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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Li, Ziyi Wang, Lijie Sun, Jianbo Lu, Xinyu Zhu, Xianwen School Of Measurement And Control Technology And Communication Engineering Harbin University Of Science And Technology China

ISBN: (纸本)9798400709272

In this paper, the fault analysis of crystalline silicon photovoltaic modules is studied. The Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) methods are used to analyze the failure mode and its causes. Based on the results of fault analysis, the infrared image analysis and recognition methods are studied for the two fault modes of infrared hot spot and component shedding of Photovoltaic(PV) modules. The YOLOv5s image recognition method based on clustering improvement and feature enhancement is discussed. The experimental results show that the YOLOv5s image recognition algorithm with clustering improvement and feature enhancement improves the training effect of the model by using the EIOU loss function to adaptively adjust the confidence loss balance coefficient, and the detection speed (Frame Per Second, FPS) can reach 42.37 FPS;by adding InRe feature enhancement modules before each detection layer, the extraction ability of target features is improved. The mean Average Precision (mAP) of hot spot and component shedding are 94.85 % and 90.67 %, respectively, which can fully meet the needs of UAV automatic inspection. © 2023 ACM.

关键词： Silicon

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Joint Task Offloading and Resource Allocation Strategy for Space-Air-Ground Integrated Vehicular Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1027-1043页

作者： Yuanshuo Gang Yuexia Zhang Zhihai Zhuo School of Information and Communication Engineering Beijing Information Science and Technology UniversityBeijing 100101China Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100101China

Space-Air-Ground integrated Vehicular Network(SAGVN)aims to achieve ubiquitous connectivity and provide abundant computational resources to enhance the performance and efficiency of the vehicular ***,there are still challenges to overcome,including the scheduling of multilayered computational resources and the scarcity of spectrum *** address these problems,we propose a joint Task Offloading(TO)and Resource Allocation(RA)strategy in SAGVN(namely JTRSS).This strategy establishes an SAGVN model that incorporates air and space networks to expand the options for vehicular TO,and enhances the edge-computing resources of the system by deploying edge *** minimize the system average cost,we use the JTRSS algorithm to decompose the original problem into a number of subproblems.A maximum rate matching algorithm is used to address the channel allocation and the Lagrangian multiplier method is employed for computational *** acquire the optimal TO decision,a differential fusion cuckoo search algorithm is *** simulation results demonstrate the significant superiority of the JTRSS algorithm in optimizing the system average cost.

关键词： edge computing Resource Allocation(RA) Space-Air-Ground integrated Vehicular Network(SAGVN) Task Offloading(TO)

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Multi-Path Fusion Transformer Network for Hyperspectral Remote Sensing Image Classification

Multi-Path Fusion Transformer Network for Hyperspectral Remo...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Li, Jiacheng Wang, Wei School of Measurement-Control Technology and Communication Engineering Harbin University of Science and Technology 52 Xuefu Road Heilongjiang Harbin150000 China

ISBN: (纸本)9781510691773

To address the challenges of insufficient spectral feature extraction and feature redundancy in hyperspectral image (HSI) classification, this paper proposes a Multi-Path Fusion Transformer Network (MPFT) that integrates Vision Transformer, hybrid residual convolution, and attention mechanisms. The proposed approach effectively utilizes both spatial and spectral information of pixels to overcome these limitations. The MPFT architecture comprises a Data Processing Module (DPM), a Dual-Stream Feature Extraction Module (DSFE), and a Vision Transformer (ViT) module. Specifically, the DPM performs dimensionality reduction and merges HSI information in three-dimensional space, while the DSFE extracts deeper features, which are subsequently processed by the ViT module for classification. Experiments conducted on three benchmark datasets—Indian Pines, University of Pavia, and Salinas—demonstrate that MPFT significantly outperforms mainstream models. Notably, MPFT achieved improvements in Overall Accuracy (OA) by up to 5.52%, Average Accuracy (AA) by up to 6.39%, and Kappa coefficient by up to 6.49% compared to state-of-the-art methods. These results highlight that MPFT not only efficiently extracts spatial-spectral features from HSI but also demonstrates superior effectiveness and robustness in various classification tasks. © 2025 SPIE.

关键词： Data streams

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FPGA-Oriented Target Detection Accelerator Design 5

FPGA-Oriented Target Detection Accelerator Design

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5th International Conference on Frontiers technology of Information and Computer, ICFTIC 2023

作者： Huang, Weizhe Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin China

ISBN: (纸本)9798350309034

Target detection is widely applied in fields such as face recognition, autonomous driving, and industrial automation. However, when deploying target detection models based on convolutional neural networks on resource-constrained embedded platforms, issues like high computational complexity, long execution times, and significant on-chip memory usage arise. To address these challenges, this paper presents a target detection accelerator design based on Field Programmable Gate Array (FPGA). Firstly, the computational workload of convolutional neural networks is reduced by merging convolutional layers with batch normalization layers. Secondly, to mitigate on-chip storage space concerns, on-chip data fixed-point quantization is employed to decrease on-chip memory consumption. Subsequently, to tackle the issue of long execution times, a convolution calculation module based on the Winograd algorithm is designed, which reduces computation latency. Finally, a dual-buffer pipeline architecture is devised to optimize memory bandwidth. Experimental results demonstrate that the proposed solution accelerates YOLOv4-tiny on the ZYNQ-7020, achieving a performance of 21.35 GOP/s. The energy efficiency is 32.56 times higher than CPU and 9.76 times higher than GPU. Additionally, the convolution calculation module in this paper reduces inference time by 15% compared to the sliding window convolution calculation method. © 2023 IEEE.

