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arXiv 2023年

作者： Tang, Chuanbo Sheng, Xihua Li, Zhuoyuan Zhang, Haotian Li, Li Liu, Dong CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possible, but the optical flows themselves may cost too many bits to encode. Second, the optical flow estimation networks were trained on synthetic data, and may not generalize well enough to real-world videos. We address the twofold limitations by enhancing the optical flows in two stages: offline and online. In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e.g. H.266/VVC, as we believe the motion information of H.266/VVC achieves a better rate-distortion tradeoff. In the online stage, we further optimize the latent features of the optical flows with a gradient descent-based algorithm for the video to be compressed, so as to enhance the adaptivity of the optical flows. We conduct experiments on a state-of-the-art deep video compression scheme, DCVC. Experimental results demonstrate that the proposed offline and online enhancement together achieves on average 12.8% bitrate saving on the tested videos, without increasing the model or computational complexity of the decoder side. Copyright © 2023, The Authors. All rights reserved.

关键词： Image coding

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Plug-And-Play TomoSAR Imaging With Prior information

Plug-And-Play TomoSAR Imaging With Prior Information

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Kun Wang Jie Li Yizhe Fan Bingchen Zhang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Synthetic aperture radar (SAR) tomography (TomoSAR) is an advanced remote sensing technology that has the ability to acquire three-dimensional information of targets. To enhance target features, different reaularization terms, including L 1 norm, total variation norm, and morphology term, have been introduced into the TomoSAR inversion model. However, there is a lack of attention to establishing a TomoSAR imaging framework with a modular structure that provides flexibility in specifically enhancing different features. In this article, a novel 3D tomographic reconstruction framework based on plug-and-play (PnP) priors and Alternating Direction Method of Multipliers (ADMM) is proposed. The PnP-ADMM framework achieves flexibility through the selection of appropriate priors for the features of targets, leading to a trade-off between the performance of feature enhancement and computational complexity. Simulation and real data experiments verify the effectiveness of the proposed method in flexibly selecting priors for feature enhancement of specific targets.

关键词： Radar remote sensing Three-dimensional displays Morphology Tomography Apertures Convex functions Computational complexity

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Analysis and Design of Application-Layer Rateless Codes Based on Reliability and Latency Trade-Off

Analysis and Design of Application-Layer Rateless Codes Base...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Huang, Haobo Fu, Yusun Yin, Junpeng Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350368369

With the Internet of Things (IoT) playing an increasingly crucial role in connecting diverse devices, applications have different requirements for communication and various evaluation metrics have emerged, such as Age of information (AoI) and estimation error. Considering the fundamental trade-off between reliability and latency of the communication system, the relationship among reliability, latency, AoI and estimation error is complex. This paper proposes a remote state observation system with application-layer rateless codes to explore above relationships, addressing how to design the system for optimal performance based on varying requirements by adjusting coding parameters. By emphasizing flexible and adaptable coding strategies, we aim to convey the idea that the error-correction codes can be designed to optimize the communication system for diverse practical metrics. It is different from the traditional designs that focus on approaching the Shannon limit. © 2025 IEEE.

关键词： Integrated circuit design

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3D Gaussian Splatting in Robotics: A Survey

arXiv

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arXiv 2024年

作者： Zhu, Siting Wang, Guangming Kong, Xin Kong, Dezhi Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai China University of Cambridge United Kingdom Imperial College London United Kingdom Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University China

