is proposed to analyze and solve the nonlinear problems in the data, for reducing the prediction error, a method of particle swarm optimization is also used to optimize SVM parameters. Finally, the above methods are combined into a prediction model, which is suitable for the current air pollution control. 12 representative data sets on the UCI (University of California, Irvine) website are used to verify the combined model, and the experimental results show that the model is feasible and effective. Show more

Keywords: Feature selection, linear regression, support vector machine, combined forecasting model, PM2.5 prediction

DOI: 10.3233/JIFS-202812

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Wei, Hui-Chuan | Li, Ai-Tzu | Wang, Wei-Ni | Liao, Yu-Hsien

Article Type: Research Article

Abstract:  By focusing on various influences arose from environmental change, sustainability has become a major conception among many fields, including utility allocation. On the other hand, game-theoretical methods have always been adopted to analyze the reasonability of utility allocation rules. In many real-world situations, however, participants and its energetic levels (decisions) should be essential factors simultaneously. By focusing on both the participants and its energetic levels (decisions), we introduce the restrained core to investigate utility allocation under fuzzy transferable-utility (TU) models. In order to analyze the reasonability for the restrained core, two axiomatic results are further provided by applying several types …of reductions. Since the restrained core infringes a specific converse steadiness property, a converse steady enlargement of the restrained core is also introduced to investigate how extensive the violation of this specific converse steadiness property is. This converse steady enlargement is smallest converse steady measuration that contains the restrained core. Show more

Keywords: Sustainability, the core, fuzzy TU models, the restrained core, reduction, converse steadiness

DOI: 10.3233/JIFS-202689

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Asim, Mohammed | Agrawal, Piyush | Tariq, Mohd | Alamri, Basem

Article Type: Research Article

Abstract: Under partial shading conditions (PSC), most traditional maximum power point tracking (MPPT) techniques may not adopt GP (global peak). These strategies also often take a considerable amount of time to reach a full power point (MPP). Such obstacles can be eliminated by the use of metaheuristic strategies. This paper shows, in partial shading conditions, the MPPT technique for the photovoltaic system using the Bat Algorithm (BA). Simulations have been performed in the MATLAB ® /Simulink setting to verify the efficacy of the proposed method. In MPPT applications, the results of the simulations emphasize the precision of the proposed technique. The algorithm …is also simple and efficient, on a low-cost microcontroller, it could be implemented. Hardwar in loop (HIL) validation is performed, with a Typhoon HIL 402 setup. Show more

Keywords: Photovoltaic system, partial shading conditions, maximum power point tracking, Bat algorithm

DOI: 10.3233/JIFS-189754

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Xing, Jingyu | Zhang, Zheng

Article Type: Research Article

Abstract: In order to predict the development trend of network security situation more accurately, this paper proposes an improved vector machine model by simulated annealing optimization to improve network security situation prediction. In the process of prediction, the sample data of phase space reconstruction network security status is first formed to form training sample set, and then the simulated annealing method is improved. The correlation vector machine is the optimization of correlation vector machine with simulated degradation algorithm embedded in the calculation process of objective function. The network security situation prediction model is obtained through super parameters to improve the learning …ability and prediction accuracy. The simulation results show that this method has higher prediction accuracy better than the correlation vector machine model optimized by Elman and simulated annealing. This method can describe the change of network security well. Show more

Keywords: Heuristic, security situation, vector machine, correlation vector machine

DOI: 10.3233/JIFS-189817

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Li-quan, Chen | You, Li | Shen, Fengjun | Shan, Zhaoqimeng | Chen, Jiaxuan

Article Type: Research Article

Abstract: Human skeleton extraction is a basic problem in the field of computer vision. With the rapid progress of science and technology, it has become a hot issue in the field of target detection such as pedestrian recognition, behavior monitoring, and pedestrian gesture recognition. In recent years, due to the development of deep neural networks, modeling of human joints in acquired images has made progress in skeleton extraction. However, most models have low modeling accuracy, poor real-time performance, and poor model availability. problem. Aiming at the above-mentioned human target detection problem, this paper uses the deep learning skeleton sequence model gesture …recognition method in sports scenes to study, aiming to provide a gesture recognition method with strong noise resistance, good real-time performance and accurate model. This article uses motion video frame images to train the VGG16 network. Using the network to extract skeleton information can strengthen the posture feature expression, and use HOG for feature extraction, and use the Adam algorithm to optimize the network to extract more posture features, thereby improving the posture of the network Recognition accuracy. Then adjust the hyperparameters and network structure of the basic network according to the training results, and obtain the key poses in the sports scene through the final classifier. Show more

Keywords: Skeleton extraction, gesture recognition, deep learning

DOI: 10.3233/JIFS-189834

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Chen, Dai | Li, Fei

Article Type: Research Article

Abstract: Under the current situation, the school enterprise cooperation education mode implemented by higher vocational colleges conforms to the needs of social and economic development, conforms to the law of vocational education, and plays an important role in cultivating a large number of enterprises’ urgently needed high-quality skilled talents. Based on the above background, the purpose of this paper is to explore the teaching practice of Higher Vocational architectural decoration engineering technology specialty based on the school enterprise cooperation education mode from the perspective of fuzzy algorithm. This paper analyzes the background of the development of higher vocational education, studies the …reform of personnel training mode, the construction of core curriculum system, the cooperation between school and enterprise under the framework of vocational education group, and the improvement of teachers’ professional ability, and puts forward the views and discussions of teaching reform. This paper also introduces the method of fuzzy algorithm model to evaluate the performance of teachers and tries to put forward an improvement scheme for the traditional performance evaluation system of teachers. Based on the actual evaluation cases, according to the principle of maximum membership, the maximum membership degree of comprehensive evaluation is 0.390, The evaluation level of the corresponding evaluation set is better, which proves the application prospect and methodological value of the model. Show more

Keywords: Fuzzy algorithm, higher vocational education, school-enterprise cooperation, talent training

DOI: 10.3233/JIFS-189826

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Xu, Zuomei

Article Type: Research Article

Abstract: This paper first introduces the relevant content of fuzzy theory system and expounds the relevant knowledge of fuzzy comprehensive evaluation in fuzzy theory. In this paper, an evaluation system for the teaching of environmental art design specialty based on the integration of production, teaching and research is established to provide scientific basis for the exploration and control of the teaching mode, and a fuzzy comprehensive evaluation model is established on the basis of the index system. Through the methods of literature retrieval, real data analysis and expert interview, 14 factors influencing the level of classroom teaching were collected, and questionnaires …were distributed to teachers to collect the factor weight data, and these data were sorted out and calculated to obtain the weight indicators at all levels. Through the collection of online teaching evaluation data, the fuzzy evaluation matrix of each level index is calculated, and the matrix is combined with the corresponding weight vector to establish the fuzzy comprehensive evaluation model. Finally, the teaching evaluation results of the integrated mode of production, teaching and research are as follows: 74.8% are excellent, 12.6% are good, 7.1% are medium, and only 5.5% are poor. Through the above results, it can be concluded that the teaching effect of the integration mode of production, teaching and research is excellent. Show more

Keywords: Fuzzy algorithm, integration of production, teaching and research, environmental art design

DOI: 10.3233/JIFS-189825

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhang, Guangchao | Kou, Xinyue

Article Type: Research Article

Abstract: In recent years, with the rapid development of VR technology, its application range gradually involves the field of urban landscape design. VR technology can simulate complex environments, breaking through the limitations of traditional environmental design on large amounts of information processing and rendering of renderings. It can display complex and abstract urban environmental design through visualization. With the support of high-speed information transmission in the 5G era, VR technology can simulate the overall urban landscape design by generating VR panoramas, and it can also bring the experiencer into an immersive and interactive virtual reality world through VR video Experience. Based …on this, this article uses the 5G virtual reality method in the new media urban landscape design to conduct research, aiming to provide an urban landscape design method with strong authenticity, good user experience and vividness. This paper studies the urban landscape design method in the new media environment; in addition, how to realize the VR panorama in the 5G environment, and also explores the image design of each node in the city in detail; and uses the park design in the city As an example, the realization process of the entire virtual reality is described in detail. The research in this article shows that the new media urban landscape design method based on 5G virtual reality, specifically to the design of urban roads, water divisions, street landscapes, and people’s living environment, makes the realization of smart cities possible. Show more

Keywords: 5G virtual reality, VR panorama, new media

DOI: 10.3233/JIFS-189836

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Li, Xingguo | Wang, Junfeng

Article Type: Research Article

Abstract: With the rapid development of the Internet, threats from the network security are emerging one after another. Driven by economic interests, attackers use malicious domain names to promote the development of botnets and phishing sites, which leads to serious information leakage of victims and devices, the proliferation of DDoS attacks and the rapid spread of viruses. Based on the above background, the purpose of this paper is to study the network detection of malicious domain name based on the adversary model. Firstly, this paper studies the generation mechanism of DGA domain name based on PCFG model, and studies the characteristics …of the domain name generated by such DGA. The research shows that the domain name generated by PCFG model is usually based on the legal domain name, so the character statistical characteristics of the domain name are similar to the legal domain name. Moreover, the same PCFG model can often generate multiple types of domain names, so it is difficult to extract appropriate features manually. The experimental results show that the accuracy, recall and accuracy of the performance parameters of the classifier are over 95%. By using the open domain name data set, comparing the linear calculation edit distance method and the detection effect under different thresholds, it is proved that the proposed method can improve the detection speed of misplanted domain names under the condition of similar accuracy. Show more

Keywords: Adversary model, malicious domain name, domain name system, PCFG model

DOI: 10.3233/JIFS-189823

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Pang, Xiaojia | Ning, Yuwen

Article Type: Research Article

Abstract: The advancement of science has made computer technology and the education industry more and more closely related, and the development of intelligent teaching systems has also opened a new path for classroom teaching. This paper studies the application of fuzzy control based on genetic algorithms in the intelligent psychology teaching system. Facing the complicated variables in the teaching process, the improved genetic algorithm can better realize dynamic teaching decisions through fuzzy control. This article aims to improve the quality of psychology classroom teaching, and develops an intelligent psychology teaching system based on the fuzzy control theory of genetic algorithm. Combined …with the current development of fuzzy control theory, the problems existing in the intelligent teaching system are studied and analyzed, and they have been optimized and improved. This paper proposes a control algorithm based on a teaching management system. The algorithm can implement fuzzy control on student models, knowledge organization structure, intelligent test papers and teaching decision-making. While restoring the real teaching process, it can better realize teaching students in accordance with their aptitude and improve teaching. The intelligence of the system. According to the system test data, the proportions of the difficulty of the system’s automatic test paper are 30.1%, 51.6%, 18.3%, which are in line with the designer’s set expectation of 3 : 5:2, which shows the improved genetic algorithm. It can realize the intelligent volume group function very well. Show more

Keywords: Genetic algorithm, fuzzy control, intelligent teaching system, intelligent decision

DOI: 10.3233/JIFS-189827

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Kang, Keming | Tian, Shengwei | Yu, Long

