Early Access

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Hyperspectral Image Super-Resolution Meets Deep Learning: A Survey and Perspective
Xinya Wang, Qian Hu, Yingsong Cheng, Jiayi Ma
, Available online  , doi: 10.1109/JAS.2023.123681
Hyperspectral image super-resolution, which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation, aims to improve the spatial resolution of the hyperspectral image, which is beneficial for subsequent applications. The development of deep learning has promoted significant progress in hyperspectral image super-resolution, and the powerful expression capabilities of deep neural networks make the predicted results more reliable. Recently, several latest deep learning technologies have made the hyperspectral image super-resolution method explode. However, a comprehensive review and analysis of the latest deep learning methods from the hyperspectral image super-resolution perspective is absent. To this end, in this survey, we first introduce the concept of hyperspectral image super-resolution and classify the methods from the perspectives with or without auxiliary information. Then, we review the learning-based methods in three categories, including single hyperspectral image super-resolution, panchromatic-based hyperspectral image super-resolution, and multispectral-based hyperspectral image super-resolution. Subsequently, we summarize the commonly used hyperspectral dataset, and the evaluations for some representative methods in three categories are performed qualitatively and quantitatively. Moreover, we briefly introduce several typical applications of hyperspectral image super-resolution, including ground object classification, urban change detection, and ecosystem monitoring. Finally, we provide the conclusion and challenges in existing learning-based methods, looking forward to potential future research directions.
Finite-Time Attack Detection and Secure State Estimation for Cyber-Physical Systems
Mi Lv, Yuezu Lv, Wenwu Yu, Haofei Meng
, Available online  
Underwater Data-Driven Positioning Estimation Using Local Spatiotemporal Nonlinear Correlation
Chengming Luo, Luxue Wang, Xudong Yang, Gaifang Xin, Biao Wang
, Available online  , doi: 10.1109/JAS.2023.123288
An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration
Zhihao Hao, Guancheng Wang, Bob Zhang, Leyuan Fang, Haisheng Li
, Available online  , doi: 10.1109/JAS.2023.123360
A Coverage Optimization Algorithm for Underwater Acoustic Sensor Networks based on Dijkstra Method
Meiqin Tang, Jiawen Sheng, Shaoyan Sun
, Available online  , doi: 10.1109/JAS.2023.123279
AUTOSIM: Automated Urban Traffic Operation Simulation via Meta-Learning
Yuanqi Qin, Wen Hua, Junchen Jin, Jun Ge, Xingyuan Dai, Lingxi Li, Xiao Wang, Fei-Yue Wang
, Available online  , doi: 10.1109/JAS.2023.123264
Online traffic simulation feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic management. It has been a challenging problem due to three aspects: 1) The diversity of traffic patterns due to heterogeneous layouts of urban intersections; 2) The nature of complex spatiotemporal correlations; 3) The requirement of dynamically adjusting the parameters of traffic models in a real-time system. To cater to these challenges, this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning (AUTOSIM). In particular, simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry attributes. Through a meta-learning technique, AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal correlations. Besides, AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a meta-learner. Through computational experiments, we demonstrate the effectiveness of the meta-learning-based framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.
Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space
Yahui Liu, Bin Tian, Yisheng Lv, Lingxi Li, Fei-Yue Wang
, Available online  , doi: 10.1109/JAS.2023.123432
Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention, but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space (content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectNN. Source code of this paper is available at https://github.com/yahuiliu99/PointConT.
