Journal of Intelligent & Fuzzy Systems - Volume 42, issue 6

Authors: Ameen, Zanyar A.

Article Type: Research Article

Abstract: As everyday problems contain a lot of data and ambiguity, it has become necessary to develop new mathematical approaches to address them and soft set theory is the best tool to deal with such problems. Hence, in this article, we introduce a non-continuous mapping in soft settings called soft U -continuous. We mainly focus on studying soft U -continuity and its connection to soft continuity. We further show that soft U -continuity preserves soft compact sets and soft connected sets. The later sets have various applications in computing …science and decision making theory. In the end, we show that if each soft U -continuous mapping f from a soft space X into a soft T 0 -space Y is soft continuous, then Y is soft T 1 . Show more

Keywords: soft continuous, soft compact, soft connected, soft separable, soft T1-space

DOI: 10.3233/JIFS-212410

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5839-5845, 2022

Authors: Wang, Xiaohan | Wang, Pei | Chen, Weilong | Hu, Wangwu | Yang, Long

Article Type: Research Article

Abstract: Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, …the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective. Show more

Keywords: Map matching, continuous window direction, road network topology, trajectory clustering

DOI: 10.3233/JIFS-212417

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5847-5862, 2022

Authors: Li, Xin | Li, Xiaoli | Wang, Kang

Article Type: Research Article

Abstract: The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the …the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs. Show more

Keywords: Niche selection, multi-objective optimization, diversity, irregular Pareto front

DOI: 10.3233/JIFS-212426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5863-5883, 2022

Authors: He, Xiaorong

Article Type: Research Article

Abstract: Earthquake prediction is one of the important themes of earthquake research, and it is also a very difficult scientific problem in the world. In this study, a bibliometric analysis is conducted on the scientific publications about earthquake prediction indexed in SCIE (Science Citation Index Expanded) and SSCI (Social Sciences Citation Index) databases during the past two decades (1998–2017). The subject categories, annual and journal distributions, leading countries/regions and institutions are investigated in this field. The main research topics are identified through text mining method. The research trends are explored by keyword co-occurrence analysis and bursting keywords detection techniques. The results …of this study are helpful for scholars in this field to find the knowledge structure and important participants. It is also helpful for scholars to seize the current research hotspots and future development trends in this field. Show more

Keywords: Earthquake prediction, visualization, bibliometric analysis, citation structure, research trends

DOI: 10.3233/JIFS-212442

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5885-5901, 2022

Authors: Chu, Yongjie | Ahmad, Touqeer | Zhao, Lindu

Article Type: Research Article

Abstract: Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain …Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot. Show more

Keywords: Domain Adaptation, discriminative learning, low-resolution face recognition, one-shot

DOI: 10.3233/JIFS-212454

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5903-5917, 2022

Authors: Li, Yang | Chen, Simeng | Bai, Ke | Wang, Hao

Article Type: Research Article

Abstract: Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with …the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots. Show more

Keywords: Safety, fire risk level, path optimisation, discrete electrostatic discharge algorithm, improved discrete electrostatic discharge algorithm

DOI: 10.3233/JIFS-212464

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5919-5930, 2022

Authors: Yang, Qingbo | Xu, Fangzhou | Leng, Jiancai

Article Type: Research Article

Abstract: Robotic arms are powerful assistants in many industrial production environments, and they run periodically in accordance with preset actions to complete specified operations. However, they may act abnormally when encountering unexpected situation and then lead to unnecessary loss. Recognizing the abnormal actions of robotic arms through surveillance video can automatically help us to understand their operating status and discover possible abnormalities in time. We designed a deep learning architecture based on 3D convolution for abnormal action recognition. The 3D convolutional layer can extract the spatial and temporal features of the robotic arm movements from the video frame difference sequence. The …features are compressed and streamlined by the maximum pooling layer to obtain concise and effective robotic arm action features. Finally, the fully connected layer is used to classify the features to recognize the abnormal robotic arm tasks. Support vector data description (SVDD) model is employed to detect abnormal actions of the robotic arm, and the well-trained SVDD model can distinguish the normal actions from the three kinds of abnormal actions with the Area Under Curve (AUC) 99.17%. Show more

Keywords: Robotic arm, action recognition, anomaly detection, 3D convolution neural network, support vector data description