Article Type: Research Article

Abstract: For deep learning’s insufficient learning ability of a small amount of data in the Chinese named entity recognition based on deep learning, this paper proposes a named entity recognition of local adverse drug reactions based on Adversarial Transfer Learning, and constructs a neural network model ASAIBC consisting of Adversarial Transfer Learning, Self-Attention, independently recurrent neural network (IndRNN), Bi-directional long short-term memory (BiLSTM) and conditional random field (CRF). However, of the task of Chinese named entity recognition (NER), there are only few open labeled data sets. Therefore, this article introduces Adversarial Transfer Learning network to fully utilize the boundary of Chinese …word segmentation tasks (CWS) and NER tasks for information sharing. Plus, the specific information in the CWS is also filtered. Combing with Self-Attention mechanism and IndRNN, this feature’s expression ability is enhanced, thus allowing the model to concern the important information of different entities from different levels. Along with better capture of the dependence relations of long sentences, the recognition ability of the model is further strengthened. As all the results gained from WeiBoNER and MSRA data sets by ASAIBC model are better than traditional algorithms, this paper conducts an experiment on the data set of Xinjiang local named entity recognition of adverse drug reactions (XJADRNER) based on manual labeling, with the accuracy, precision, recall and F-Score value being 98.97%, 91.01%, 90.21% and 90.57% respectively. These experimental results have shown that ASAIBC model can significantly improve the NER performance of local adverse drug reactions in Xinjiang. Show more

Keywords: Transfer learning, self-Attention mechanism, IndRNN, named entity recognition of adverse drug reactions, deep learning

DOI: 10.3233/JIFS-201017

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Hu, Jinjuan | Xie, Chao

Article Type: Research Article

Abstract: After entering the 21st century, the electronic commerce system has affected all aspects of our lives. Whether we read news on our mobile phones or computers or purchase items on our online websites, it greatly facilitates our lives. With the rapid development of short videos, many people like to watch small videos that interest them. The rapid development of e-commerce has facilitated our lives, so that we no longer have to go to many shopping malls to buy our favorite items, and we also no need to change TV stations one by one after watching a program to find our …favorite programs. However, due to the rapid development of electronic commerce, there has been a lot of information overload. When users browse the website, items they are not interested in will appear, and even information about online fraud appears. How to filter this information and how to intelligently recommend to users more favorite items is the main research direction of this article. The research of this article is mainly divided into four parts. The first part analyzes the current situation of intelligent recommendation technology research and puts forward the idea of this article. The second part introduces the commonly used collaborative filtering algorithm and the principle and process of the fuzzy clustering algorithm used in this experiment, analyzes the shortcomings of the traditional collaborative filtering algorithm and illustrates the adaptability of the fuzzy clustering algorithm in practical applications. The third part introduces an intelligent recommendation system based on fuzzy clustering, which comprehensively analyzes the characteristics of users and products, makes full use of users’ evaluation information of products, and realizes intelligent recommendations based on content and collaborative filtering. At the end of the article, the comparative analysis experiment with the intelligent recommendation system of collaborative recommendation algorithm further proves the superiority of the intelligent recommendation system of electronic commerce based on fuzzy clustering algorithm in this paper and improves the accuracy of intelligent recommendation. Show more

Keywords: Electronic business, fuzzy clustering, collaborative filtering

DOI: 10.3233/JIFS-189824

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhou, Wei | Metawea, Maha Saad

Article Type: Research Article

Abstract: As is known that, one of the challenges in ensuring the quality and safety of agricultural products in China is how to organize plenty of scattered small farmers and integrate them into the modern agricultural products supply chain system. In this paper, in order to promote the tight integration of agricultural products supply chain, based on multi-agent system, a computer simulation model of agricultural products supply chain is proposed. Through a series of simulation experiments, the evolution of the organizational structure of the agricultural products supply chain, its impact on the quality and safety of agricultural products under different government …regulations are explored and discussed in detail. It follows from these simulation results that the more long-term-contract farmers and sellers, the more conducive to the improvement of the quality and safety of agricultural products, and the corresponding countermeasures and suggestions are also provided. Show more

Keywords: Multi-agent system, quality and safety, farm produce, supply chain

DOI: 10.3233/JIFS-189822

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Huang, Meng | Liu, Shuai | Zhang, Yahao | Cui, Kewei | Wen, Yana

Article Type: Research Article

Abstract: The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of …Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators. Show more

Keywords: Non-linear, artificial intelligence technology, training model, Gis

DOI: 10.3233/JIFS-189820

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Hu, Jie | Deng, Sier

Article Type: Research Article

Abstract: With the increase in the intelligence of the production process and the increase in reliability requirements, the monitoring of the bearing life status after the event has been unable to meet the needs of industrial production. Performance degradation assessment and life monitoring have attracted more attention as intelligent methods based on condition maintenance. Hidden Markov model is a statistical probability model based on time series, which is very suitable for modeling the performance degradation process of equipment. Therefore, this paper proposes a life monitoring algorithm based on hidden Markov model. First, the continuous wavelet transform is introduced to obtain the …optimal value of the shape factor or the stretch factor. Secondly, a hidden Markov model of multi-channel information fusion is proposed. The algorithm significantly improves the effectiveness and robustness of life monitoring. The hidden Markov model explicitly expresses the state duration distribution, making the model more suitable for life monitoring. Show more

Keywords: Monitoring, bearing fatigue life, hidden Markov model

DOI: 10.3233/JIFS-189815

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Ayyub, Kashif | Iqbal, Saqib | Nisar, Muhammad Wasif | Ahmad, Saima Gulzar | Munir, Ehsan Ullah

Article Type: Research Article

Abstract:  Sentiment analysis is the field that analyzes sentiments, and opinions of people about entities such as products, businesses, and events. As opinions influence the people’s behaviors, it has numerous applications in real life such as marketing, politics, social media etc. Stance detection is the sub-field of sentiment analysis. The stance classification aims to automatically identify from the source text, whether the source is in favor, neutral, or opposed to the target. This research study proposed a framework to explore the performance of the conventional (NB, DT, SVM), ensemble learning (RF, AdaBoost) and deep learning-based (DBN, CNN-LSTM, and RNN) machine learning …techniques. The proposed method is feature centric and extracted the (sentiment, content, tweet specific and part-of-speech ) features from both datasets of SemEval2016 and SemEval2017. The proposed study has also explored the role of deep features such as GloVe and Word2Vec for stance classification which has not received attention yet for stance detection. Some base line features such as Bag of words, N-gram, TF-IDF are also extracted from both datasets to compare the proposed features along with deep features. The proposed features are ranked using feature ranking methods such as (information gain, gain ration and relief-f). Further, the results are evaluated using standard performance evaluation measures for stance classification with existing studies. The calculated results show that the proposed feature sets including sentiment, (part-of-speech, content , and tweet specific) are helpful for stance classification when applied with SVM and GloVe a deep feature has given the best results when applied with deep learning method RNN. Show more

Keywords: Stance classification, deep learning, deep features, sentiment analysis, content based

DOI: 10.3233/JIFS-202269

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2021

Authors: Ding, Xiangwen | Wang, Shengsheng

Article Type: Research Article

Abstract: Melanoma is a very serious disease. The segmentation of skin lesions is a critical step for diagnosing melanoma. However, skin lesions possess the characteristics of large size variations, irregular shapes, blurring borders, and complex background information, thus making the segmentation of skin lesions remain a challenging problem. Though deep learning models usually achieve good segmentation performance for skin lesion segmentation, they have a large number of parameters and FLOPs, which limits their application scenarios. These models also do not make good use of low-level feature maps, which are essential for predicting detailed information. The Proposed EUnet-DGF uses MBconv to implement …its lightweight encoder and maintains a strong encoding ability. Moreover, the depth-aware gated fusion block designed by us can fuse feature maps of different depths and help predict pixels on small patterns. The experiments conducted on the ISIC 2017 dataset and PH2 dataset show the superiority of our model. In particular, EUnet-DGF only accounts for 19% and 6.8% of the original Unet in terms of the number of parameters and FLOPs. It possesses a great application potential in practical computer-aided diagnosis systems. Show more

Keywords: Skin lesion segmentation, dermoscopic images, deep learning, Unet, gated fusion

DOI: 10.3233/JIFS-202566

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Singh, Saumya | Srivastava, Smriti

Article Type: Research Article

Abstract: In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five …data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM). Show more

Keywords: Kernel fuzzy C means, TLBO, metaheuristic, multi-objective

DOI: 10.3233/JIFS-189771

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Barma, Partha Sarathi | Dutta, Joydeep | Mukherjee, Anupam | Kar, Samarjit

Article Type: Research Article

Abstract: This study designs a new variant of the capacitated vehicle routing problem (CVRP) under a fuzzy environment. In CVRP, several vehicles start their journey from a central depot to provide services to different cities and finally return to the depot. This paper introduces an additional time beyond the service time at each city to fulfill the pre-ordered demands. The need for this excess service time is to provide the services to new customers who are not enlisted at the start of the process. It is a market enhancement step. The proposed model’s main objective is to find the maximum time-dependent …profit by using the optimum number of vehicles in an appropriate route and spending optimum excess service time in each city. The model considers travel time and travel cost as fuzzy numbers. An expected value model (EVM) is formulated using the credibility approach on fuzzy variables. A hybrid meta-heuristic method combining a genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) is designed to solve the proposed model. The proposed model is explained with the help of some numerical examples. Sensitivity analyses based on different independent parameters of the algorithms are also conducted. Show more

Keywords: Capacitated vehicle routing problem, profit maximization, fuzzy credibility theory, hybrid algorithm, genetic algorithm, bacteria foraging optimization algorithm

DOI: 10.3233/JIFS-192134

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Luo, Sheng

Article Type: Research Article

Abstract: An information system as a database that represents relationships between objects and attributes is an important mathematical model in the field of artificial intelligence. Hybrid data means boolean, categorical, real-valued, set-valued data and missing data in this paper. A hybrid information system is an information system where its attribute is hybrid data. This paper proposes a three-way decision method based on hybrid data. First, the distance between two objects based on the conditional attribute set in a given hybrid information system is developed and Gaussian kernel based on this distance is acquired. Then, the fuzzy T cos -equivalence relation, …induced by this information system, is obtained by using Gaussian kernel. Next, the decision-theoretic rough set model in this hybrid information system is presented. Moreover, a three-way decision method is given by means of this decision-theoretic rough set model and inclusion degree between two fuzzy sets. Finally, an example is employed to illustrate the feasibility of the proposed method, which may provide an effective method for hybrid data analysis in real applications. Show more

Keywords: Three-way decision, hybrid data, decision-theoretic rough set, gaussian kernel, feasibility

DOI: 10.3233/JIFS-182764

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Zhang, Shu | Chen, Jianhua

Article Type: Research Article

Abstract: This paper provides an in-depth analysis of the optimization of energy-efficient dynamic task allocation in wireless sensor networks through an improved particle swarm optimization algorithm, and introduces the idea of software-defined networking into wireless sensor network to propose a software-defined wireless sensor network non-uniform cluster routing protocol. The protocol decouples the data layer from the control layer, and the base station performs the cluster head election, network clustering, and routing control operations. The base station optimizes the cluster head election process by electing cluster head nodes using an improved particle cluster algorithm. Based on the elected cluster head nodes, the …base station calculates their corresponding contention radius and plans the data transmission path. The results of the calculation are sent to the corresponding nodes for cluster creation and data transmission. The simulation results fully show that the use of this protocol can achieve the purpose of significantly extending the service life of the network. This paper comprehensively analyses the whole process of mobile charging of UAVs under improved conditions and proposes a path planning algorithm. The multi-level weighted charging path planning proposed in this paper considers both fairness and timeliness. Finally, the paper verifies the effectiveness of the algorithm. Show more

