【CAA期刊】IEEE/CAA JAS第10卷第7期

       本期导读

主题

    强化学习、深度迁移学习、多模态多目标优化、无人机、鲁棒控制、复杂网络、多无人机系统...

全球科研机构

    美国North Carolina State University；英国Brunel University London；加拿大Memorial University of Newfoundland；韩国Pohang University of Science and Technology、Yonsei University；清华大学、北京理工大学、北京航空航天大学、电子科技大学、南京邮电大学、华中科技大学、西南大学...

        A. Joshi, S. Capezza, A. Alhaji, and  M.-Y. Chow,  “Survey on AI and machine learning techniques for microgrid energy management systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1513–1529, Jul. 2023. doi: 10.1109/JAS.2023.123657

        > Future of scalable microgrids relies on the proper use of machine learning.

        > AI in EMS has shown improved efficiency, accuracy, and robustness of MG systems.

        > AI in EMS keeps the design generalized by adapting variations and human factors.

        G. J. Ma, Z. D. Wang, W. B. Liu, J. Z. Fang, Y. Zhang, H. Ding, and  Y. Yuan,  “Estimating the state of health for lithium-ion batteries: A particle swarm optimization-assisted deep domain adaptation approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1530–1543, Jul. 2023. doi: 10.1109/JAS.2023.123531

        > A novel deep domain adaptation method is developed for personalized LIB SOH estimation.

        > Domain loss of labels is considered in the developed deep domain adaptation method.

        > Conditioning strategy of the CGAN is employed to analyze the conditional distribution.

        W. H. Li, X. Y. Yao, K. W. Li, R. Wang, T. Zhang, and  L. Wang,  “Coevolutionary framework for generalized multimodal multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1544–1556, Jul. 2023. doi: 10.1109/JAS.2023.123609

        > A coevolutionary framework for GMMOPs is proposed, which integrates a novel algorithm CoMMEA.

        > ϵ-dominance method and local convergence indicator are used to balance diversity and convergence.

        > Framework can handle almost all types of MMOPs with different characteristics and complexities.

        Z. W. Zheng, J. Z. Li, Z. Y. Guan, and  Z. Y. Zuo,  “Constrained moving path following control for UAV with robust control barrier function,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1557–1570, Jul. 2023. doi: 10.1109/JAS.2023.123573         

        > Present a MPF guidance law for UAV with output constraints and wind disturbances.

        > MPF problem is transferred into a time-varying tracking control problem.

        > A robust CBF is proposed to address the safety constraints.

        L. F. Wang, Z. F. Li, G. T. Zhao, G. Guo, and  Z. Kong,  “Input structure design for structural controllability of complex networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1571–1581, Jul. 2023. doi: 10.1109/JAS.2023.123504

        > Present a new method that gives the minimum set of controlled nodes to achieve complete controllability of the networks.

        > Propose the number of input node theorem, which provides the range of the number of input nodes to achieve minimal structural controllability.

        > Propose the input configuration method, which gives the connection relationship between the input node set and the controlled node set.

        C. C. Wang, Y. L. Wang, Q.-L. Han, and  Y. K. Wu,  “MUTS-based cooperative target stalking for a multi-USV system,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1582–1592, Jul. 2023. doi: 10.1109/JAS.2022.106007   

        > A novel V-type probabilistic data extraction method is proposed for the first time. The advantages of this data extraction method include reducing the amount of data and shortening training time, and filtering out more important data in the experience buffer for training.

        > An action constraint network is pre-trained to reduce collisions of USVs. It can circumvent infeasibility problems in the action correction step by using soft constraints.

        > Different cooperative target stalking scenarios are studied for the multi-USV system. The MUTS algorithm is tested in three different scenarios and shown to be effective.

        L. L. Fan, S. Li, Y. Li, B. Li, D. P. Cao, and  F.-Y. Wang,  “Pavement cracks coupled with shadows: A new shadow-crack dataset and a shadow-removal-oriented crack detection approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1593–1607, Jul. 2023. doi: 10.1109/JAS.2023.123447

        > Proposed a new pavement shadow and crack dataset, which contains a variety of shadow and pavement pixel size combinations. It also covers all common cracks (linear cracks and network cracks), placing higher demands on crack detection methods.

        > Designed a two-step shadow-removal-oriented crack detection approach: SROCD.

        > Explored the mechanism of how shadows affect crack detection.

        H. R. Wu, X. Y. Chen, Z. F. Hu, J. Shi, S. Xu, and  B. Xu,  “Local-to-global causal reasoning for cross-document relation extraction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1608–1621, Jul. 2023. doi: 10.1109/JAS.2023.123540

         > Realizing the filtering of confusing information from the causal perspective.

        > End-to-end causal reasoning algorithm to estimate the contribution of features.

        > Controlling message propagation in graph convolution using causality.

        C. F. Wang, Z. C. Bi, and Y. P. Wan, “Secure underwater distributed antenna systems: A multi-agent reinforcement learning approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1622–1624, Jul. 2023. doi: 10.1109/JAS.2023.123366

        Q. Xu, C. T. Yu, X. Yuan, Z. Fu, and H. Z. Liu, “A privacy-preserving distributed subgradient algorithm for the economic dispatch problem in smart grid,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1625–1627, Jul. 2023. doi: 10.1109/JAS.2022.106028

        L. Chen and X. Luo, “Tensor distribution regression based on the 3D conventional neural networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1628–1630, Jul. 2023. doi: 10.1109/JAS.2023.123591

        Y. B. Wang, C. C. Hua, and P. Park, “Relaxed stability criteria for delayed generalized neural networks via a novel reciprocally convex combination,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1631–1633, Jul. 2023. doi: 10.1109/JAS.2022.106025

        L. F. Hua, H. Zhu, S. M. Zhong, K. B. Shi, and J. D. Cao, “Novel criteria on finite-time stability of impulsive stochastic nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1634–1636, Jul. 2023. doi: 10.1109/JAS.2023.123276

        Z. J. Gong, C. Li, and R. Y. Su, “Fundamental limits of doppler shift-based, ToA-based, and TDoA-based underwater localization,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1637–1639, Jul. 2023. doi: 10.1109/JAS.2023.123282

        Q. W. Zhu, Q. Y. Xiong, Z. Y. Yang, and Y. Yu, “RGCNU: Recurrent graph convolutional network with uncertainty estimation for remaining useful life prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1640–1642, Jul. 2023. doi: 10.1109/JAS.2023.123369