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An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

Science China(Information Sciences) 2024年第2期67卷 5-19页

作者： Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN School of electrical and Information engineering Zhengzhou University State Key Laboratory of Intelligent Agricultural Power Equipment School of electrical engineering and Automation Henan Institute of Technology School of Electronics and Information Zhongyuan University of Technology KINDI Center for computing Research College of EngineeringQatar University

Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs) have been extensively used to address the feature selection problem due to their strong global search capability. However, most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process. In addressing the aforementioned issue, this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition. The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity. Specifically, an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets. Furthermore, a novel knowledge transfer mechanism that considers evolutionary characteristics is developed, allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods. The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of electrical engineering and Industrial computing University Institute of TechnologyDoualaCameroon Department of Computer engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon electrical engineering Department and Industrial computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang School of electrical and Information engineering Zhengzhou UniversityZhengzhou 450001 School of electrical engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of electrical and Information engineering Zhengzhou UniversityZhengzhou 450001China KINDI Center for computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore College of electrical and Information engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China IEEE

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.

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Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1819-1835页

作者： Kangjia Qiao Jing Liang Kunjie Yu Xuanxuan Ban Caitong Yue Boyang Qu Ponnuthurai Nagaratnam Suganthan School of electrical and Information engineering Zhengzhou UniversityZhengzhou 450001China School of electrical engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electronic and Information Zhongyuan University of TechnologyZhengzhou 450007China KINDI Center for computing Research College of EngineeringQatar UniversityDoha 999043Qatar

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for *** all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto ***,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single ***,this paper attempts to identify promising single constraints and utilize them to help solve *** be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint ***,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance ***,an improved tentative method is designed to find and transfer useful knowledge among *** results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

关键词： Constrained multi-objective optimization(CMOPs) evolutionary multitasking knowledge transfer single constraint.

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Model Predictive Current Control for Low-Cost Shunt Active Power Filter

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1589-1598页

作者： Ali A.Abdel-Aziz Mohamed A.Elgenedy Barry W.Williams the Electronic and electrical engineering Department Strathclyde UniversityGlasgowG11XQUK School of computing Engineering and the Built Environment(SCEBE)Glasgow Caledonian UniversityGlasgowG40BAUK

Performance of a three-phase shunt active power filter(SAPF)relies on the capability of the controller to track the reference ***,designing an accurate current controller is crucial to guarantee satisfactory SAPF *** paper presents a model predictive current controller(MPCC)for a low-cost,four-switch,shunt active power filter for power quality improvement.A four-switch,B4,converter topology is adopted as an SAPF,hence offering a simple,robust,and low-cost *** addition,to further reduce overall cost,only two interfacing filter inductors,instead of three,are used to eliminate switching current *** proposed SAPF model MPCC is detailed for implementation,where simulation and experimental results validate effectiveness of the proposed control algorithm showing a 20%improvement in total harmonic distortion compared with a conventional hysteresis band current controller.

关键词： Four-switch converter harmonic distortion model predictive current control power quality shunt active power filter

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Multi-Smart Meter Data Encryption Scheme Basedon Distributed Differential Privacy

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Big Data Mining and Analytics 2024年第1期7卷 131-141页

作者： Renwu Yan Yang Zheng Ning Yu Cen Liang School of Electronic Electrical Engineering and PhysicsFujian University of TechnologyFuzhou 350118China Department of computing Sciences at The College at Brockport State University of New YorkNew YorkNY 14420USA

Under the general trend of the rapid development of smart grids,data security and privacy are facing serious challenges;protecting the privacy data of single users under the premise of obtaining user-aggregated data has attracted widespread *** this study,we propose an encryption scheme on the basis of differential privacy for the problem of user privacy leakage when aggregating data from multiple smart ***,we use an improved homomorphic encryption method to realize the encryption aggregation of users’***,we propose a double-blind noise addition protocol to generate distributed noise through interaction between users and a cloud platform to prevent semi-honest participants from stealing data by colluding with one ***,the simulation results show that the proposed scheme can encrypt the transmission of multi-intelligent meter data under the premise of satisfying the differential privacy *** if an attacker has enough background knowledge,the security of the electricity information of one another can be ensured.

关键词： smart grid homomorphic encryption data aggregation differential privacy cloud computing

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Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2339-2356页

作者： R.Saravanan R.Muthaiah A.Rajesh School of computing SASTRA Deemed UniversityThanjavur613401India School of electrical&Electronics engineering SASTRA Deemed UniversityThanjavur613401India

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio *** threshold identification method is implemented in the received signal at the secondary user based on the square *** proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division ***,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio *** the dynamic threshold,the signal ratio-based threshold is *** threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and *** general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user *** limitations undermine the sensing accuracy of the energy identification ***,the ETBED technique is developed to enhance the energy efficiency of cognitive radio *** projected approach is executed and analyzed with performance and comparison *** proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

关键词： Cognitive radio network spectrum sensing noise uncertainty modified black widow optimization algorithm energy detection technique

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Superposed probabilistically shaped QAM constellation design based on nonlinear coding for MIMO visible light communication systems

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Chinese Optics Letters 2024年第6期22卷 109-115页

作者： Xinyue Guo Meixia Lu Qibin Zheng Key Laboratory of Optical Technology and Instrument for Medicine Ministry of EducationSchool of Optical-Electrical and Computing EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Laboratory of Radiation Detection and Medical Imaging School of Health Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

In this study, we propose a superposed probabilistically shaped(PS) quadrature amplitude modulation(QAM) constellation scheme for multiple-input multiple-output visible light communication systems. PS QAM signals are generated from a nonlinear coding equation that converts uniformly distributed 8-level signals into PS 9-or 10-level signals, which are then mapped into PS 9QAM or 10QAM signals. Square-shaped 9QAM and trapezoid-shaped 10QAM constellations are introduced to maximize the minimum Euclidean distance(MED) of the superposed constellation. Finally, the PS 9QAM and 10QAM signals are superposed with the 4QAM signals in a flipped manner to obtain PS 36QAM or 40QAM signals at the receiver, *** exploit the temporal correlation of the resulting signal from nonlinear coding, we developed a detection algorithm based on Viterbi decoding. Experimental results confirmed the superiority of the proposed schemes by achieving a higher MED and stronger ability to resist nonlinearity. Compared with the traditional scheme, the peak-to-peak voltage dynamic ranges of the superposed 36QAM and 40QAM constellation schemes were improved by 52% and 48%, respectively.

关键词： visible light communication superposed constellation nonlinear coding

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Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

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Journal of Energy Chemistry 2024年第3期90卷 120-132,I0005页

作者： Ziyou Zhou Yonggang Liu Chengming Zhang Weixiang Shen Rui Xiong State Key Laboratory of Mechanical Transmission for Advanced Equipment&College of Mechanical and Vehicle engineering Chongqing UniversityChongqing 400000China Department of Vehicle engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of electrical engineering Harbin Institute of TechnologyHarbin 150000HeilongjiangChina School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

关键词： Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and electrical Communications engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and electrical Communications engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and electrical Communications engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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