Keywords: Particle swarm algorithms, wireless sensors, network energy conservation, dynamic task allocation optimization

DOI: 10.3233/JIFS-189814

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Wang, Pin | Wang, Peng | Fan, En

Article Type: Research Article

Abstract: Nowadays, energy has become a hot issue of concern to the whole society. With the unbalanced distribution of resources in the world and more severe climate change, the constraints of resource conditions and environmental status on global energy development are becoming stronger and stronger. The rapid development of the Internet, as well as the proposal of the energy Internet, has a better application in the analysis of energy demand, which can effectively alleviate the contradiction between energy and environment. Aiming at the big data of energy Internet and based on the advantages of fuzzy rough model, this paper studies a …method of big data analysis and prediction of multidimensional space-time characteristics of energy Internet based on fuzzy rough model. Firstly, according to the spatio-temporal characteristics of energy Internet data, extract the multidimensional spatio-temporal characteristics of energy internet. Secondly, rough set and fuzzy set are two commonly used mathematical tools, and the combination of the two fuzzy rough models can more fully mine data information. In view of the shortcomings of the commonly used fuzzy rough set reduction algorithm, a reduction algorithm based on conditional entropy is proposed. Finally, taking multidimensional space-time characteristics as input, combining the advantages of fuzzy rough model and neural network, a prediction model is established to analyze and forecast energy demand. The simulation experiments show that the design method is feasible and superior, and can achieve the prediction of energy demand well, so as to make more rational use of energy. Show more

Keywords: Energy internet, big data, multidimensional spatial and temporal characteristics, fuzzy rough model

DOI: 10.3233/JIFS-189819

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Zhou, Jia-Jia | Li, Xiang-Yang

Article Type: Research Article

Abstract:  In present paper, we put forward four types of hesitant fuzzy β covering rough sets (HFβ CRSs) by uniting covering based rough sets (CBRSs) and hesitant fuzzy sets (HFSs). We firstly originate hesitant fuzzy β covering of the universe, which can induce two types of neighborhood to produce four types of HFβ CRSs. We then make further efforts to probe into the properties of each type of HFβ CRSs. Particularly, the relationships of each type of rough approximation operators w.r.t. two different hesitant fuzzy β coverings are groped. Moreover, the relationships between our proposed models and some …other existing related models are established. Finally, we give an application model, an algorithm, and an illustrative example to elaborate the applications of HFβ CRSs in multi-attribute decision making (MADM) problems. By making comparative analysis, the HFβ CRSs models proposed by us are more general than the existing models of Ma and Yang and are more applicable than the existing models of Ma and Yang when handling hesitant fuzzy information. Show more

Keywords: Hesitant fuzzy β covering, hesitant fuzzy β neighborhoods, hesitant fuzzy complement β neighborhoods, HFβCRSs, MADM

DOI: 10.3233/JIFS-190959

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Malik, Hasmat | Alotaibi, Majed A. | Almutairi, Abdulaziz

Article Type: Research Article

Abstract: Maintaining the reliable, efficient, secure and multifunctional IEC 61850 based substation is an extremely challenging task, especially in the ever-evolving cyberattacks domain. This challenge is also exacerbated with expending the modern power system (MPS) to meet the demand along with growing availability of hacking tools in the hacker community. Few of the most serious threats in the substation automation system (SAS) are DoS (Denial of Services), MS (Message Suppression) and DM (Data Manipulation) attacks, where DoS is due to flood bogus frames. In MS, hacker inject the GOOSE sequence (sqNum)) and GOOSE status (stNum) number. In the DM attacks, attacker …modify current measurements reported by the merging units, inject modified boolean value of circuit breaker and replay a previously valid message. In this paper, an intelligent cyberattacks identification approach in IEC 61850 based SAS using PSVM (proximal support vector machine) is proposed. The performance of the proposed approach is demonstrated using experimental dataset of recorded signatures. The obtained results of the demonstrated study shows the effectiveness and high level of acceptability for real side implementation to protect the SAS from the cyberattacks in different scenarios. Show more

Keywords: False data injection, Man-In-The_Middle, intrusion detection system, GOOSE, MMS, SVM, information and communication technologies, substation automation system, telephone switching based remote control unit, digital communication network

DOI: 10.3233/JIFS-189783

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Cui, Chongyu | Li, Zhaoxia | Zang, Kun | Quan, Xuezheng

Article Type: Research Article

Abstract: In this paper, in-depth analysis, and evaluation of the transient stability of a renewable energy power system are carried out, and a transient stability evaluation method based on stacked autoencoders is proposed. The method breaks through the traditional two-stage evaluation mode. The method proposes a deep network model with a multi-branch structure for different physical natures of different measured data. With the help of different sparse denoising coding networks (branches), features are extracted from each measurement separately and fused on top of the model to finally complete the transient stability assessment. The simulation results show that the method can effectively …handle the simultaneous input of multiple measurements, and has the advantages of high accuracy and robustness in noisy environments. To effectively determine the model hyperparameters, the orthogonal test is used for model selection, which can significantly reduce the hyperparameter seeking space and save computational resources. The simulation results show that the method can effectively use multiple measurements to improve the accuracy of wind power prediction. Also, the input variable selection index based on mutual information can provide a good guide for further improvement of model performance. Show more

Keywords: Renewable energy, power systems, transient stability, analytical assessment

DOI: 10.3233/JIFS-189816

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Aziz, Fehmi | Tahir, Faheem | Midhat, Sadia | Naz, Shafaq | Qureshi, Naveeda Akhtar

Article Type: Research Article

Abstract:  Present study is an interdisciplinary approach towards rapid and efficient medical diagnosis. The research articulated on data set of cross-sectional study of pregnant females dwelling rural area of Pakistan. The prognosis of gestational wellbeing followed through analyzing heterogenic medical information to develop a holistic picture of ongoing pregnancy. Therefore, for rapid medical diagnosis and precision in decision-making, Fuzzy Soft Set (denoted as FSS) theory selected to develop an algorithm. The algorithm constructed as single point, multipoint and cumulative diagnosis for predicting health status with respect of Hemoglobin, Body Mass Index and Random Glucose Concentration (Respectively denoted as Hb, BMI and …RGC) of subjects under study. We successfully proposed novel approach for complex modeling and provision of algorithm for medical diagnosis. The algorithms successfully dealt with analyzing diversely attributed detailed medical tests/reports as input. The output of complex modeling effectively served efficient decision-making in predicting gestational wellbeing. Show more

Keywords: Medical diagnosis, fuzzy set, soft set, BMI, maternal anemia

DOI: 10.3233/JIFS-190452

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Kumar, Neeraj | Tripathi, M.M.

Article Type: Research Article

Abstract:  Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power increasing immensely across the globe. Solar energy is widely expanding in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for accurate electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due …to intermittency issue. In this paper, investigation has been done on the effect of solar energy generation on electricity price forecasting. Different state of the art Machine learning (ML) models have been applied and compared with LSTM model for electricity price forecasting and the evaluation of the impact of solar energy generation on electricity price has been done. During the investigation it was found from the results that the LSTM model outperform all other models and impact of solar energy generation on electricity price is evaluated using forecasting metrics. The forecasted electricity price considering the factor of solar energy generation was lower as compared with the forecast without solar energy generation. The reliability test of the MAPE values has been performed by calculating confidence interval for proposed model. Show more

Keywords: Price forecasting, renewable energy, LSTM, LASSO, decision tree, random forest, XGBoost

DOI: 10.3233/JIFS-189781

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Zhang, Na | Yan, Shuli | Fang, Zhigeng | Yang, Baohua

Article Type: Research Article

Abstract: In view of the situation that tasks or activities in the GERT model may have multiple realizations, this paper explores the time dependence of each repeated execution node under the condition of fuzzy information, and studies the characteristics of the z-tag fuzzy GERT model and its analytic algorithm. Firstly, the F-GERT model related to the number of executions of activities is defined, and the simplified rules, related properties and theorems of the network model are examined. Secondly, solving algorithm, conditional moment generating function and process arrival time of the F-GERT model for repeated execution time are studied. Finally, the application …of F-GERT queuing system based on element execution time in weapon equipment management is discussed. The feasibility and effectiveness of the model and algorithm are verified by the practical application of the project. Show more

Keywords: Project management, GERT model, fuzzy information, z-tag, moment generating function, network structure

DOI: 10.3233/JIFS-201731

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Mi, Xiangjun | Tian, Ye | Kang, Bingyi

Article Type: Research Article

Abstract: Describing and processing complex as well as ambiguous and uncertain information has always been an inescapable and challenging topic in multi-attribute decision analysis (MADA) problems. As an extension of Dempster-Shafer (D-S) evidence theory, D numbers breaks through the constraints of the constraint framework and is a new way of expressing uncertainty. The soft likelihood function based on POWA operator is one of the most useful tools recently developed for dealing with uncertain information, since it provides a more excellent performance for the aggregation of multiple compatible evidence. Recently, a new MADA model based on D numbers has been proposed, called …DMADA. In this paper, inspired by the above mentioned theories, based on soft likelihood functions, POWA aggregation and D numbers we design a novel model to improve the performance of representing and processing uncertain information in MADA problems as an improvement of the DMADA approach. In contrast, our advantages include mainly the following. Firstly, the proposed method considers the reliability characteristics of each initial D number information. Secondly, the proposed method empowers decision makers with the possibility to express their perceptions through attitudinal features. In addition, an interesting finding is that the preference parameter in the proposed method can clearly distinguish the variability between candidates by adjusting the space values between adjacent alternatives, making the decision results clearer. Finally, the effectiveness and superiority of this model are proved through analysis and testing. Show more

Keywords: Multi-attribute decision analysis (MADA), D numbers, ordered weighted averaging (OWA), power OWA (POWA), soft likelihood function (SLF), reliability

DOI: 10.3233/JIFS-202413

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-25, 2021

Authors: Srikanth, Pullabhatla | Koley, Chiranjib

Article Type: Research Article

Abstract: A convolution neural network (CNN) based deep learning method has been proposed for automatic classification and localization of nonlinear loads present in an interconnected power system. The identification of nonlinear loads has been previously dealt with the use of Nonlinear Auto Regression neural network with eXogenous inputs (NARX), Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Artificial Neural Networks (ANN) and Fuzzy Logic (FL). However, these techniques had not explored the area of classification of industrial and domestic nonlinear loads in an interconnected power system. Also, a Deep learning-based solution for identification of the type of nonlinear load has not …been reported in the literature to date. Hence, to address these shortcomings, an IEEE-9 Bus system with industrial nonlinear loads has been used to obtain various current waveforms with distortions. The recorded current waveforms are transformed into a time-frequency (TF) domain plane, and the obtained images are then fed to the deep learning algorithm. The colored images of the TF plots of each type of nonlinear load in Red-Green-Blue (RGB) index provide the best visual features for extraction. The TF domain signatures of individual events are scaled to a standard size before feeding to the algorithm. Through these TF signatures, unique features were extracted with the deep learning algorithm, and then passed on to different stages of convolution and max-pooling with fully connected layers. The softmax classifier at the end classifies the input data into the type of nonlinear present in the power system. The algorithm, when run at different buses, also identifies the location of the nonlinear load. The proposed methodology avoids the usage of any additional fusion layer for obtaining unique features, reduces the training time and maintains the highest accuracy of 100%. Show more

Keywords: Nonlinear loads, localization, identification, deep learning, time-frequency representation

DOI: 10.3233/JIFS-189780

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Choudhary, Anu | Raj, Kuldip | Mursaleen, M.

