A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

Vol. 9,  No. 7, 2022

Display Method:
An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem
Bingjie Li, Guohua Wu, Yongming He, Mingfeng Fan, Witold Pedrycz
2022, 9(7): 1115-1138. doi: 10.1109/JAS.2022.105677
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The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed significantly to the development of this field, these approaches either are limited in problem size or need manual intervention in choosing parameters. To solve these difficulties, many studies have considered learning-based optimization (LBO) algorithms to solve the VRP. This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches. We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms. Finally, we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.
Towards Long Lifetime Battery: AI-Based Manufacturing and Management
Kailong Liu, Zhongbao Wei, Chenghui Zhang, Yunlong Shang, Remus Teodorescu, Qing-Long Han
2022, 9(7): 1139-1165. doi: 10.1109/JAS.2022.105599
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Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.
Disagreement and Antagonism in Signed Networks: A Survey
Yuxin Wu, Deyuan Meng, Zheng-Guang Wu
2022, 9(7): 1166-1187. doi: 10.1109/JAS.2022.105680
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Signed networks refer to a class of network systems including not only cooperative but also antagonistic interactions among nodes. Due to the existence of antagonistic interactions in signed networks, the agreement of nodes may not be established, instead of which disagreement behaviors generally emerge. This paper reviews several different 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 considered. For the static signed networks with the adjacency weights as (time-varying) scalars, we investigate the convergence behaviors and the fluctuation behaviors with respect to fixed topologies and switching topologies, respectively, and give some brief introductions on the disagreement behaviors of general time-varying signed networks. Correspondingly, several classes of behavior analysis approaches are also provided. For the dynamic signed networks with the adjacency weights as transfer functions or linear time-invariant systems, we show the specific descriptions and characteristics of them such that the disagreement behaviors can be obtained by resorting to the derived static signed graphs. Furthermore, we give their applications to the behavior analysis of static signed networks in the presence of high-order dynamics or communication delays.
Finite-Time Distributed Identification for Nonlinear Interconnected Systems
Farzaneh Tatari, Hamidreza Modares, Christos Panayiotou, Marios Polycarpou
2022, 9(7): 1188-1199. doi: 10.1109/JAS.2022.105683
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In this paper, a novel finite-time distributed identification method is introduced for nonlinear interconnected systems. A distributed concurrent learning-based discontinuous gradient descent update law is presented to learn uncertain interconnected subsystems’ dynamics. The 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. The state information of neighboring interconnected subsystems is acquired through direct communication. The overall update laws for all subsystems form coupled continuous-time gradient flow dynamics for which finite-time Lyapunov stability analysis is performed. As a byproduct of this Lyapunov analysis, easy-to-check rank conditions on data stored in the distributed memories of subsystems are obtained, under which finite-time stability of the distributed identifier is guaranteed. These rank conditions replace the restrictive persistence of excitation (PE) conditions which are hard and even impossible to achieve and verify for interconnected subsystems. Finally, simulation results verify the effectiveness of the presented distributed method in comparison with the other methods.
SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer
Jiayi Ma, Linfeng Tang, Fan Fan, Jun Huang, Xiaoguang Mei, Yong Ma
2022, 9(7): 1200-1217. doi: 10.1109/JAS.2022.105686
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This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global interaction. More specifically, the proposed method involves an intra-domain fusion unit based on self-attention and an inter-domain fusion unit based on cross-attention, which mine and integrate long dependencies within the same domain and across domains. Through long-range dependency modeling, the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global perspective. In particular, we introduce the shifted windows mechanism into the self-attention and cross-attention, which allows our model to receive images with arbitrary sizes. On the other hand, the multi-scene image fusion problems are generalized to a unified framework with structure maintenance, detail preservation, and proper intensity control. Moreover, an elaborate loss function, consisting of SSIM loss, texture loss, and intensity loss, drives the network to preserve abundant texture details and structural information, as well as presenting optimal apparent intensity. Extensive experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the state-of-the-art unified image fusion algorithms and task-specific alternatives. Implementation code and pre-trained weights can be accessed at https://github.com/Linfeng-Tang/SwinFusion.
Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems
Chuxiong Hu, Ran Zhou, Ze Wang, Yu Zhu, Masayoshi Tomizuka
2022, 9(7): 1218-1232. doi: 10.1109/JAS.2022.105689
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With regard to precision/ultra-precision motion systems, it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances. In this paper, to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control (ILC), a novel real-time iterative compensation (RIC) control framework is proposed for precision motion systems without changing the inner closed-loop controller. Specifically, the RIC method can be divided into two parts, i.e., accurate model prediction and real-time iterative compensation. An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times. In light of predicted errors, a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation. Both the prediction and compensation processes are finished in a real-time motion control sampling period. The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions. Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability, which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.
Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels
Yiming Lei, Haiping Zhu, Junping Zhang, Hongming Shan
2022, 9(7): 1233-1247. doi: 10.1109/JAS.2022.105668
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The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal property in nature, e.g., the development from benign to malignant tumor, CE loss cannot take into account such ordinal information to allow for better generalization. To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework. The merits of the proposed MORF come from the following two components: A tree-wise weighting net (TWW-Net) and a grouped feature selection (GFS) module. First, the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree. Hence, all the trees possess varying weights, which is helpful for alleviating the tree-wise prediction variance. Second, the GFS module enables a dynamic forest rather than a fixed one that was previously used, allowing for random feature perturbation. During training, we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian matrix. Experimental results on two medical image classification datasets with ordinal labels, i.e., LIDC-IDRI and Breast Ultrasound datasets, demonstrate the superior performances of our MORF method over existing state-of-the-art methods.
Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble
Yi-Bo Wang, Jun-Yi Hang, Min-Ling Zhang
2022, 9(7): 1248-1261. doi: 10.1109/JAS.2022.105518
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Multi-label learning deals with objects associated with multiple class labels, and aims to induce a predictive model which can assign a set of relevant class labels for an unseen instance. Since each class might possess its own characteristics, the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning, where the predictive model is induced based on tailored features specific to each class label instead of the identical instance representations. As a representative approach, LIFT generates label-specific features by conducting clustering analysis. However, its performance may be degraded due to the inherent instability of the single clustering algorithm. To improve this, a novel multi-label learning approach named SENCE (stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble) is proposed, which stabilizes the generation process of label-specific features via clustering ensemble techniques. Specifically, more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization (EM) algorithm. Extensive experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.
Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control
Mingming Ha, Ding Wang, Derong Liu
2022, 9(7): 1262-1272. doi: 10.1109/JAS.2022.105692
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The core task of tracking control is to make the controlled plant track a desired trajectory. The traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps increases. In this paper, a new cost function is introduced 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. A novel stability analysis method is developed to guarantee that the tracking error converges to zero. The discounted iterative scheme under the new cost function for the special case of linear systems is elaborated. Finally, the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

Input-to-State Stabilization of Nonlinear Impulsive Delayed Systems: An Observer-Based Control Approach
Yuhan Wang, Xiaodi Li, Shiji Song
2022, 9(7): 1273-1283. doi: 10.1109/JAS.2022.105422
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This paper addresses the problems of input-to-state stabilization and integral input-to-state stabilization for a class of nonlinear impulsive delayed systems subject to exogenous dis- turbances. Since the information of plant’s states, time delays, and exogenous disturbances is often hard to be obtained, the key design challenge, which we resolve, is the construction of a state observer-based controller. For this purpose, we firstly propose a corresponding observer which is independent of time delays and exogenous disturbances to reconstruct (or estimate) the plant’s states. And then based on the observations, we establish an observer-based control design for the plant to achieve the input-to-state stability (ISS) and integral-ISS (iISS) properties. With the help of the comparison principle and average impulse interval approach, some sufficient conditions are presented, and moreover, two different linear matrix inequalities (LMIs) based criteria are proposed to design the gain matrices. Finally, two numerical examples and their simulations are given to show the effectiveness of our theoretical results.
