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arXiv 2025年

作者： Shi, Jianxin Chen, Jinhao Wang, Yuandong Sun, Li Liu, Chunyang Xiong, Wei Wo, Tianyu Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Information Engineering College Capital Normal University Beijing100048 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Didi Chuxing Beijing100094 China

In recent years, the field of autonomous driving has attracted increasingly significant public interest. Accurately forecasting the future behavior of various traffic participants is essential for the decision-making of Autonomous Vehicles (AVs). In this paper, we focus on both scenario-based and perception-based motion forecasting for AVs. We propose a formal problem formulation for motion forecasting and summarize the main challenges confronting this area of research. We also detail representative datasets and evaluation metrics pertinent to this field. Furthermore, this study classifies recent research into two main categories: supervised learning and self-supervised learning, reflecting the evolving paradigms in both scenario-based and perception-based motion forecasting. In the context of supervised learning, we thoroughly examine and analyze each key element of the methodology. For self-supervised learning, we summarize commonly adopted techniques. The paper concludes and discusses potential research directions, aiming to propel progress in this vital area of AV technology. © 2025, CC BY.

关键词： Self-supervised learning

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The strong substructure and feature attention mechanism for image semantic segmentation

The strong substructure and feature attention mechanism for ...

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作者： Zhang, Yuhang Ren, Hongshuai Yang, Wensi Wang, Yang Ye, Kejiang Xu, Cheng-Zhong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Alibaba Cloud Alibaba Group Hangzhou China State Key Laboratory of IoT for Smart City University of Macau China

Semantic segmentation is a hot topic in computer vision and various deep learning networks are designed to achieve higher accuracy on that by fully exploring the capability of neural networks. This paper aims to address the issue and proposes the substructures with novelty for popular networks. Meanwhile, we present a cross-channel structure, which simultaneously reduces parameter while the kernel size becomes larger. After that, to overcome the weakness of insufficient dataset which refers to satellite image data, we propose a feature attention mechanism with generative adversarial network to enhance the images' features. We show the recognition result on the satellite image dataset with a large picture. This paper evaluates substructures on the PASCAL VOC2012 dataset and improves the mIOU from 74.68% to 88.15%. © 2020 John Wiley & Sons, Ltd.

关键词： Image recognition

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Dynamic Event-Triggered Optimized Backstepping Containment control of a Class of Stochastic Nonlinear MASs Using Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Teng, Ao Wen, Guoguang Wang, Yidi Xu, Jiayang Meng, Yunhe Huang, Tingwen Beijing Jiaotong University School of Mathematics and Statistics Beijing100044 China National University of Defense Technology College of Aerospace Science and Engineering Hunan Changsha410073 China School of Artificial Intelligence Zhuhai519082 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper focuses on the fully distributed dynamic event-triggered optimized backstepping (OB) containment control for a class of stochastic nonlinear high-order strict-feedback multiagent systems (MASs) based on reinforcement learning (RL) algorithm. Firstly, an OB control scheme is proposed using the critic-actor structure. In this scheme, neural networks are applied to approximate the value function and the controllers are derived from the gradients of simple positive functions. This approach simplifies the algorithm and effectively addresses two fundamental requirements commonly encountered in RL-based optimal control: known dynamics and persistent excitation. Furthermore, the static and dynamic event-triggered mechanisms are designed in order to reduce the control actions in the highest layer for nonlinear high-order stochastic MASs. Then, the distributed event-triggered control protocols based on RL are proposed for the strict-feedback nonlinear MASs with stochastic disturbances. Finally, simulation results are shown to demonstrate the effectiveness of the theoretical analysis. © 2014 IEEE.

关键词： Stochastic control systems

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Bias-Eliminated Semantic Refinement for Any-Shot Learning

arXiv

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arXiv 2022年

作者： Feng, Liangjun Zhao, Chunhui Li, Xi The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China

When training samples are scarce, the semantic embedding technique, i.e., describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manual annotation, resulting in weak consistency between descriptions and visual features. In this paper, we refine the coarse-grained semantic description for any-shot learning tasks, i.e., zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new model, namely, the semantic refinement Wasserstein generative adversarial network (SRWGAN) model, is designed with the proposed multihead representation and hierarchical alignment techniques. Unlike conventional methods, semantic refinement is performed with the aim of identifying a bias-eliminated condition for disjoint-class feature generation and is applicable in both inductive and transductive settings. We extensively evaluate model performance on six benchmark datasets and observe state-of-the-art results for any-shot learning;e.g., we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the standard GZSL setting. Various visualizations are also provided to show the bias-eliminated generation of SRWGAN. Our code is available. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite Systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Sparse Pose Graph Optimization in Cycle Space

arXiv

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arXiv 2022年

作者： Bai, Fang Vidal-Calleja, Teresa Grisetti, Giorgio University of Technology Sydney Sydney Australia The Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Rome Italy

The state-of-the-art modern pose-graph optimization (PGO) systems are vertex based. In this context the number of variables might be high, albeit the number of cycles in the graph (loop closures) is relatively low. For sparse problems particularly, the cycle space has a significantly smaller dimension than the number of vertices. By exploiting this observation, in this paper we propose an alternative solution to PGO, that directly exploits the cycle space. We characterize the topology of the graph as a cycle matrix, and re-parameterize the problem using relative poses, which are further constrained by a cycle basis of the graph. We show that by using a minimum cycle basis, the cycle-based approach has superior convergence properties against its vertex-based counterpart, in terms of convergence speed and convergence to the global minimum. For sparse graphs, our cycle-based approach is also more time efficient than the vertex-based. As an additional contribution of this work we present an effective algorithm to compute the minimum cycle basis. Albeit known in computer science, we believe that this algorithm is not familiar to the robotics community. All the claims are validated by experiments on both standard benchmarks and simulated datasets. To foster the reproduction of the results, we provide a complete open-source C++ implementation of our approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph theory

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A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System 8

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiao Zhang, Lijun Yang, Longzhi Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China Northumbria University Faculty of Engineering and Environment Department of Computer Science and Digital Technologies Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350300178

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time. © 2023 IEEE.