Article Type: Research Article

Abstract: Tauberian theorem serves the purpose to recuperate Pringsheim’s convergence of a double sequence from its (C , 1, 1) summability under some additional conditions known as Tauberian conditions. In this article, we intend to introduce some Tauberian theorems for fuzzy number sequences by using the de la Vallée Poussin mean and double difference operator of order r  . We prove that a bounded double sequence of fuzzy number which is Δ u r - convergent is ( C , 1 , 1 ) Δ u r - summable to the …same fuzzy number L  . We make an effort to develop some new slowly oscillating and Hardy-type Tauberian conditions in certain senses employing de la Vallée Poussin mean. We establish a connection between the Δ u r - Hardy type and Δ u r - slowly oscillating Tauberian condition. Finally by using these new slowly oscillating and Hardy-type Tauberian conditions, we explore some relations between ( C , 1 , 1 ) Δ u r - summable and Δ u r - convergent double fuzzy number sequences. Show more

Keywords: Fuzzy number, difference operator, double sequences, Tauberian theorem, (C, 1, 1)- summability

DOI: 10.3233/JIFS-202921

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Ajith Kumar, S.P. | Banyal, Siddhant | Bharadwaj, Kartik Krishna | Thakur, Hardeo Kumar | Sharma, Deepak Kumar

Article Type: Research Article

Abstract:  Opportunistic IoT networks operate in an intermittent, mobile communication topology, employing peer-to-peer transmission hops on a store-carry-forward basis. Such a network suffers from intermittent connectivity, lack of end-to-end route definition, resource constraints and uncertainties arising from a dynamic topology, given the mobility of participating nodes. Machine learning is an instrumental tool for learning and many histories-based machine learning paradigms like MLPROPH, KNNR and GMMR have been proposed for digital transformations in the field with varying degrees of success. This paper explores the dynamic topology with a plethora of characteristics guiding the node interactions, and consequently, the routing decisions. Further, the …study ascertains the need for better representation of the versatility of node characteristics that guide their behavior. The proposed scheme Opportunistic Fuzzy Clustering Routing (OFCR) protocol employs a three-tiered intelligent fuzzy clustering-based paradigm that allows representation of multiple properties of a single entity and the degree of association of the entity with each property group that it is represented by. Such quantification of the extent of association allows OFCR a proper representation of multiple node characteristics, allowing a better judgement for message routing decisions based on these characteristics. OFCR performed 33.77%, 6.07%, 3.69%, 6.88% and 78.14% better than KNNR, GMMR, CAML, MLPRoPH and HBPR respectively across Message Delivery probability. OFCR, not only shows improved performance from the compared protocols but also shows relatively more consistency across the change in simulation time, message TTL and message generation interval across performance metrics. Show more

Keywords: Analytical models, clustering, fuzzy logic, Internet of Things, opportunistic networks, routing protocols, machine learning, ONE simulator

DOI: 10.3233/JIFS-189782

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Hong, Jie | Qin, Xiansheng

Article Type: Research Article

Abstract: Over past two decades, steady-state evoked potentials (SSVEP)-based brain computer interface (BCI) systems have been extensively developed. As we all know, signal processing algorithms play an important role in this BCI. However, there is no comprehensive review of the latest development of signal processing algorithms for SSVEP-based BCI. By analyzing the papers published in authoritative journals in nearly five years, signal processing algorithms of preprocessing, feature extraction and classification modules are discussed in detail. In addition, other aspects existed in this BCI are mentioned. The following key problems are solved. (1) In recent years, which signal processing algorithms are frequently …used in each module? (2) Which signal processing algorithms attract more attention in recent years? (3) Which modules are the key to signal processing in BCI field? This information is very important for choosing the appropriate algorithms, and can also be considered as a reference for further research. Simultaneously, we hope that this work can provide relevant BCI researchers with valuable information about the latest trends of signal processing algorithms for SSVEP-based BCI systems. Show more

Keywords: Steady-state visual potentials (SSVEP), brain computer interface (BCI), signal processing, feature extraction, classification

DOI: 10.3233/JIFS-201280

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Zhu, Xiuying

Article Type: Research Article

Abstract: Aiming at the competition conflict problem of task allocation of sensor node in wireless sensor network multi-target tracking, a discrete particle swarm optimization tracking task allocation optimization algorithm based on nearest neighbor is proposed. By constructing the mathematical model and objective function of the multi-objective multi-sensor node alliance cooperative tracking task allocation problem, the nearest neighbor method is used to initialize the particle group node task allocation, the objective function is used as the fitness function to guide the particle flight, and the optimal node allocation can be quickly realized. Experiments show that in the case of sparse node coverage, …the particle swarm optimization node task allocation method has greatly reduced energy consumption compared with the nearest neighbor method, and can effectively solve the problem of multi-target tracking node task allocation conflict and multiple monitoring alliances on sensor resources the problem of increased system energy consumption during competition conflicts. Discrete particle swarm optimization has superiority for wireless sensor network multi-target tracking in actual environment. Show more

Keywords: Wireless sensor network, discrete particle swarm optimization, multi-target tracking

DOI: 10.3233/JIFS-189813

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: He, Weiming | Wu, You | Xiao, Jing | Cao, Yang

Article Type: Research Article

Abstract: Feature pyramids are commonly applied to solve the scale variation problem for object detection. One of the most representative works of feature pyramid is Feature Pyramid Network (FPN), which is simple and efficient. However, the fully power of multi-scale features might not be completely exploited in FPN due to its design defects. In this paper, we first analyze the structure problems of FPN which prevent the multi-scale feature from being fully exploited, then propose a new feature pyramid structure named Mixed Group FPN (MGFPN) , to mitigate these design defects of FPN. Concretely, MGFPN strengthens the feature utilization by two …modules named Mixed Group Convolution(MGConv) and Contextual Attention(CA) . MGConv reduces the spatial information loss of FPN in feature generation stage. And CA narrows the semantic gaps between features of different receptive field before lateral summation. By replacing FPN with MGFPN in FCOS, our method can improve the performance of detectors in many major backbones by 0.7 to 1.2 Average Precision(AP) on MS-COCO benchmark without adding too much parameters and it is easy to be extended to other FPN-based models. The proposed MGFPN can serve as a simple and strong alternative for many other FPN based models. Show more

Keywords: Object Detection, Feature Pyramids, FPN, Mixed Group Convolution, Contextual Attention

DOI: 10.3233/JIFS-202372

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Liu, Hui | He, Boxia | He, Yong | Tao, Xiaotian

Article Type: Research Article

Abstract: The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing …three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost. Show more

Keywords: Deep learning, feature extraction network, lightweight algorithm, multiscale classification, surface defect detection, O-rings

DOI: 10.3233/JIFS-202614

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Virk, Jitender Singh | Singh, Mandeep | Singh, Mandeep | Panjwani, Usha | Ray, Koshik

Article Type: Research Article

Abstract:  Most of the people who do not take required sleep are prone to sleep-deprived mental fatigue. This mental fatigue due to sleep deprivation is very harmful to persons involved in critical jobs like Pilots, Surgeons, Air traffic controllers and others. The present research paper proposes an intelligent method based on re-enforced learning, followed by classification supported by the adaptive threshold. Moreover, the method proposed by us is non-intrusive, in which the subject is unaware of being monitored during the test; it helps prevent biased results. The novelty lies in the use of the Inter-frame interval of an open and close …eye for feature extraction that leads to the detection of “Alertness” or “Fatigue” based on the adaptive threshold. The proposed self-learning framework is real-time in nature and has a detection accuracy of 97.5 %. Since the method is self-learning, as the size of the data set increases, its accuracy and sensitivity are likely to increase further. Show more

Keywords: Alertness, computer vision, self-learning, visual cues

DOI: 10.3233/JIFS-189784

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Zhang, Wenning | Zhou, Qinglei

Article Type: Research Article

Abstract: Combinatorial testing is a statute-based software testing method that aims to select a small number of valid test cases from a large combinatorial space of software under test to generate a set of test cases with high coverage and strong error debunking ability. However, combinatorial test case generation is an NP-hard problem that requires solving the combinatorial problem in polynomial time, so a meta-heuristic search algorithm is needed to solve the problem. Compared with other meta-heuristic search algorithms, the particle swarm algorithm is more competitive in terms of coverage table generation scale and execution time. In this paper, we systematically …review and summarize the existing research results on generating combinatorial test case sets using particle swarm algorithm, and propose a combinatorial test case generation method that can handle arbitrary coverage strengths by combining the improved one-test-at-a-time strategy and the adaptive particle swarm algorithm for the variable strength combinatorial test problem and the parameter selection problem of the particle swarm algorithm. To address the parameter configuration problem of the particle swarm algorithm, the four parameters of inertia weight, learning factor, population size and iteration number are reasonably set, which makes the particle swarm algorithm more suitable for the generation of coverage tables. For the inertia weights. Show more

Keywords: Software test data, neural network, polymorphic particle swarm, model

DOI: 10.3233/JIFS-189811

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Shao, Jingfeng | Yang, Zhigang

Article Type: Research Article

Abstract: Automobile styling design is an important part of the design chain. In the traditional automobile modeling evaluation, the process of project evaluation is more in-depth, and designers exchange ideas. Different designers have different evaluations of automobile styling. The evaluation process lasts a long time, which leads to the design cycle being too long and the efficiency of automobile modeling evaluation is greatly reduced. The introduction of virtual reality in automobile modeling evaluation can effectively optimize the evaluation process and promote the rapid adjustment of the model on the basis of development. From the virtual reality system based on mechanical engineering, …we only need the parameters of the car model to observe the actual situation through VR technology, and use the measurement tools to directly and accurately evaluate the driver’s field of vision. Through the application of virtual reality technology in the automobile design stage, the interactive and network-based remote research on automobile modeling will also make the automobile design process more convenient, easier to communicate with designers, and reduce the development cycle and cost of automobile design. Show more

Keywords: Virtual reality, automobile modeling, optimization design, man-machine evaluation

DOI: 10.3233/JIFS-189806

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Jiang, Bichuan | Shu, Lan

Article Type: Research Article

Abstract: In this paper, we study the evolutionary game dynamics of the death-birth process with interval payoffs on graphs. First of all, we derive the interval replication dynamic equation. Secondly, we derive the fixation probability of the B-C prisoner’s dilemma game based on the death-birth process under the condition of weak selection, analyze the condition of the strategy fixed in the population, that is the condition of strategy A being dominant is analyzed. So we can judge whether natural selection is beneficial to strategy A in the game process through this condition. Finally, the feasibility of this method is …verified by several examples. Show more

Keywords: Interval-valued functions, death-birth process, fixation probability, evolutionary dynamics

