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[转载]【CAA期刊】IEEE/CAA JAS第10卷第11期

已有 440 次阅读 2023-12-7 09:32 |个人分类:行业观察|系统分类:博客资讯|文章来源:转载

【CAA期刊】IEEE/CAA JAS第10卷第11期


( 本期导读 )


主题

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..


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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


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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.


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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.


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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.


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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.


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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.


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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.


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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.


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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.


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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.


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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 


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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 


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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 


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