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【当期目录】IEEE/CAA JAS第10卷第11期

已有 528 次阅读 2023-12-1 14:36 |系统分类:博客资讯

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分布式优化、图神经网络、多智能体系统、强化学习、分布式控制、无人驾驶、多目标优化...

全球科研机构

美国Indiana University-Purdue University Indianapolis；澳大利亚University of Sydney、Monash University；新加坡Motion G, Inc；日本National Institute for Materials Science、RIKEN AIP；中国科学院自动化研究所、东北大学、中南大学、北京科技大学、南京航空航天大学、东南大学、北京林业大学、广东工业大学、安徽大学、山东师范大学..

Y. T. Wang, X. Wang, X. X. Wang, J. Yang, O. Kwan, L. X. Li, and F.-Y. Wang, “The ChatGPT after: Building knowledge factories for knowledge workers with knowledge automation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2041–2044, Nov. 2023. doi: 10.1109/JAS.2023.123966

M. N. Zhai, Q. Y. Sun, R. Wang, and H. G. Zhang, “Containment-based multiple PCC voltage regulation strategy for communication link and sensor faults,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2045–2055, Nov. 2023. doi: 10.1109/JAS.2023.123747

> Investigates the regulation of the voltage at the common coupling point in a multi-microgrid system.

> Introduce the control objective of containment control.

> Designed a novel adaptive follower-based observer to handle communication and sensor faults, avoiding the use of global information of directed communication network and fault-related parameters.

H. X. Ma, M. Chen, and Q. X. Wu, “Disturbance observer-based safe tracking control for unmanned helicopters with partial state constraints and disturbances,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2056–2069, Nov. 2023. doi: 10.1109/JAS.2022.105938

> To handle the time-varying partial state constraints of the UAH, a SPA is proposed to generate the safe desired trajectories.

> A developed second-order disturbance observer method is investigated to estimate the disturbances, and the unbiased estimation of their time derivatives is achieved.

> Closed-loop system convergence is guaranteed by the Lyapunov method, which shows that all closed-loop system signals are bounded.

Y. F. Wang, M. Z. Kang, Y. L. Liu, J. J. Li, K. Xue, X. J. Wang, J. Q. Du, Y. L. Tian, Q. H. Ni, and F.-Y. Wang, “Can digital intelligence and cyber-physical-social systems achieve global food security and sustainability?” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2070–2080, Nov. 2023. doi: 10.1109/JAS.2023.123951

> Advanced technologies of big data, artificial intelligence, digital twins, metaverses, and parallel intelligence are employed to achieve a harmonious equilibrium between carbon and water resources for ensuring food security and sustainability.

> A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin.

> Envisioning the future of agriculture involves the integration of digital, robotic and biological farming techniques, incorporating small tasks, big models, and deep intelligence.

Z. Chen and N. Li, “An optimal control-based distributed reinforcement learning framework for a class of non-convex objective functionals of the multi-agent network,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2081–2093, Nov. 2023. doi: 10.1109/JAS.2022.105992

> Considers a novel distributed optimization problem, where the decision variable is a time-varying continuous function and the objective functional is the integration of a non-convex function over a continuous time interval.

> Converts the optimization of the functional into an optimal control problem.

> Considers the privacy protection in distributed optimization.

S. H. Teng, Z. F. Zheng, N. Q. Wu, L. Y. Teng, and W. Zhang, “Adaptive graph embedding with consistency and specificity for domain adaptation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2094–2107, Nov. 2023. doi: 10.1109/JAS.2023.123318

> Consistency and specificity components are deeply mined to transfer more knowledge.

> A graph learning unified framework is built to acquire additional knowledge.

> An algorithm is implemented to adaptively adjust the significance of consistency and specificity.

X. H. Wang, S. S. Zhao, L. Guo, L. Zhu, C. R. Cui, and L. C. Xu, “GraphCA: Learning from graph counterfactual augmentation for knowledge tracing,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2108–2123, Nov. 2023. doi: 10.1109/JAS.2023.123678

> Focus on the data sparsity issue of knowledge tracing and solve it by leveraging the counterfactual data in an innovatively devised counterfactual contrasting graph learning method, namely GraphCA.

> Obtain the counterfactual positive samples by generating interrupted sub-graphs based on two observational facts and learn an enhanced user representation by a contrastive graph learning method.

> Consider the multiple relationships among students, questions, and concepts in a unified heterogeneous graph to enhance the representations of students by the concepts involved in questions.

X. L. Wang, L. M. Liu, L. Duan, and Q. Liao, “Multi-objective optimization for an industrial grinding and classification process based on PBM and RSM,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2124–2135, Nov. 2023. doi: 10.1109/JAS.2023.123333

> Mapping relationship between mill power and load is considered.

> Mill power model based on Response Surface Method is established.

> A multi-objective optimization model for maximizing power and product quality is established to improve throughput and grinding quality.

B. S. Shi and K. X. Liu, “Regularization by multiple dual frames for compressed sensing magnetic resonance imaging with convergence analysis,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2136–2153, Nov. 2023. doi: 10.1109/JAS.2023.123543

> Propose a trainable bounded Gaussian denoiser.

> Propose a novel regularization model using multiple dual frames.

> Prove the convergence of the proposed CSMRI algorithm.

H. N. Huang, G. X. Zhou, N. Y. Liang, Q. B. Zhao, and S. L. Xie, “Diverse deep matrix factorization with hypergraph regularization for multi-view data representation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2154–2167, Nov. 2023. doi: 10.1109/JAS.2022.105980

> Under the assumption of diverse information among multiple views of data, a diversity-enhanced deep matrix factorization- based multi-view representation learning model is established to explore the structural complementarity that exists inter-and intra-views.

> Hypergraph regularization is performed to preserve the intrinsic geometrical structure.

> Develop an efficient algorithm for optimizing the HDDMF and demonstrate that it decreases the objective function of the HDDMF monotonically and converges to a stationary point.

L. J. Wang, X. J. Du, and C. Li, “A range-based node localization scheme for UWASNs considering noises and aided with neurodynamics model,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2168–2170, Nov. 2023. doi: 10.1109/JAS.2023.123348

T. Chen and C. W. Gao, “Intelligent electric vehicle charging scheduling in transportation-energy nexus with distributional reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2171–2173, Nov. 2023. doi: 10.1109/JAS.2023.123285

J. Y. Yang, Y. J. Zhang, T. Yildirim, and J. W. Zhang, “A model predictive control algorithm based on biological regulatory mechanism and operational research,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2174–2176, Nov. 2023. doi: 10.1109/JAS.2023.123303