DOI: 10.3233/JIFS-202774

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Long, Fei

Article Type: Research Article

Abstract: The difficulty of English text recognition lies in fuzzy image text classification and part-of-speech classification. Traditional models have a high error rate in English text recognition. In order to improve the effect of English text recognition, guided by machine learning ideas, this paper combines ant colony algorithm and genetic algorithm to construct an English text recognition model based on machine learning. Moreover, based on the characteristics of ant colony intelligent algorithm optimization, a method of using ant colony algorithm to solve the central node is proposed. In addition, this paper uses the ant colony algorithm to obtain the characteristic points …in the study area and determine a reasonable number, and then combine the uniform grid to select some non-characteristic points as the central node of the core function, and finally use the central node with a reasonable distribution for modeling. Finally, this paper designs experiments to verify the performance of the model constructed in this paper and combines mathematical statistics to visually display the experimental results using tables and graphs. The research results show that the performance of the model constructed in this paper is good. Show more

Keywords: Ant colony algorithm, genetic algorithm, text recognition, English text, machine learning

DOI: 10.3233/JIFS-189807

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Mao, Yipu | Jiang, Muliang | Zhao, Fanyu | Long, Liling

Article Type: Research Article

Abstract: Currently, DSC has been extensively studied in the diagnosis, differential diagnosis and prognosis evaluation of brain lymphoma, but it has not obtained a uniform standard. By combining DSC imaging features, this study investigated the imaging features and diagnostic value of several types of tumors such as primary brain lymphoma. At the same time, this study obtained data from brain lymphoma patients by data collection and set up different groups to conduct experimental studies to explore the correlation between IVIM-MRI perfusion parameters and DSC perfusion parameters in brain lymphoma. Through experimental research, it can be seen that the combination of two …perfusion imaging techniques can more fully reflect the blood flow properties of the lesion, which is beneficial to determine the nature of the lesion. Show more

Keywords: DSC procedure, impact characteristics, brain lymphoma, feature capture, image analysis

DOI: 10.3233/JIFS-189808

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Yanyan, Zhou

Article Type: Research Article

Abstract: The motion characteristics of particulate matter are very wonderful. With the development of science and technology, the motion of granular materials has gradually become a hot topic, which has attracted the attention of many scientists and experts. The research of granular matter has gradually become specialized and systematic. With the gradual improvement of the system, a frontier research field particle physics has been formed. Under the combined action of external force and internal force, particles can reflect the properties of fluid, but in the process of flow, it will show different size separation phenomenon from the fluid. The problem of …particle separation was formally introduced into the field of physics in 1987. In reality, the existence of particles is not unique. In view of this, the author makes a systematic research and Analysis on the behavior and factors of vibration. Show more

Keywords: Image recognition, machine learning, multiple mixed particles, vibration separation

DOI: 10.3233/JIFS-189804

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: He, Peng | Wang, Xue-ping

Article Type: Research Article

Abstract: This paper first describes a characterization of a lattice L which can be represented as the collection of all up-sets of a poset. It then obtains a representation of a complete distributive lattice L 0 which can be embedded into the lattice L such that all infima, suprema, the top and bottom elements are preserved under the embedding by defining a monotonic operator on a poset. This paper finally studies the algebraic characterization of a finite distributive.

Keywords: 03E72, 06D05, L-fuzzy set, cut set, complete distributive lattice, embedding, monotonic operator

DOI: 10.3233/JIFS-201430

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhou, Yue | Wang, Xiujun | Guo, Shu | Wen, Yi | He, Jingsha

Article Type: Research Article

Abstract: The rapid development of object oriented programming (OOP) technology has made it one of the mainstream programming technologies that has been widely used in the design and development of object oriented software (OOS). The inheritance, encapsulation and polymorphism properties of object-oriented language can improve the reusability, scalability and interoperability of software while increasing the difficulty of testing OOS. Researchers have proposed a variety of testing methods to test OOS among which random testing (RT) has been widely used due to its simplicity and ease of use. An OMISS-ARTsum algorithm is proposed in this paper that uses improved OMISS random test …FSCS-ART with max-sum standard, which is an implementation version of fixed-sized-candidate-set ART. The OMISS-ARTsum algorithm calculates the total distance between a candidate test case and the executed test case set before the next test case is selected from the set of candidate test cases. Unlike the traditional max-sum based FSCS-ART algorithm, OMISS-ARTsum does not calculate the distance between each executed test case and the candidate case and then sum up the total distance, but uses the method of summing up all the executed test cases and the candidate cases. The information of executing test cases is saved as a whole and the distance between the executed test case set and candidate cases is calculated at the same time. Experiment shows that compared to the OMISS-ART algorithm, the proposed OMISS-ARTsum algorithm can reduce the time overhead. Show more

Keywords: Object oriented software, adaptive random testing, test input, time cost

DOI: 10.3233/JIFS-189701

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Meniz, Busra | Bas, Sema Akin | Ozkok, Beyza Ahlatcioglu | Tiryaki, Fatma

Article Type: Research Article

Abstract: Decision making (DM) is an important process encountered in every moment of life. Since it is difficult to interpret life depending on a single criterion, Multi-Criteria Decision Making (MCDM) enables to make decisions easier by creating appropriate choice in situations of uncertainty, complexity, and conflicting objectives. Therefore, we have studied the Analytic Hierarchy Process (AHP) which is one of the MCDM methods based on binary comparison logic. When uncertainties concerning the nature of life are considered, the solution procedure of AHP has been addressed by using Interval Type-2 Fuzzy Numbers (IT2FN)s to obtain more realistic results. The usability of AHP …with IT2FN is increased by amplifying hierarchy with sub-levels. Since sub-criterion may also need to be evaluated on sub-criteria in some cases of real multi-criteria problems, it is explicitly essential that each of sub-sub-criterion is included in the hierarchy at the own level in the real sense. In this paper, a new multilevel type-2 fuzzy AHP method is expanded by adding sub-criteria to the Interval Type-2 Fuzzy AHP (IT2FAHP) method developed by Kahraman et al. [C. Kahraman, B. Öztaysi, I. Sari and B. Turanoglu, Fuzzy analytic hierarchy process with interval type-2 fuzzy sets, Knowledge-Based Systems 59 (2014), 48–57.]. Thanks to the extended method, another aim is to ensure that even complex situations that have multiple levels can be solved simply. Also, the proposed method is illustrated with a portfolio selection problem. Thus, the AHP method with type-2 fuzzy sets is carried out to the portfolio selection problem, which is in the scope of finance theory, for the first time in the literature. Show more

Keywords: Interval type-2 fuzzy numbers, multilevel AHP, multi-criteria decision making, portfolio selection

DOI: 10.3233/JIFS-200512

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Mao, Lijun

Article Type: Research Article

Abstract: In recent years, the field of computer vision is promoted by the development of intelligent technology and computer technology, and has made breakthrough progress. Intelligent hardware technology and computer technology lay the foundation for the development of computer vision field. At the same time, the continuous improvement and development of artificial intelligence technology has also promoted the rapid development of educational video system, and the video tracking of educational video system has made breakthrough progress. By fully using intelligent hardware and computer technology, and combining with artificial intelligence technology, the video tracking and recognition technology of educational video system has …been further developed, and new recognition algorithm has been adopted. The accuracy of tracking recognition is greatly improved, which can accurately identify the action of the characters. At the same time, through the use of new action recognition algorithm, not only improve the accuracy of educational video recognition, but also improve the speed of recognition, which can accurately capture the changes of people’s behavior in the classroom. The time consumed by the action recognition algorithm is very short, and the speed of the algorithm is very high. This new algorithm greatly improves the efficiency of the education recording and broadcasting system, and improves the accuracy and accuracy of the education recording and broadcasting system. This paper studies a set of intelligent image recognition system for students’ classroom behavior. It compiles and explains the intelligent system software systematically. The operation of this system is no single. It operates through the joint operation of many modules. It can realize online distributed homework, accurately and quickly identify students’ classroom behavior, and can also help students to identify their classroom behavior accurately and quickly. The classroom behavior of the accurate analysis of students’ incorrect classroom behavior to make timely reminders, greatly improve the efficiency of the classroom, improve the degree of concentration of students. In this paper, many classroom behaviors are simulated, and the performance of this software platform is predicted through many experiments. Show more

Keywords: Improved neural network, face recognition, remote education, classroom action recognition

DOI: 10.3233/JIFS-189803

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Gu, Ning

Article Type: Research Article

Abstract: In recent years, China has increased its investment in science and technology, and digital technologies such as mobile Internet, big data, and cloud computing have continuously made breakthroughs. The integration with the modern financial industry has stimulated online lending, third-party payment, digital insurance, and New financial forms such as digital wealth management are booming. With the rapid development of digital financial inclusion, the development of traditional financial industry has broken through time and geographical constraints, allowing more groups excluded from the traditional financial system to participate in financial activities and enjoy more convenient and faster personalized financial products and services, …meet their financial needs and improve the reach of financial services. While the development of digital financial inclusion has benefited more groups, it has not changed the original risks of the financial industry. It has also brought about some negative external effects of financial technology, which poses greater challenges to the protection of financial consumers’ rights and interests. Therefore, this research aims at the digital inclusive risk prediction of financial institutions and personal risk prediction respectively, and proposes a financial risk prediction method based on the adaptive fusion of multi-source heterogeneous data, which can improve the effect of financial risk prediction through the effective use of multi-source data. purpose. Show more

Keywords: Machine learning, neural network, digital financial inclusion, risk prediction and prevention

DOI: 10.3233/JIFS-189805

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Wang, Junbin | Qin, Zhongfeng

Article Type: Research Article

Abstract: The hub maximal covering location problem aims to find the best locations for hubs so as to maximize the total flows covered by predetermined number of hubs. Generally, this problem is defined in the framework of binary coverage. However, there are many real-life cases in which the binary coverage assumption may yield unexpected decisions. Thus, the partial coverage is considered by stipulating that the coverage of an origin-destination pair is determined by a non-increasing decay function. Moreover, as this problem contains strategic decisions in long range, the precise information about the parameters such as travel times may not be obtained …in advance. Therefore, we present uncertain hub maximal covering location models with partial coverage in which the travel times are depicted as uncertain variables. Specifically, the partial coverage parameter is introduced in uncertain environment and the expected value of partial coverage parameter is further derived and simplified with specific decay functions. Expected value model and chance constrained programming model are respectively proposed and transformed to their deterministic equivalent forms. Finally, a greedy variable neighborhood search heuristic is presented and the efficiency of the proposed models is evaluated through computational experiments. Show more

Keywords: Hub maximal covering location problem, partial coverage, decay function, uncertain variable

DOI: 10.3233/JIFS-202635

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Liu, Chenguang

Article Type: Research Article

Abstract: The construction industry is an indispensable and important support in the national economic industry. The characteristics of the construction industry, such as long production cycle, large number of participants and various types, determine that the development of the construction industry is undoubtedly very difficult. In order to realize the rapid development of the construction industry, transformation is the inevitable development direction of the construction industry in the future, which requires the help of science and technology. With the development of science and technology, information technology and big data have been applied to all walks of life, and these are also …important means to support the transformation of the construction industry. In order to achieve green development, reducing energy consumption is an inevitable measure. Energy consumption analysis and reduction can be realized by establishing energy consumption monitoring platform based on big data. The application of BIM system is an information-based energy consumption analysis method. This technology can realize the analysis and prediction of energy consumption, so as to determine the appropriate way to save energy, and even estimate the corresponding cost. It is of great significance to establish a suitable energy-saving scheme. Show more

Keywords: BIM system, improved neural network, green building, energy consumption simulation

DOI: 10.3233/JIFS-189802

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Li, Long | Xie, Zhongqu | Luo, Xiang | Li, Juanjuan | He, Yufeng