Development of a Bias Compensating Q-Learning Controller for a Multi-Zone HVAC Facility
Syed Ali Asad Rizvi, Amanda J. Pertzborn, Zongli Lin
, Available online  , doi: 10.1109/JAS.2023.123624
We present the development of a bias compensating reinforcement learning (RL) algorithm that optimizes thermal comfort (by minimizing tracking error) and control utilization (by penalizing setpoint deviations) in a multi-zone heating, ventilation, and air-conditioning (HVAC) lab facility subject to unmeasurable disturbances and unknown dynamics. It is shown that the presence of unmeasurable disturbance results in an inconsistent learning equation in traditional RL controllers leading to parameter estimation bias (even with integral action support), and in the extreme case, the divergence of the learning algorithm. We demonstrate this issue by applying the popular Q-learning algorithm to linear quadratic regulation (LQR) of a multi-zone HVAC environment and showing that, even with integral support, the algorithm exhibits bias issue during the learning phase when the HVAC disturbance is unmeasurable due to unknown heat gains, occupancy variations, light sources, and outside weather changes. To address this difficulty, we present a bias compensating learning equation that learns a lumped bias term as a result of disturbances (and possibly other sources) in conjunction with the optimal control parameters. Experimental results show that the proposed scheme not only recovers the bias-free optimal control parameters but it does so without explicitly learning the dynamic model or estimating the disturbances, demonstrating the effectiveness of the algorithm in addressing the above challenges.
Echo State Network With Probabilistic Regularization for Time Series Prediction
Xiufang Chen, Mei Liu, Shuai Li
, Available online  , doi: 10.1109/JAS.2023.123489
Recent decades have witnessed a trend that the echo state network (ESN) is widely utilized in field of time series prediction due to its powerful computational abilities. However, most of the existing research on ESN are conducted under the assumption that data is free of noise or polluted by the Gaussian noise, which lacks robustness or even fails to solve real-world tasks. This work handles this issue by proposing a probabilistic regularized ESN (PRESN) with robustness guaranteed. Specifically, we design a novel objective function for minimizing both the mean and variance of modeling error, and then a scheme is derived for getting output weights of the PRESN. Furthermore, generalization performance, robustness, and unbiased estimation abilities of the PRESN are revealed by theoretical analyses. Finally, experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN. The source code is publicly available at https://github.com/LongJin-lab/probabilistic-regularized-echo-state-network.
Constrained Moving Path Following Control for UAV With Robust Control Barrier Function
Zewei Zheng, Jiazhe Li, Zhiyuan Guan, Zongyu Zuo
, Available online  , doi: 10.1109/JAS.2023.123573
This paper studies the moving path following (MPF) problem for fixed-wing unmanned aerial vehicle (UAV) under output constraints and wind disturbances. The vehicle is required to converge to a reference path moving with respect to the inertial frame, while the path following error is not expected to violate the predefined boundaries. Differently from existing moving path following guidance laws, the proposed method removes complex geometric transformation by formulating the moving path following problem into a second-order time-varying control problem. A nominal moving path following guidance law is designed with disturbances and their derivatives estimated by high-order disturbance observers. To guarantee that the path following error will not exceed the prescribed bounds, a robust control barrier function is developed and incorporated into controller design with quadratic program based framework. The proposed method does not require the initial position of the UAV to be within predefined boundaries. And the safety margin concept makes error-constraint be respected even if in a noisy environment. The proposed guidance law is validated through numerical simulations of shipboard landing and hardware-in-the-loop (HIL) experiments.
Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach
Lili Fan, Shen Li, Ying Li, Bai Li, Dongpu Cao, Fei-Yue Wang
, Available online  , doi: 10.1109/JAS.2023.123447
Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety. The task is challenging because the shadows on the pavement may have similar intensity with the crack, which interfere with the crack detection performance. Till to the present, there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows. To fill in the gap, we made several contributions as follows. First, we 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. Second, we designed a two-step shadow-removal-oriented crack detection approach: SROCD, which improves the performance of the algorithm by first removing the shadow and then detecting it. In addition to shadows, the method can cope with other noise disturbances. Third, we explored the mechanism of how shadows affect crack detection. Based on this mechanism, we propose a data augmentation method based on the difference in brightness values, which can adapt to brightness changes caused by seasonal and weather changes. Finally, we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters, and the algorithm improves the performance of the model overall. We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset, and the experimental results demonstrate the superiority of our method.
