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Aperiodically semi-intermittent-based fixed-time stabilization and synchronization of delayed discontinuous inertial neural networks

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Science China(Information Sciences) 2025年第1期68卷 238-254页

作者： Guodong ZHANG Jinde CAO school of Mathematics and Statistics South-Central Minzu University school of Mathematics Southeast University

This article concentrates on the fixed-time stabilization(FS) and fixed-time synchronization(FTS) of discontinuous inertial neural networks(DINNs) with distributed delays. Using mathematical induction to a new differential equality, a novel fixed-time stability lemma is constructed. Then, by designing an aperiodically semi-intermittent switching control and combining it with the theory of nonsmooth analysis, some novel criteria on the FS and FTS of DINNs are obtained. Unlike the methods used in most existing studies, the FS and FTS results are structured using the newly proposed fixed-time stability lemma and the nonreduced-order approach, resulting in broader practical applications and strengthening the scientific quality of the derived results. Lastly, numerical simulations and applications proffer the effectiveness of the established FS and FTS criteria.

关键词： inertial neural networks fixed-time synchronization semi-intermittent switching control nonreduced-order approach fixed-time stabilization

Optimizing local search-based partial MaxSAT solving via initial assignment prediction

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Science China(Information Sciences) 2025年第2期68卷 59-73页

作者： Chanjuan LIU Guangyuan LIU Chuan LUO Shaowei CAI Zhendong LEI Wenjie ZHANG Yi CHU Guojing ZHANG school of Computer Science and Technology Dalian University of Technology school of Software Beihang University Institute of Software Chinese Academy of Sciences school of Computer Science Peking University

Partial maximum satisfiability(PMS) is a significant generalization of Boolean satisfiability(SAT) and maximum satisfiability(MaxSAT), by introducing hard clauses and soft clauses. Compared with SAT and MaxSAT, the PMS problem has more real-world applications where both hard and soft constraints are involved. Local search is an effective incomplete method for solving PMS and is useful for important domains where good-quality solutions are desired within reasonable *** local search PMS solvers, the approach for initial assignment generation is crucial because its effectiveness significantly affects practical performance. In this study, we propose a novel initial assignment prediction approach, called InitPMS. When predicting an assignment for PMS, InitPMS considers the specific structure of PMS instances, i.e., distinguishing hard and soft clauses. Our experiments on extensive PMS instances from MaxSAT evaluations(MSEs) 2020 and 2021 show that InitPMS significantly boosts the performance of five state-of-the-art local search PMS solvers, demonstrating its generality. In addition,our results indicate that incorporating InitPMS could improve the performance of one of the best incomplete PMS solvers in MaxSAT Evaluation 2021, indicating that InitPMS might help advance the state of the art in PMS solving.

关键词： MaxSAT local search initial assignment incomplete method assignment prediction

Predefined-Time Constrained Optimization of Multi-Agent Systems Under Impulsive Effects

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 255-257页

作者： Zhuyan Jiang Xiaoyang Liu Xiang Jiang Jinde Cao the school of Computer Science and Technology Jiangsu Normal University IEEE the school of Mathematics Southeast University

Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.

关键词： Constrained optimization

Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG school of Journalism and Communication Sun Yat-sen University school of Software Technology Zhejiang University school of Software Engineering Sun Yat-sen University school of Software Dalian University of Technology school of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking university) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University school of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences school of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University school of Computer and Information Technology Beijing Jiaotong University school of Computer Science and Technology Harbin Institute of Technology school of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

Constructing electrochemically stable single crystal Ni-rich cathode material via modification with high valence metal oxides

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Journal of Energy Chemistry 2025年第02期101卷 392-401页

作者： Hancheng Shi Jiongzhi Zheng Tao Wan Hongqiang Wang Zeping Wen Fenghua Zheng Mingru Su Aichun Dou Yu Zhou Ahmad Naveed Panpan Zhang Hailong Wang Ruiqiang Guo Yunjian Liu Dewei Chu school of Material Science and Engineering Jiangsu University Thayer school of Engineering Dartmouth College school of Materials Science and Engineering The University of New South Wales school of Chemistry and Pharmaceutical Sciences Guangxi Normal University school of Materials and New Energy Ningxia University Thermal Science Research Center Shandong Institute of Advanced Technology

Single crystal Ni-rich cathode materials（SCNCM） are a good supplement in the market of nickel-based materials due to their safety and excellent electrochemical ***,the challenges of cation mixing,phase change during charge/discharge,and low thermal stability remain unresolved in single crystal *** address these issues,SCNCM are rationally modified by incorporating transition metal（TM） oxides,and the influence of metal ions with different valence states on the electrochemical properties of SCNCM is methodically explored through experimental results and theoretical *** structural stability is demonstrated in SCNCM after the modifications,and the degree of improvement in the matrix materials varies depending on the valence state of doped TM *** highest structural stability is found in WO3-modified SCNCM,due to the smaller effective ion radii,higher electro-negativity,stronger W-O bond,and efficient suppression of oxygen vacancy *** a result,WO3-modified SCNCM have outstanding cycle performance,with a capacity retention rate of90.2% after 200 *** study provides an insight into the design of advanced SCNCM with enhanced reversibility and cyclability.

