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

Authors: Chen, Yao-Mei | Chen, Yenming J. | Tsai, Yun-Kai | Ho, Wen-Hsien | Tsai, Jinn-Tsong

Article Type: Research Article

Abstract: A multi-layer convolutional neural network (MCNN) with hyperparameter optimization (HyperMCNN) is proposed for classifying human electrocardiograms (ECGs). For performance tests of the HyperMCNN, ECG recordings for patients with cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR) were obtained from three PhysioNet databases: MIT-BIH Arrhythmia Database, BIDMC Congestive Heart Failure Database, and MIT-BIH Normal Sinus Rhythm Database, respectively. The MCNN hyperparameters in convolutional layers included number of filters, filter size, padding, and filter stride. The hyperparameters in max-pooling layers were pooling size and pooling stride. Gradient method was also a hyperparameter used to train the MCNN model. …Uniform experimental design approach was used to optimize the hyperparameter combination for the MCNN. In performance tests, the resulting 16-layer CNN with an appropriate hyperparameter combination (16-layer HyperMCNN) was used to distinguish among ARR, CHF, and NSR. The experimental results showed that the average correct rate and standard deviation obtained by the 16-layer HyperMCNN were superior to those obtained by a 16-layer CNN with a hyperparameter combination given by Matlab examples. Furthermore, in terms of performance in distinguishing among ARR, CHF, and NSR, the 16-layer HyperMCNN was superior to the 25-layer AlexNet, which was the neural network that had the best image identification performance in the ImageNet Large Scale Visual Recognition Challenge in 2012. Show more

Keywords: Convolutional neural network, hyperparameter, human electrocardiogram, PhysioNet, uniform experimental design approach

DOI: 10.3233/JIFS-189610

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

Authors: Wang, Xiaodong | Wang, Xiaoming | Wu, Junfeng | Zheng, Kai | Pang, Yanhong | Gang, Song

Article Type: Research Article

Abstract: For the weak part of the quality traceability ability of cigarette logistics and products, this paper proposes a quality traceability method which integrates PDCA quality cycle with information strategy. This method firstly establishes the quality cycle process of cigarette logistics through PDCA quality cycle process, and then constructs IS-PDCA process based on information strategy (IS) to analyze the quality traceability process of cigarette products. The key links are determined according to the traceability process of cigarette logistics, and the traceability resource scheduling function is determined through the product. Then, according to the determined scheduling function and RFID technology, the optimal …allocation strategy is constructed to complete the feature extraction and classification identification of cigarette quality labels. For assessing the quality of cigarette evaluation, classification based on fuzzy is proposed and artificial neural network are utilized for calculating the grade of cigarette. Finally, a process of cigarette quality traceability combining PDCA quality cycle and information strategy is formed, and the quality traceability results are constructed by means of QR code technology, so as to realize the process system of cigarette quality traceability and improve the quality control ability of cigarettes. The simulation results show that the cigarette quality traceability method constructed in this paper can obtain the cigarette quality control with good adaptive performance, and the control process shows a strong ability, which improves the feasibility and effectiveness of the cigarette quality traceability. Show more

Keywords: Information strategy, Artificial Intelligence (AI), PDCA quality cycle, cigarette logistics, quality traceability, fuzzy, artificial neural network

DOI: 10.3233/JIFS-189644

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

Authors: Jing, Wu | Jie, Tian

Article Type: Research Article

Abstract: China remains one among largest agricultural countries in the world and one of the fastest growing Internet countries in the world. The further thorough and penetration of the Internet into the rural hinterland indicates that the combination of the Internet and the rural areas already has the technical foundation. The transformation of rural economy with Internet as medium is the future direction of rural development. Starting from the current situation of rural Internet development, based on the annotation of “Internet plus”, this paper explores the theoretical and practical application of rural e-commerce development. We utilize Intuitionistic Fuzzy Sets operator for …modelling multiple attribute issues related to decision making to estimate influence of e-commerce in industries. This paper puts forward the measure method of industrial transformation and proves that “Internet plus” e-commerce has exerted a great influence on the commercial civilization, industrial structure and economic structure transformation of rural areas. The steady and lasting impact of this continuous release may become a new paradigm of technological economy. It is of great significance to the formation of the “four modernizing” linkage mechanism with information as media and the innovation of the conventional path of rural economic development and the rapid growth of rural economy. Show more

Keywords: Rural e-commerce, industrial structure, transformation, artificial intelligence (AI), “Internet plus”, fuzzy sets

DOI: 10.3233/JIFS-189643

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

Authors: Luo, Xiaolin

Article Type: Research Article

Abstract: Along with improvement of technology in network and continuous expansion of network economy and network applications, the Internet has gradually become an indispensable part of the modern society. However, an endless stream of hacker attacks and network virus events make network security issues stand out. Therefore, network security has become a hot spot in computer network research and development. This paper aims at establishing a real-time detection and dynamic defense security system and makes an in-depth study of intrusion detection technology and defense decision-making technology. The strategy involved in finding the intrusion behavior since the fuzzy base contains the better …group of rules. We have utilized an automated fuzzy rule generation strategy. An adaptive network intrusion detection and defense system model is established, and the architecture of the model is discussed in detail. The platform independence, good self-adaptability, expansibility, multi-level data analysis and dynamic defense decision-making are expounded. The experiment proves that the model proposed in this article has a good self-adaptability and open construction, and effectively combines the functions of intrusion detection and defense decision-making. Show more

Keywords: Artificial intelligence, adaptive network, defense system, fuzzy rule

DOI: 10.3233/JIFS-189645

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

Authors: Wang, Jie | Yan, Linhuang | Yang, Qiaohe | Yuan, Minmin

Article Type: Research Article

Abstract: In this paper, a single-channel speech enhancement algorithm is proposed by using guided spectrogram filtering based on masking properties of human auditory system when considering a speech spectrogram as an image. Guided filtering is capable of sharpening details and estimating unwanted textures or background noise from the noisy speech spectrogram. If we consider the noisy spectrogram as a degraded image, we can estimate the spectrogram of the clean speech signal using guided filtering after subtracting noise components. Combined with masking properties of human auditory system, the proposed algorithm adaptively adjusts and reduces the residual noise of the enhanced speech spectrogram …according to the corresponding masking threshold. Because the filtering output is a local linear transform of the guidance spectrogram, the local mask window slides can be efficiently implemented via box filter with O(N) computational complexity. Experimental results show that the proposed algorithm can effectively suppress noise in different noisy environments and thus can greatly improve speech quality and speech intelligibility. Show more

Keywords: Auditory masking properties, guided filtering, guided spectrogram filtering, spectrogram, speech enhancement

DOI: 10.3233/JIFS-202278

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

Authors: Abu Saleem, M.

Article Type: Research Article

Abstract: The main aim of this article is to present neutrosophic folding and neutrosophic retractions on a single-valued neutrosophic graph Ğ from the viewpoint of geometry and topology. For this reason, we use a sequence of neutrosophic transformations on Ğ to obtain a new single-valued neutrosophic graph G ˇ 1 which contains different parameters under new conditions. We deduce the isometric neutrosophic folding on neutrosophic spheres and neutrosophic torii. Also, we determine the relationship between the limit neutrosophic folding and the limit of neutrosophic retraction on Ğ. Theorems regulating these relations are attained.