Self-adaptive Bat Algorithm With Genetic Operations
Jing Bi, Haitao Yuan, Jiahui Zhai, MengChu Zhou, H. Vincent Poor
2022, 9(7): 1284-1294. doi: 10.1109/JAS.2022.105695
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Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. Their integration provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of GA, or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only. Differing from them, this work proposes an improved self-adaptive bat algorithm with genetic operations (SBAGO) where GA and BA are combined in a highly integrated way. Specifically, SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality. Guided by these exemplars, SBAGO improves both BA’s efficiency and global search capability. We evaluate this approach by using 29 widely-adopted problems from four test suites. SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems. Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness, search accuracy, local optima avoidance, and robustness.
Two-Stage Robust Optimization Under Decision Dependent Uncertainty
Yunfan Zhang, Feng Liu, Yifan Su, Yue Chen, Zhaojian Wang, João P. S. Catalão
2022, 9(7): 1295-1306. doi: 10.1109/JAS.2022.105512
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In the conventional robust optimization (RO) context, the uncertainty is regarded as residing in a predetermined and fixed uncertainty set. In many applications, however, uncertainties are affected by decisions, making the current RO framework inapplicable. This paper investigates a class of two-stage RO problems that involve decision-dependent uncertainties. 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. A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision coupling. The computational tractability, robust feasibility and optimality, and convergence performance of the proposed algorithm are guaranteed with theoretical proof. Four motivating application examples that feature the decision-dependent uncertainties are provided. Finally, the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.
Self-Supervised Monocular Depth Estimation via Discrete Strategy and Uncertainty
Zhenyu Li, Junjun Jiang, Xianming Liu
2022, 9(7): 1307-1310. doi: 10.1109/JAS.2022.105698
Abstract(194) HTML (23) PDF(57)
Modeling and Analysis of Matthew Effect Under Swit-ching Social Networks via Distributed Competition
Mei Liu, Suibing Li, Long Jin
2022, 9(7): 1311-1314. doi: 10.1109/JAS.2022.105527
Abstract(218) HTML (21) PDF(47)
Differentiable Automatic Data Augmentation by Proximal Update for Medical Image Segmentation
Wenxuan He, Min Liu, Yi Tang, Qinghao Liu, Yaonan Wang
2022, 9(7): 1315-1318. doi: 10.1109/JAS.2022.105701
Abstract(223) HTML (14) PDF(44)
A Novel Dynamic Watermarking-Based EKF Detection Method for FDIAs in Smart Grid
Xue Li, Ziyi Wang, Changda Zhang, Dajun Du, Minrui Fei
2022, 9(7): 1319-1322. doi: 10.1109/JAS.2022.105704
Abstract(178) HTML (27) PDF(40)
Safety-Critical Model-Free Control for Multi-Target Tracking of USVs with Collision Avoidance
Shengnan Gao, Zhouhua Peng, Haoliang Wang, Lu Liu, Dan Wang
2022, 9(7): 1323-1326. doi: 10.1109/JAS.2022.105707
Abstract(221) HTML (26) PDF(69)
Collision and Deadlock Avoidance in Multi-Robot Systems Based on Glued Nodes
Zichao Xing, Xinyu Chen, Xingkai Wang, Weimin Wu, Ruifen Hu
2022, 9(7): 1327-1330. doi: 10.1109/JAS.2022.105710
Abstract(160) HTML (10) PDF(39)
Structured Controller Design for Interconnected Systems via Nonlinear Programming
Yanpeng Guan, Junpeng Du, Xinchun Jia
2022, 9(7): 1331-1334. doi: 10.1109/JAS.2022.105713
Abstract(181) HTML (7) PDF(48)
Driving as well as on a Sunny Day? Predicting Driver’s Fixation in Rainy Weather Conditions via a Dual-Branch Visual Model
Han Tian, Tao Deng, Hongmei Yan
2022, 9(7): 1335-1338. doi: 10.1109/JAS.2022.105716
Abstract(142) HTML (9) PDF(35)