MUTS-Based Cooperative Target Stalking for A Multi-USV System
Chengcheng Wang, Yulong Wang, Qing-Long Han, Yunkai Wu
, Available online  , doi: 10.1109/JAS.2022.106007
This paper is concerned with the cooperative target stalking for a multi-unmanned surface vehicle (multi-USV) system. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, a multi-USV target stalking (MUTS) algorithm is proposed. Firstly, a V-type probabilistic data extraction method is proposed for the first time to overcome shortcomings of the MADDPG algorithm. The advantages of the proposed method are twofold: 1) it can reduce the amount of data and shorten training time; 2) it can filter out more important data in the experience buffer for training. Secondly, in order to avoid the collisions of USVs during the stalking process, an action constraint method called Safe DDPG is introduced. Finally, the MUTS algorithm and some existing algorithms are compared in cooperative target stalking scenarios. In order to demonstrate the effectiveness of the proposed MUTS algorithm in stalking tasks, mission operating scenarios and reward functions are well designed in this paper. The proposed MUTS algorithm can help the multi-USV system avoid internal collisions during the mission execution. Moreover, compared with some existing algorithms, the newly proposed one can provide a higher convergence speed and a narrower convergence domain.
RGCNU: Recurrent Graph Convolutional Network With Uncertainty Estimation for Remaining Useful Life Prediction
Qiwu Zhu, Qingyu Xiong, Zhengyi Yang, Yang Yu
, Available online  , doi: 10.1109/JAS.2023.123369
Fundamental Limits of Doppler Shift-Based, ToA-Based, and TDoA-Based Underwater Localization
Zijun Gong, Cheng Li, Ruoyu Su
, Available online  , doi: 10.1109/JAS.2023.123282
Relaxed Stability Criteria for Delayed Generalized Neural Networks via a Novel Reciprocally Convex Combination
Yibo Wang, Changchun Hua, PooGyeon Park
, Available online  , doi: 10.1109/JAS.2022.106025
Novel Criteria on Finite-Time Stability of Impulsive Stochastic Nonlinear Systems
Lanfeng Hua, Hong Zhu, Shouming Zhong, Kaibo Shi, Jinde Cao
, Available online  , doi: 10.1109/JAS.2023.123276
A Privacy-Preserving Distributed Subgradient Algorithm for the Economic Dispatch Problem in Smart Grid
Qian Xu, Chutian Yu, Xiang Yuan, Zao Fu, Hongzhe Liu
, Available online  , doi: 10.1109/JAS.2022.106028
Tensor Distribution Regression Based on the 3D Conventional Neural Networks
Lin Chen, Xin Luo
, Available online  , doi: 10.1109/JAS.2023.123591
Secure Underwater Distributed Antenna Systems: A Multi-Agent Reinforcement Learning Approach
Chaofeng Wang, Zhicheng Bi, Yaping Wan
, Available online  , doi: 10.1109/JAS.2023.123366
Local-to-Global Causal Reasoning for Cross-Document Relation Extraction
Haoran Wu, Xiuyi Chen, Zefa Hu, Jing Shi, Shuang Xu, Bo Xu
, Available online  , doi: 10.1109/JAS.2023.123540
Cross-document relation extraction (RE), as an extension of information extraction, requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts. Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity. However, similarity-based methods cannot distinguish valid information from highly similar retrieved documents well. How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue. To address this problem, we design a novel local-to-global causal reasoning (LGCR) network for cross-document RE, which enables efficient distinguishing, filtering and global reasoning on complex information from a causal perspective. Specifically, we propose a local causal estimation algorithm to estimate the causal effect, which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE. Furthermore, based on the causal effect, we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning. Experimental results under the closed and the open settings of the large-scale dataset CodRED demonstrate our LGCR network significantly outperforms the state-of-the-art methods and validate the effectiveness of causal reasoning in confusing information processing.
