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Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

Energy Engineering 2025年第1期122卷 243-264页

作者： Liyao Yang Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Institute of Distributed Energy Storage Safety Big Data Beijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China

Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is *** address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and ***,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance ***,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution *** Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction ***,the proposed models are validated using NASA and CALCE lithium-ion battery *** results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.

关键词： State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm

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A Frequency and Polarization Reconfigurable Transparent Water Antenna

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Progress in Electromagnetics Research C 2025年 152卷 177-186页

作者： Li, Lei Gao, Jing Nan, Jingchang School of Electronics and Information Engineering Liaoning Technical University Huludao125105 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China

A novel frequency and polarization reconfigurable water patch antenna is proposed for radio communication in the UHF band. Based on theoretical analysis and simulation results, water is an ideal material for designing transparent liquid ground. Water enhances outstanding transparency, excellent aesthetics, and high optical stealth performance for a wider range of application scenarios. The entire structure is made with polyvinyl chloride material and distilled water, except for the feed structure. By filling different cavities with liquid water, five different operating states are obtained in 1.924–2.5 GHz (26.1%), 1.67–2.33 GHz (33%), 0.644–2.288 GHz (112.1%), 1.975–2.54 GHz (25%), and 1.748–2.108 GHz (18.7%), achieving frequency reconfigurability. The antenna can be flexibly switched between linear polarization (LP) and two right-handed circular polarization (RHCP) states. The results show that the 3 dB axial ratio (AR) bandwidth covers 1.93–2.08 GHz (7.5%) and 2.06–2.132 GHz (3.5%). The antenna achieves high optical transparency of 100% and a peak gain of 7.97 dBi. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Circular polarization

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A blockchain-empowered authentication scheme for worm detection in wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 265-272页

作者： Yuling Chen Xiong Yang Tao Li Yi Ren Yangyang Long State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Computing Science University of East AngliaNorwichNR57TJUK

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and *** special structure of WSN brings both convenience and *** example,a malicious participant can launch attacks by capturing a physical ***,node authentication that can resist malicious attacks is very important to network ***,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for *** our scheme,all nodes are managed by utilizing the identity information stored on the ***,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection *** experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

关键词： Wireless Sensor Network(WSN) Node authentication Blockchain Tangle Worm detection

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Type 1 Diabetes Blood Glucose Management Based on Fuzzy Logic and Reinforcement Learning 4

Type 1 Diabetes Blood Glucose Management Based on Fuzzy Logi...

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4th International Symposium on Artificial Intelligence and intelligent Manufacturing, AIIM 2024

作者： Liu, Xuan Hu, Feng Su, Zuqiang Yang, Fusong Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798331541729

Developing a personalized insulin regimen for the treatment of Type 1 Diabetes (T1D) is a healthcare task with very high safety requirements. Compared to traditional control algorithms, current reinforcement learning-based methods for blood glucose control can increase the duration of blood glucose within the target range while reducing risks. However, they do not account for the uncertainties of factors affecting blood glucose levels, such as diet, age, weight, etc. These factors do not have a simple linear relationship with blood glucose changes. For example, consuming a certain amount of carbohydrates does not always immediately result in the same degree of blood glucose increase, and this variability is precisely the key to personalized insulin regimens. To address this issue, this work proposes a personalized insulin regimen based on fuzzy logic reasoning and reinforcement learning, namely the Fuzzy Rule-Based TD3-BC model (FRBTD3-BC). This regimen learns from real-world treatment data of T1D patients, uses membership functions to obtain the initial conditions and outcome categories of the data, and then calculates the weight of each rule based on confidence levels. Subsequently, a fuzzy rule base is obtained and embedded into the Actor module of the TD3-BC algorithm in the form of a fuzzy reasoning logic layer. By interacting with the FDA-approved UVA/Padova glucose kinetics simulator, the utility of FRBTD3-BC in blood glucose control was studied in 30 virtual patients. The results show that the FRBTD3-BC model outperforms other comparable methods, with the TIR indicator increasing by 5.53 ± 0.64% compared to the original TD3-BC. © 2024 IEEE.

关键词： Reinforcement learning

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DCEL:classifier fusion model for Android malware detection

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Journal of Systems Engineering and Electronics 2024年第1期35卷 163-177页

作者： XU Xiaolong JIANG Shuai ZHAO Jinbo WANG Xinheng Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computing and Engineering University of West LondonLondon W55RFUK

The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android ***,the current methods of Android malware detection need a lot of time in the feature engineering ***,these models have the defects of low detection rate,high complexity,and poor practicability,*** analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other *** classify the software’s threat levels based on the correlation of ***,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware ***,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray ***,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single *** based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.