Dense 3D representations of the environment have been a long-term goal in the robotics field. While previous Neural Radiance Fields (NeRF) representation have been prevalent for its implicit, coordinate-based model, the recent emergence of 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential in its explicit radiance field representation. By leveraging 3D Gaussian primitives for explicit scene representation and enabling differentiable rendering, 3DGS has shown significant advantages over other radiance fields in real-time rendering and photo-realistic performance, which is beneficial for robotic applications. In this survey, we provide a comprehensive understanding of 3DGS in the field of robotics. We divide our discussion of the related works into two main categories: the application of 3DGS and the advancements in 3DGS techniques. In the application section, we explore how 3DGS has been utilized in various robotics tasks from scene understanding and interaction perspectives. The advance of 3DGS section focuses on the improvements of 3DGS own properties in its adaptability and efficiency, aiming to enhance its performance in robotics. We then summarize the most commonly used datasets and evaluation metrics in robotics. Finally, we identify the challenges and limitations of current 3DGS methods and discuss the future development of 3DGS in robotics. The paper lists of our survey are available at 3D Gaussian Splatting in Robotics. Copyright © 2024, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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Pointer Meter Reading Under Noise Based on Virtual Data Generation

Pointer Meter Reading Under Noise Based on Virtual Data Gene...

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Chinese Automation Congress (CAC)

作者： Jinyang Zhu Zhezhuang Xu Ye Lin College of Electrical Engineering and Automation Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Using machine vision for meter reading significantly enhances the efficiency of industrial monitoring. However, meters in outdoor environment are often subject to the noise such as rain and fog, which affect the accuracy of meter readings. Additionally, the meter images and labels under noise are hard to obtain. To address these challenges, We propose a method for pointer meter reading under noise based on virtual data generation. Firstly, We utilize Unity to construct an industrial meter scene, which generates the images of meter under noise and theri labels. Secondly, we develop a U-Net network with an ASPP module, which expands the receptive field of network and enhances the ability of segmentation network to capture global semantic information under noise. Finally, the angular method is used to calculate the meter readings. Experimental results show that this method can effectively read meters under noise in outdoor environments.

关键词： Meters Image segmentation Rain Accuracy Noise Semantics Object segmentation Data collection Meter reading Monitoring

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MAML MOT: Multiple Object Tracking Based on Meta-Learning

MAML MOT: Multiple Object Tracking Based on Meta-Learning

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IEEE International Conference on systems, Man and Cybernetics

作者： Jiayi Chen Chunhua Deng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the advancement of video analysis technology, the multi-object tracking (MOT) problem in complex scenes involving pedestrians is gaining increasing importance. This challenge primarily involves two key tasks: pedestrian detection and re-identification. While significant progress has been achieved in pedestrian detection tasks in recent years, enhancing the effectiveness of re-identification tasks remains a persistent challenge. This difficulty arises from the large total number of pedestrian samples in multi-object tracking datasets and the scarcity of individual instance samples. Motivated by recent rapid advancements in meta-learning techniques, we introduce MAML MOT, a meta-learning-based training approach for multi-object tracking. This approach leverages the rapid learning capability of meta-learning to tackle the issue of sample scarcity in pedestrian re-identification tasks, aiming to improve the model's generalization performance and robustness. Experimental results demonstrate that the proposed method achieves high accuracy on mainstream datasets in the MOT Challenge. This offers new perspectives and solutions for research in the field of pedestrian multi-object tracking.

关键词： Metalearning Training Pedestrians Accuracy Robustness Object tracking Optimization Cybernetics

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CMAC-Based Sliding-Mode control for Large-Scale Fuzzy Descriptor systems

CMAC-Based Sliding-Mode Control for Large-Scale Fuzzy Descri...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhong, Zhixiong Shao, Zhenghua Huan, Zhijie Wang, Xingyi Minjiang University Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150080 China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781665481090

This paper addresses the problem of sliding-mode control for large-scale fuzzy descriptor systems subject to unknown uncertainties. First, a CMAC neural network is used to approximate the unknown uncertainties, and the CMAC-based sliding-mode controller is proposed. Then, a compensation technique is employed to eliminate the influence on the approximation error stemmed from CMAC model. We use the gradient-descent method to online tune the parameters of CMAC and the Lyapunov function is introduced to ensure the stability of the closed-loop control system and the CMAC's convergence. Finally, the effectiveness and correctness of the obtained theories are verified by the tracking control of multi-autonomous underwater vehicles (AUVs). © 2022 IEEE.