Keywords: Single valued neutrosophic graph, Neutrosophic folding, Neutrosophic retraction, 51H20, 57N10, 57M05, 14F35, 20F34

DOI: 10.3233/JIFS-201957

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Zhou, Wei

Article Type: Research Article

Abstract: Decentralized application (DAPP), replacing traditional business logic and data access layer with block chain, is a new form of Internet service. Testing DAPP requires large-scale distributed systems. Performing experiments in a real system is costly and difficult. This article carefully analyses the process of block generation and synchronization and explains the reasons for the low efficiency of block chain system simulation. We incorporate fuzzy rule based model for enhancing the logging system in blockchain. Rules based on fuzzy are utilized inside system of fuzzy logic to obtain outcome on basis of input variables. The data of Ethereum and Bitcoin proves …that the block generation interval conforms to the exponential distribution, and the real PoW calculation can be replaced with random numbers. Both block verification and network propagation processes have latency, which can be simulated with asynchronous messaging. Based on the above analysis, this article proposes a high-performance simulation method based on event-driven model, which is suitable for describing the communication and synchronization behave our of block chain networks. The method can effectively describe the block generation, the synchronization process between nodes, and supports different equity proof forms. Using this method, the performance of the PoW systemis tested. Under the ecs.c6.xlargeinstance,the simulation running speed reaches 782 times of actual system. Further experiments show that this method can be efficiently used in larger-scale networks and is an effective tool for DAPP developing and testing. Show more

Keywords: Decentralized applications, blockchain, complex network, distributed system, artificial intelligence, fuzzy logic, fuzzy rule

DOI: 10.3233/JIFS-189633

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Wang, Qian | Wang, Jianxu | Li, Hua | Li, Xiang

Article Type: Research Article

Abstract: Equipment manufacturing industry is the core industry of national economy. The development of artificial intelligence technology provides new development opportunities for the transformation and upgrading of equipment manufacturing industry, but in this process, China’s equipment manufacturing enterprises are faced with serious financing constraints and financing efficiency needs to be improved. Based on the panel data of Listed Companies in equipment manufacturing industry from 2009 to 2018, the article constructs a panel data regression model by using stochastic frontier analysis to measure the financing efficiency of equipment manufacturing industry and study its influencing factors. The results show that the average financing …efficiency of China’s equipment manufacturing enterprises is in the medium level, while the traditional equipment manufacturing industry is lower; external financing has a positive impact on the financing efficiency of enterprises, and labor input has a negative impact; in the analysis of influencing factors, the Capital structure, R&D investment, Accounts receivable turnover rate, Fixed assets turnover rate have a great impact on the financing efficiency. The research results have a certain reference significance for equipment manufacturing enterprises to improve financing efficiency. Show more

Keywords: Equipment manufacturing, financing efficiency, stochastic frontier analysis

DOI: 10.3233/JIFS-189635

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

Authors: Yafeng, Wen

Article Type: Research Article

Abstract: With the promotion of BIM Technology, prefabricated building is developed rapidly in China. However, BIM technology has been only partially applied to prefabricated building, and there is still a gap between prefabricated building and intelligent construction. This paper focus on BIM 5D, together with relevant information technologies, all of which will be highly integrated and applied to prefabricated building, with the mission to get related information and enable the rapid flow of information, as well as bringing human perception, memory, knowledge and wisdom into prefabricated building, driving the development of prefabricated buildings to intelligence and leanness. Intelligent construction is …an innovated construction model based on the combination of latest information technology and engineering construction. Thus, it is particularly important to train personnel with corresponding knowledge structure, knowledge system and professional ability for intelligent construction. This paper also discusses about how to train personnel on prefabricated building and intelligent construction. Show more

Keywords: BIM5D, prefabricated building, intelligent construction, personnel training

DOI: 10.3233/JIFS-189625

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

Authors: Pan, Nan | Shen, Xin | Guo, Xiaojue | Cao, Min | Pan, Dilin

Article Type: Research Article

Abstract: In recent years, electricity stealing has been repeatedly prohibited, and as the methods of stealing electricity have become more intelligent and concealed, it is growing increasingly difficult to extract high-dimensional data features of power consumption. In order to solve this problem, a correlation model of power-consumption data based on convolutional neural networks (CNN) is established. First, the original user signal is preprocessed to remove the noise. The user signal with a fixed signal length is then intercepted and the parallel class labelled. The segmented user signals and corresponding labels are input into the convolutional neural network for training, and the …trained convolutional neural network is then used to detect and classify the test user signals. Finally, the actual steal leak dataset is used to verify the effectiveness of this algorithm, which proves that the algorithm can effectively carry out anti–-electricity stealing by warning of abnormal power consumption behavior. There are lots of line traces on the surface of the broken ends which left in the cable cutting case crime scene along the high-speed railway in China. The line traces usually present nonlinear morphological features and has strong randomness. It is not very effective when using existing image-processing and three-dimensional scanning methods to do the trace comparison, therefore, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is put forward to make fast trace analysis and infer the criminal tools. The proposed algorithm first applies wavelet decomposition to the 1-D signals which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to do constant iteration. The experiment results of cutting line traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm. Show more

Keywords: Anti–electricity stealing, high-dimensional data features, convolutional neural network, early warning

DOI: 10.3233/JIFS-189621

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Yousaf, Waqas | Umar, Arif | Shirazi, Syed Hamad | Khan, Zakir | Razzak, Imran | Zaka, Mubina

Article Type: Research Article

Abstract: Automatic logo detection and recognition is significantly growing due to the increasing requirements of intelligent documents analysis and retrieval. The main problem to logo detection is intra-class variation, which is generated by the variation in image quality and degradation. The problem of misclassification also occurs while having tiny logo in large image with other objects. To address this problem, Patch-CNN is proposed for logo recognition which uses small patches of logos for training to solve the problem of misclassification. The classification is accomplished by dividing the logo images into small patches and threshold is applied to drop no logo area …according to ground truth. The architectures of AlexNet and ResNet are also used for logo detection. We propose a segmentation free architecture for the logo detection and recognition. In literature, the concept of region proposal generation is used to solve logo detection, but these techniques suffer in case of tiny logos. Proposed CNN is especially designed for extracting the detailed features from logo patches. So far, the technique has attained accuracy equals to 0.9901 with acceptable training and testing loss on the dataset used in this work. Show more

Keywords: Logo detection, logo recognition, deep learning, AlexNet, ResNet, CNN

DOI: 10.3233/JIFS-190660

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

Authors: Lee, Bor-Hon | Yang, Albert Jing-Fuh | Chen, Yenming J.