关键词： Android malware detection deep learning ensemble learning model fusion

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Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

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Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Puning Zhang Lei Tan Zhigang Yang Fengyi Huang Lijun Sun Haiying Peng School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing Innovation Center of Industrial Big-Data Co.Lt

The lack of facial features caused by wearing masks degrades the performance of facial recognition systems. Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer. Besides, previous research fails to consider the facial characteristics including occluded and unoccluded parts. To solve the above problems, we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion. Specifically, the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition. Then, a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial. Furthermore, a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load, computational power, bandwidth, and delay tolerance constraints of the edge. This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy. The experimental results show that the proposed method achieves an average gain of about 21% in recognition latency, while the accuracy of the face recognition task is basically the same compared to the baseline method.

关键词： Occluded face recognition Cross-domain feature fusion Device-edge collaboration

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A Multi-Objective Artificial Physics Optimization Algorithm Based on Two-Phase Search 8

A Multi-Objective Artificial Physics Optimization Algorithm ...

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8th International Symposium on Computer Science and intelligent Control, ISCSIC 2024

作者： Zhang, Huihua Xie, Liping Taiyuan University of Science and Technology Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan China

ISBN: (纸本)9798350380286

Aiming at the problems of insufficient utilization of information about elite particles in archive and instability of particle motion in the population in the multi-objective artificial physics optimization algorithm (MOAPO) in solving multiobjective optimization problems, A multi-objective artificial physics optimization algorithm based on two-phase search (TPMOAPO) is proposed. To begin with, the algorithm improves the calculation of the mass of particles, so that the strength and weakness of the particles can be accurately transformed into the corresponding masses while improving the efficiency of particle mass calculation. Next, a two-phase search strategy is proposed, which makes the algorithm have strong exploration ability in the first phase, and the second phase gradually enhances the exploitation capability with iterations, which solves the problem of instability motion of particles in the search process. Finally, the simulated binary crossover (SBX) and polynomial-based mutation (PM) operators are adopted in the archive to further enhance the search capability of the algorithm. For verifying the performance of TP-MOAPO, 21 benchmark functions were selected to compare with the classical multi-objective particle swarm optimization algorithms: MOPSO, dMOPSO, SMPSO, MMOPSO, and NMPSO, and the experimental results show the superiority of TP-MOAPO in these functions. © 2024 IEEE.

关键词： Optimization algorithms

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SMILE: A Cost-Effective System for Serving Massive Pretrained Language Models in the Cloud 23

SMILE: A Cost-Effective System for Serving Massive Pretraine...

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2023 ACM/SIGMOD International Conference on Management of data, SIGMOD 2023

作者： Wang, Jue Chen, Ke Shou, Lidan Jiang, Dawei Chen, Gang Key Lab of Intelligent Computing Based Big Data of Zhejiang Province Zhejiang University Hangzhou China

ISBN: (纸本)9781450395076

Deep learning models, particularly pre-trained language models (PLMs), have become increasingly important for a variety of applications that require text/language processing. However, these models are resource-intensive and often require costly hardware such as dedicated GPU servers. In response to this issue, we present SMILE, a novel prototype system for efficient deployment and management of such models in the cloud. Our goal is to build a cloud platform from which tenants can easily derive their own custom models, and rent PLM processors to run inference services on these models at reduced costs. To facilitate this, we present a co-design of cost-effective storage and computation scheme for managing massive customized PLMs with constrained hardware resources via effective resource sharing and multiplexing. Our system consists of four core components: vPLM creator, vPLM storage appliance, vPLM trainer, and vPLM processor, which allow tenants to easily create, store, train, and use their customized PLM in the cloud without the need for dedicated hardware or maintenance. In particular, vPLM processors are virtualized from a physical machine, and are designed to have a multi-tenant nature, enabling efficient utilization of resources by precomputing the intermediate representation of PLMs and using adapters to provide customization instead of training the entire model. This allows tenants to host their PLMs in the cloud at minor costs. In our demonstration, we show that over 10,000 models can be hosted on one single machine without compromising the inference speed and accuracy. Overall, our system provides a convenient and cost-effective solution for tenants to host and manage PLMs in the cloud for their customized tasks. © 2023 ACM.

关键词： Cost effectiveness

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Hierarchical Clustering Combination Method Using Intuitionistic Fuzzy Similarity in Software Module Clustering

Hierarchical Clustering Combination Method Using Intuitionis...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Xia, Hong Wang, Bo Zhang, Yongkang Chen, Yanping School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing China Xi 'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9798350327380

In order to solve the problem that the similarity method used in software module clustering can produce arbitrary decision, and the description matrix of dendrogram generated by base clustering in hierarchical clustering combination does not consider more characteristics of entities, a hierarchical clustering combination method based on intuitionistic fuzzy similarity (HCC-IFS) was proposed. In the process of entity similarity calculation, the intuitionistic fuzzy set theory and similarity calculation method are used to provide membership and non-membership attributes for each entity, so as to improve the merging efficiency and solve arbitrary decision problems. In the description matrix representation of base clustering, the absolute and relative features obtained from the dendrogram are used to construct the description matrix, and the generated description matrix is combined. Finally, the proposed algorithm is verified by experiments. Experimental results show that HCC-IFS can produce better clustering results and improve the recovery quality of software architecture. © 2023 IEEE.

关键词： Fuzzy set theory

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