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arXiv 2021年

作者： Yuan, Ye Liu, Jun Jin, Dou Yue, Zuogong Chen, Ruijuan Wang, Maolin Sun, Chuan Xu, Lei Hua, Feng He, Xin Yi, Xinlei Yang, Tao Zhang, Hai-Tao Sui, Shaochun Ding, Han School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Department of Applied Mathematics University of Waterloo Canada State Key Laboratory of Synthetical Automation for Process Industries Northeastern University United States School of Electrical Engineering and Computer Science Digital Futures Kth Royal Institute of Technology Co. Ltd.

Traditional machine learning relies on a centralized data pipeline, i.e., data are provided to a central server for model training. In many applications, however, data are inherently fragmented. Such a decentralized nature of these databases presents the biggest challenge for collaboration: sending all decentralized datasets to a central server raises serious privacy concerns. Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client. Here we propose a principled decentralized federated learning algorithm (DeceFL), which does not require a central client and relies only on local information transmission between clients and their neighbors, representing a fully decentralized learning framework. It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1/T) (where T is the number of iterations in gradient descent) as centralized federated learning when the loss function is smooth and strongly convex. Finally, the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions, demonstrating its applicability to a wide range of real-world medical and industrial applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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Zeroth-order algorithms for stochastic distributed nonconvex optimization

arXiv

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arXiv 2021年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

In this paper, we consider a stochastic distributed nonconvex optimization problem with the cost function being distributed over n agents having access only to zeroth-order (ZO) information of the cost. This problem has various machine learning applications. As a solution, we propose two distributed ZO algorithms, in which at each iteration each agent samples the local stochastic ZO oracle at two points with a time-varying smoothing parameter. We show that the proposed algorithms achieve the linear speedup convergence rate O(√p=(nT)) for smooth cost functions under the state-dependent variance assumptions which are more general than the commonly used bounded variance and Lipschitz assumptions, and O(p=(nT)) convergence rate when the global cost function additionally satisfies the Polyak-Lojasiewicz (P-L) condition in addition, where p and T are the dimension of the decision variable and the total number of iterations, respectively. To the best of our knowledge, this is the first linear speedup result for distributed ZO algorithms, which enables systematic processing performance improvements by adding more agents. We also show that the proposed algorithms converge linearly under the relative bounded second moment assumptions and the P-L condition. We demonstrate through numerical experiments the efficiency of our algorithms on generating adversarial examples from deep neural networks in comparison with baseline and recently proposed centralized and distributed ZO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Cost functions

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Recursive Weighted Null-Space Fitting Method for Identification of Multivariate Systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

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Ray-Based Characterization of the AMPLE Model from 0.85 to 5 GHz

arXiv

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arXiv 2025年

作者： Zhou, Lingyou Dong, Xin Qiu, Kehai Yu, Gang Zhang, Jie Zhang, Jiliang School of Electrical and Electronic Engineering The University of Sheffield SheffieldS10 2TN United Kingdom Ranplan Wireless Network Design Ltd. CambridgeCB23 3UY United Kingdom Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom College of Engineering Design and Physical Sciences Brunel University of London UxbridgeUB8 3PH United Kingdom 5G/6G Innovation Centre University of Surrey SurreyGU2 7XH United Kingdom State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang110819 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

In this paper, we characterize the adaptive multiple path loss exponent (AMPLE) radio propagation model under urban macrocell (UMa) and urban microcell (UMi) scenarios from 0.85-5 GHz using Ranplan Professional. We first enhance the original AMPLE model by introducing an additional frequency coefficient to support path loss prediction across multiple carrier frequencies. By using measurement-validated Ranplan Professional simulator, we simulate four cities and validate the simulations for further path loss model characterization. Specifically, we extract the close-in (CI) model parameters from the simulations and compare them with parameters extracted from measurements in other works. Under the ray-based model characterization, we compare the AMPLE model with the 3rd Generation Partnership Project (3GPP) path loss model, the CI model, the alpha-beta-gamma (ABG) model, and those with simulation calibrations. In addition to standard performance metrics, we introduce the prediction-measurement difference error (PMDE) to assess overall prediction alignment with measurement, and mean simulation time per data point to evaluate model complexity. The results show that the AMPLE model outperforms existing models while maintaining similar model complexity. Copyright © 2025, The Authors. All rights reserved.

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A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model

arXiv

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arXiv 2024年

作者： Fu, Ming-Xiang Song, Yu Lv, Jia-Meng Cao, Liang Jia, Peng Li, Nan Li, Xiang-Ru Liu, Ji-Feng Luo, A-Li Qiu, Bo Shen, Shi-Yin Tu, Liang-Ping Wang, Li-Li Wei, Shou-Lin Yang, Hai-Feng Yi, Zhen-Ping Zou, Zhi-Qiang National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing101408 China Key lab of Space Astronomy and Technology National Astronomical Observatories Beijing100101 China School of Computer Science South China Normal University Guangzhou510631 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Beijing100101 China University of Chinese Academy of Sciences Nanjing211135 China University of Science and Technology Beijing Beijing100083 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Computer and Information Dezhou University Dezhou253023 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan30024 China School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China Nanjing University of Posts & Telecommunications Nanjing210023 China School of Science University of Science and Technology LiaoNing Anshan114051 China

