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【当期目录】IEEE/CAA JAS 第9卷 第7期
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B. J. Li, G. H. Wu, Y. M. He, M. F. Fan, and W. Pedrycz, “An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1115–1138, Jul. 2022. > Briefly introduces the applications of LBO algorithms to the VRP to aid beginners in understanding the development of this field. > Discusses the advantages and disadvantages of different learning-based optimization algorithms based on extensive experiments on different datasets. > Suggests several potential research directions of applying the LBO algorithms in the VRP from these limitations. K. L. Liu, Z. B. Wei, C. H. Zhang, Y. L. Shang, R. Teodorescu, and Q.-L. Han, “Towards long lifetime battery: AI-based manufacturing and management,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1139–1165, Jul. 2022. > AI-based manufacturing and smart battery to benefit battery health are showcased. > Advanced AI solutions for battery life diagnostic and ageing prediction are reviewed. > Control-oriented model and health-conscious charging to enhance battery longevity are presented. Y. X. Wu, D. Y. Meng, and Z.-G. Wu, “Disagreement and antagonism in signed networks: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1166–1187, Jul. 2022. > Reviews several disagreement behaviors in signed networks under the single-integrator linear dynamics, where two classes of topologies, namely, the static topology and the dynamic topology, are both involved. > For the static signed networks with adjacency weights as (time-varying) scalars, both the convergence behaviors and the fluctuation behaviors subject to fixed topologies and switching topologies are discussed, respectively. Some brief introductions on the disagreement behaviors of general time-varying signed networks are also reported. Simultaneously, several classes of behavior analysis approaches with respect to different topology conditions are introduced. F. Tatari, H. Modares, C. Panayiotou, and M. Polycarpou, “Finite-time distributed identification for nonlinear interconnected systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1188–1199, Jul. 2022. > A novel finite-time distributed identification method is introduced for nonlinear interconnected systems using concurrent learning. > Concurrent learning approach continually minimizes the identification error for a batch of previously recorded data collected from each subsystem as well as its neighboring subsystems. > State information of neighboring interconnected subsystems is acquired through direct communication. Jiayi Ma, Linfeng Tang, Fan Fan, Jun Huang, Xiaoguang Mei, and Yong Ma, "SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer," IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1200-1217, Jul. 2022. > Proposed a general image fusion method based on cross-domain long-range learning and Swin Transformer, called SwinFusion, which could handle multi-modal image fusion and digital photography image fusion in a unified framework. > Devised a unified loss function consisting of SSIM loss, texture loss, and intensity loss to constrain the network to fulfill the corresponding functions. > A self-attention-based intra-domain fusion unit and a cross-attention-based inter-domain fusion unit have been developed to adequately integrate the long-range dependencies and global interactions within the same domain and across domains. C. X. Hu, R. Zhou, Z. Wang, Y. Zhu, and M. Tomizuka, “Real-time iterative compensation framework for precision mechatronic motion control systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1218–1232, Jul. 2022. > A novel RIC framework is proposed for precision mechatronic motion control systems. > The feedforward trajectory compensation of RIC is developed by accurate predicted tracking errors. > Both prediction and compensation processes are merely finished in a real-time sampling period. Y. M. Lei, H. P. Zhu, J. P. Zhang, and H. M. Shan, “Meta ordinal regression forest for medical image classification with ordinal labels,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1233–1247, Jul. 2022. > A meta ordinal regression forest (MORF) is proposed for medical image classification with ordinal labels. > MORF alleviates tree-wise variance and incorporates feature random perturbation for better generalization. > The effectiveness of MORF has been evaluated on lung nodule classification and breast cancer classification. Y.-B. Wang, J.-Y. Hang, and M.