Input Structure Design for Structural Controllability of Complex Networks
Lifu Wang, Zhaofei Li, Guotao Zhao, Ge Guo, Zhi Kong
, Available online  , doi: 10.1109/JAS.2023.123504
This paper addresses the problem of the input design of large-scale complex networks. Two types of network components, redundant inaccessible strongly connected component (RISCC) and intermittent inaccessible strongly connected component (IISCC) are defined, and a subnetwork called a driver network is developed. Based on these, an efficient method is proposed to find the minimum number of controlled nodes to achieve structural complete controllability of a network, in the case that each input can act on multiple state nodes. The range of the number of input nodes to achieve minimal control, and the configuration method (the connection between the input nodes and the controlled nodes) are presented. All possible input solutions can be obtained by this method. Moreover, we give an example and some experiments on real-world networks to illustrate the effectiveness of the method.
Coevolutionary Framework for Generalized Multimodal Multi-Objective Optimization
Wenhua Li, Xingyi Yao, Kaiwen Li, Rui Wang, Tao Zhang, Ling Wang
, Available online  , doi: 10.1109/JAS.2023.123609
Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). However, in real-world problems, decision makers (DMs) may be also interested in local PSs. Also, searching for both global and local PSs is more general in view of dealing with MMOPs, which can be seen as generalized MMOPs. Moreover, most state-of-the-art MMEAs exhibit poor convergence on high-dimension MMOPs and are unable to deal with constrained MMOPs. To address the above issues, we present a novel multimodal multi-objective coevolutionary algorithm (CoMMEA) to better produce both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs. Specifically, the CoMMEA introduces two archives to the search process, and coevolves them simultaneously through effective knowledge transfer. The convergence archive assists the CoMMEA to quickly approach the Pareto optimal front. The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the $\epsilon$-dominance-based method to obtain global and local PSs effectively. Experimental results show that CoMMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.
Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach
Guijun Ma, Zidong Wang, Weibo Liu, Jingzhong Fang, Yong Zhang, Han Ding, Ye Yuan
, Available online  , doi: 10.1109/JAS.2023.123531
The state of health (SOH) is a critical factor in evaluating the performance of the lithium-ion batteries (LIBs). Due to various end-user behaviors, the LIBs exhibit different degradation modes, which makes it challenging to estimate the SOHs in a personalized way. In this article, we present a novel particle swarm optimization-assisted deep domain adaptation (PSO-DDA) method to estimate the SOH of LIBs in a personalized manner, where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy. The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method. The proposed PSO-DDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials, ambient temperatures and charge-discharge configurations. Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method. The PyTorch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.
Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems
Aditya Joshi, Skieler Capezza, Ahmad Alhaji, Mo-Yuen Chow
, Available online  , doi: 10.1109/JAS.2023.123657
In the era of an energy revolution, grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level. Microgrids are considered a driving component for accelerating grid decentralization. To optimally utilize the available resources and address potential challenges, there is a need to have an intelligent and reliable energy management system (EMS) for the microgrid. The artificial intelligence field has the potential to address the problems in EMS and can provide resilient, efficient, reliable, and scalable solutions. This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids. We analyze EMS methods for centralized, decentralized, and distributed microgrids separately. Then, we summarize machine learning techniques such as ANNs, federated learning, LSTMs, RNNs, and reinforcement learning for EMS objectives such as economic dispatch, optimal power flow, and scheduling. With the incorporation of AI, microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources. However, challenges such as data privacy, security, scalability, explainability, etc., need to be addressed. To conclude, the authors state the possible future research directions to explore AI-based EMS’s potential in real-world applications.
Evolutionary Multitasking with Global and Local Auxiliary Tasks for Constrained Multi-objective Optimization
Kangjia Qiao, Jing Liang, Zhongyao Liu, Kunjie Yu, Caitong Yue, Boyang Qu
, Available online  , doi: 10.1109/JAS.2023.123336
Constrained multi-objective optimization problems (CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers. To solve CMOPs, constrained multi-objective evolutionary algorithms (CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking (EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front, and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.