Article Type: Research Article

Abstract: A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated …distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices. Show more

Keywords: Distribution entropy, spatial diffusion, stochastic automata (SA), evolutionary game (EG), machine learning

DOI: 10.3233/JIFS-189609

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Hei, Hongzhong | Jian, Xianzhong | Xiao, Erliang

Article Type: Research Article

Abstract: The widespread application of infrared human action recognition in intelligent surveillance has attracted significant attention. However, the infrared action recognition dataset is limited, which limits the development of infrared action recognition. Existing methods for infrared action recognition are based on features in the same sample, without paying attention to within-class differences. Motivated by the idea of weighting video information, this paper proposes a novel infrared action recognition framework to rew eight the s amples of training sets named REWS to solve the problems of limited infrared action data and the large within-class differences in the infrared action recognition dataset. In …the proposed framework, we first map infrared action video data to a low-dimensional feature space, and use the cosine similarity between the feature data of the training set and the testing set to determine the weight of the training set samples. Each training set sample has an independent weight. Then, a support vector machine (SVM) is trained by the training sets with weights to recognize the infrared actions. Experimental results demonstrate that our approach can achieve state-of-the-art performance compared with hand-crafted features based methods on the benchmark InfAR dataset. Show more

Keywords: Infrared, action recognition, within-class differences, samples reweighting, cosine similarity

DOI: 10.3233/JIFS-192068

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

Authors: Bai, Haoyue | Zhang, Haofeng | Wang, Qiong

Article Type: Research Article

Abstract: Zero Shot learning (ZSL) aims to use the information of seen classes to recognize unseen classes, which is achieved by transferring knowledge of the seen classes from the semantic embeddings. Since the domains of the seen and unseen classes do not overlap, most ZSL algorithms often suffer from domain shift problem. In this paper, we propose a Dual Discriminative Auto-encoder Network (DDANet), in which visual features and semantic attributes are self-encoded by using the high dimensional latent space instead of the feature space or the low dimensional semantic space. In the embedded latent space, the features are projected to both …preserve their original semantic meanings and have discriminative characteristics, which are realized by applying dual semantic auto-encoder and discriminative feature embedding strategy. Moreover, the cross modal reconstruction is applied to obtain interactive information. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method. Show more

Keywords: Zero shot learning, domain shift, dual auto-encoder, discriminative projection

DOI: 10.3233/JIFS-201920

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

Authors: Lin, Rongde | Li, Jinjin | Chen, Dongxiao | Huang, Jianxin | Chen, Yingsheng

Article Type: Research Article

Abstract: Fuzzy covering rough set model is a popular and important theoretical tool for computation of uncertainty, and provides an effective approach for attribute reduction. However, attribute reductions derived directly from fuzzy lower or upper approximations actually still occupy large of redundant information, which leads to a lower ratio of attribute-reduced. This paper introduces a kind of parametric observation sets on the approximations, and further proposes so called parametric observational-consistency, which is applied to attribute reduction in fuzzy multi-covering decision systems. Then the related discernibility matrix is developed to provide a way of attribute reduction. In addition, for multiple observational parameters, …this article also introduces a recursive method to gradually construct the multiple discernibility matrix by composing the refined discernibility matrix and incremental discernibility matrix based on previous ones. In such case, an attribute reduction algorithm is proposed. Finally, experiments are used to demonstrate the feasibility and effectiveness of our proposed method. Show more

Keywords: Attribute reduction, fuzzy discernibility matrix, fuzzy multi-covering systems, incremental discernibility matrix, observational consistency, refined discernibility matrix

DOI: 10.3233/JIFS-201998

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

Authors: Zhou, Zaohong | Zou, Yongwen

Article Type: Research Article

Abstract: Problems involving decision-making and management of engineering projects call for attention from different quarters and the issue of decision-making for projects under the state planning in particular should be the major concern of project management. This study takes a traditional village protection project —- the preservation of Zaoshi Village, Xingan County, Jiangxi Province, China—- as a case in point. Treating the decision-making process as a system, the study employs ISM model to examine the system-level relationship between engineering projects. Artificial Intelligence is utilized for analyzing the planning of project structure and Fuzzy TOPSIS model is useful in estimating the weights …of the scenario and find the ranking of structure finally. Then, using the analytical data thus derived, the research focuses on identifying the optimum option for decision-making. By this process, the study intends to gain and share some insight into the issue and establish precedents for similar engineering projects. Show more

Keywords: Planning engineering, Artificial Intelligence (AI), ISM, Grey situation decision-making, Fuzzy TOPSIS

DOI: 10.3233/JIFS-189641

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

Authors: Phawinee, Suphawimon | Cai, Jing-Fang | Guo, Zhe-Yu | Zheng, Hao-Ze | Chen, Guan-Chen

Article Type: Research Article

Abstract: Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then …the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app. Show more

Keywords: Face recognition, intelligent lock, ResNet, deep learning

DOI: 10.3233/JIFS-189624

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

Authors: Leea, Chien-Cheng | Gao, Zhongjian | Huanga, Xiu-Chi

Article Type: Research Article

Abstract: This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used …to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective. Show more

Keywords: Human movement detection, Wi-Fi, CNN, ResNet, channel state information

DOI: 10.3233/JIFS-189629

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

Authors: Konstantakopoulos, Grigorios D. | Gayialis, Sotiris P. | Kechagias, Evripidis P. | Papadopoulos, Georgios A. | Tatsiopoulos, Ilias P.

Article Type: Research Article

Abstract: Routing of vehicles and scheduling of deliveries play a crucial role in logistics operations as they affect both the distribution cost and customer satisfaction. That is why researchers have intensively studied this problem in conjunction with the multiple variables and constraints involved in the logistics operations. In this paper, the cases of time windows and simultaneous pickups and deliveries, where goods are simultaneously delivered and collected from customers within a predetermined time slot, are studied. The objective of our research is to create efficient routes that minimize both the number of vehicles and the total distance travelled, as both of …them affect the total distribution cost. Considering various plans of routes that are differentiated by the number of routes and the sequence of visitations, can be beneficial for decision-makers, since they have the opportunity to select the plan that better fits their needs. Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services. Through the proposed MOEA a set of solutions (route plans), known as Pareto-optimal, is obtained, while single biased solutions are avoided. The proposed algorithm is tested in two well-known datasets in order to evaluate the algorithm’s efficiency. The results indicate that the algorithm’s solutions have small deviation from the best-published and some non-dominated solutions are also obtained. Show more

Keywords: Vehicle routing problem, time windows, simultaneous pickups and deliveries, multiobjective optimization, evolutionary algorithm, logistics

DOI: 10.3233/JIFS-202129

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

Authors: Yu, Guo | Li, Weijian | Zhou, Xiaobo

Article Type: Research Article

Abstract: The Belt and Road (abbreviated as B&R) creates opportunities for the economy and trade development and the improvement of national relations of the Belt and Road countries. Development of service trade plays an important role in the Belt and Road. Henan Province takes the opportunity of B&R to develop the international service industry and makes some achievements. In the new era of development, analyzing the trade competitiveness of international service in Henan Province under the background of the Belt and Road is of great significance to help Henan Province recognize its own strengths and weaknesses in service trade development and …make targeted improvements. Studies have shown that the share of international market, revealed, advantages of comparative competitiveness trade and service trade openness of service trade in Henan Province under the background of the Belt and Road are constantly improving, but the overall development level is still low and slow development occurs. We utilize the concept of fuzzy embedded along with Analytic Network Process (ANP) which makes it suitable for managing vagueness of the linguistics information of assessment system. Therefore, in order to further improve the international competitiveness of service trade in Henan Province, it is necessary to optimize the international market share, competitiveness, and degree of openness. Specifically, it is necessary to transform and upgrade the traditional service trade industry, continue to expand the breadth and depth of service trade openness, strengthen the talent team building to improve the quality of employees, and promote the brand construction of service industry to enhance the industry’s development potential. Show more

