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Real-Time control Allocation for Autonomous Surface Vehicle Using Constrained Quadratic Programming

Guidance, Navigation and control 2021年第4期1卷 116-133页

作者： Xiaocheng Liu Zhihuan Hu Ziheng Yang Weidong Zhang Department of Automation Shanghai JiaoTong University Shanghai 200240 China Key laboratory of System Control and Information Processing Ministry of Education Shanghai 200240 China

control system is very important for each autonomous surface vehicle(ASV),which involves the problem of maintaining the vehicle's position and heading using feedback controller and achieving the desired forces through thrust *** this paper,we present a practical thrust allocator for under-actuated and fully-actuated vehicles,which can be represented as a quadratic programming(QP)problem with linear *** an optimization method allows us to consider common propulsion system,including tunnel thruster,azimuth thruster,and-xed propeller with *** linear constraints enable us to explicitly account for the rate,amplitude and azimuth constraints of each propeller on the *** proposed methods have been illustrated by simulated and experimental maneuvers for di®erent thruster layout of a vehicle.

关键词： control allocation autonomous surface vehicle convex optimization

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Towards Efficient On-Chip Learning for Spiking Neural Networks Accelerator with Surrogate Gradient

Towards Efficient On-Chip Learning for Spiking Neural Networ...

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IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Kexin Huang Wei Liu Yue Liu Shanlin Xiao Zhiyi Yu School of Microelectronics Science and Technology Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems

Spiking Neural Networks (SNNs) have advantages in low power consumption, however, there is a problem of non-differentiability in the backpropagation (BP) of SNN. In this work, we propose a high-performance, low-cost SNN accelerator supports on-chip learning and utilizes a surrogate gradient (SG) for supervised learning, addressing the non-differentiability issue. The design is implemented on the VC707 FPGA, operating at a clock frequency of 115 MHz with a power consumption of 798 mW, achieving an accuracy of 95.49% on the MNIST dataset. The training and inference speeds are 1183 frame/s and 4163 frame/s. The training energy is 0.67 mJ/img and the inference energy is 0.21 mJ/img, reducing the energy consumption by 10–40 times compared to state-of-the-art (SOTA) unsupervised works.

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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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A Distributed Fixed-Time Neurodynamic Algorithm and Its Application in Multi-Autonomous Underwater Vehicle Collaborative Escorting

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IEEE Transactions on Network Science and Engineering 2025年

作者： Zhang, Peng He, Xing Yu, Junzhi Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Peking University Nanchang Innovation Institute Nanchang330224 China

In this paper, a distributed fixed-time neurodynamic algorithm (DFxTNA) is designed for solving distributed optimization problem with time-varying (TV) objective function and constraints. The DFxTNA consists of consensus part based on sliding-mode control technique and optimization part based on Hessian matrix and penalty function. In contrast to existing distributed TV algorithms, DFxTNA based on fixed-time sliding mode control can effectively improve the convergence performance of the algorithm and has better stability with the addition of TV disturbance. In addition, the consistency and fixed-time convergence of DFxTNA are proved by Lyapunov theory and fixed-time stability, respectively. The effectiveness of DFxTNA is verified by numerical experiments. Finally, a distributed TV optimization problem model is established for a multi-autonomous underwater vehicle (AUV) cooperative escort scenario, and DFxTNA is applied to solve the problem. Simulation results confirm the effectiveness and stability of DFxTNA in target tracking, formation control, and dynamic obstacle avoidance. © 2013 IEEE.

关键词： Lyapunov functions

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Attention Multiscale Network for Semantic Segmentation of Multimodal Remote Sensing Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Ye, Zhen Li, Yuan Li, Zhen Liu, Huan Zhang, Yuxiang Li, Wei Chang'an University School of Electronics and Control Engineering Xi'an710064 China Beijing Institute of Technology School of Information and Electronics Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China

Due to recent advancements in deep learning, techniques for urban structure extraction and semantic segmentation of multimodal remote sensing images have significant improvements. However, the challenge arises from the variable color intensity and complex texture of urban structures in optical images, particularly in buildings and roads. Fortunately, the light detection and ranging (LiDAR) images promote the task of developing an optimal multimodal fusion network that effectively leverages information from different modalities. In this article, we propose an attention multiscale network (AMSNet) for binary semantic segmentation tasks focused on building extraction, as well as multiclass semantic segmentation tasks, by integrating optical and LiDAR remote sensing images. AMSNet introduces two feature fusion modules - spatial scale adaptive fusion (S2AF) and semantic guided fusion (SGF). S2AF facilitates feature fusion between optical and LiDAR images within the same layer. This module contains a spatial scale selection strategy and an adaptive weight learning strategy, which enables the network to adaptively extract and intentionally select multiscale features from multimodal data. SGF addresses the semantic gap between different layered block features through semantic feature guidance strategy while achieving feature fusion. Furthermore, we introduce robust feature learning (RFL) to ensure the network robustness in rotation and variation in objects, making it resilient to images captured from different viewpoints and sensors. RFL incorporates point-to-point similarity learning strategy and multiscale feature reuse strategy. Experimental results on publicly available datasets demonstrate that AMSNet outperforms other state-of-the-art models. Extensive ablation studies further confirm the significance of all key components in the proposed approach. The source code of this method is available at https://***/B-LG-J/***. © 1980-2012 IEEE.