Position Errors and Interference Prediction-Based Trajectory Tracking for Snake Robots
Dongfang Li, Yilong Zhang, Ping Li, Rob Law, Zhengrong Xiang, Xin Xu, Li-Min Zhu, Edmond Q. Wu
, Available online  , doi: 10.1109/JAS.2023.123612
This work presents a trajectory tracking control method for snake robots. This method eliminates the influence of time-varying interferences on the body and reduces the offset error of a robot with a predetermined trajectory. The optimized line-of-sight (LOS) guidance strategy drives the robot’s steering angle to maintain its anti-sideslip ability by predicting position errors and interferences. Then, the predictions of system parameters and viscous friction coefficients can compensate for the joint torque control input. The compensation is adopted to enhance the compatibility of a robot within ever-changing environments. Simulation and experimental outcomes show that our work can decrease the fluctuation peak of the tracking errors, reduce adjustment time, and improve accuracy.
Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism
Zhijia Zhao, Jian Zhang, Shouyan Chen, Wei He, Keum-Shik Hong
, Available online  , doi: 10.1109/JAS.2023.123453
Helicopter systems present numerous benefits over fixed-wing aircraft in several fields of application. Developing control schemes for improving the tracking accuracy of such systems is crucial. This paper proposes a neural-network (NN)-based adaptive finite-time control for a two-degree-of-freedom helicopter system. In particular, a radial basis function NN is adopted to solve uncertainty in the helicopter system. Furthermore, an event-triggering mechanism (ETM) with a switching threshold is proposed to alleviate the communication burden on the system. By proposing an adaptive parameter, a bounded estimation, and a smooth function approach, the effect of network measurement errors is effectively compensated for while simultaneously avoiding the Zeno phenomenon. Additionally, the developed adaptive finite-time control technique based on an NN guarantees finite-time convergence of the tracking error, thus enhancing the control accuracy of the system. In addition, the Lyapunov direct method demonstrates that the closed-loop system is semiglobally finite-time stable. Finally, simulation and experimental results show the effectiveness of the control strategy.
Recurrent Neural Network Inspired Finite-Time Control Design
Jianan Liu, Shihua Li, Rongjie Liu
, Available online  , doi: 10.1109/JAS.2023.123297
Autonomous Recommendation of Fault Detection Algorithms for Spacecraft
Wenbo Li, Baoling Ning
, Available online  , doi: 10.1109/JAS.2023.123423
Resilient Event-Triggered Control of Connected Automated Vehicles Under Cyber Attacks
Ning Zhao, Xudong Zhao, Ning Xu, Liang Zhang
, Available online  , doi: 10.1109/JAS.2023.123483
Parallel Light Fields: A Perspective and A Framework
Fei-Yue Wang, Yu Shen
, Available online  , doi: 10.1109/JAS.2023.123174
Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning
Jia Wu, Yican Lou
, Available online  , doi: 10.1109/JAS.2023.123270
Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies
Xiaohua Ge, Qing-Long Han, Xian-Ming Zhang, Derui Ding
, Available online  , doi: 10.1109/JAS.2023.123507
Platooning represents one of the key features that connected automated vehicles can possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge of accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the inter-vehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.
Nonconvex Noise-Tolerant Neural Model for Repetitive Motion of Omdierectional Mobile Manipulators
Zhongbo Sun, Shijun Tang, Jiliang Zhang, Junzhi Yu
, Available online  , doi: 10.1109/JAS.2023.123273
Cascading Delays for the High-speed Rail Network Under Different Emergencies: A Double Layer Network Approach
Xingtang Wu, Mingkun Yang, Wenbo Lian, Min Zhou, Hongwei Wang, Hairong Dong
, Available online  , doi: 10.1109/JAS.2022.105530
High-speed rail (HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.
Relay-Switching-Based Fixed-Time Tracking Controller for Nonholonomic State-Constrained Systems: Design and Experiment
Zhongcai Zhang, Jinshan Bian, Kang Wu
, Available online  , doi: 10.1109/JAS.2022.106046
Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey
Minfeng Qi, Ziyuan Wang, Qing-Long Han, Jun Zhang, Shiping Chen, Yang Xiang
, Available online  , doi: 10.1109/JAS.2022.106058
To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.