Article Type: Research Article

Abstract: Gait pattern generation has an important influence on the walking quality of biped robots. In most gait pattern generation method, it is usually assumed that the torso remains vertial during walking. It is very intuitive and simple. However, is the gait pattern of keeping the torso vertical the most efficient? This paper presents a gait pattern in which the torso has pitch motion during walking. We define the cyclic gait of a seven-link biped robot with multiple gait parameters. The gait parameters are determined by optimization. The optimization criterion is choosen to minimize the energy consumption per unit distance of …the biped robot. In order to compare the energy consumption of the proposed gait pattern with the one of torso vertical gait pattern, we generate two sets of optimal gait with various walking step lengths and walking periods. The results show that the proposed gait pattern is more energy-efficiency than the torso vertical gait pattern. Show more

Keywords: Biped robot, gait pattern generation, optimal gait, walking efficient, torso pitch motion

DOI: 10.3233/JIFS-189699

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Ansar, Wazib | Goswami, Saptarsi | Chakrabarti, Amlan | Chakraborty, Basabi

Article Type: Research Article

Abstract: Aspect-Based Sentiment Analysis (ABSA) has become a trending research domain due to its ability to transform lives as well as the technical challenges involved in it. In this paper, a unique set of rules has been formulated to extract aspect-opinion phrases. It helps to reduce the average sentence length by 84% and the complexity of the text by 50%. A modified rank-based version of Term-Frequency - Inverse-Document-Frequency (TF-IDF) has been proposed to identify significant aspects. An innovative word representation technique has been applied for aspect categorization which identifies both local as well as global context of a word. For sentiment …classification, pre-trained Bidirectional Encoder Representations from Transformers (BERT) has been applied as it helps to capture long-term dependencies and reduce the overhead of training the model from scratch. However, BERT has drawbacks like quadratic drop in efficiency with an increase in sequence length which is limited to 512 tokens. The proposed methodology mitigates these drawbacks of a typical BERT classifier accompanied by a rise in efficiency along with an improvement of 8% in its accuracy. Furthermore, it yields enhanced performance and efficiency compared to other state-of-the-art methods. The assertions have been established through extensive analysis upon movie reviews and Sentihood data-sets. Show more

Keywords: Aspect-based sentiment analysis, aspect extraction, BERT, TF-IDF, word embedding

DOI: 10.3233/JIFS-202140

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Iranmanesh, Seyed Mehdi | Nasrabadi, Nasser M.

Article Type: Research Article

Abstract: In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the …estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines. Show more

Keywords: Generative adversarial network, adversarial training, mode collapse, network distillation, autoregressive model

DOI: 10.3233/JIFS-201202

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Saleem, Naeem | Işık, Hüseyin | Furqan, Salman | Park, Choonkil

Article Type: Research Article

Abstract: In this paper, we introduce the concept of fuzzy double controlled metric space that can be regarded as the generalization of fuzzy b -metric space, extended fuzzy b -metric space and controlled fuzzy metric space. We use two non-comparable functions α and β in the triangular inequality as: M ( x , z , t α ( x , y ) + s β ( y , z ) ) ≥ M ( x , y , t ) ∗ M ( y , z , s ) . We prove Banach contraction …principle in fuzzy double controlled metric space and generalize the Banach contraction principle in aforementioned spaces. We give some examples to support our main results. An application to existence and uniqueness of solution for an integral equation is also presented in this work. Show more

Keywords: Extended fuzzy b-metric space, controlled fuzzy metric space, fixed point

DOI: 10.3233/JIFS-202594

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Elngar, Ahmed A.

Article Type: Editorial

DOI: 10.3233/JIFS-189844

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-1, 2021

Authors: Aski, Baharak Shakeri | Haghighat, Abolfazl Toroghi | Mohsenzadeh, Mehran

Article Type: Research Article

Abstract: Using Web services to assess data in a distributed configuration, apart from different hardware and software platforms for employing standard criteria, is practical because of development in the Internet and network infrastructure. Distributed applications can transfer data using web services. Trust is the main criterion to select the appropriate web service. Neuro-fuzzy systems including clustering are applied to assess the trust of single web services. This paper considers nine criteria including quality of service, subjective perspectives, user preference, credibility of raters, objective perspectives, dynamic computing, bootstrapping, independency and security. To obtain a neuro-fuzzy system with high prediction accuracy, the paper …considers eight neuro-fuzzy membership functions (i.e., trapmf, gbellmf, trimf, gaussmf, dsigmf, psigmf, gauss2mf, pimf) using the k-means clustering. Also, to increase the speed and reduce the fuzzy rules, a three-level neuro-fuzzy system (13 neuro-fuzzy) is investigated. The main target of this paper is evaluating the trust of single web services using the nine aforementioned criteria, as web services selection is a main issue which is still absorbing researchers to conduct research works on this field and analyze it. Ultimately, the results show reasonable root mean square error (RMSE) amount, precision value, recall value, and F-score value. In comparison to previous research works, this study obtained the lower amounts of errors and presents the more accurate trust of single web services. Show more

Keywords: Web service, internet service, trust, neuro-fuzzy system, k-means

DOI: 10.3233/JIFS-201560

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Devarapalli, Ramesh | Venkateswara Rao, B. | Dey, Bishwajit | Vinod Kumar, K. | Malik, H. | Márquez, Fausto Pedro García

Article Type: Research Article

Abstract: Nowadays, improvement in power system performance is essential to obtaine economic and technical benifits. To achieve this, optimize the large number of parameters in the system based on optimal power flow(OPF). For solving OPF problem efficiently, it needs robust and fast optimization techniques. This paper proposes the application of a newly developed hybrid Whale and Sine Cosine optimization algorithm to solve the OPF. It has been implemented for optimization of the control variables. The reduction of true power generation cost, emission, true power losses, and voltage deviation are considered as different objectives. The hybrid Whale and Sine Cosine optimization is …validated by solving OPF problem with various intentions using IEEE30 bus system. To varidate the proposed technique, the results obtained from this are compared with other methods in the literature. The robustness achieved with the proposed algorithm has been analyzed for the considered OPF problem using statistical analysis and whisker plots. Show more

Keywords: Optimal power flow, sine cosine optimization, voltage deviation, whale optimization

DOI: 10.3233/JIFS-189763

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Oguz, Gulay | Davvaz, Bijan

Article Type: Research Article

Abstract: Molodtsov proposed the theory of soft sets which can be considered as a recent mathematical tool to deal with uncertainties. The main purpose of this paper is to give the definition of soft topological hypergrupoid by examining the concept of hypergrupoid which is one of the hyperystructures with soft set theory from the topological point of view. Also, the relation between soft topological hypergroupoids and soft hypergroupoids is examined and some theoretical results are obtained. By introducing the concept of soft good topological homomorphism, the category of soft topological hypergrupoids is constructed. At last, the definition of soft topological subhypergrupoid …is presented and some related properties are studied. Show more

Keywords: Soft set, topological hypergroupoid, soft hypergrupoid, soft topological hypergroupoid

DOI: 10.3233/JIFS-200242

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Srikanth, Pullabhatla | Koley, Chiranjib

Article Type: Research Article

Abstract: In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, …and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM. Show more

Keywords: Power system faults, localization, identification, fuzzy logic, signal processing

DOI: 10.3233/JIFS-189769

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Zhan, Keqiang

Article Type: Research Article

Abstract: In recent years, the application of network technology has become popular, and the application of the Internet has occupied a large proportion in people’s daily life. The issue of network security has also begun to be paid attention to. In recent years, due to the rapid expansion of network applications, malicious events such as network threats have occurred from time to time. Many computers do not have a good protection structure so that they are often vulnerable to attacks during network interconnection. Traditional computer protection measures are border-based protection, but with the development of network technology, this protection system can …no longer guarantee computer security. Therefore, in order to keep computers safe in the current network environment, the role of artificial intelligence in the computer field should be given full play. The artificial intelligence analysis system can analyze and predict the situation of computer network security based on the situation of network security. This paper integrates convolutional neural network algorithms on the basis of traditional machine learning to establish a new network intrusion model. This paper verifies the feasibility of the model through experiments, and the experimental results show that the accuracy of the new model proposed in this paper can reach more than 90% for KDDCUP99 data detection. In addition, traditional computer protection systems have many errors when dealing with DNN attack detection. In order to reduce the occurrence of this situation, this paper proposes a standardized attack detection model based on deep nerves. The detection precision of this model is higher and the results obtained are more accurate. In addition, this new model can also synthesize the impact of different network attacks on the security situation, and construct attack situation predictions for computer systems. Show more

Keywords: Artificial intelligence, neural network, network security, defense system

DOI: 10.3233/JIFS-189794

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Feng, Lv

Article Type: Research Article

Abstract: In recent years, scientists have begun to introduce dynamic elements into wireless networks. With the introduction of mobile sink node, the phenomenon of “hot node” and “energy hole” can be effectively avoided, so as to realize more network connection and improve network flexibility. Therefore, it is imperative to design energy-saving algorithm with popular sink code. In this paper, a multi hop data forwarding algorithm is proposed for solar powered wireless sensor networks. The algorithm divides the monitoring area of the network and the communication area of the node. Through the sensor node, the next hop node is selected from the appropriate …area, thus forming the path from the data source point to the base station. At the same time, in order to reduce the energy consumption and delay in the network, a multi-objective programming model of the next hop data forwarding node is established. The reasonable area of static and dynamic area is calculated by mathematical analysis. Finally, the paper calculates the network’s life cycle, energy consumption and transmission time, and compares the static sink with the network using only mobile sink. Show more

Keywords: Wireless sensor, network, energy saving algorithm, simulation test

DOI: 10.3233/JIFS-189789

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Qiu, Chenye | Liu, Ning

Article Type: Research Article

Abstract: Feature selection (FS) is a vital data preprocessing task which aims at selecting a small subset of features while maintaining a high level of classification accuracy. FS is a challenging optimization problem due to the large search space and the existence of local optimal solutions. Particle swarm optimization (PSO) is a promising technique in selecting optimal feature subset due to its rapid convergence speed and global search ability. But PSO suffers from stagnation or premature convergence in complex FS problems. In this paper, a novel three layer PSO (TLPSO) is proposed for solving FS problem. In the TLPSO, the particles …in the swarm are divided into three layers according to their evolution status and particles in different layers are treated differently to fully investigate their potential. Instead of learning from those historical best positions, the TLPSO uses a random learning exemplar selection strategy to enrich the searching behavior of the swarm and enhance the population diversity. Further, a local search operator based on the Gaussian distribution is performed on the elite particles to improve the exploitation ability. Therefore, TLPSO is able to keep a balance between population diversity and convergence speed. Extensive comparisons with seven state-of-the-art meta-heuristic based FS methods are conducted on 18 datasets. The experimental results demonstrate the competitive and reliable performance of TLPSO in terms of improving the classification accuracy and reducing the number of features. Show more

Keywords: Feature selection, particle swarm optimization, three layer structure, random exemplar selection, local search operator

DOI: 10.3233/JIFS-202647

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Sarin, Sumit | Mittal, Antriksh | Chugh, Anirudh | Srivastava, Smriti

Article Type: Research Article

Abstract: Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product …fusion rules work best for the data used in the experiments. Show more

Keywords: Multimodal, touchless, biometric system, gait recognition, speech recognition