DOI: 10.3233/JIFS-212468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5931-5937, 2022

Authors: Ramaraju, Satish Kumar | Kaliannan, Thenmalar | Androse Joseph, Sheela | Kumaravel, Umadevi | Albert, Johny Renoald | Natarajan, Arun Vignesh | Chellakutty, Gokul Prasad

Article Type: Research Article

Abstract: A Voltage lift performance is an excellent role to DC/DC conversion topology. The Voltage Lift Multilevel Inverter (VL-MLI) topology is suggested with minimal number of components compared to the conventional multilevel inverter (MLI). In this method, the Modified Particle Swarm Optimization (MPSO) conveys a primary task for the VL-MLI using Half Height (H-H) method, it determine the required optimum switching angles to eliminate desired value of harmonics. The simulation circuit for fifteen level output uses single switch voltage-lift inverter fed with resistive and inductive loads (R & L load). The power quality is developed by voltage-lift multilevel inverter with minimized …harmonics under the various Modulation Index (MI) while varied from 0.1 up to 1. The circuit is designed in a Field Programmable Gate Array (FPGA), which includes the MPSO rules for fast convergence to reduce the lower order harmonics and finds the best optimum switching angle values. To report this problem the H-H has implemented with MPSO to reduce minimum Total Harmonic Distortion (THD) for simulation circuit using Proteus 7.7 simulink tool. Due to the absence of multiple switches, filter and inductor element exposes for novelty of the proposed system. The comparative analysis has been carried-out with existing optimization and modulation methods. Show more

Keywords: Solar-Photovoltaic, voltage lift-multilevel inverter, particle swarm optimization algorithm, half height, field program gate array

DOI: 10.3233/JIFS-212583

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5939-5956, 2022

Authors: Wang, Chunye | Sun, Jian | Xu, Xiaoxin | Zou, Bin | Zhang, Min | Tang, Yang | Zeng, Min

Article Type: Research Article

Abstract:  The denial-of-service (DoS) attacks block the communications of the power grids, which affects the availability of the measurement data for monitoring and control. In order to reduce the impact of DoS attacks on measurement data, it is essential to predict missing measurement data. Predicting technique with measurement data depends on the correlation between measurement data. However, it is impractical to install phasor measurement units (PMUs) on all buses owing to the high cost of PMU installment. This paper initializes the study on the impact of PMU placement on predicting measurement data. Considering the data availability, this paper proposes a scheme …for predicting states using the LSTM network while ensuring system observability by optimizing phasor measurement unit (PMU) placement. The optimized PMU placement is obtained by integer programming with the criterion of the node importance and the cost of PMU deployment. There is a strong correlation between the measurement data corresponding to the optimal PMU placement. A Long-Short Term Memory neural network (LSTM) is proposed to learn the strong correlation among PMUs, which is utilized to predict the unavailable measured data of the attacked PMUs. The proposed method is verified on an IEEE 118-bus system, and the advantages compared with some conventional methods are also illustrated. Show more

Keywords: Integer linear programming, DoS attacks, deep learning, state prediction

DOI: 10.3233/JIFS-212593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5957-5971, 2022

Authors: Lv, Zhaoming | Peng, Rong

Article Type: Research Article

Abstract: The grasshopper optimization algorithm (GOA) has received extensive attention from scholars in various real applications in recent years because it has a high local optima avoidance mechanism compared to other meta-heuristic algorithms. However, the small step moves of grasshopper lead to slow convergence. When solving larger-scale optimization problems, this shortcoming needs to be solved. In this paper, an enhanced grasshopper optimization algorithm based on solitarious and gregarious states difference is proposed. The algorithm consists of three stages: the first stage simulates the behavior of solitarious population learning from gregarious population; the second stage merges the learned population into the gregarious …population and updates each grasshopper; and the third stage introduces a local operator to the best position of the current generation. Experiments on the benchmark function show that the proposed algorithm is better than the four representative GOAs and other metaheuristic algorithms in more cases. Experiments on the ontology matching problem show that the proposed algorithm outperforms all metaheuristic-based method and beats more the state-of-the-art systems. Show more

Keywords: Meta-heuristic algorithms, grasshopper optimization algorithm, solitarious and gregarious states, chemotaxis operator, ontology matching

DOI: 10.3233/JIFS-212633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5973-5986, 2022