Keywords: The belt and road, henan province, service trade, international competitiveness, fuzzy, analytic network process (ANP)

DOI: 10.3233/JIFS-189642

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

Authors: Hsieh, Wen-Hsiang | Chen, Yi-Syun | Wu, Shang-Teh

Article Type: Research Article

Abstract: Iterative Learning Control is a branch of intelligent control which combines artificial intelligence and control theory. This objective of this study aims at reducing the cyclic error of an inverse ball screw transmission system by using iterative learning control approach. Firstly, kinematic and dynamic analyses are conducted by using the vectorial loop closure and Lagrange equations, respectively. Then, system identification is performed followed by controller design. Moreover, controller parameters are optimized to minimize the error. Finally, the feasibility and the effectiveness of the proposed approach are verified by computer simulation and prototype experiment. The experimental results showed that the reducing …percentage of the square error sum of the output speed is 90.64% by using PID control only. If ILC is applied additionally, the error is further reduced to 94.21%. Therefore, the proposed approach is not only feasible and but also effective. Show more

Keywords: Ball screw, ILC controller, PID controller, Oldham coupling

DOI: 10.3233/JIFS-189627

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

Authors: Lai, Yi-Horng

Article Type: Research Article

Abstract: OBJECTIVES: Efavirenz therapy plays an important role in controlling the progression of HIV/AIDS. However, efavirenz often causes short-term side effects for the central nervous system, and it remained controversial as to whether efavirenz leads to depression or even suicidal attempt when applied for a longer period of time. The purpose of this study is to determine the association between the use of efavirenz and depressive disorders. METHODS: This study explored the use of efavirenz on HIV-infected patients using National Health Insurance Research Database (NHIRD) in Taiwan by Bayesian survival analysis and investigated whether the use of efavirenz has …the risk of depressive disorders. To reduce the dependence of statistical modeling assumptions, this study applied propensity score matching to research data. RESULTS: Based on the result of this study, it can be found that the use of efavirenz (HR = 1.009, 95% CI=–0.505 0.554), gender (HR = 0.324, 95% CI = –2.544 0.381) were not significantly associated with the occurrence of depressive disorders, whereas age of HIV diagnosis (HR = 1.021, 95% CI = 0.011 0.055) was significantly associated with the occurrence of depressive disorders. This study concludes that the use of efavirenz does not in-crease the risk of depressive disorders among HIV-treated patients. CONCLUSIONS: For the care of HIV-infected patients (especially the older ones), the psychological harm from society, such as lack of social support, social stigma or unemployment is higher than the harm of medicine. Show more

Keywords: Human immunodeficiency virus (HIV), active antiretroviral therapy, depressive disorder, propensity score matching, Bayesian cox regression

DOI: 10.3233/JIFS-189628

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

Authors: Jeong, Sang-Ki | Ji, Dea-Hyeong | Oh, Ji-Youn | Seo, Jung-Min | Choi, Hyeung-Sik

Article Type: Research Article

Abstract: In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance …by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12 ]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9 ]. Show more

Keywords: USV (Unmanned surface vehicles), RNN (Recurrent neural network), LSTM (Long short-term memory models), ANN-PID (Artificial neural networks-PID)

DOI: 10.3233/JIFS-189622

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

Authors: Chou, Hsi-Chiang | Han, Kai-Yu

Article Type: Research Article

Abstract: This study developed a smart cane with remote electrocardiogram (ECG) and fall detection. The cane comprises a self-developed ECG detection circuit, fall detection module composed of a three-axis gyroscope and three-axis accelerator, and two wireless transmission modules. The data reception end features a human–machine interface with self-developed ECG analysis and fall detection programs, providing reference data for identifying an abnormal situation. The hardware of the proposed system is divided into two parts. First, ECG detection is achieved using a copper column-shaped detector in place of conventional ECG electrodes. The self-developed sensor circuit amplifies the collected signals and filters unwanted noise …to generate complete ECG signals. An Arduino MEGA microcontroller board and the two wireless transmission modules then transmit the signals to the human–machine interface. Second, fall detection is achieved using the aforementioned fall detection module to collect numerical data, which are then transmitted to the human–machine interface through the Arduino MEGA and wireless transmission modules. The proposed system can be applied to real-time monitoring and provide reference data for health care professionals and nursing personnel. Show more

Keywords: Electrocardiogram, fall detection, wireless transmission, human–machine interface

DOI: 10.3233/JIFS-189630

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

Authors: Choi, Hey-Min | Kim, Min-Kyu | Yang, Hyun

Article Type: Research Article

Abstract: Recently, abnormally high water temperature (AHWT) phenomena are occurring more often due to the global warming and its impact. These phenomena have damaged extensively to the maritime economy around the southern coast of Korea and caused an illness by exacerbating the propagation of Vibrio pathogens. To mitigate damages by AHWT phenomena, it is necessary to respond as quickly as possible or predict them in advance. In this study, therefore, we proposed a deep learning-based methodology to predict the occurrences of AHWT phenomena using the long short-term memory (LSTM) model. First, a LSTM model was trained using the satellite-derived water temperature …data over the past ten years. Then, the water temperatures after a few days were estimated using the trained LSTM model. In a performance evaluation, when estimating water temperatures after one-day, the model achieved results of 1.865 and 0.412 in terms of mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively. Second, we developed a decision algorithm based on the Markov state transition in order to predict the AHWT occurrence probability. As a result, we obtained 0.88 of F1 score for predicting AHWT phenomena after 1 day in case of the southern coast of Korea. Show more

Keywords: Long short-term memory, deep learning, satellite data, abnormally high water temperature

DOI: 10.3233/JIFS-189623

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

Authors: Deng, Jiawen | Li, Junqing | Li, Chengyou | Han, Yuyan | Liu, Qingsong | Niu, Ben | Liu, Lili | Zhang, Biao

Article Type: Research Article

Abstract: This paper investigates the electric vehicle routing problem with time windows and nonlinear charging constraints (EVRPTW-NL), which is more practical due to battery degradation. A hybrid algorithm combining an improved differential evolution and several heuristic (IDE) is proposed to solve this problem, where the weighted sum of the total trip time and customer satisfaction value is minimized. In the proposed algorithm, a special encoding method is presented that considers charging stations features. Then, a battery charging adjustment (BCA) strategy is integrated to decrease the charging time. Furthermore, a novel negative repair strategy is embedded to make the solution feasible. Finally, …several instances are generated to examine the effectiveness of the IDE algorithm. The high performance of the IDE algorithm is shown in comparison with two efficient algorithms. Show more

Keywords: Vehicle routing problem, time windows, nonlinear charging, differential evolution algorithm