DOI: 10.3233/JIFS-189765

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Gupta, Sangeeta | Varshney, Pragya | Srivastava, Smriti

Article Type: Research Article

Abstract: This paper proposes a scheme to synchronize fractional order chaotic systems employing fractional PID controller. The parameters of FOPID are tuned using Swarm based optimization techniques, viz.: Whale optimization algorithm and Particle swarm optimization techniques. To assert the complete synchronization, master-slave method has been implemented. Chaotic systems are highly dependent upon initial conditions and parameter perturbations. Therefore, taking these properties into consideration, synchronization of two identical fractional order financial chaotic systems is performed with distinct initial conditions. To show the efficacy of the proposed method, analysis is performed for orders between 0 to 1, and also for sensitivity to initial …conditions. Show more

Keywords: Fractional order chaotic system (FOCS), fractional order financial chaotic system (FOFCS), whale optimization algorithm (WOA), particle swarm optimization (PSO), proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller

DOI: 10.3233/JIFS-189761

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Sun, Zhuo | Zhang, Shuang | Liu, Mairu

Article Type: Research Article

Abstract: Horse racing is different from other competitive sports. It is an entertainment sport which combines commercialization and sports spirit. Therefore, horse racing has the characteristics of sports and commercial events. In addition, horse racing also has the nature of social welfare, and part of the funds for holding horse racing will be used for fund-raising. Now horse racing has become a complete industry and an important part of the tertiary industry. Horse racing, as a new industry, has developed rapidly. However, there are still some defects in the management of horse racing events. The main problem is the lack of …information management of horse racing industry. The introduction of information technology into the management of horse racing industry can realize the efficient management of industrial information, which is more professional and orderly for horse racing. Show more

Keywords: Machine learning, data mining, speed racing, industry information management

DOI: 10.3233/JIFS-189801

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Hasan, Mashhood | Alhazmi, Waleed Hassan | Zakri, Waleed

Article Type: Research Article

Abstract: In this paper, a solar photovoltaic model integrated with brushless DC motor via DC to DC zeta converter is controlled in two stage. In first stage, a fuzzy rule based maximum power point tracking (PPT) is proposed to generate the pulse for DC to DC zeta converter. It is efficient intelligent control approach to extract maximum power from the solar PV system and enhance the speed to track the maximum power. The basic three process of fuzzy logic controller (FLC) are fuzzifier, inference and defuzzifier where the defuzzification process is used center of gravity (COG) method to convert its original …value. The FLC to extract maximum PPT for solar PV based brushless DC motor can be examined the performance under transient and dynamic condition with different solar insolation. Moreover, in second stage a trapezoidal control approach based electronic commutation is chosen to generate the pulses of voltage source inverter (VSI) and it offers the smooth control of the brushless DC motor which can easily applicable for water pumping or irrigation purpose. A second stage, trapezoidal control approach is close loop control algorithm using sensorless drive. The performance of proposed fuzzy rule based control algorithm is shown using simulation results on MATLAB platform. Show more

Keywords: Center of gravity, fuzzy logic controller, fuzzy rule, membership function, solar photovoltaic

DOI: 10.3233/JIFS-189767

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Meghana, Pulimamidi | Yammani, Chandrasekhar | Salkuti, Surender Reddy

Article Type: Research Article

Abstract: This paper proposes an energy scheduling mechanism among multiple microgrids (MGs) and also within the individual MGs. In this paper, electric vehicle (EV) energy scheduling is also considered and is integrated in the operation of the microgrid (MG). With the advancements in the battery technologies of EVs, the significance of Vehicle-to-Grid (V2G) is increasing tremendously. So, designing the strategies for energy management of electric vehicles (EVs) is of paramount importance. The battery degradation cost of an EV is also taken into account. Vickrey second price auction is used for truthful bidding. To enhance the security and trust, blockchain technology can …be incorporated. The market is shifted to decentralized state by using blockchain. To encourage the MGs to generate more, contribution index is allotted to each prosumer of a MG and to the MGs as a whole, depending on which priority is given during auction. The system was simulated using IEEE 118 bus feeder which consists of 5 MGs, which in turn contain EVs and prosumers. Show more

Keywords: Blockchain technologies, distributed generators, electric vehicles, green energy, microgrid, vehicle-to-grid

DOI: 10.3233/JIFS-189766

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Songyun, Wang

Article Type: Research Article

Abstract: With the development of social economy and the improvement of science and technology, digital video on the Internet is increasing rapidly, and it has become a new force to promote the development of the times. Most of these videos are stored in the memory, which poses a great challenge to the research and development of the system. The reader service system is an important part of library service. The library uses it to collect information resources, not just for service and work. The document combines the video of library service, the analysis of video recovery and video software requirements of …digital library, puts forward the design goal and conception of video search, and puts forward a foundation. From the video data of digital library, video retrieval experiments are gradually carried out. These experimental results show that the number of enhanced dynamic clustering algorithm increases to ensure the complexity of the image. Show more

Keywords: Artificial intelligence, machine learning, intelligent library, reader service system

DOI: 10.3233/JIFS-189800

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Tyagi, Shikhar | Chawla, Bhavya | Jain, Rupav | Srivastava, Smriti

Article Type: Research Article

Abstract: Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional …neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates. Show more

Keywords: Multimodal biometrics, face, finger vein, convolutional neural network, score level fusion

DOI: 10.3233/JIFS-189762

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Gan, Weichao | Ma, Zhengming | Liu, Shuyu

Article Type: Research Article

Abstract: Tensor data are becoming more and more common in machine learning. Compared with vector data, the curse of dimensionality of tensor data is more serious. The motivation of this paper is to combine Hilbert-Schmidt Independence Criterion (HSIC) and tensor algebra to create a new dimensionality reduction algorithm for tensor data. There are three contributions in this paper. (1) An HSIC-based algorithm is proposed in which the dimension-reduced tensor is determined by maximizing HSIC between the dimension-reduced and high-dimensional tensors. (2) A tensor algebra-based algorithm is proposed, in which the high-dimensional tensor are projected onto a subspace and the projection coordinate …is set to be the dimension-reduced tensor. The subspace is determined by minimizing the distance between the high-dimensional tensor data and their projection in the subspace. (3) By combining the above two algorithms, a new dimensionality reduction algorithm, called PDMHSIC, is proposed, in which the dimensionality reduction must satisfy two criteria at the same time: HSIC maximization and subspace projection distance minimization. The proposed algorithm is a new attempt to combine HSIC with other algorithms to create new algorithms and has achieved better experimental results on 8 commonly-used datasets than the other 7 well-known algorithms. Show more

Keywords: Dimensionality reduction, tensor mode product, hilbert-schmidt independence criterion, reproducing kernel hilbert space

DOI: 10.3233/JIFS-202582

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Malik, Shaily | Bansal, Poonam

Article Type: Research Article

Abstract: The real-world data is multimodal and to classify them by machine learning algorithms, features of both modalities must be transformed into common latent space. The high dimensional common space transformation of features lose their locality information and susceptible to noise. This research article has dealt with this issue of a semantic autoencoder and presents a novel algorithm with distinct mapped features with locality preservation into a commonly hidden space. We call it discriminative regularized semantic autoencoder (DRSAE). It maintains the low dimensional features in the manifold to manage the inter and intra-modality of the data. The data has multi labels, …and these are transformed into an aware feature space. Conditional Principal label space transformation (CPLST) is used for it. With the two-fold proposed algorithm, we achieve a significant improvement in text retrieval form image query and image retrieval from the text query. Show more

Keywords: Semantic autoencoder, hypergraph, twofold validation, cross model retrieval

DOI: 10.3233/JIFS-189759

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Bhati, Nitesh Singh | Khari, Manju

Article Type: Research Article

Abstract: With the increase in the amount of data available today, the responsibility of keeping that data safe has also taken a more severe form. To prevent confidential data from getting in the hands of an attacker, some measures need to be taken. Here comes the need for an effective system, which can classify the traffic as an attack or normal. Intrusion Detection Systems can do this work with perfection. Many machine learning algorithms are used to develop efficient IDS. These IDS provide remarkable results. However, ensemble-based IDS using voting have been seen to outperform individual approaches (Support Vector Machine and …ExtraTree). Since the Voting methodology is able to work around both, theoretically similar and different classifiers and produce a single classifier based on the majority characteristics, it proved to be better than the other ensemble based techniques. In this paper, an ensemble IDS implementation is presented based on the voting ensemble method, using the two algorithms, Support Vector Machine (SVC) and ExtraTree. The experiment is performed on the KDDCup99 Dataset. The evaluation of the performance of the proposed method is based on the comparison with an unoptimized implementation of the same. The results based on performing the experiment in Python fetched an accuracy of 99.90%. Show more

Keywords: Security, intrusion detection system, network security, ensemble, voting, machine learning

DOI: 10.3233/JIFS-189764

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Venkateswara Rao, B. | Devarapalli, Ramesh | Malik, H. | Bali, Sravana Kumar | Márquez, Fausto Pedro García | Chiranjeevi, Tirumalasetty

Article Type: Research Article

Abstract: The trend of increasing demand creates a gap between generation and load in the field of electrical power systems. This is one of the significant problems for the science, where it require to add new generating units or use of novel automation technology for the better utilization of the existing generating units. The automation technology highly recommends the use of speedy and effective algorithms in optimal parameter adjustment for the system components. So newly developed nature inspired Bat Algorithm (BA) applied to discover the control parameters. In this scenario, this paper considers the minimization of real power generation cost with …emission as an objective. Further, to improve the power system performance and reduction in the emission, two of the thermal plants were replaced with wind power plants. In addition, to boost the voltage profile, Static VAR Compensator (SVC) has been integrated. The proposed case study, i.e., considering wind plant and SVC with BA, is applied on the IEEE30 bus system. Due to the incorporation of wind plants into the system, the emission output is reduced, and with the application of SVC voltage profile improved. Show more

Keywords: Bat algorithm, emission, optimal power flow, SVC, wind power

DOI: 10.3233/JIFS-189770

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Setiawan, Noor Akhmad | Nugroho, Hanung Adi | Persada, Anugerah Galang | Yuwono, Tito | Prasojo, Ipin | Rahmadi, Ridho | Wijaya, Adi

Article Type: Research Article

Abstract: Arrhythmia is a disease often encountered in patients with cardiac problems. The presence of arrhythmia can be detected by an electrocardiogram (ECG) test. Automatic observation based on machine learning has been developed for long time. Unfortunately, only few of them have capability of explaining the knowledge inside themselves. Thus, transparency is important to improve human understanding of knowledge. To achieve this goal, a method based on cascaded transparent classifier is proposed, a method was prepared. Firstly, ECG signals were separated and every single signal was extracted using feature extraction method. Several of extracted feature’s attributes were selected, and the final …step was classifying data using cascade classifier which consists of decision tree and the rule based classifier. Classification performance was evaluated with publicly available dataset, the MIT-BIH Physionet Dataset. The methods were tested using 10-fold cross validation. The average of both accuracy and number of rules generated was considered. The best result using rule-based classifier achieves the accuracy and the number of rules 92.40% and 40, respectively. And the best result using cascade classifier achieves the accuracy and the number of rules 92.84% and 80, respectively. As a conclusion, transparent classifier shows a competitive performance with reasonable accuracy compared with previous research and promising in addressing the need for interpretability model. Show more

Keywords: Physionet, arrhythmia, cascade, transparent classifier

DOI: 10.3233/JIFS-189768

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Anees, Mohd. Anas | Mohammad, | Lodi, Kaif Ahmed | Alam, Mahetab | Chakrabortty, Ripon K. | Ryan, Michael J.