关键词： Supercapacitor

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Multi-service collaboration and composition of cloud manufacturing customized production based on problem decomposition

arXiv

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arXiv 2024年

作者： Yue, Hao Wu, Yingtao Wang, Min Hu, Hesuan Wu, Weimin Zhang, Jihui Shandong Qingdao266580 China College of Information Engineering Yangzhou University Jiangsu Yangzhou225127 China School of Electro-Mechanical Engineering Xidian University Shanxi Xi’an710071 China School of Computer Science and Engineering College of Engineering Nanyang Technological University 639798 Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou310027 China Institute of Cyber-Systems and Control Zhejiang University Zhejiang Hangzhou310027 China Institute of Complexity Science School of Automation Qingdao University Shandong Qingdao266071 China Shandong Key Laboratory of Industrial Control Technology Shandong Qingdao266071 China

Cloud manufacturing system is a service-oriented and knowledge-based one, which can provide solutions for the large-scale customized production. The service resource allocation is the primary factor that restricts the production time and cost in the cloud manufacturing customized production (CMCP). In order to improve the efficiency and reduce the cost in CMCP, we propose a new framework which considers the collaboration among services with the same functionality. A mathematical evaluation formulation for the service composition and service usage scheme is constructed with the following critical indexes: completion time, cost, and number of selected services. Subsequently, a problem decomposition based genetic algorithm is designed to obtain the optimal service compositions with service usage schemes. A smart clothing customization case is illustrated so as to show the effectiveness and efficiency of the method proposed in this paper. Finally, the results of simulation experiments and comparisons show that these solutions obtained by our method are with the minimum time, a lower cost, and the fewer selected services. © 2024, CC BY-NC-ND.

关键词： Genetic algorithms

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LONG-TERM MONITORING OF IRRELEVANT SPEECH NOISE IN OPEN-PLAN OFFICES WITH AND WITHOUT LIGHTING FEEDBACK TO THE OCCUPANTS 10

LONG-TERM MONITORING OF IRRELEVANT SPEECH NOISE IN OPEN-PLAN...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Fissore, Virginia Isabella Saugo, Martina Puglisi, Giuseppina Emma Shtrepi, Louena Riente, Fabrizio Servetti, Antonio Pellerey, Franco Lecis, Roberto Gilotta, Silvia Sansone, Francesco Bianchi, Gian Carlo D'Apuzzo, Luca Giusti, Lorenzo Astolfi, Arianna Department of Energy Politecnico di Torino Italy Department of Control and Computer Engineering Politecnico di Torino Italy Department of Electronics and Telecommunications Politecnico di Torino Italy Department of Mathematical Sciences Politecnico di Torino Italy Adequat s.r.l. Via Drovetti 20 Turin Italy Intesa Sanpaolo Piazza San Carlo 156 Turin Italy

ISBN: (纸本)9788888942674

Irrelevant speech noise is one of the main sources of noise that negatively affect health, well-being, comfort and performance in densely occupied environments. The use of devices that provide lighting feedback based on noise levels generated by occupants could promote a change in their behavior and therefore an improvement of acoustic conditions. In the present work, a four-weeks monitoring campaign was performed in two bank open-plan offices in Milan (Italy) with six S3EM (Speech and Sound SEMaphore) devices. S3EM monitors noise levels and visualizes their variation through an integrated colored lighting feedback (i.e., red, yellow and green) based on the results of an advanced algorithm. In the first and fourth weeks the lighting feedback was off, in the second and third weeks it was on, to evaluate the lighting feedback effect on occupants. The ethnographic observation method was applied to describe interactions of occupants with the task, the work environment and with the S3EM devices. Results show that the sound level decreased only when users had the possibility to control their voice in relation to the work activity performed. © 2023 First author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Feedback

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Data-Driven Force control of an Automated Scratch Test

Data-Driven Force Control of an Automated Scratch Test

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Florian Diepers Dominik Polke Elmar Ahle Dirk Söffker Electrical Engineering and Computer Science University of Applied Sciences Niederrhein Krefeld Germany Dynamics and Control University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781665490498

The development of chemicals, like coatings, is increasingly automated in high-throughput plants. The assessment of the quality of the coating formulation is also automated to achieve precise and reproducible results. Usually, a cross-cut test is performed to characterize the adhesion of coatings. To obtain comparable conditions and comply with standards, the force during scratching has to be controlled. In this paper, the force control of an automated testing equipment is optimized. There are different substrates, coating formulations and process parameters influencing the quality of the adhesion of the coatings. Because of this, the force controller has to cover a variety of dynamics. The different coatings and nonlinear behavior of the scratching makes modeling the system extremely difficult. Therefore, a data-driven approach is used. Two data-driven methods, the intelligent PID (iPID) and Virtual Reference Feedback Tuning (VRFT), are applied to the field of force control. The results for both control methods are compared. Additionally, both methods are combined with an Iterative Learning control (ILC) to optimize the control performance over multiple iterations. The qualities of the data-driven controllers are evaluated. All results are compared to the currently implemented controller.

关键词： Mechatronics Adhesives Force Nonlinear dynamical systems Coatings Force control Tuning

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