DOI: 10.3233/JIFS-202164

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

Authors: Chen, Peng | Nie, Yingzhi

Article Type: Research Article

Abstract: Based on the company cases published in China over the past ten years, both theoretical methods and Artificial intelligence technologies were applied to analysis cases data on the effectiveness of clauses restricting equity transfer in articles of association of limited liability companies (LLCs). With its unique characters based on shareholders and strong vitality, limited liability company (LLC), as the “evergreen tree” among the market players, is a company form adopted by many investors. Nevertheless, due to its prominent closed characteristics, equity transfer has become a bottleneck for the development of LLCs. According to this paper, it is necessary to distinguish …between the effectiveness of clauses restricting internal and external equity transfer in articles of association of LLCs. Fuzzy Analytic Hierarchical Process (AHP) is utilized for which involves process of analytic hierarchy modelled with utilizing theory of fuzzy logic. Moreover, instead of being confined to the existing legal norms, the judgment standard of clauses restricting equity transfer in articles of association of LLCs should be comprehensively measured by the golden rules, i.e. “fairness”, “autonomy” and “operability”. Show more

Keywords: Characters based on shareholders, nature of articles of association, effectiveness of clauses, judgment standard, fuzzy logic, fuzzy Analytic Hierarchical Process (AHP)

DOI: 10.3233/JIFS-189637

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

Authors: Wang, Xi | Chen, Qinyi | Wang, Jingyi

Article Type: Research Article

Abstract: The lightening system inside the residential or commercial building consumes the highest electrical power. For an energy efficient smart city development, some sustainable and low power consumption methods need to be explored. In this direction, we proposed solar energy based auto-intelligent LED light controlling system that uses wireless sensor network (WSN) with computation and control model for LED on/off and dimming of LED lights inside the building area. The WSN is employed with some sensor devices that sense and gather ambient context information which is transmitted to computation model. LEDs get power supply from photovoltaic solar panel systems that have …inbuilt battery banks. Fuzzy rough set is a simplification of a rough set, obtained from the normalization of fuzzy set in a approximation of crisp value. Fuzzy is utilized for analyzing the energy consumed in the system additionally. Performance evaluation of proposed Auto-intelligent LED system is carried out based on the comparative analysis of energy consumption of ac-grid system with solar energy based dc-grid system. Result analysis shows that proposed system saves 78% of energy consumption as compared to the traditional AC power grid system. The proposed DC power grid system presents 3% of voltage drop and maximum power loss of 1.25%. The statistics of battery charger and LED drives are also represented experimentally. Show more

Keywords: Energy efficient, wireless sensor networks, power consumption, artificial intelligence (AI), smart grid, fuzzy rough set

DOI: 10.3233/JIFS-189640

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

Authors: Deng, Guangzhe | Fu, Yingkai

Article Type: Research Article

Abstract: As the stability of surrounding rock of coal roadway is affected by many factors, which makes the classification result hard to be consistent with the field practice. To solve the above problems, this paper proposes a method for the classification of stability of rock which is present in roadway of coal using the artificial intelligence algorithm. In this paper, the influencing factors of stability of rock which is present in roadway are analyzed, and seven influential factors are selected as classification indexes. To solve the problem of slow convergence speed and easy to fall into the local minimum of the …back propagation artificial neural network (BP-ANN), an improved BP-ANN algorithm based on additional momentum and Levenberg-Marquardt optimization is proposed based on the analysis of the existing improved methods, which improves the convergence speed and avoids the local minimum effectively. Based on the learning model available, classification system based on fuzzy rule have been implemented and yielded better behavior in the situation of uncertain data sets. Finally, the stability classification model of surrounding rocks of coal roadway using BP-ANN was established in MATLAB environment, and the model was applied to 13 data samples of coal roadway for testing, with the identification rate of 92.3%. The experimental results verify that the method proposed based on fuzzy rule classification system in this paper has a high accuracy of type identification and is applicable to the stability classification of surrounding rock in the coal roadway. Show more

Keywords: Coal roadway, surrounding rock, artificial intelligence, BP-ANN, fuzzy rule, classification system

DOI: 10.3233/JIFS-189639

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

Authors: Wen, Bor-Jiunn | Kao, Chia-Hung | Yeh, Che-Chih

Article Type: Research Article

Abstract: Labor force is gradually becoming insufficient owing to the aging population. The quality and safety of workforces are increasingly important, and thus, a set of intelligent wearable devices that assist the transport of loads by laborers, provide auxiliary standing support, and prevent falls were designed in this study. By applying an auxiliary force to the knee joint externally, an intelligent wearable device saves labor and reduces the burden on this joint, thereby protecting it. This study utilizes a Bayesian backpropagation algorithm for intelligent control. The intelligent wearable device provides the most suitable velocity and torsion depending on the initial driving …torsion of the user by a Bayesian backpropagation algorithm based on the current angle position, velocity, and torsion load of the device motor, thereby achieving an intelligent control effect of auxiliary standing support. A triaxial accelerometer is utilized to sense a fall and prevent it by a so-called fuzzy-Bayesian backpropagation control (FBC). Eventually, this study successfully designed and manufactured an intelligent wearable device by the FBC method. For a single motor control, two knee auxiliary devices can generate a torsion of 18.6 Nm. For dual motor control, two knee auxiliary devices can generate a torsion of 43.2 Nm. Thus, the laborers can not only perform their work efficiently and safely but also reduce costs and raise the working market competitiveness. Show more

DOI: 10.3233/JIFS-189620

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

Authors: Dong, Wang | Yong, Zhao | Hong, Lin | Xin, Zuo

Article Type: Research Article

Abstract: Chinese fill-in-the-blank questions contain both objective and subjective characteristics, and thus it has always been difficult to score them automatically. In this paper, fill-in-the-blank items are divided into those with word-level or sentence-level granularity; then, the items are automatically scored by different strategies. The automatic scoring framework combines semantic dictionary matching and semantic similarity calculations. First, fill-in-the-blank items with word-level granularity are divided into two types of test sites: the subject term test site, and the common word test site. We propose an algorithm for identifying an item’s test site. Then, a subject term dictionary with self-feedback learning ability is …constructed to support the scoring of subject term test sites. The Tongyici Cilin semantic dictionary is used for scoring common word test sites. For fill-in-the-blank items with sentence-level granularity, an improved P-means model is used to generate a sentence vector of the standard answer and the examinee’s answer, and then the semantic similarity between the two answers is obtained by calculating the cosine distance of the sentence vector. Experimental results on actual test data show that the proposed algorithm has a maximum accuracy of 94.3% and achieves good results. Show more

Keywords: Fill-in-the-blank question, automatic scoring, dictionary matching, semantic similarity, word vector, sentence vector, subject terminology, P-means model

DOI: 10.3233/JIFS-202317

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

Authors: Gu, Xungang | Li, Gang | Cao, Shengli | Zhang, Yumeng | Wang, Ran

Article Type: Research Article

Abstract: The reasonable cost budget of the e-government scheme can effectively promote the construction of the digital government. To analyze the cost impact components of the e-government system and find out the impact factor model works in China, this paper reviews relevant literature on software cost impact factors and proposes the impact factors model based on COCOMO II. Besides, combined with the actual construction of digital government and specific cases, this paper analyzes the mechanism of each impact factor in detail. The model can be used to guide the cost estimation of e-government software in China, especially with artificial intelligence estimation …method. An enhanced decision theory of theory based on fuzzy set has been adopted for analysis of cost factor on E-government software cost. Show more

