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【当期目录】IEEE/CAA JAS 第10卷 第1期
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Q.-L. Han, “Editorial: The era of quality and metaverse,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 1–2, Jan. 2023.
Q.-G. Wang, “Control design for transient performance,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 3–7, Jan. 2023.
H. L. Wei and Y. Shi, “MPC-based motion planning and control enables smarter and safer autonomous marine vehicles: Perspectives and a tutorial survey,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 8–24, Jan. 2023. > A comprehensive review of recent advances and developments of MPC-based motion planning and control methods for autonomous marine vehicles is presented. > Key issues on MPC-based motion planning and control for smarter and safer autonomous marine vehicles are highlighted. > Future trends in this substantial research area of autonomous marine vehicles are suggested. X. T. Feng, X. G. Zhu, Q.-L. Han, W. Zhou, S. Wen, and Y. Xiang, “Detecting vulnerability on IoT device firmware: A survey,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 25–41, Jan. 2023. > Collect different techniques which are commonly used in existing vulnerability detection of IoT firmware. > Propose a taxonomy which classifies vulnerability detection of IoT firmware into different categories by test perspectives. Moreover, we list challenges and corresponding solutions of different vulnerability detection categories. > Discuss the limitations of existing vulnerability detections and the future directions for readers to follow. U. Lee, G. Jung, E.-Y. Ma, J. S. Kim, H. Kim, J. Alikhanov, Y. Noh, and H. Kim, “Toward data-driven digital therapeutics analytics: Literature review and research directions,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 42–66, Jan. 2023. > Data-driven DTx analytics enable contextual analysis and causal inference. > Data-driven DTx analytics offer novel ways for improving DTx and behavior engagement. > Key components and processes of data-driven DTx analytics are reviewed. Y. K. Shi, Y. Q. Wang, and J. Y. Tuo, “Distributed secure state estimation of multi-agent systems under homologous sensor attacks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 67–77, Jan. 2023. > Considering the system affected by the homologous sensor attack, two observers are designed for communication network with and without time delay. > Proposed observers have less computation and communication pressure, and hence are more suitable for online operation. > Some constructive sufficient conditions are derived for the two observers and can guide the design of the gain matrices of the observers. X. Y. Wang, J. Y. Ma, and J. J. Jiang, “Contrastive learning for blind super-resolution via a distortion-specific network,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 78–89, Jan. 2023. doi: 10.1109/JAS.2022.105914 > Our novel CRDNet realizes the best parameter-performance trade-off. > Propose a novel contrastive regularization without extra computation for better SR. > Extracted prior capturing degradation makes our network sensitive to distortion. A. Perrusquía and W. Guo, “Optimal control of nonlinear systems using experience inference human-behavior learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 90–102, Jan. 2023. > Gives a solution to the migration problem from simulations to real-world experiments to ensure safety critical control. > A novel optimal control solution of nonlinear systems based on a human-behaviour learning approach. > Algorithm does not require to solve a HJB equation and does not require knowledge of the real parameters of the system. Q. B. Ge, X. M. Hu, Y. Y. Li, H. L. He, and Z. H. Song, “A novel adaptive Kalman filter based on credibility measure,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 103–120, Jan. 2023. > A trust factor, which evaluated online, is designed to express the creditable degree of the Kalman filter. > Optimization method proposed is successfully used to deal with the estimation with two unknown noise covariances. > Directly numerical solving way is presented to estimate the noise covariances, meanwhile, the Sage-Husa technology is also introduced to estimate the trust factor. X. Li, Y. X. Xu, N. P. Li, B. Yang, and Y. G. Lei, “Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 121–134, Jan. 2023. > An intelligent data-driven prognostic method is proposed for remaining useful life prediction. > Challenging problem is addressed, where partial sensor malfunction occurs in the testing scenarios. > Deep adversarial learning is proposed for extraction of generalized features from different sensors and entities. S. Gupta, S. Singh, R. Su, S. Gao, and J. C. Bansal, “Multiple elite individual guided piecewise search-based differential evolution,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 135–158, Jan. 2023. > A new Differential Evolution (DE) variant called MIDE is proposed. > MIDE proposed a new mutation operator to perform a piecewise search in DE. > Control parameters are tuned based on their success rates in past evolutionary stages. W. Q. Cao, J. Yan, X. Yang, X. Y. Luo, and X. P. Guan, “Communication-aware formation control of AUVs with model uncertainty and fading channel via integral reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 159–176, Jan. 2023. > Channel prediction, formation control and model uncertainty are considered together. > An IRL-based estimator with M-PCM-OFFD is designed to predict the SNR of AUVs in positions that they have not yet visited and can effectively avoid failing into local optimum. > An IRL-based formation controller with input saturation of thrusters is developed to guarantee communication-aware formation control of AUVs. Q. Xu, M. Wu, E. Khoo, Z. H. Chen, and X. L. Li, “A hybrid ensemble deep learning approach for early prediction of battery remaining useful life,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 