Distributed Minimum-Energy Containment Control of Continuous-Time Multi-Agent Systems by Inverse Optimal Control
Fei Yan, Xiangbiao Liu, Tao Feng
, Available online  , doi: 10.1109/JAS.2022.106067
Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers
Meiling Xie, Derui Ding, Xiaohua Ge, Qing-Long Han, Hongli Dong, Yan Song
, Available online  , doi: 10.1109/JAS.2022.105941
Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks. A proportional-integral-observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Then, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control strategy.
Underwater Cable Localization Method Based on Beetle Swarm Optimization Algorithm
Wenchao Huang, Zhijun Pan, Zhezhuang Xu
, Available online  , doi: 10.1109/JAS.2022.106073
Multi-Feature Fusion-Based Instantaneous Energy Consumption Estimation for Electric Buses
Mingqiang Lin, Shouxin Chen, Wei Wang, Ji Wu
, Available online  , doi: 10.1109/JAS.2022.106010
Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication
Maojiao Ye, Qing-Long Han, Lei Ding, Shengyuan Xu, Guobiao Jia
, Available online  , doi: 10.1109/JAS.2022.105857
This paper is concerned with distributed Nash equilibrium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization of players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized information flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respectively. Through Lyapunov stability analysis, it is shown that players’ actions can be steered to a neighborhood of the Nash equilibrium with logarithmic and uniform quantizers, and the quantified convergence error depends on the parameter of the quantizer for both undirected and directed cases. A numerical example is given to verify the theoretical results.
Deep Transfer Ensemble Learning-Based Diagnostic of Lithium-Ion Battery
Dongxu Ji, Zhongbao Wei, Chenyang Tian, Haoran Cai, Junhua Zhao
, Available online  , doi: 10.1109/JAS.2022.106001
Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-view Data Representation
Haonan Huang, Guoxu Zhou, Naiyao Liang, Qibin Zhao, Shengli Xie
, Available online  , doi: 10.1109/JAS.2022.105980
Deep matrix factorization (DMF) has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data. However, existing multi-view DMF methods mainly explore the consistency of multi-view data, while neglecting the diversity among different views as well as the high-order relationships of data, resulting in the loss of valuable complementary information. In this paper, we design a hypergraph regularized diverse deep matrix factorization (HDDMF) model for multi-view data representation, to jointly utilize multi-view diversity and a high-order manifold in a multi-layer factorization framework. A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data. Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view. An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis. Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms state-of-the-art multi-view learning approaches.
Disturbance Observer-Based Safe Tracking Control for Unmanned Helicopters With Partial State Constraints and Disturbances
Haoxiang Ma, Mou Chen, Qingxian Wu
, Available online  , doi: 10.1109/JAS.2022.105938
In this paper, a disturbance observer-based safe tracking control scheme is proposed for a medium-scale unmanned helicopter with rotor flapping dynamics in the presence of partial state constraints and unknown external disturbances. A safety protection algorithm is proposed to keep the constrained states within the given safe-set. A second-order disturbance observer technique is utilized to estimate the external disturbances. It is shown that the desired tracking performance of the controlled unmanned helicopter can be achieved with the application of the backstepping approach, dynamic surface control technique, and Lyapunov method. Finally, the availability of the proposed control scheme has been shown by simulation results.
An Optimal Control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network
Zhe Chen, Ning Li
, Available online  , doi: 10.1109/JAS.2022.105992
This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation. Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning (RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.
Supplementary File of “Push-Sum Based Algorithm for Constrained Convex Optimization Problem and Its Potential Application in Smart Grid”
Qian Xu, Zao Fu, Bo Zou, Hongzhe Liu, Lei Wang
, Available online  
Supplementary Material for “Collision and Deadlock Avoidance in Multi-Robot Systems Based on Glued Nodes”
Zichao Xing, Xinyu Chen, Xingkai Wang, Weimin Wu, Ruifen Hu
, Available online