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PltcRB:a practical distributed randomness beacon with optimal amortized communication complexity

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Science China(Information Sciences) 2025年第2期68卷 269-284页

作者： Zheyi WU Haolin LIU Lei WANG School of electronic Information and electrical engineering Shanghai Jiao Tong University

The distributed randomness beacon(DRB) is a crucial tool for continuously generating unpredictable, bias-resistant,and publicly verifiable random numbers on a regular basis. This is particularly useful for applications such as lotteries, electronic voting, and cryptographic parameter generation. However, existing studies either require complex communication or rely on a public bulletin board to meet security requirements. This brings a performance bottleneck when dealing with a large number of participants. This paper introduces a novel DRB protocol PltcRB based on timed commitments, eliminating the need for a public bulletin board. Our approach achieves optimal communication complexity of O(n) while maintaining the desired properties of a DRB protocol. The computation complexity is also O(n) when n is larger than the security parameter κ. These results demonstrate the practicality and performance of PltcRB, supported by our experimental analysis under various configurations.

关键词： timed commitment randomness beacon random number optimal communication complexity permissionlessłinebreak setting

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Constrained Networked Predictive Control for Nonlinear Systems Using a High-Order Fully Actuated System Approach

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 478-480页

作者： Yi Huang Guo-Ping Liu Yi Yu Wenshan Hu the School of electrical engineering and Automation Wuhan University IEEE the Center for Control Science and Technology Southern University of Science and Technology the department of electrical and electronic engineering The Hong Kong Polytechnic University

Dear Editor,In this letter, a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated (HOFA) systems with noises. The method can effectively deal with nonlinearities, constraints, and noises in the system, optimize the performance metric, and present an upper bound on the stable output of the system.

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Logic-in-memory cell enabling binary and ternary Boolean logics

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Science China(Information Sciences) 2025年第2期68卷 370-379页

作者： Jeongyun OH Juhee JEON Yunwoo SHIN Kyougah CHO Sangsig KIM department of electrical engineering Korea University

In computing systems, processing and memory units have been integrated into logic-in-memory(LiM) to enhance the computational efficiency and performance. LiM has been attempted to perform not only binary but also ternary logic functions, which reduces computing complexity. Herein, we demonstrate a binary and ternary LiM(BT-LiM) cell with eight triple-gated(TG) feedback field-effect transistors(FBFETs) reconfigured into n-or p-channel modes. The TG FBFETs exhibit symmetrical latch-up voltages and an on-current ratio of 1.02 between the n-and p-channel modes, which indicates a high potential for reconfigurable logic applications. The BT-LiM cell can perform YES, NOT, AND, OR, NAND, NOR, XNOR, and XOR logic functions in a single cell because of these reconfigurable characteristics. Further, binary and ternary logic functions are realized in the cell without a programming stage, and the cell maintains the results of the logic functions under zero-bias conditions. This study achieves multifunctional LiM that can operate all binary and ternary Boolean logics.

关键词： logic-in-memory reconfigurable channel modes ternary logic triple-gated feedback field-effect transistors

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Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

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Journal of Energy Chemistry 2025年第2期101卷 524-534页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chi Yung Chung School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong department of electrical and electronic engineering and Research Centre for Grid Modernisation The Hong Kong Polytechnic University Photonics Research Institute The Hong Kong Polytechnic University School of electrical Computer and Telecommunications Engineering University of Wollongong

Accurate prediction of the remaining useful life(RUL) is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

关键词： Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning

Method for Estimating the State of Health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network

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Energy engineering 2025年第1期122卷 203-220页

作者： Yu Zhang Daoyu Zhang TiezhouWu School of electrical and electronic engineering Hubei University of TechnologyWuhan430068China

Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery ***,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity ***,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model *** address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural ***,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity ***,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network *** validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging *** experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.

关键词： Lithium-ion battery state of health differential thermal voltammetry Sparrow Search Algorithm

Distributed State and Fault Estimation for Cyber-Physical Systems Under DoS Attacks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 261-263页

作者： Limei Liang Rong Su Haotian Xu the School of Control Science and engineering Shandong University IEEE the School of electrical and electronic engineering Nanyang Technological University

Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.

关键词： Observers Estimation error Sensors Physical layer Sensor systems Security Power system stability

Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of electrical and electronic engineering Nanyang Technological University School of Computer Science and engineering Nanjing University of Science and Technology School of Information Science and engineering East China University of Science and Technology department of electrical and Computer engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第03期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication engineering Dalian University of Technology College of Information engineering Zhejiang University of Technology School of Information and electronics Beijing Institute of Technology department of electrical and Computer engineering The University of Texas at Dallas department of electrical and Computer engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Physics-informed battery degradation prediction:Forecasting charging curves using one-cycle data

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Journal of Energy Chemistry 2025年第2期101卷 825-836页

作者： Aihua Tang Yuchen Xu Jinpeng Tian Xing Shu Quanqing Yu Key Laboratory of Advanced Manufacturing Technology for Automobile Parts Ministry of Education Chongqing University of Technology department of electrical and electronic engineering and Research Centre for Grid Modernization The Hong Kong Polytechnic University School of Automotive engineering Harbin Institute of Technology

Accurately predicting battery degradation is crucial for battery system ***,due to the complexities of aging mechanisms and limitations of historical data,comprehensively indicating battery degradation solely through maximum capacity loss assessment is *** machine learning offers promising solutions,it often overlooks domain knowledge,resulting in reduced accu racy,increased computational burden and decreased ***,this study proposes a method to predict the voltage-capacity（V-Q） curve during battery degradation with limited historical *** process is achieved through two physically interpretable components:a lightweight interpretable physical model and a physics-informed neural *** components incorporate domain knowledge into machine learning to improve V-Q curve prediction performance and enhance *** validation was conducted on 52 batteries of different types under different testing *** proposed method can accurately predict future *** for hundreds of cycles using only one-present-cycle V-Q curve,with root mean square error and mean absolute error basically less than 0.035 Ah and R2basically less than 98.5%.This means that incremental capacity curves can be extracted from the predicted results for a more comprehensive and accurate battery degradation ***,the method can flexibly adjust prediction length and density to cater to the practical needs of long-cycle prediction and data *** study provides a viable method for rapid degradation prediction and is expected to be generalized to in-vehicle implementations.

关键词： Degradation prediction Lightweight interpretable physical model Lithium-ion battery Physics-informed neural network Voltage-capacity curve