关键词： Semantic Segmentation

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Secrecy Performance Analysis of RIS Assisted Ambient Backscatter Communication Networks

arXiv

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arXiv 2024年

作者： Pei, Yingjie Yue, Xinwei Huang, Chongwen Lu, Zhiping The Key Laboratory of Information and Communication Systems Ministry of Information Industry The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China The Key Laboratory of Information and Communication Systems Ministry of Information Industry China The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing100191 China

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) have been envisioned as two promising technologies due to their high transmission reliability as well as energy-efficiency. This paper investigates the secrecy performance of RIS assisted AmBC networks. New closed-form and asymptotic expressions of secrecy outage probability for RIS-AmBC networks are derived by taking into account both imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) cases. On top of these, the secrecy diversity order of legitimate user is obtained in high signal-to-noise ratio region, which equals zero and is proportional to the number of RIS elements for ipSIC and pSIC, respectively. The secrecy throughput and energy efficiency are further surveyed to evaluate the secure effectiveness of RIS-AmBC networks. Numerical results are provided to verify the accuracy of theoretical analyses and manifest that: i) The secrecy outage behavior of RIS-AmBC networks exceeds that of conventional AmBC networks;ii) Due to the mutual interference between direct and backscattering links, the number of RIS elements has an optimal value to minimise the secrecy system outage probability;and iii) Secrecy throughput and energy efficiency are strongly influenced by the reflecting coefficient and eavesdropper’s wiretapping ability. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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A Small UAV Detection Method Based on Optical Flow and Visual Feature Fusion 23

A Small UAV Detection Method Based on Optical Flow and Visua...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Li, Miao Wang, Hanzhuo Mao, Shengjian Shi, Zhiguo Tao, Ran College of Control Science and Engineering Zhejiang University Hangzhou310027 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350325959

The rapid growth of the unmanned aerial vehicle (UAV) market poses threats to public safety and personal privacy. Visual detection has become a cost-effective and important method for UAV detection, but it struggles with detecting small and distant UAVs that lack clear morphological features. To address this, we propose Fusion Net, a network detection approach that combines optical flow features with visual features for improved detection. Fusion Net utilizes Convolutional Neural Networks (CNNs) and Transformers for feature extraction and fusion, achieving excellent detection results. We also introduce Mask Augmentation, a new data augmentation method, to enhance network convergence and diversity in UAV scenes. Fusion Net, pre-trained on the Drone-vs-Bird dataset, exhibits excellent performance in detecting small drones, as demonstrated by its high detection accuracy in our self-made dataset evaluation. © 2023 IEEE.

关键词： feature fusion Unmanned aerial vehicle detection video object detection

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Hybrid evolutionary algorithm with sequence difference-based differential evolution for multi-objective fuzzy flow-shop scheduling problem

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International Journal of Internet Manufacturing and Services 2022年第4期8卷 308-329页

作者： Zhang, Wenqiang Li, Chen Yang, Weidong Gen, Mitsuo College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou450001 China Fuzzy Logic Systems Institute Tokyo University of Science Tokyo162-8601 Japan

In the actual production process of a factory, there are often many uncertain factors, and researchers usually use fuzzy time to express this uncertainty. In this regard, a hybrid evolutionary algorithm with sequence difference-based differential evolution (HEA-SDDE) is proposed to solve fuzzy flow-shop scheduling problem (FFSP). Firstly, the algorithm uses a hybrid sampling strategy based a multi-objective evolutionary algorithm to guide the population to quickly converge to multiple areas of the Pareto front (PF). Secondly, the proposed algorithm applies a sequence difference-based differential evolution (SDDE) strategy, which uses exchanging sequences to determine the sequence differences between individuals, thereby improving the poorly performing individuals in the population. The experiment compares HEA-SDDE with multiple algorithms on 12 problems of different scales for the multi-objective fuzzy flow-shop scheduling problem (MoFFSP). The results demonstrate that the proposed HEA-SDDE has good convergence and distribution performance. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Machine shop practice

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Dispersion Compensation Optimization Based on Neural Network 20

Dispersion Compensation Optimization Based on Neural Network

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20th International Conference on Optical Communications and Networks, ICOCN 2022

作者： Chang, Dengpan Huang, Xiatao Zhangl, Jing Xu, Bo Yi, Xingwen Qiu, Kun Key Lab. of Optical Fiber Sensing and Communications University of Electronic Science and Technology of China Chengdu China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou510275 China

In this paper, a dispersion compensation algorithm based on neural network is proposed. We conduct a simulation to transmit a 120-Gb/s 16-QAM signal over 400-km SSMF in coherent optical systems to prove the effectiven... 详细信息

ISBN: (数字)9781665458986

ISBN: (纸本)9781665458986

关键词： Dispersion compensation

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Robust Platoon control of Industrial Heavy-Load Autonomous Vehicles 12

Robust Platoon Control of Industrial Heavy-Load Autonomous V...

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12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Liu, Zhe Li, Xiang Wang, Hesheng MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China School of Mechanical Engineering and Electronic Information China University of Geosciences 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 Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Platoon control of autonomous industrial vehicles contributes to improving the safety and reliability of cargo transportation in complex industrial scenarios, increasing traffic efficiency and saving energy. The platoon control of autonomous heavy-load industrial vehicles is much harder, as the high robustness requirements, low hardware ability and complex un-structural environments. This paper considers the platoon control task from the view-point of practical industrial applications. Firstly, the longitudinal dynamic model of the heavy-load industrial vehicle is transferred into a first-order damping acceleration response model by introducing the low-level feedback linearization control and compensations. Secondly, the synchronization control concept is introduced and a fully distributed control law is presented, which only requires the coupling information of the two nearest neighbors. In particular, in order to ensure the practical applicability, the information processing delays, data samplings and external control disturbances are explicitly modelled and the global stability analysis approach is presented. Finally, we provide real experiments using full-scale industrial heavy-load vehicles to demonstrate our effectiveness and robustness. © 2022 IEEE.

关键词： Accident prevention

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