Keywords: E-government, information system, software cost estimation, impact factors, fuzzy set

DOI: 10.3233/JIFS-189638

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

Authors: Ko, Joonho | Cho, Hyun Woong | Kim, Jung In | Kim, Hyunmyung | Lee, Young-Joo | Suh, Wonho

Article Type: Research Article

Abstract: Traffic simulation tools are becoming more popular as complexity and intelligence are growing in transportation systems. The need for more accurate and intelligent traffic modeling is increasing rapidly as transportation systems are having more congestion problems. Although traffic simulation models have been continuously updated to represent various traffic conditions more realistically, most simulation models still have limitations in overcapacity congested traffic conditions. In traditional traffic simulation models, when there is no available space due to traffic congestion, additional traffic demand may never be allowed to enter the network. The objective of this paper is to investigate one possible method to …address the issue of unserved vehicles in overcapacity congested traffic conditions using the VISSIM trip chain. The VISSIM trip chain is used for this analysis as it has the advantage of holding a vehicle without eliminating it when traffic congestion prevents its entrance onto a network. This will allow the vehicle to enter when an acceptable gap becomes available on the entry link. To demonstrate the difference between the simulation using standard traffic input and the trip chain method, a sample congested traffic network is built and congested traffic scenarios are created. Also, simulations with different minimum space headway parameters in the priority rules are analyzed to model congested traffic conditions more realistically. This will provide the insight about the sensitivity of the model to this parameter. Based on the analysis conducted it is concluded that, with appropriate calibrations, the trip chain feature in VISSIM has the potentials to be useful in modeling overcapacity congested traffic conditions more realistically. Show more

Keywords: Traffic simulation environments, traffic congestion modeling, intelligence of traffic simulation, simulation analysis, network simulation

DOI: 10.3233/JIFS-189614

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Qian, Junbin | Zhao, Chengjun | Pan, Nan | Xie, Tao

Article Type: Research Article

Abstract: In order to effectively control the vibration transmitted from the ground to the precision equipment, permanent magnet linear synchronous motor (PMLSM) can be effectively applied in the active vibration absorber systems due to its good characteristics. The design of a PMLSM is very important because of the special requirements of the damping system on the response speed, working temperature, working bandwidth and installation size of the PMLSM. In this paper, the multi-objective design problem of the PMLSM in vibration damping system is proposed. Based on the equivalent magnetic circuit analysis of the PML SM’s air gap magnetic field, the performance …and design of the PMLSM are effectively analyzed in combination with the design objectives. The design and performance of the prototype meet the design requirements. Show more

Keywords: Damping, amplitude-frequency characteristics, Laplace transform, linear synchronous motor

DOI: 10.3233/JIFS-189602

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

Authors: Liu, Hsiao-Man | Huang, Chung-Chi | Huang, Chung-Lin | Ke, Yen-Ting

Article Type: Research Article

Abstract: This study proposes a health assessment and predictive assistance system for intelligent health monitoring. Through machine learning, the tool features a customized set of quantitative measurements and web analysis systems for physical and mental fitness. The system replaces the manpower and time requirements of the past necessary to conduct interviews and keep paper records, allowing users to observe and analyze physical and mental fitness status through the webpage. To achieve this, ECG, EEG, and EMAS are used to follow physiological, psychological, and meridian energy states. ASP.NET software is used as a development tool for the system cloud page, which constructs, …documents, evaluates, and predicts functions for the smart health assistance system. The measurement data is entered and recorded in the cloud database. The data is used to construct an assessment and prediction of the user’s state of mind and body through machine learning methods, as well as the individual’s physical and mental fitness. Show more

Keywords: Intelligent assessment, intelligent prediction, somatic fitness, healthcare, machine learning

DOI: 10.3233/JIFS-189618

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

Authors: Chen, Joy Iong-Zong | Hengjinda, P.

Article Type: Research Article

Abstract: Smart Robot embedded with GMM-UBM (Gaussian mixture model- universal background model) based on the machine learning scheme is presented in the article. Authors have designed a smart robot for the farmer and which is designed controlled by the concept of machine learning. On the other hand, the techniques of machine learning are applied to develop a smart robot for helping farmers recognize the environment conditions, e.g . weather, and disease protection in rice or plant. The smart robot is implemented to detect and to recognize the environment conditions around a fixed area. The sensing way through vision devices, such as …camera, look like a human’s eye to distinguish various types of target. The QR code is deployed to simulate working conditions allows the robot to separate conditions and act according to conditions precisely. Besides, the smart robot is embedded with GMM-UBM algorithm for promoting the accuracy of recognition from the deployment of machine learning. The smart robot, mainly combines with AI (Artificial intelligence) techniques, consists of the following equipments: 1) a control movement subsystem, 2) a sensor control subsystem, and 3) an analysis subsystem. The researcher has determined the condition of the message options via QR code. In addition, the contents of the QR code tag will be processed a text message and saved to a memory device, once the reading is finished. The data analysis subsystem then reads the text and recommends the robot to move according to the specified conditions. The results from QR code data allow the smart robot to accurately collect many kinds of prefer data (e.g ., climate data) in the farm at the specified location. Show more

Keywords: Artificial intelligence, GMM-UBM, machine learning, smart robot, vision devices

DOI: 10.3233/JIFS-189615

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

Authors: Chou, Fu-I | Ho, Wen-Hsien | Chen, Yenming J. | Tsai, Jinn-Tsong

Article Type: Research Article

Abstract: This study proposes a framework implementing triangular estimation for better modeling and forecasting time series. In order to improve the performance of estimation, we employ two sources of triangulation to generate a time series, which is statistically indistinguishable with the latent time series hidden in a system. Thanks to Bayesian hierarchical estimation, which is akin to deep learning but more sophisticate and longer history, the framework has been validated by a large amount of records in vegetable auctions. The hierarchical Bayesian estimation and Monte Carlo Markov Chain particle filters used in hidden Markov model are appreciated during the massive bootstrapping …of data. Our results demonstrate excellent estimation performance in discovering hidden states. Show more

Keywords: Generative estimation, time series forecasting, triangulation data assimilation

DOI: 10.3233/JIFS-189611

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Yu, Meng | Lu, Bao | Li, Xiong | Li, Weidong

Article Type: Research Article

Abstract: Online Distance teaching for multiple smart classrooms by famous teachers, as an effective solver for the problem of lack of excellent teachers, has become a new popular teaching mode. However, one of the key problems to be solved urgently for this teaching mode is how to monitor children’s class status and effectively feedback their listening standing to teachers. Installation of intelligent pressure cushion on the chair of smart classroom to monitor children’s classroom state can be a powerful way to improve teaching effectiveness for the online distance teaching mode. This paper presents a new method for monitoring children’s classroom behavior …based on intelligent cushion, which can identify basic children’s classroom behavior by classifying the original intelligent cushion pressure signal and evaluating the effectiveness of the classifier. To be concrete, the present method uses intelligent pressure cushion to collect data and denoises the original data by digital filter, and then extracts the time-domain and frequency-domain features of time-series pressure signals based on sliding time window. Finally, it uses machine learning to identify children’s status. In addition, by feature selection to reduce the data dimension, integrating different classifier to classify the extracted features, the efficiency of the present method is greatly improved. Show more