Article Type: Research Article

Abstract: This paper proposes a model predictive control strategy for 15 level Packed-U-Cell inverter that satisfies multiple-objectives of low current total harmonic distortion (THD), capacitor voltage balances, supply of desired active and reactive power, as well as lower switching and lower voltage stresses on the switching devices. The proposed device performs well under dynamic conditions and can successfully track the current command during step changes in the power demand. A detailed modeling is presented and discussed. MATLAB/Simulink is used for obtaining the simulation results, and the results are validated in the real time by using a hardware-in-the-loop (HIL) Typhoon 402 real-time …emulator. Show more

Keywords: Model predictive control, packed-U-Cell, reactive power compensation, multilevel inverter

DOI: 10.3233/JIFS-189749

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Mirsadeghpour Zoghi, S.M. | Saneie, M. | Tohidi, G. | Banihashemi, Sh. | Modarresi, N.

Article Type: Research Article

Abstract: According to modern finance theory and increasing need for efficient investments, we evaluate the portfolio performance based on the data envelopment analysis method. By the fact that stock market’s return distributions usually exhibit skewness, kurtosis and heavy-tails, we consider some appropriate underlying distributions that affect the input and output of the model. In this regard, the multivariate skewed t and the multivariate generalized hyperbolic as the heavy-tailed distributions of Normal mean-variance mixture are applied. The models are inspired by the Range Directional Measure (RDM) model to deal with negative values. The value-at-risk (VaR) and conditional VaR (CVaR) as risk …measures are used in these optimization problems. We estimate the parameters of such distributions by Expectation Maximization algorithm. Then we present an empirical investigation to measure the relative efficiency of two sets of seven groups of companies from different industries of Iran stock exchange market. By comparing the results of introduced models with previous RDM approach, we show that how well the distribution of assets affect the performance evaluation. Show more

Keywords: Data envelopment analysis, normal mean-variance mixture distributions, portfolio optimization, VaR, CVaR

DOI: 10.3233/JIFS-202332

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Bisht, Vimal Singh | Hasan, Mashhood | Malik, Hasmat | Sunori, Sandeep

Article Type: Research Article

Abstract: For estimation of the RUL (Remaining useful life) of Lithium ion battery we are required to do its health assessment using online facilities. For identifying the health of a battery its internal resistance and storage capacity plays the major role. However the estimation of both these parameters is not an easy job and requires lot of computational work to be done. So to overcome this constraint an easy alternate way is simulated in the paper through which we can estimate the RUL. For formation of a linear relationship between health index of the battery (HI) and its actual capacity used …of power transformation method is done and later on to validate the result a comparison study is done with Pearson & Spearman methods. Transformed value of Health Index is used for developing a neural network. The results demonstrated in the paper shows the feasibility of the proposed technique resulting in great saving of time Show more

Keywords: Remaining-useful-life, health indicator, lithium-Ion battery, Box-Cox, data-driven

DOI: 10.3233/JIFS-189758

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Huang, Jinfang | Jin, Xin | Lee, Shin-Jye | Huang, Shanshan | Jiang, Qian

Article Type: Research Article

Abstract: Since the intuitionistic fuzzy set (IFS) was proposed by Atanassov, many explorations of this particular fuzzy set were conducted. One of the most important areas is the study of similarity and distance between IFSs, which can measure the degree of deviation of objects with uncertain and vague features, and this technique has great value and potential to solve the fuzzy and uncertain problems in the real world. Based on our previous similarity/distance measure model D JJ  (α , β ), a new method is proposed for improving the performance of similarity/distance measure model of IFSs, which is derived from …the sum of the areas of two triangles constructed by the transformed isosceles triangles of two IFSs. A great effort is made to prove the validity of the proposed method by mathematical derivation. In order to further demonstrate the performance of the proposed method, we apply this method to solve some practical problems such as pattern recognition, medical diagnosis, and cluster analysis. In addition, we also list a series of the existing methods which are used to compare with the proposed method to prove the effectiveness and superiority. The experimental results confirm that the performance of the proposed method exceeds most of the existing methods. Show more

Keywords: Intuitionistic fuzzy set, similarity/distance measure, transformed isosceles triangle fuzzy number, decision-making, cluster analysis

DOI: 10.3233/JIFS-201763

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Lijun, Wang | Yaqian, Pang | Mengdong, Chen

Article Type: Research Article

Abstract: Data envelopment analysis (DEA) was used to measure the comprehensive efficiency, pure technical efficiency and scale efficiency of science and technology business incubators in 11 provinces and cities of the Yangtze River economic belt from 2011 to 2017, and the situation of incubators in the Yangtze River economic belt was analyzed from the overall, horizontal and vertical perspectives. Results show that the overall operation efficiency of science and technology business incubators in the Yangtze River economic belt is relatively high, but it shows a downward trend in the sample period, and it is found that the development of science and …technology business incubators in the Yangtze River economic belt is unbalanced, there are regional differences, and some provinces and cities have serious redundancy of incubator personnel and incubation funds. On this basis, some suggestions are put forward, such as reducing the number of managers and tutors, adjusting the dominant position of government investment in science and technology business incubators, and creating resource input sharing enterprise output circulation chain. Show more

Keywords: DEA, operating efficiency, business incubator, yangtze river economic belt

DOI: 10.3233/JIFS-189916

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Srivastava, Vishal | Srivastava, Smriti

Article Type: Research Article

Abstract: Ball and beam is a popular benchmark problem in control engineering. Various control strategies have been proposed on ball & beam system in literature, In this paper, hybrid optimization algorithms have been implemented on PID controller to control ball position and beam angle. Hybrid algorithms combine exploration and exploitation ability of individual algorithm and find optimized value of performance index. In this paper, two hybrid algorithms namely PSO-GSA and PSO-GWO are used to tune controller parameters which in turn improve the system performance. Simulation results show effective and efficient improvement in system performance with these hybrid algorithms. To analyze the …performance of these algorithms, time domain parameters and mean square error (MSE) has been taken as performance index. A comparative study of these algorithms with that of individual algorithms namely PSO, GWO, GSA has also been done. Show more

Keywords: Ball and beam, particle swarm optimization (PSO), gravitational search algorithm (GSA), grey wolf optimization (GWO), mean square error (MSE), robustness

DOI: 10.3233/JIFS-189760

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Alzubi, Omar A.

Article Type: Research Article

Abstract: Industrial Wireless Sensor Network (IWSN) includes numerous sensor nodes that collect data about target objects and transmit to sink nodes (SN). During data transmission among nodes, intrusion detection is carried to improve data security and privacy. Intrusion detection system (IDS) examines the network for intrusions based on user activities. Several works have been done in the field of intrusion detection and different measures are carried out to increase data security from the issues related to black hole, Sybil attack, Worm hole, identity replication attack and etc. In various existing approaches, secure data transmission is not achieved, therefore resulted in compromising …the security and privacy of IWSNs. Accurate intrusion detection is still challenging task in terms of improving security and intrusion detection rate. In order to improve intrusion detection rate (IDR) with minimum time, generalized Frechet Hyperbolic Deep and Dirichlet Secured (FHD-DS) data communication model is introduced. At first, Frechet Hyperbolic Deep Traffic (FHDT) feature extraction method is designed to extract more relevant network activities and inherent traffic features. With the help of extracted features, anomalous or normal data is predicted. Followed by Statistical Dirichlet Anomaly-based Intrusion Detection model is applied to discover intrusion. Here, Dirichlet distribution is evaluated to attain secure data transmission and significantly detect intrusions in WSNs. Experimental evaluation is carried out with KDD cup 99 dataset on factors such as IDR, intrusion detection time (IDT) and data delivery rate (DDR). The observed results show that the generalized FHD-DS data communication method achieves higher IDR with minimum time. Show more

Keywords: Deep learning, intrusion detection, industrial wireless sensor networks, IWSN security, Fréchet hyperbolic, statistical dirichlet distribution, machine learning, security

DOI: 10.3233/JIFS-189756

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Li, Yanling

Article Type: Research Article

Abstract: The teaching resource bank is very important for the education of linguistics. In the construction of teaching Chinese as a foreign language, we should first build a teaching resource library. Only in this way can we meet the requirements of the Ministry of education and achieve better teaching objectives. The purpose of the construction of teaching resources database is to make the teaching resources can be systematically and scientifically planned, stored in the computer in the form of data, and can be directly extracted from the computer when necessary, so as to realize the efficient utilization of resources. Based on …the existing problems of teaching Chinese as a foreign language, this paper puts forward some suggestions on the construction of teaching resource database. In recent years, the rise of artificial intelligence technology provides a new idea for the establishment of teaching resource database. According to the basic idea of artificial intelligence, scientists have established a new database of teaching Chinese as a foreign language, updated and improved the original database, and established a new standard for teaching database according to the characteristics of artificial intelligence technology. A teaching resource database system based on teaching resources and artificial intelligence is established. The system is divided into data layer, logic layer and presentation layer. Artificial intelligence is used as a bridge connecting different parts. In the process of establishing the multimedia database of TCFL, we should develop in all aspects, and pave the way for the future research after laying a solid foundation of data. Show more

Keywords: Artificial intelligence, chinese as a foreign language, teaching resource library, XML, net

DOI: 10.3233/JIFS-189793

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Jing, Wu

Article Type: Research Article

Abstract: In year 2020, a large-scale outbreak of pneumonia caused by new coronavirus has affected the development of many industries and enterprises in China. Under the strong leadership of the Chinese government, the development of the epidemic situation in China has been well controlled. The development of various industries also began to show a good situation, many large-scale sports competitions also need to be restored. In order to ensure the normal development of large-scale sports events, we need to consider the development of epidemic situation to determine the time of sports events. Based on the study of FPGA theory, this paper …designs a specific scheme of programming and system debugging, which includes a variety of program operations. In order to better predict the situation of the epidemic situation, this paper also uses the basic knowledge of machine learning to establish a relevant model to evaluate the situation of large-scale sports events under the development of the epidemic situation, and provide feasible suggestions for the recovery of large-scale sports events under the epidemic situation. Show more

Keywords: FPGA system, machine learning, epidemic prevention and control, sports events

DOI: 10.3233/JIFS-189791

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Yang, Yang

Article Type: Research Article

Abstract: In order to improve the effect of sports movement training, this paper builds a sports movement training model based on artificial intelligence technology based on the generation of confrontation network model. Moreover, in order to achieve the combination of model and model-free deep reinforcement learning algorithm, this paper implements the model’s guidance and constraints on deep reinforcement learning algorithm from the perspective of reward value and behavior strategy and divides the model into two situations. In one case, the existing or manually established expert rules are used as model constraints, which is equivalent to online learning by experts. In another …case, expert samples are used as model constraints, and an imitation learning method based on generative adversarial networks is introduced. Moreover, using expert samples as training data, the mechanism that the model is guided by the reward value is combined with the model-free algorithm by generating a confrontation network structure. Finally, this paper studies the performance of the model through experimental research. The research results show that the model constructed in this paper has a certain effect. Show more

Keywords: Generative confrontation network, artificial intelligence, sports action, machine learning

DOI: 10.3233/JIFS-189799

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021