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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Mu, Baoyan Chen, Shuai Gao, Liang Shenyang Institute of Automation Chinese Academy of Sciences Shengyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shengyang110169 China Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province Shengyang100179 China

ISBN: (纸本)9789811966125

Dot matrix character recognition is a challenging task with many difficulties, such as lack of datasets, complex background pattern, character distortion caused by uneven outer packaging of products. In this paper, we propose a new method of synthesizing dot matrix character image, which can automatically generate a large number of datasets and annotation files. The synthesized dot matrix character image is very similar to the real image. On this basis, we propose a high-precision dot matrix character recognition method in complex background, which constructs a new neural network architecture by inserting the spatial transformer network (STN) into the convolutional recurrent neural network (CRNN). The STN can transform an input image to a rectified image by performing explicit spatial transformations, which can help to improve the performance of dot matrix character recognition. Experiments show that the proposed method achieves the state-of-the-art recognition result, without the loss of the inference speed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach

arXiv

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arXiv 2024年

作者： Kang, Jiawen Zhong, Yue Xu, Minrui Nie, Jiangtian Wen, Jinbo Du, Hongyang Ye, Dongdong Huang, Xumin Niyato, Dusit Xie, Shengli China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore The Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual exploration. UAV Twins (UTs), as the digital twins of UAVs that revolutionize UAV applications by making them more immersive, realistic, and informative, are deployed and updated on ground base stations, e.g., RoadSide Units (RSUs), to offer metaverse services for UAV Metaverse Users (UMUs). Due to the dynamic mobility of UAVs and limited communication coverages of RSUs, it is essential to perform real-time UT migration to ensure seamless immersive experiences for UMUs. However, selecting appropriate RSUs and optimizing the required bandwidth is challenging for achieving reliable and efficient UT migration. To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Specifically, we formulate a multi-leader multi-follower Stackelberg model considering a new immersion metric of UMUs in the utilities of UAVs. Then, we design a Tiny Multi-Agent Deep Reinforcement Learning (Tiny MADRL) algorithm to obtain the tiny networks representing the optimal game solution. Specifically, the actor-critic network leverages the pruning techniques to reduce the number of network parameters and achieve model size and computation reduction, allowing for efficient implementation of Tiny MADRL. Numerical results demonstrate that our proposed schemes have better performance than traditional schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Efficient Graph Neural Network Driven Recurrent Reinforcement Learning for GNSS Position Correction 36

Efficient Graph Neural Network Driven Recurrent Reinforcemen...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zhao, Haoli Tang, Jianhao Li, Zhenni Wu, Zhuoyu Xie, Shengli Wu, Zhaofeng Liu, Ming Kumara, Banage T.G.S. School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510000 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (纸本)9780936406350

With the wide applications of the Global Navigation Satellite System (GNSS) in autonomous driving scenarios, the demand for high-precision positioning of navigation systems has increased dramatically in complex multipath environments. Conventional model-based methods are constrained by strict assumptions about noise models and can hardly model complex environment errors. In contrast, approaches based on artificial intelligent learning have become an important direction to solving the problem of high-precision positioning because learning-based approaches only require simple assumptions. However, current learning-based approaches are facing the following issues. The existing Graph Neural Network-based (GNN) method could hardly adapt to dynamically changing driving environment scenarios since it considers positioning discretely. On the other hand, existing Reinforcement Learning-based (RL) approaches ignore the relationship between multi-constellation satellites, resulting in an inadequate description of the driving correction environment observations. In this paper, we construct a GNN-driven recurrent reinforcement learning method to consider the GNSS measurement of multi-constellation satellites and to learn real-time correction strategy in the dynamic driving environment. To establish a comprehensive positioning correction environment, we construct a multi-constellation graph observation, based on the feature vector concerning GNSS measurement of multi-constellation satellites and edges for satellites in and between constellations. To make more effective use of GNSS measurements, we employ the graph embedding module to deal with the multi-constellation graph inputs, to extract hidden topological features to form the brief states about relationships between multi-constellation satellites for the RL environment. Finally, we construct a recurrent actor-critic structured RL model with cumulative reward and continuous action space to exploit historical information and a