-L. Zhang, “Stable label-specific features generation for multi-label learning via mixture-based clustering ensemble,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1248–1261, Jul. 2022. > Problem of generating label-specific features for multi-label learning is investigated. > A novel approach for label-specific features generation is proposed, which stabilizes the generation process of the label-specific features via clustering ensemble techniques. > Specifically, the final clustering used to construct label-specific features is obtained by fitting a mixture model on instances augmented with base cluster assignments via the EM algorithm. M. M. Ha, D. Wang, and D. Liu, “Discounted iterative adaptive critic designs with novel stability analysis for tracking control,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1262–1272, Jul. 2022. > Based on the new performance index function, a novel stability analysis method for the tracking control problem is established. It is guaranteed that the tracking error can be eliminated completely. The effect of the presence of the approximation errors derived from the value function approximator is discussed with respect to the stability of controlled systems. For linear systems, the new VI-based adaptive critic scheme between the kernel matrix and the state feedback gain is developed. > Adopts a new performance index function to develop the value-iteration-based adaptive critic framework to solve the tracking control problem. Unlike the regulator problem, the iterative value function of tracking control problem cannot be regarded as a Lyapunov function. Y. H. Wang, X. D. Li, and S. J. Song, “Input-to-state stabilization of nonlinear impulsive delayed systems: An observer-based control approach,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1273–1283, Jul. 2022. > The problems of input-to-state stabilization for impulsive delayed systems are addressed. > A novel observer-based output feedback controller is designed for stabilization. > The designed state observer can be applied to the case involving unmeasurable time delays. J. Bi, H. T. Yuan, J. H. Zhai, M. C. Zhou, and H. V. Poor, “Self-adaptive bat algorithm with genetic operations,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1284–1294, Jul. 2022. > Designs a novel self-adaptive bat algorithm with genetic operations (SBAGO). > SBAGO performs genetic operations on BA solutions to produce high-quality exemplars. > Guided by exemplars, SBAGO improves both BA’s efficiency and global search capability. Y. F. Zhang, F. Liu, Y. F. Su, Y. Chen, Z. J. Wang, and J. P. S. Catalão, “Two-stage robust optimization under decision dependent uncertainty,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1295–1306, Jul. 2022. > Investigates a class of two-stage RO problems that involve DDUs. We introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem. > Propose a novel iterative solution algorithm involving one master problem and two subproblems, based on Benders dual decomposition. > Proposed model and solution algorithm cater for a variety of application problems. Four motivating DDU-featured examples are provided Z. Y. Li, J. J. Jiang, and X. M. Liu, “Self-supervised monocular depth estimation via discrete strategy and uncertainty,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1307–1310, Jul. 2022. M. Liu, S. B. Li, and L. Jin, “Modeling and analysis of Matthew effect under switching social networks via distributed competition,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1311–1314, Jul. 2022. W. X. He, M. Liu, Y. Tang, Q. H. Liu, and Y. N. Wang, “Differentiable automatic data augmentation by proximal update for medical image segmentation,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1315–1318, Jul. 2022. X. Li, Z. Y. Wang, C. D. Zhang, D. J. Du, and M. R. Fei, “A novel dynamic watermarking-based EKF detection method for FDIAs in smart grid,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1319–1322, Jul. 2022. S. N. Gao, Z. H. Peng, H. L. Wang, L. Liu, and D. Wang, “Safety-critical model-free control for multi-target tracking of USVs with collision avoidance,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1323–1326, Jul. 2022. Z. C. Xing, X. Y. Chen, X. K. Wang, W. M. Wu, and R. F. Hu, “Collision and deadlock avoidance in multi-robot systems based on glued nodes,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1327–1330, Jul. 2022. Y. P. Guan, J. P. Du, and X. C. Jia, “Structured controller design for interconnected systems via nonlinear programming,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1331–1334, Jul. 2022. H. Tian, T. Deng, and H. M. Yan, “Driving as well as on a sunny day? Predicting driver’s fixation in rainy weather conditions via a dual-branch visual model,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1335–1338, Jul. 2022.
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