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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Chen, Jinjian Li, Zuoyong Lai, Taotao Xie, Huosheng College of Computer and Data Science Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350319156

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM. © 2023 IEEE.

关键词： Image enhancement

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Multi-adversarial Testing and Retraining for Improving the Robustness of Autonomous Driving Policies 42

Multi-adversarial Testing and Retraining for Improving the R...

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42nd Chinese control Conference, CCC 2023

作者： Liu, keyu Zhou, Junjie Wang, Lin School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai202400 China Key Laboratory of System Control and Information Process Ministry of Education of China Shanghai202400 China

ISBN: (纸本)9789887581543

Autonomous vehicles, especially those based on deep reinforcement learning, are known for their susceptibility to the adversarial perturbations. To enhance their robustness, it is imperative to not only detect their decision errors through testing, but also fortify their robustness against these errors. This paper proposes an iterative optimization method that trains multiple adversarial agents with varying adversarial intensities to identify decision errors in the driving vehicle and enhance its robustness by retraining to counteract these adversarial agents. The effectiveness of the method is evaluated in a lane-changing scenario and the results demonstrate improved robustness of deep reinforcement learning based autonomous driving strategies compared to the adversarial reinforcement learning with a single adversary. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Autonomous vehicles

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Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory Prediction

arXiv

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arXiv 2025年

作者： He, Yushen Zhao, Lei Deng, Tianchen Fang, Zipeng Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Service mobile robots are often required to avoid dynamic objects while performing their tasks, but they usually have only limited computational resources. So we present a lightweight multi-modal framework for 3D object detection and trajectory prediction. Our system synergistically integrates LiDAR and camera inputs to achieve real-time perception of pedestrians, vehicles, and riders in 3D space. The framework proposes two novel modules: 1) a Cross-Modal Deformable Transformer (CMDT) for object detection with high accuracy and acceptable amount of computation, and 2) a Reference Trajectory-based Multi-Class Transformer (RTMCT) for efficient and diverse trajectory prediction of mult-class objects with flexible trajectory lengths. Evaluations on the CODa benchmark demonstrate superior performance over existing methods across detection (+2.03% in mAP) and trajectory prediction (-0.408m in minADE5 of pedestrians) metrics. Remarkably, the system exhibits exceptional deployability - when implemented on a wheelchair robot with an entry-level NVIDIA 3060 GPU, it achieves realtime inference at 13.2 fps. To facilitate reproducibility and practical deployment, we release the related code of the method at https://***/TossherO/3D Perception and its ROS inference version at https://***/TossherO/ros packages. Copyright © 2025, The Authors. All rights reserved.

关键词： Mobile robots

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Digital-Twin-Based Clock Synchronization in Industrial IoT systems 13

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Cheng, Ting Liu, Shuai Xu, Qimin Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798350315196

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources. © 2023 IEEE.

关键词： Internet of things

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Event-Triggered Cooperative Tracking control of Multiagent systems with a Dynamic Leader via Approximate Dynamic Programming

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2752-2765页

作者： Fu, Hao He, Haodong Chen, Yang Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System The School of Computer Science and Technology Wuhan430081 China Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

This article investigates the optimal cooperative tracking control problem for unknown nonlinear multiagent systems with a real dynamic leader and uncertainties. Its main difficulty lies in eliminating effect of the unmatched uncertainty and noncooperation in the event-triggered manner. To overcome this difficulty, a distributed robust event-triggered control algorithm is developed with the aid of a disturbance observer and robust approximate dynamic programming (ADP). Specifically, this problem is converted into a cooperative multiplayer game for the distributed auxiliary systems employing observer-based feedforward compensation, which contributes to reducing conservatism;via an adaptive event-triggered mechanism and a new weight tuning law, a distributed robust ADP approach is proposed with hope to obviate judging the existence of the saddle point beforehand. Moreover, the local neighbor tracking error and the weight estimation error are demonstrated to be uniformly ultimately bounded by means of Lyapunov stability theory. We also prove that Zeno behavior is excluded. A comparative simulation example for multiquadrotor systems is carried out to verify the effectiveness and superiority of our algorithm. © 2020 IEEE.

关键词： Dynamic programming

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Advancing Few-Shot Instance Segmentation of Military Aircraft in Remote Sensing Imagery with Large Vision Models

Advancing Few-Shot Instance Segmentation of Military Aircraf...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiyu Qi Yifan Wu Wenhui Zhang Zhan Chen Yidan Zhang Lei Wang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Target Cognition and Application Technology (TCAT) Key Laboratory of Network Information System Technology (NIST) Key Laboratory of Technology in Geo-Spatial Information Processing and Application System

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study introduces a novel few-shot instance segmentation framework MAR FSIS for identifying and segmenting aircraft in high-resolution remote sensing imagery under stringent few-shot conditions. Utilizing large vision models in conjunction with a sophisticated detector and a novel object prompter, MAR FSIS demonstrates superior performance in discerning intricate details of aircraft with minimal training examples. The effectiveness of this approach is confirmed through experimental evaluations on the proposed MAR20IS dataset, achieving remarkable precision in both detection and segmentation tasks.