关键词： Autonomous underwater vehicles

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Task scheduling for transport and pick robots in logistics:a comparative study on constructive heuristics

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Autonomous Intelligent systems 2021年第1期1卷 273-285页

作者： Hanfu Wang Weidong Chen Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghai 200240China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of EducationDepartment of AutomationShanghai Jiao Tong UniversityShanghai 200240China

We study the Transport and Pick Robots Task Scheduling(TPS)problem,in which two teams of specialized robots,transport robots and pick robots,collaborate to execute multi-station order fulfillment tasks in logistic *** objective is to plan a collective time-extended task schedule with the minimization of ***,for this recently formulated problem,it is still unclear how to obtain satisfying results *** this research,we design several constructive heuristics to solve this problem based on the introduced sequence ***,we give time complexity analysis or feasibility guarantees of these heuristics;empirically,we evaluate the makespan performance criteria and computation time on designed *** results demonstrate that coupled append heuristic works better for the most cases within reasonable computation *** heuristics work better than decoupled heuristics prominently on instances with relative few pick robot numbers and large work *** law of diminishing marginal utility is also observed concerning the overall system performance and different transport-pick robot numbers.

关键词： Multi-robot task allocation Multi-robot system Complex-schedule constraints Heterogeneous robotic order fulfillment system

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A New Dual-Population Evolutionary Algorithm Leveraging Objective-Constraint Relationships for Constrained Multi-Objective Optimization

SSRN

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SSRN 2025年

作者： Ye, Jialu Tan, Chaogui Xia, Yizhang Hou, Zhanglu Liu, Yuan Zou, Juan Hunan Engineering Research Center of intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Hunan National Applied Mathematics Center China Xiangtan University China

Population co-evolution strategies are widely used to handle constrained multi-objective optimization problems (CMOPs). However, existing coevolutionary algorithms oversimplify population collaboration and are rigid in evolving the auxiliary population, ignoring the importance of maintaining population diversity, especially for CMOPs with irregular feasible regions. To tackle this issue, we propose a new dual-population evolutionary algorithm, denoted as AACMO, which attempts to mine the relational features between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF) to better adapt to CMOPs with various constraints. Specifically, the method is categorized into two phases: a learning phase and an evolving phase. First, the main population (MP) aims to quickly learn the relationships between the UPF and the CPF by leveraging the advantages of different operators, namely, genetic algorithm operators and differential evolution operators, during the learning phase. Secondly, two auxiliary strategies are designed to collaborate with the MP during the evolving phase. These strategies are capable of employing a more effective co-evolution method to assist the MP in maintaining diversity, based on the degree of alignment between the learned UPF and CPF at the end of the learning phase. The effectiveness of AACMO is confirmed through comparative analysis with eight state-of-the-art algorithms across four CMOP benchmark suites. © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

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A Reinforcement Learning PID Compound Protocol Design Method for Ultrasonic Motor control*

A Reinforcement Learning PID Compound Protocol Design Method...

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control Conference (ANZCC), Australian and New Zealand

作者： Qiang Wang Yunlong Yang Xiongjun Wu Jinqi Wang Guanyu Liu The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Aiming at overcoming drawback brought by the nonlinear and time-varying characteristics of ultrasonic motor, this paper proposed a PID compound controller of ultrasonic motor based on reinforcement learning to improve the dynamic performance and adaptability of the system. In practice, the output control value of the reinforcement learning controller is observed in general to be uncertain, and sometimes even destroys the learning strategy, which makes the system unstable. To overcome this limitation, this paper ultilized the idea of expert rules to improve the controller performance, and the Lyapunov stability is also guaranteed. The simulation results show that the improved composite control algorithm can significantly improve the learning efficiency, and meanwhile shorten the number of learning iterations. At the same time, the composite controller based on expert rules significantly improves the control performance of the system, in terms of reducing the rise time and adjustment time of the system, as well as reducing the steady-state error of the system.

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