177–187, Jan. 2023. > An innovative hybrid deep model is proposed for early prediction of Lithium-ion battery remaining useful life. > A non-linear correlation-based approach is proposed for feature selection from excessive domain knowledge-based features. > A novel snapshot ensemble learning strategy upon the proposed deep learning framework is developed to further enhance the generalization capabilities of deep model. P. Y. Zhang, M. C. Zhou, C. X. Li, and A. Abusorrah, “Dynamic evolutionary game-based modeling, analysis and performance enhancement of blockchain channels,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 188–202, Jan. 2023. > A novel dynamic evolutionary game model is proposed based on its nodes’ behaviors. A defense strategy is developed, which considers attack cost and attack success rate. > To increase cooperation among nodes, the model considers the bounded rationality of nodes, which only know a part of the game state of a blockchain channel network. Each node adopts its strategy with the goal to maximize its profits. Driven by their profits, nodes prefer to cooperate instead of attacks or non-cooperation. I. Birs, C. Muresan, D. Copot, and C. Ionescu, “Model identification and control of electromagnetic actuation in continuous casting process with improved quality,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 203–215, Jan. 2023. > Theoretical framework for modeling steel material properties in continuous casting line process. > Non-Newtonian dynamics captured using fractional order transfer function models. > Data driven identification using a real continuous casting line. G.-P. Liu, “Tracking control of multi-agent systems using a networked predictive PID tracking scheme,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 216–225, Jan. 2023. > A networked predictive PID tracking scheme is proposed to achieve the desired tracking performance for multi-agent systems. > Scheme actively compensates for communication delays. > Proposed scheme is simply implemented in a distributed way so that each agent does not need to have information on the dynamics of its neighbor agents. Z. B. Wei, H. X. Zhao, Z. S. Li, X. J. Bu, Y. Y. Chen, X. Q. Zhang, Y. S. Lv, and F.-Y. Wang, “STGSA: A novel spatial-temporal graph synchronous aggregation model for traffic prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 226–238, Jan. 2023. > Proposed a spatial-temporal graph synchronous aggregation model for traffic prediction. > Proposed method can capture more trends of relevant nodes by implementing the heuristic spatial adjacency matrix optimization algorithm which makes it contain more useful information. > Designed temporal graph is easily to be constructed, and the overall performance is significantly improved. Y. Xiu, D. F. Li, M. M. Zhang, H. B. Deng, R. Law, Y. Huang, E. Q. Wu, and X. Xu, “Finite-time sideslip differentiator-based LOS guidance for robust path following of snake robots,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 239–253, Jan. 2023. > Finite-time-based sideslip differentiator and anti-sideslip LOS guidance method for snake robots are proposed to counteract sideslip caused by the cross-track velocity. > A barrier Lyapunov function-based backstepping adaptive path following controller for snake robots is designed to improve the robustness of the robot to the environment. > A virtual velocity control input is derived to achieve the exponential convergence of the snake robot’s velocity error. W. Lu, J. C. Li, H. H. Qin, L. Shu, and A. G. Song, “On dual-mode driving control method for a novel unmanned tractor with high safety and reliability,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 254–271, Jan. 2023. > A dual-mode switching control method with high security and reliability was proposed. > Experiments were carried out for evaluating the performance of the strategy which results demonstrate that the driving system can respond to the control commands in time and complete the agricultural operations with high precision using the proposed dual-mode switching control method. K. L. Liu, Q. Peng, R. Teodorescu, and A. M. Foley, “Knowledge-guided data-driven model with transfer concept for battery calendar ageing trajectory prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 272–274, Jan. 2023. Z. Y. Zhang and R. L. Deng, “Impact analysis of moving target defense the frequency stability in smart grid,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 275–277, Jan. 2023. R. Qi, Y. Zhang, and K. D. Kumar, “Design and robustness analysis of a wave-based controller for tethered towing of defunct satellites,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 278–280, Jan. 2023. B. K. Gao, Y.-J. Liu, and L. Liu, “Fixed-time neural control of a quadrotor UAV with input and attitude constraints,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 281–283, Jan. 2023. X. Tian, W. Zhang, D. Yu, and J. Y. Ma, “Sparse tensor prior for hyperspectral, multispectral, and panchromatic image fusion,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 284–286, Jan. 2023. S. Gao, H. Zhang, Z. P. Wang, C. Huang, and H. C. Yan, “Optimal injection attack for UCPS under vertical depth-keeping task via game approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 287–289, Jan. 2023. Z. H. Peng, M. G. Lv, L. Liu, and D. Wang, “Data-driven learning extended state observers for nonlinear systems: Design, analysis and hardware-in-loop simulations,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 290–293, Jan. 2023. P. H. Du, W. M. Zhong, X. Peng, L. L. Li, and Z. Li, “Data-driven fault compensation tracking control for coupled wastewater treatment process,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 294–297, Jan. 2023. C. Z. Jiang and X. C. Xiao, “Norm-based adaptive coefficient ZNN for solving the time-dependent algebraic Riccati equation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 298–300, Jan. 2023.
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