智慧起航，共创未来
主题
信息物理系统、多标签不平衡、广泛学习系统、数据科学、电力系统、优化控制、图像增强、迭代预测改进、分布式在线优化、联合学习、故障检测滤波器、非线性系统、深度强化学习...
全球科研机构
美国Columbia University、Monmouth University、New Jersey Institute of Technology；加拿大Concordia University；上海交通大学、复旦大学、同济大学、哈尔滨工业大学、华南理工大学、武汉大学、北方工业大学、重庆邮电大学、西南大学、东南大学；鹏城实验室...
M. Taheri, K. Khorasani, and N. Meskin, “On zero dynamics and controllable cyber-attacks in cyber-physical systems and dynamic coding schemes as their countermeasures,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2191–2203, Nov. 2024. doi: 10.1109/JAS.2024.124692
> Vulnerability of CPS to zero dynamics and controllable cyber-attacks is studied.
> Cyber-attacks are derived in terms of nonzero Markov parameters of the CPS and the entries of the observability matrix.
> Number of actuators that need to be compromised for zero dynamics and controllable cyber-attacks is studied.
Y. Lin, Z. Yu, K. Yang, Z. Fan, and C. L. P. Chen, “Boosting adaptive weighted broad learning system for multi-label learning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2204–2219, Nov. 2024. doi: 10.1109/JAS.2024.124557
> Aiming at the serious multi-label imbalance problem, this paper innovatively proposes a MLW-BLS.
> Proposes the MLAW-BLS to adaptively adjust corresponding label weights and values of MLW-BLS to construct an efficient imbalanced classifier set.
> Extensive comparative experiments are conducted on 30 datasets with 4 metrics to evaluate the effectiveness of MLAW-BLS compared with 7 mainstream algorithms.
J. Chen, K. Liu, X. Luo, Y. Yuan, K. Sedraoui, Y. Al-Turki, and M. C. Zhou, “A state-migration particle swarm optimizer for adaptive latent factor analysis of high-dimensional and incomplete data,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2220–2235, Nov. 2024. doi: 10.1109/JAS.2024.124575
> An SPSO algorithm injects particles’ historical position and velocity into the evolution process, enhancing its search ability.
> SPSO’s theoretical convergence is rigorously proved via the analyses of the stochastic convergence conditions on the particles’ position expectations.
> An SPSO-incorporated LFA model implements efficient hyper-parameter adaptation without accuracy loss.
K. Nosrati, J. Belikov, A. Tepljakov, and E. Petlenkov, “Revisiting the LQR problem of singular systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2236–2252, Nov. 2024. doi: 10.1109/JAS.2024.124665
> Examine the conditions for the existence of the LQR algorithm for discrete singular systems.
> Derive LQR algorithm via dynamic programming and penalized LSs over a finite horizon.
> Link the problem to a system using Hamiltonian diagonalization for steady-state analysis.
K. Jiang, R. Wang, Y. Xiao, J. Jiang, X. Xu, and T. Lu, “Image enhancement via associated perturbation removal and texture reconstruction learning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2253–2269, Nov. 2024. doi: 10.1109/JAS.2024.124521
> Investigates the image enhancement tasks from a fresh perspective that involves the joint representation of perturbation removal, texture reconstruction and their association.
> Develops a PerTEM to associate degradation simulation and texture restoration, facilitating the learning capability while maintaining the model compactness.
> Experiments on various mainstream image enhancement tasks, such as image deraining, image dehazing and low-light image enhancement have demonstrated that PerTeRNet delivers competitive performance compared to the state-of-the-art method.
Z. Yin, J. Pu, Y. Zhou, and X. Xue, “Two-stage approach for targeted knowledge transfer in self-knowledge distillation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2270–2283, Nov. 2024. doi: 10.1109/JAS.2024.124629
> Propose a novel two-stage self-knowledge distillation approach for selective dark knowledge transfer.
> Generate class medoids from logit vectors to represent typical samples per class.
> Distill under-trained data using past predictions on half batch size.
Z. Zhao, Z. Yang, L. Jiang, J. Yang, and Q. Ge, “Privacy preserving distributed bandit residual feedback online optimization over time-varying unbalanced graphs,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2284–2297, Nov. 2024. doi: 10.1109/JAS.2024.124656
> Differential privacy in distributed online optimization with precise noise control.
> Derives optimal prediction residual feedback boundedness, reducing estimation variance.
> Distributed algorithm with privacy and one-point feedback, handling unbalanced comms.
J. Zhang, B. Du, S. Zhang, and S. Ding, “A double sensitive fault detection filter for positive Markovian jump systems with a hybrid event-triggered mechanism,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2298–2315, Nov. 2024. doi: 10.1109/JAS.2024.124677
> A non-monotonic adaptive triggering law is established for PMJSs.
> Asynchronous filters with double sensitivity are proposed for PMJSs.
> A simple analysis and design approach is presented by combining stochastic co-positive Lyapunov function and linear programming.
Z. Song and P. Li, “General Lyapunov stability and its application to time-varying convex optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2316–2326, Nov. 2024. doi: 10.1109/JAS.2024.124374
> The general Lyapunov stability criteria of nonlinear systems are proposed.
> A less conservative upper bound of settling-time function is provided.
> A fixed-time stable approach is raised for resolving TV convex optimization problem.
M. Yang, G. Liu, Z. Zhou, and J. Wang, “Probabilistic automata-based method for enhancing performance of deep reinforcement learning systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2327–2339, Nov. 2024. doi: 10.1109/JAS.2024.124818
> Develop a novel framework that utilizes probabilistic automata to enhance DRL models.
> Implement reverse breadth-first search to identify and correct key weaknesses in DRL models. Improve the robustness of DRL models through targeted, minimal modifications based on identified vulnerabilities.
> Experiments in different environments verify the effectiveness of the framework in optimizing DRL for real-world industrial applications.
B. Yang, C. Tang, Y. Liu, G. Wen, and G. Chen, “A linear programming-based reinforcement learning mechanism for incomplete-information games,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2340–2342, Nov. 2024. doi: 10.1109/JAS.2024.124464
J. Wang, W. Li, and X. Luo, “A distributed adaptive second-order latent factor analysis model,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2343–2345, Nov. 2024. doi: 10.1109/JAS.2024.124371
Y. Liu, X. Wu, Y. Bo, J. Wang, and L. Ma, “A transfer learning framework for deep multi-agent reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2346–2348, Nov. 2024. doi: 10.1109/JAS.2023.124173
C.-C. Wang, Y.-L. Wang, and L. Jia, “Multi-USV formation collision avoidance via deep reinforcement learning and COLREGs,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2349–2351, Nov. 2024. doi: 10.1109/JAS.2023.123846
Z.-H. Pang, Q. Cao, H. Guo, and Z. Dong, “Prediction-based state estimation and compensation control for networked systems with communication constraints and DoS attacks,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2352–2354, Nov. 2024. doi: 10.1109/JAS.2024.124605
END
往期文章
联系我们
地址：北京市海淀区中关村东路95号
邮编：100190
电话：010-82544542（综合）
010-62522472（会员）
010-62522248（宣传出版及大赛 ）
010-62624980（财务）
010-82544541（学术活动）
传真：010-62522248
邮箱：caa@ia.ac.cn
中国自动化学会新媒体矩阵
点
阅读原文了解更多
喜欢的话点击在看哟~