Keywords: Smart cushion, child behavior recognition, pressure sensor

DOI: 10.3233/JIFS-189616

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

Authors: Pan, Nan | Jiang, Xuemei | Pan, Dilin | Liu, Yi

Article Type: Research Article

Abstract: The practical applications of the bullet rifling linear traces are severely restricted due of the complex shape and strong randomness. We propose a model of position and attitude parameters distribution at the end of specimens based on multimode elastic driving adaptive control method to achieve feature decomposition and error compensation correction of the attitude transformation of the specimen seat. The isolated forest algorithm was employed for abnormal processing of detection signals, non-small features were removed based on variable-scale morphological filtering algorithm, the trace curve profiles were extracted using the multiscale registration framework, and the square speed function optimization elastic shape …metric algorithm was used to map the profiles into an embedding. Afterwards, a parametric shared conjoined triple deep learning model suitable for feature tracing and optimization of triplet selection and data augmentation strategies is proposed. This system is trained by minimizing a triplet loss function so that a similarity measure is defined by the L2 distance in this embedding. Finally, the trained model is used to do the similarity matching for the test set, try to solve the technical problems in the manual construction of guns and the inspection of bullet rifling traces. Show more

Keywords: Laser measurement, multiscale registration, elastic shape metric, triplet loss function, convolutional neural network

DOI: 10.3233/JIFS-189617

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-6, 2021

Authors: Feng, Rui | Huang, Cheng-Chen | Luo, Kun | Zheng, Hui-Jun

Article Type: Research Article

Abstract: The West Lake of Hangzhou, a world famous landscape and cultural symbol of China, suffered from severe air quality degradation in January 2015. In this work, Random Forest (RF) and Recurrent Neural Networks (RNN) are used to analyze and predict air pollutants on the central island of the West Lake. We quantitatively demonstrate that the PM2.5 and PM10 were chiefly associated by the ups and downs of the gaseous air pollutants (SO2 , NO2 and CO). Compared with the gaseous air pollutants, meteorological circumstances and regional transport played trivial roles in shaping PM. The predominant meteorological factor …for SO2 , NO2 and surface O3 was dew-point deficit. The proportion of sulfate in PM10 was higher than that in PM2.5 . CO was strongly positively linked with PM. We discover that machine learning can accurately predict daily average wintertime SO2 , NO2 , PM2.5 and PM10 , casting new light on the forecast and early warning of the high episodes of air pollutants in the future. Show more

Keywords: Random forest, recurrent neural network, air pollutants prediction

DOI: 10.3233/JIFS-201964

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

Authors: Huang, Chung-Lin | Huang, Chung-Chi

Article Type: Research Article

Abstract: Knowledge graphs are useful sources for various AI applications, however the basic paradigm to support pilot training is still unclear. In the paper, It is proposed to generate the customized knowledge graph of flight trainings using machine learning method for the flight training program. In order to provide the successful key to the further understanding of the learning problems between the students and the instructors. In this research, we collected data from an aeronautical academic in Taiwan that students were trained for Recreation Pilot License Program. We performed a test on 24 students at the first of each training course, …16 data of collected been used on building the module, 8 of them used to exam the module. There are 12 courses in the training program, and 30 hours total time were suggested by academic. The score which we applied on test were based on LCG method which is the sum of Maneuver and SRM Grades. For the indicators of course component in Learner Centered Grading, namely (a) CCS1: Operation & Effect of Controls; (b) CCS2: Straight & Level; (c) CCS3: Climbing & Descending; (d) CCS4: Turning; (e) CCS5: Stalling; (f) CCS6: Revision; (g) CCS7: Circuits; (h) CCS8: Cross-Wind Training; (i) CCS9: Circuit Emergency; (j) CCS10: Solo Circuit; (k) CCS11: Forced Landing; and (l) CCS12: Precautionary & Searching Landing. Through the method of Knowledge Graph, we deduct and predict the number of hours that need to be added for each student’s learning. Using the dynamic knowledge graph to display the key issues of the course learning continuously, and make follow-up decisions for the students, instructors and airliners. Show more

Keywords: Customized knowledge graph, FAA-industry training standards, machine learning

DOI: 10.3233/JIFS-189619

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

Authors: Bouteraa, Yassine | Abdallah, Ismail Ben | Ibrahim, Atef | Ahanger, Tariq Ahamed

Article Type: Research Article

Abstract: In this paper, a robotic system dedicated to remote wrist rehabilitation is proposed as an Internet of Things (IoT) application. The system offers patients home rehabilitation. Since the physiotherapist and the patient are on different sites, the system guarantees that the physiotherapist controls and supervises the rehabilitation process and that the patient repeats the same gestures made by the physiotherapist. A human-machine interface (HMI) has been developed to allow the physiotherapist to remotely control the robot and supervise the rehabilitation process. Based on a computer vision system, physiotherapist gestures are sent to the robot in the form of control instructions. …Wrist range of motion (RoM), EMG signal, sensor current measurement, and streaming from the patient’s environment are returned to the control station. The various acquired data are displayed in the HMI and recorded in its database, which allows later monitoring of the patient’s progress. During the rehabilitation process, the developed system makes it possible to follow the muscle contraction thanks to an extraction of the Electromyography (EMG) signal as well as the patient’s resistance thanks to a feedback from a current sensor. Feature extraction algorithms are implemented to transform the EMG raw signal into a relevant data reflecting the muscle contraction. The solution incorporates a cascade fuzzy-based decision system to indicate the patient’s pain. As measurement safety, when the pain exceeds a certain threshold, the robot should stop the action even if the desired angle is not yet reached. Information on the patient, the evolution of his state of health and the activities followed, are all recorded, which makes it possible to provide an electronic health record. Experiments on 3 different subjects showed the effectiveness of the developed robotic solution. Show more

Keywords: Gesture control, human robot interaction, internet of things, rehabilitation robotics

DOI: 10.3233/JIFS-201671

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

Authors: Martínez, Oscar Sanjuán | Fenza, Giuseppe | Crespo, Ruben Gonzalez

Article Type: Editorial

DOI: 10.3233/JIFS-189631

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-2, 2021

Authors: Nazari, Mohammad Hassan | Sanjareh, Mehrdad Bagheri | Moradi, Mohammad Bagher | Hosseinian, Seyed Hossein

Article Type: Research Article

Abstract: This paper presents an economical approach for reliability improvement, harmonic mitigation and loss reduction in microgrids and active distribution networks that include of the distributed generations (DGs) considering technical constraints. The proposed method is a stochastic approach based on the calculation of the locational marginal price (LMP) in each DG bus. The problem is as a game-theoretic that each DG is taken as a single player considering its contributions on the aforementioned objectives. In this regard, each player gets a financial incentive as incremental price, based on a fair method using cooperative game-theoretic sharing strategy. In other words, each DG …that aligns its generation with the aforementioned objectives will increase the price of selling energy. This increase in prices will lead to higher profits. Therefore, DGs are interested in volunteering to accomplish network goals. As a tool for system management, the proposed method can control the impact of the pricing in the form of incentives to satisfy each objective depending on its decision in the incentive allocation procedure. To obtain a more realistic framework, demands are considered as the uncertainty parameters. To validate the proposed method, it is evaluated on the real Taiwan Power Company (TPC) network. The promising results indicate that the total loss is decreased by 54.5%, harmonics are mitigated by 12.3% and the reliability is improved by 12.6%. Show more

