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【当期目录】IEEE/CAA JAS第10卷第6期
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主题 深度学习、机器学习、数据科学、图像重建、多智能体系统、数据增强、数据驱动建模、滑模控制... 全球科研机构 英国Imperial College London、Brunel University London、University of Leeds；法国IMT Atlantique Bretagne、Paris-Saclay University、Electricite de France SA、Sorbonne University、CEREA；加拿大University of Waterloo；德国KU Eichstaett-Ingolstadt；意大利University of Bologna；挪威NERSC；日本RIKEN Center for Computational Science；东北大学、哈尔滨工业大学、西安交通大学、同济大学、香港城市大学、大连海事大学、天津大学、南京邮电大学、西南大学、东华大学、东南大学... Q.-L. Han, “Editorial: Driving into future with reliable, secure, efficient and intelligent metavehicles,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1355–1356, Jun. 2023. X. Xue, X. N. Yu, and F.-Y. Wang, “ChatGPT chats on computational experiments: From interactive intelligence to imaginative intelligence for design of artificial societies and optimization of foundational models,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1357–1360, Jun. 2023. S. B. Cheng, C. Quilodrán-Casas, S. Ouala, A. Farchi, C. Liu, P. Tandeo, R. Fablet, D. Lucor, B. Iooss, J. Brajard, D. H. Xiao, T. Janjic, W. P. Ding, Y. K. Guo, A. Carrassi, M. Bocquet, and R. Arcucci, “Machine learning with data assimilation and uncertainty quantification for dynamical systems: A review,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1361–1387, Jun. 2023. F. H. Bi, X. Luo, B. Shen, H. L. Dong, and Z. D. Wang, “Proximal alternating-direction-method-of-multipliers-incorporated nonnegative latent factor analysis,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1388–1406, Jun. 2023. > A proximal ADMM-based nonnegative latent factor analysis (PAN) model is proposed. > Detailed algorithm design and analysis for a PAN model are presented. > Theoretical proof of PAN’s convergence is given. D. Zhang, Q. S. Lian, Y. M. Su, and T. F. Ren, “Dual-prior integrated image reconstruction for quanta image sensors using multi-agent consensus equilibrium,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1407–1420, Jun. 2023. > A dual-prior integrated reconstruction algorithm for Quanta image sensors using multi-agent consensus equilibrium framework is proposed. > Two priors with complementary properties are embedded into the multi-agent consensus equilibrium framework in a plug-and-play manner. > From the view of projection, a clear exposition of the working mechanism for multi-agent consensus equilibrium framework is provided. H. H. Guo, M. Meng, and G. Feng, “Lyapunov-based output containment control of heterogeneous multi-agent systems with Markovian switching topologies and distributed delays,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1421–1433, Jun. 2023. > Both randomly switching topologies and distributed delays are considered of MASs. > A novel distributed containment observer is proposed. > A novel distributed output feedback containment controller is designed. M. Ye, Q.-L. Han, L. Ding, and S. Xu, “Fully distributed Nash equilibrium seeking for high-order players with actuator limitations,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1434–1444, Jun. 2023. > Designing distributed Nash equilibrium seeking strategies for high-order integrators. > Achieving fully distributed Nash equilibrium seeking under directed graphs. > Accommodating games with bounded control inputs. X. Y. Jiang, X. Y. Kong, and Z. Q. Ge, “Augmented industrial data-driven modeling under the curse of dimensionality,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1445–1461, Jun. 2023. > Systematically explores and discusses the necessity, feasibility, and effectiveness of augmented industrial data-driven modeling in the context of the curse of dimensionality and virtual big data. > Process of data augmentation modeling is analyzed, and the concept of data boosting augmentation is proposed. > Proposed method is verified using practical examples of fault diagnosis systems and virtual measurement systems in the industry. J. S. Sang, D. Z. Ma, and Y. Zhou, “Group-consensus of hierarchical containment control for linear multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1462–1474, Jun. 2023. > Hierarchical containment control strategy is developed which multiple control targets for large-scale cooperation can be achieved simultaneously. > Transformed three-layer topology facilitates the decoupled of large-scale networks, which provides the precondition for parallel control targets. > Dynamic hierarchical containment control protocol is designed such that the group-consensus behavior can be achieved in the convex hull. W. Chen and Q. L. Hu, “Sliding-mode-based attitude tracking control of spacecraft under reaction wheel uncertainties,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1475–1487, Jun. 2023. > Novel sliding manifold is presented to ensure fixed settling time for the sliding phase and circumvent the unwinding issue of the quaternion-based expressions. > Adaptive sliding mode attitude tracking controller is developed to deal with spacecraft actuator uncertainties, bounded external disturbances and inertia uncertainty simultaneously. > A definitive actuator misalignment angle range that can be handled here without small angle approximation operation is provided via some algebraic analyses. Y. B. Wu, Z. Y. Sun, G. T. Ran, and L. Xue, “Intermittent control for fixed-time synchronization of coupled networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1488–1490, Jun. 2023. M. Shang and X. P. Hong, “Recurrent ConFormer for WiFi activity recognition,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1491–1493, Jun. 2023. Y. P. Xu, L. Liu, N. Gu, D. Wang, and Z. H. Peng, “Multi-ASV collision avoidance for point-to-point transitions based on heading-constrained control barrier functions with experiment,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1494–1497, Jun. 2023. Y. Zhang, M. R. Fei, Q. Sun, D. J. Du, A. Rakić, and K. Li, “A multi-objective and multi-constraint optimization model for cyber-physical power systems considering renewable energy and electric vehicles,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1498–1500, Jun. 2023. Z.-X. Liu, X.-C. Jin, Y.-A. Xie, and Y. Yang, “Joint slot scheduling and power allocation in clustered underwater acoustic sensor networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1501–1503, Jun. 2023. J. C. Huang, Z. X. Li, and Z. Zhou, “A simple framework to generalized zero-shot learning for fault diagnosis of industrial processes,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1504–1506, Jun. 2023. G. Wang and Y. F. Chen, “MCNet: Multiscale clustering network for two-view geometry learning and feature matching,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1507–1509, Jun. 2023. Y. Lin, Z. Z. Xu, D. Chen, Z. J. Ai, Y. Qiu, and Y. Z. Yuan, “Wood crack detection based on data-driven semantic segmentation network,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1510–1512, Jun. 2023.
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