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Temporal-Spatial Scheduling of Energy and Computation Resources for Charging and Computing Service Vehicles

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IEEE Internet of Things Journal 2025年

作者： Rong, Shichu Deng, Xiongtian Zhong, Weifeng Huang, Xumin Kang, Jiawen Xie, Shengli Yuen, Chau Guangdong University of Technology School of Automation Key Laboratory of Intelligent Detection and Manufacturing Internet of Things Ministry of Education Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China Guangdong University of Technology School of Automation 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore

The growing adoption of electric vehicles (EVs) and expansion of Internet-of-Things (iot) in-vehicle applications enhance vehicle intelligence and connectivity but also drive higher demand for both charging and computing services. Charging and Computing Stations (CCSs), integrating bidirectional chargers and edge computing servers and allowing optimal joint energy-computation management, has been taken as an effective solution to address this demand. This paper introduces a new concept called charging and computing service vehicle (CCSV) fleets, which are equipped with high-capacity batteries and edge servers, serving as mobile resources to support the stationary CCSs at different locations in a wide area. We propose a two-timescale model integrating temporal-spatial scheduling, charging/discharging management, and computation task offloading of the CCSV fleets. Our goal is to minimize the total system cost by optimizing the energy-computation coordination between the mobile CCSV fleets and the stationary CCSs. We construct an extended time-space network (TSN) with congestion nodes, providing a clearer depiction of the time-varying congestion conditions in the traffic network. For practical implementation, we develop a heuristic based on the convex-concave procedure (CCP) and penalty alternating direction method (PADM) to solve the problem quickly. Simulation results in a traffic network based on Guangzhou city demonstrate that the proposed model effectively leverages the mobility and multi-dimensional resources of the CCSV fleets to reduce the system cost significantly. © 2014 IEEE.

关键词： Traffic congestion

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Deep sparse representation via deep dictionary learning for reinforcement learning 41

Deep sparse representation via deep dictionary learning for ...

引用

第41届中国控制会议

作者： Jianhao Tang Zhenni Li Shengli Xie Shuxue Ding Shaolong Zheng Xueni Chen School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Technology (GDUT) 111 Center for Intelligent Batch Manufacturing Based on IoT Technology(GDUT) The Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing (GDUT) School of Artificial Intelligence Guilin University of Electronic Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Reinforcement Learning(RL) currently has been widely applied for various fields of decision-making,which relies on the function approximation for tackling complicated ***,most RL models including Deep Reinforcement Learning(DRL) suffer from the catastrophic interference,resulting in poor control ***,the sparse representation has been proven effective for avoiding the interference and is easy to train,but shallow sparse representations have limited representation *** this paper,we proposed to employ the deep dictionary learning(DDL) model for reinforcement learning as a joint optimization problem over the deep sparse representation and the function approximation,which considers a multi-layer dictionary for obtaining a deeper latent sparse representation with better data representation ***,to address the difficulties in optimization of DDL,we employ the decomposition method that we can decompose the multilayer problem into a set of single-layer ***,to solve the joint optimization problem efficiently,we apply the alternating update scheme for updating the sparse representation,multi-layer dictionary,and weight for value prediction ***,we can directly obtain the closed-form solution of the sparse representation by the Proximal Operator(PO),multi-layer dictionary and weight are trained by the gradient descent ***,the agent utilizes the pre-trained multilayer dictionary and weight for RL control,and the weight is updated dynamically at each *** experimental results on two benchmark environments demonstrate that the performance of the RL control training based on the DDL model can be significantly improved.

关键词： Reinforcement Learning Deep sparse representation Deep dictionary learning Proximal operator

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A Phase Error Compensation Algorithm for White Light Phase Shift Interferometry 8

A Phase Error Compensation Algorithm for White Light Phase S...