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe. However, effectively analyzing this vast amount of data poses a significant challenge. Astronomers are turning to deep learning techniques to address this, but the methods are limited by their specific training sets, leading to considerable duplicate workloads too. Hence, as an example to present how to overcome the issue, we built a framework for general analysis of galaxy images, based on a large vision model (LVM) plus downstream tasks (DST), including galaxy morphological classification, image restoration, object detection, parameter extraction, and more. Considering the low signal-to-noise ratio of galaxy images and the imbalanced distribution of galaxy categories, we have incorporated a Human-in-the-loop (HITL) module into our large vision model, which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively. The proposed framework exhibits notable few-shot learning capabilities and versatile adaptability to all the abovementioned tasks on galaxy images in the DESI legacy imaging surveys. Expressly, for object detection, trained by 1000 data points, our DST upon the LVM achieves an accuracy of 96.7%, while ResNet50 plus Mask R-CNN gives an accuracy of 93.1%;for morphology classification, to obtain AUC ∼ 0.9, LVM plus DST and HITL only requests 1/50 training sets compared to ResNet18. Expectedly, multimodal data can be integrated similarly, which opens up possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-message astronomy. © 2024, CC BY.

关键词： Object detection

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A Unified Alternating Optimization Framework for Joint Sensor and Actuator Configuration in LQG Systems

arXiv

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arXiv 2025年

作者： Yang, Nachuan Li, Yuzhe Shi, Ling Chen, Tongwen Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 China Department of Electronic and Computer Engineering Department of Chemical and Biological Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

This paper fills a gap in the literature by considering a joint sensor and actuator configuration problem under the linear quadratic Gaussian (LQG) performance without assuming a predefined set of candidate components. Different from the existing research, which primarily focuses on selecting or placing sensors and actuators from a fixed group, we consider a more flexible formulation where these components must be designed from scratch, subject to general-form configuration costs and constraints. To address this challenge, we first analytically characterize the gradients of the LQG performance with respect to the sensor and actuator matrices using algebraic Riccati equations. Subsequently, we derive first-order optimality conditions based on the Karush–Kuhn–Tucker (KKT) analysis and develop a unified alternating direction method of multipliers (ADMM)based alternating optimization framework to address the general-form sensor and actuator configuration problem. Furthermore, we investigate three representative scenarios: sparsity promoting configuration, low-rank promoting configuration, and structure-constrained configuration. For each scenario, we provide in-depth analysis and develop tailored computational schemes. The proposed framework ensures numerical efficiency and adaptability to various design constraints and configuration costs, making it well-suited for integration into numerical solvers. Copyright © 2025, The Authors. All rights reserved.

关键词： Matrix algebra

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Internal short circuit fault diagnosis for automotive lithium-ion batteries using an adaptive parameter identification model

引用

Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Tao, Qian Wang, Yiming Tian, Engang Tao, Hongfeng Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi Jiangsu214122 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

This paper develops a model-based diagnostic method for internal short circuit (ISC) faults in lithium-ion batteries. This method utilizes a second-order equivalent circuit model (ECM) combined with a recursive least squares method with an adaptive forgetting factor for online parameter identification, with initial parameters optimized by a particle swarm optimization (PSO) algorithm. An extended Kalman filter (EKF) is then employed for state estimation to generate residuals, which are analyzed using the refined cumulative sum (CUSUM) statistical method to detect and diagnose ISC faults. Experimental results show that this method effectively identifies ISC faults in automotive power batteries under various initial state of charge (SOC) levels and fault-free conditions. At 100% initial SOC, fault detection times are 104 seconds for a 15Ω fault, 77 seconds for a 10Ω fault, and 27 seconds for a 1Ω fault;at 90% initial SOC, detection for a 10Ω fault takes 97 seconds, demonstrating high accuracy and robustness. © 2025 Elsevier Ltd

关键词： Lithium-ion batteries

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Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

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Pinning boundary sampled-data synchronization of coupled reaction-diffusion neural networks

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Neurocomputing 2025年

作者： Zi-Peng Wang Bo-Ming Chen Junfei Qiao Biao Luo Huai-Ning Wu Tingwen Huang Guangwei Chen School of Information Science and Technology Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System and Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing 100124 China School of Automation Central South University Changsha 410083 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen 518107 China

In this paper, the pinning boundary sampled-data (SD) synchronization problem of coupled reaction–diffusion neural networks (CRDNNs) with SD-based couplings is investigated under boundary and distributed SD measurements. First, a pinning boundary SD controller is designed to effectively address the synchronization problem of CRDNNs, utilizing boundary and distributed SD measurements and requiring only a small number of sensors and actuators. Then, linear matrix inequalities provide adequate conditions that ensure exponential stability of the synchronization error system by using the designed pinning boundary SD controller, derived through inequality techniques and Lyapunov functionals. Finally, we present a numerical example to showcase the practical applicability and effectiveness of the proposed pinning boundary SD synchronization method.

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

引用

SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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