关键词： Instance segmentation Training Atmospheric modeling Detectors Military aircraft Data models Proposals

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Bearing-Based Target Entrapping control of Multiple Uncertain Agents With Arbitrary Maneuvers

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IFAC-PapersOnLine 2023年第2期56卷 8821-8826页

作者： Haifan Su Ziwen Yang Shanying Zhu Cailian Chen Wenbin Yu Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

This paper is concerned with bearing-based cooperative target entrapping control of multiple uncertain agents with arbitrary maneuvers including shape deformation, rotations, scalings, etc. A leader-follower structure is used, where the leaders move with the predesigned trajectories, and the followers are steered by an estimation-based control method, integrating a distance estimator using bearing measurements and a stress matrix-based formation controller. The signum functions are used to compensate for the uncertainties so that the agents’ accelerations can be piecewise continuous and bounded to track the desired dynamics. With proper design of the leaders’ trajectories and a geometric configuration, an affine matrix is determined so that the inter-agent relative bearings can be persistently exciting since the bearing rates are related to different weighted combinations of the affine matrix vectors. The asymptotic convergence of the estimation and control error is proved using Filipov properties and cascaded system theories. A sufficient condition for inter-agent collision avoidance is also proposed. Finally, simulation results are given to validate the effectiveness of the method.

关键词： Target entrapping control bearing measurement persistent excitation uncertainty

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A Study on Semi-Supervised Semantic Segmentation of Ship Flame Images 29

A Study on Semi-Supervised Semantic Segmentation of Ship Fla...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Wan, Junwei Chen, Yaojie Zheng, Xuecong Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350330717

In addressing the challenges of limited semantic labeling data and imprecise segmentation regions prevalent in the semantic segmentation of ship flame images, this study introduces a semi-supervised semantic segmentation method tailored for ship flame images. This approach is grounded in the principles of pairwise similarity with strong-weak perturbations. Adopting a pseudo-label self-learning strategy, the method employs weak-strong perturbation pairwise similarity, ensuring the propagation of high-confidence predictions during label generation, and thereby enhancing performance. Additionally, to tackle the inherent difficulties presented by smoke enveloping and obscuring flame regions in ship images, we have conceived a Vertical Continuity Feature Enhancement module. This module empowers the model to discern more explicit inter-class boundaries. Empirical evaluations on our proprietary dataset substantiate the superiority of our method, registering an improvement of 4.12 percentage points in mIoU over other semi-supervised approaches. Such results attest to the efficacy of the proposed pairwise similarity approach and the Vertical Continuity Feature Enhancement module in flame semantic segmentation. © 2023 IEEE.

关键词： Feature enhancemen Pairwise similarity Semantic segmentation Semi-Supervised learning

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High-Resolution Urban Flows Forecasting With Coarse-Grained Spatiotemporal Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 315-327页

作者： Xu, Zhenyi Kang, Yu Cao, Yang Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Department of Automation University of Science and Technology of China Hefei230026 China Chinese Academy of Sciences Key Laboratory of Technology in GeoSpatial Information Processing and Application System Beijing100192 China

Urban flow monitoring and forecasting systems play important roles in smart city management. However, due to the long-lasting and enormous deployment cost of ubiquitous traffic monitoring devices (e.g., loop detectors, traffic video detection), it is very difficult to predict flow in high-resolution (HR) with limited monitoring devices. The existing spatiotemporal network based methods usually predict the urban flow in the same spatial scale without considering the spatial correlations between coarse-grained and fine-grained urban flows. To tackle these issues, we propose a HR spatiotemporal transformer network (HRSTT) to predict fine-grained urban flow. Specifically, residual convolution units are employed to constructs the high-level features of three-view temporal (e.g., closeness, period, and trend) flow data. Then, a transformer block is designed to jointly learn the spatiotemporal dynamic features of each temporal flow with self-attention mechanism. For the external factors (e.g., holidays, weather conditions) are extracted by embedding dense networks, which are fused with high level coarse-grained flow feature maps with gated-fusion scheme. Finally, the coarse-grained fusion feature maps are transferred to the distributional upsampling module, generating the fined-grained flow map of target predicted time. Furthermore, the proposed model is evaluated with several baselines on real-world TaxiBJ datasets, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow forecasting problem. © 2020 IEEE.

关键词： Deep neural networks

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Cyber topology design guaranteed structural controllability for networked systems

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Science China(information Sciences) 2022年第11期65卷 303-304页

作者： Jianbin MU Shaoyuan LI Jing WU Ning LI Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University

Dear editor,Structure analysis is of crucial importance for achieving reliable system functionality. In many real-world systems, the exact parameters of system model are not available owing to modeling uncertainties or measurement noise, and then structural controllability is pursued in control configuration design. As first proposed by Lin in [1],

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