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8th Symposium on Novel Photoelectronic detection Technology and Applications

作者： Tan, Junwen Gao, Jian Zheng, Zhuojun Zhang, Lanyu State Key Laboratory of Precision Electronic Manufacturing Equipment and Technology Guangdong University of Technology Guangzhou510006 China Key Laboratory of Intelligent Detection The Internet of Things In Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Due to its sensitivity to noise, the instability of phase-shifting devices and environmental vibration, white light phase shift interferometry is prone to modulation positioning errors and phase errors. This paper proposes a high-precision phase error compensation method, and establishes a white light phase shift interference model with phase error estimation. This method can calculate and compensate the phase error in the classical phase shift algorithm. The simulation results show that the proposed method can effectively improve the accuracy of phase shift interferometry compared with the classical phase shift interferometry algorithm. © 2022 SPIE

关键词： Interferometry

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Adaptive neural consensus tracking control for nonlinear multiagent systems with dead zone and sensor faults

Adaptive neural consensus tracking control for nonlinear mul...

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第42届中国控制会议

作者： Chengjie Huang Ci Chen Zhi Liu Zhenni Li Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong Key Laboratory of IoT Information Technology 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education

In this paper, an adaptive neural tracking control design is investigated for uncertain multiagent systems(MASs)with actuator dead zone and sensor faults. The adaptive control technique based on neural networks is employed to handle the uncertainty in the systems, which, however, results in a bounded tracking error from most existing works. To achieve the asymptotic tracking performance, we proposed a direct adaptive strategy via the tuning function method. Technically, a Lyapunov function with a series of smooth functions is designed, which benefits proposing of an asymptotic controller. Then, under the framework of direct adaptive control, an inverse compensation scheme, as well as a fault-tolerant mechanism, are considered for disposing of the actuator dead zone and sensor failures. As a result, user-preset tracking performance is achieved and the system error in the MASs converges to a predefined range. An example in the simulation section illustrates the effectiveness of the proposed method.

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Multi-Robot Cross-Station 3D Perception System for Automatic Assembly

Multi-Robot Cross-Station 3D Perception System for Automatic...

引用

2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Tan, Guangchao Du, Jinsong Shao, Shiliang Guo, Rui Su, Rui Chang, Kai Shenyang Institute of Automation Chinese Academy of Sciences ShenYang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences ShenYang China University of Chinese Academy of Sciences Beijing China Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province ShenYang China

ISBN: (纸本)9798350316308

This paper presents a robot automatic perception and assembly method for the situation that the measurement and assembly processes are not conducted at the same workstation. This method allows the assembly unit and the subassembly unit can be measured at different workstation. The key point can be transformed between different station with reflective markers and unified to the coordinate system of the assembly robot. The assembly process can be performed at any workstation. The method has been testified both in simulated experiments and in the real industrial field. © 2023 IEEE.

关键词： Feature extraction

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FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning

arXiv

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arXiv 2025年

作者： Li, Daoyuan Yang, Zuyuan Xie, Shengli School of Automation Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangdong University of Technology China Key Laboratory of iDetection and Manufacturing-IoT Ministry of Education China

Federated learning is essential for enabling collaborative model training across decentralized data sources while preserving data privacy and security. This approach mitigates the risks associated with centralized data collection and addresses concerns related to data ownership and compliance. Despite significant advancements in federated learning algorithms that address communication bottlenecks and enhance privacy protection, existing works overlook the impact of differences in data feature dimensions, resulting in global models that disproportionately depend on participants with large feature dimensions. Additionally, current single-view federated learning methods fail to account for the unique characteristics of multi-view data, leading to suboptimal performance in processing such data. To address these issues, we propose a Self-expressive Hypergraph Based Federated Multi-view Learning method (FedMSGL). The proposed method leverages self-expressive character in the local training to learn uniform dimension subspace with latent sample relation. At the central side, an adaptive fusion technique is employed to generate the global model, while constructing a hypergraph from the learned global and view-specific subspace to capture intricate interconnections across views. Experiments on multi-view datasets with different feature dimensions validated the effectiveness of the proposed method. © 2025, CC BY-NC-SA.

关键词： Federated learning

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RHNet: A Real-Time High-Resolution Network for Surface Defect detection 43

RHNet: A Real-Time High-Resolution Network for Surface Defec...

引用

43rd Chinese Control Conference, CCC 2024

作者： Wang, Yijie Ren, Zhigang Cai, Jianpu Wu, Zongze Xie, Shengli Shenzhen518060 China Guangdong University of Technology School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Guangzhou510006 China

ISBN: (纸本)9789887581581

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network (RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small defects. Therefore, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module (SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Semantic Segmentation

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