关键词： In-situ coating Ion doping Lithium-ion battery Single crystal Ni-rich material Strengthening mechanism analysis

Privacy Distributed Constrained Optimization Over Time-Varying Unbalanced Networks and Its Application in Federated Learning

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 335-346页

作者： Mengli Wei Wenwu Yu Duxin Chen Mingyu Kang Guang Cheng the school of Cyber Science and Engineering Southeast University IEEE the school of Mathematics Frontiers Science Center for Mobile Information Communication and Security Southeast University the Purple Mountain Laboratories the school of Mathematics Southeast University

This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into account recent progress and addressing these concerns separately, there remains a lack of solutions offering theoretical guarantees for both privacy protection and constrained ZOO over time-varying unbalanced *** hereby propose a novel algorithm, termed the differential privacy(DP) distributed push-sum based zeroth-order constrained optimization algorithm(DP-ZOCOA). Operating over time-varying unbalanced graphs, DP-ZOCOA obviates the need for supplemental suboptimization problem computations, thereby reducing overhead in comparison to distributed primary-dual methods. DP-ZOCOA is specifically tailored to tackle constrained ZOO problems over time-varying unbalanced graphs,offering a guarantee of convergence to the optimal solution while robustly preserving privacy. Moreover, we provide rigorous proofs of convergence and privacy for DP-ZOCOA, underscoring its efficacy in attaining optimal convergence without constraints. To enhance its applicability, we incorporate DP-ZOCOA into the federated learning framework and formulate a decentralized zeroth-order constrained federated learning algorithm(ZOCOA-FL) to address challenges stemming from the timevarying imbalance of communication topology. Finally, the performance and effectiveness of the proposed algorithms are thoroughly evaluated through simulations on distributed least squares(DLS) and decentralized federated learning(DFL) tasks.

关键词： Constrained distributed optimization decentralized federated learning (DFL) differential privacy (DP) time-varying unbalanced graphs zeroth-order gradient

Federated local causal structure learning

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Science China(Information Sciences) 2025年第03期68卷 110-124页

作者： Kui YU Chen RONG Hao WANG Fuyuan CAO Jiye LIANG school of Computer and Information Hefei University of Technology school of Computer and Information Technology Shanxi University

Local causal structure learning(LCS) efficiently identifies a set of direct neighbors of a specified variable from observational data. Additionally, it distinguishes direct causes and direct effects of this variable without learning the entire causal structure. While many LCS algorithms have been proposed, they do not consider the data privacy-preserving problem,which has attracted extensive attention from academia and industry. To address this issue, we propose a federated local causal structure learning(FedLCS) algorithm to learn local causal structures in privacy-preserving data in a federated ***, FedLCS introduces a layer-wise federated local skeleton learning algorithm to construct the local skeleton. Based on this skeleton, it introduces a federated local skeleton orientation algorithm and an extension-and-backtracking orientation algorithm to orient the edges. Finally, FedLCS uses a federated local extension-and-backtracking orientation algorithm to orient the remaining edges. Extensive experiments on benchmark, synthetic, and real datasets demonstrate that FedLCS can learn the local causal structure of a given variable in a federated setting.

关键词： local causal structure learning federated learning directed acyclic graph privacy-preserving data federated layer-wise strategy

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Improved Zero-Dynamics Attack Scheduling With State Estimation

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 472-474页

作者： Zhe Wang Heng Zhang Chaoqun Yang Xianghui Cao the school of Computer Engineering Jiangsu Ocean University the school of Marine Technology and Geomatics Jiangsu Ocean University IEEE the school of Automation Southeast University

Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of a...

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The optimal control theory——a scientific approach to fundamentally solving control problems

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Science China(Information Sciences) 2025年第1期68卷 389-391页

作者： Huanshui ZHANG school of Electrical and Automation Engineering Shandong University of Science and Technology school of Control Science and Engineering Shandong University

The maximum principle has bridged mathematical optimization to optimal control,ushering in significant developments and refinements in optimal control theory,notably during the 1960s with the advent of linear quadratic (LQ)control and linear quadratic estimation (LQE).This progression propelled optimal control theory into further advancements,encompassing stochastic control,robust/H-infinity control,model predictive control (MPC),networked control,and reinforcement learning *** control,established upon a rigorous mathematical foundation,extends static optimization theory to dynamic systems,exhibiting scientific essence,unity,and ***,since its inception,optimal control theory has served as an indispensable core role across all control-related domains,including communication-constrained control in networked systems,consensus control,cooperative control,and reinforcement learning control.

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