Keywords: Reliability, loss, pricing, harmonic, microgrid, active distribution network, game theory

DOI: 10.3233/JIFS-201703

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

Authors: Xu, Yu-Heng | Cheng, Si-Yi | Zhang, Hu-Biao

Article Type: Research Article

Abstract: To solve the problem of the missing data of radiator during the aerial war, and to address the problem that traditional algorithms rely on prior knowledge and specialized systems too much, an algorithm for radiator threat evaluation with missing data based on improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been proposed. The null estimation algorithm based on Induced Ordered Weighted Averaging (IOWA) is adopted to calculate the aggregate value for predicting missing data. The attribute reduction is realized by using the Rough Sets (RS) theory, and the attribute weights are reasonably allocated with the theory …of Shapley. Threat degrees can be achieved through quantization and ranking of radiators by constructing a TOPSIS decision space. Experiment results show that this algorithm can solve the incompleteness of radiator threat evaluation, and the ranking result is in line with the actual situation. Moreover, the proposed algorithm is highly automated and does not rely on prior knowledge and expert systems. Show more

Keywords: IOWA, Shapley, attribute reduction, TOPSIS, incompleteness

DOI: 10.3233/JIFS-202245

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

Authors: Ahmadini, Abdullah Ali H. | Ahmad, Firoz

Article Type: Research Article

Abstract: This paper investigates novel intuitionistic fuzzy preferences relations to determine the imprecise linguistic terms with fuzzy goals. The proposed intuitionistic fuzzy goal programming (IFGP) considers the degree of vagueness and hesitations simultaneously. Different sorts of membership functions such as linear, exponential, parabolic, and hyperbolic have been introduced to depict the linguistic importance term. The overall satisfaction level is achieved by maximizing the convex combination of each fuzzy goals and the preference relations simultaneously. To verify and validate the proposed IFGP model, a numerical example is presented with the comparative study. Further, it is also applied to a banking financial statement …management system problem. The proposed IFGP approach outperforms over others. At last, the conclusion and future research direction are suggested based on the performed study. Show more

Keywords: Intuitionistic fuzzy set, membership and non-membership function, score functions, imprecise goal hierarchy

DOI: 10.3233/JIFS-201588

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

Authors: Bakhthemmat, Ali | Izadi, Mohammad

Article Type: Research Article

Abstract: Many scientists apply fully dynamic bin packing problem solving for resource allocation of virtual machines in cloud environments. The goal of problem-solving is to reduce the number of allocated hosts (bins) and virtual machines (items) migration rates for reducing energy consumption. This study demonstrates a greedy futuristic algorithm (proposed algorithm) for fully dynamic bin packaging with an average asymptotic approximation ratio of 1.231, better than other existing algorithms. The proposed algorithm identifies inappropriate local selections using special futuristic conditions to prevent them as much as possible. Eventually, suitable choices determine and discard the improper ones. The proposed algorithm illustrates an …asymptotic approximation ratio of (t/ (t-1)) OPT, where the value of t depends on the distribution of the arrived and departed items. Also, OPT is the number of bins by an optimal solution. Finally, in experiments of datasets using a maximum utilization of 80% of each bin, the average migration rate is 0.338. Using the proposed method for allocating resources in the cloud environment can allocate hosts to a virtual machine using almost optimal use. This allocation can reduce the cost of maintaining and purchasing hosts. Also, this method can reduce the migration rate of virtual machines. As a result, decreasing migration improves the energy consumption cost in the cloud environment. Show more

Keywords: Fully dynamic bin packing, special futuristic conditions, futuristic greedy, migration reducing

DOI: 10.3233/JIFS-201581

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2021

Authors: Bin, Guo | Liang, Ge

Article Type: Research Article

Abstract: Public-Private Partnership (PPP) model is an important measure for transferring government functions and promoting socialist market economy in China. After four years of high-speed development and standardized development in recent years, PPP model has achieved good effect in our country. However, multiple problems emerged. In China, the “public” party of the model has always been dominated by state-owned enterprises. Although private enterprises have the willingness to participate, the actual participation degree and volume are not high which is not good for the sustainable development of the PPP model. This paper applied grounded theory to carry out in-depth interviews with the …representatives from government, private enterprise, and SPV company in some key PPP projects with private participation, propose four coral categories to construct the whole process of private enterprises’ participation and summarize the behavioral features, and explore private enterprises’ realization of their participation willingness into actual participation behavior and the generation of their continuous participation willingness so as to clear the private enterprises’ participation path in PPP projects. Show more

Keywords: PPP model, grounded theory, Artificial Intelligence (AI), private enterprises, participation behavior

DOI: 10.3233/JIFS-189636

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

Authors: Hsieh, Yi-Chih | You, Peng-Sheng | Chuang, Hao-Chun

Article Type: Research Article

Abstract: In this paper, we study the forest harvesting problem (FHP). A forest is assumed to be divided into several identical square units, and each unit has its harvesting value based on its type. Harvesting a unit will affect the growth and values of its neighboring units. In this FHP, the best harvesting plan of a unit must be identified to maximize three various objectives simultaneously. The FHP is a multiobjective mathematical and an NP-hard problem. We apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, for maximizing the weighted objective to solve the FHP. We …also solve the following two sets of test problems: (i) a set of randomly generated FHP problems and (ii) a practical problem in Taiwan. Numerical results show the performance of the three algorithms for the test problems. Finally, we compare and discuss the effects of various weights for the three objectives. Show more

Keywords: Forest harvesting problem, optimization, immune algorithm, genetic algorithm, particle swarm optimization

DOI: 10.3233/JIFS-189597

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2020

Authors: Shen, Xin | Shu, Hongchun | Cao, Min | Qian, Junbin | Pan, Nan

Article Type: Research Article

Abstract: Power quality of distribution network is an emerging issue due to rapid increase in usage of non-linear loads on the one hand and utilization of sensitive devices on the other hand. Especially, harmonic emission is an important concern in both electric utilities and end users of electric power. Therefore, an accurate and rapid harmonic analysis method is of interest. New technologies have enabled the investigation of electricity consumption mode at an unprecedented scale and in multiple dimensions. However, an effective method that can capture the complexity of all the factors relevant to understanding a phenomenon such as ultrahigh harmonics (2–15 kHz). …How to detect the super high order harmonic accurately has become the premise and foundation of the study of super high order harmonic. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can identify super high harmonics and predict outcomes, by measured data across several dimensions variation. In this paper, the data integration, current methods and available implementation is discussed. Finally, the current challenges in integrative methods is discussed. Show more

Keywords: Power quality, harmonic emission, unprecedented scale, multiple dimensions

DOI: 10.3233/JIFS-189600

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2020