关键词： CNN FPGA target detection Winograd algorithm

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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The Reconstruction of Seismic Reflection Events by Optimized Matching Pursuit Algorithm 6

The Reconstruction of Seismic Reflection Events by Optimized...

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6th International Conference on Intelligent control, measurement and Signal Processing, ICMSP 2024

作者： Yu, Pingyang Liu, Yanping Wan, Defu Yao, Xue The College of Electronic Engineering Xi'an Shiyou University Dept. of Measurement and Control Technology and Instrument China

ISBN: (纸本)9798331516550

This paper mainly introduces an optimized matching pursuit algorithm for the reconstruction of seismic reflection events. It adopts intelligent optimization algorithms of genetic algorithm (GA) and particle swarm algorithm (PSO) to matching pursuit algorithm for improving the processing effect and efficiency. Through experiments on synthetic seismic records and field seismic data, it demonstrates that both GA-MP and PSO-MP can reconstruct the reflection events effectively and efficiency, while PSO-MP performs better especially in waveform reconstruction. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Thickness measurement of Single-Mode Fiber Coating Layer Based on Forward Brillouin Scattering

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IEEE Sensors Journal 2025年第11期25卷 19332-19340页

作者： Zhang, Yongqi Wang, Tao Liu, Yijia Liu, Li Zhang, Mingjiang Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics and Optoelectronic Engineering Taiyuan030024 China Taiyuan University of Technology Shanxi Key Laboratory of Precision Measurement Physics The College of Physics and Optoelectronic Engineering Taiyuan030024 China

The coating layer is essential for protecting and applying optical fibers. Nowadays, the thickness measurement of the coating layer relies on point measurement schemes, such as the side-looking light distribution method and mechanical method. However, there are some problems with the above methods, such as the tradeoff between resolution and field of view (FOV), the cutting of the measurement position, and the long measurement time. To overcome these problems, we propose a new method based on forward Brillouin scattering (FBS), which measures the coating layer thickness of single mode fibers (SMFs). The physical properties of the transverse acoustic field involved in FBS make it possible to measure the thickness of the fiber coating. The corresponding relationship between the transverse acoustic mode frequency and the diameter of the coating layer can be calculated. The coating layer thickness can be measured with high precision by measuring the frequency spectrum of FBS corresponding to multiple transverse acoustic modes at a given temperature. In the verification experiment, three types of SMF coated with polyimide are used as the sensing fiber to measure the thickness of the coating layer at multiple temperatures. The experimental results show that the error in the coating layer thickness obtained is less than 0.05 μm. To the best of our knowledge, this is the first demonstration of nondestructive, high-precision monitoring of fiber coating thickness. © 2025 IEEE.

关键词： Acoustic field measurement

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Node-Decomposition Privacy-Preserving for Finite-Time Secondary control of DC Microgrids

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IEEE Transactions on Smart Grid 2025年第4期16卷 3295-3304页

作者： Wu, Han Zhu, Si-Min Chai, Li Zhu, Zhen-Hua Tian, Yu-Chu Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology School of Artificial Intelligence and Automation Wuhan430081 China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou China Queensland University of Technology School of Computer Science BrisbaneQLD4001 Australia

In the distributed secondary control of DC microgrids, achieving both rapid convergence and robust information security is critical for enhancing control performance and maintaining power quality. However, current privacy-preserving approaches often compromise either control performance or privacy preservation performance. To address this challenge, this paper presents a node-decomposition privacy-preserving (NDPP) method for fully distributed, finite-time secondary control in DC microgrids. By splitting each node in the communication graph into multiple sub-nodes, the method enhances both convergence speed and privacy preservation without sacrificing control accuracy. Sub-nodes communicate within groups, and the topology of the generated graph is concealed from malicious nodes and eavesdroppers. Theoretical analysis is provided to prove finite-time voltage regulation and current sharing with privacy preservation against malicious nodes and external eavesdroppers. Simulations are conducted to verify the effectiveness of the method in maintaining control performance and power quality. © 2010-2012 IEEE.

关键词： Privacy by design

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Interaction Quantification of MTDC Systems Connected with Weak AC Grids

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2088-2099页

作者： Wanning Zheng Jiabing Hu Li Chai Bing Liu Zixia Sang Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and TechnologyWuhan 430081China State Key Laboratory of Advanced Electromagnetic Engineering and Technology and School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China State Grid Hubei Economic Research Institute Wuhan 430077China

The small-signal stability of multi-terminal high voltage direct current(HVDC)systems has become one of the vital issues in modern power *** among voltage source converters(VSCs)have a significant impact on the stability of the *** paper proposes an interaction quantification method based on the self-/en-stabilizing coefficients of the general N-terminal HVDC system with a weak AC network ***,we derive the explicit formulae of self-/en-stabilizing coefficients for any N-terminal HVDC system,which can quantify the interactions through different paths *** relation between the self-/en-stabilizing coefficients and the poles of the system can be used to evaluate the impact of the interactions on the system stability ***,we employ the obtained formulae to analyze the parameter sensitivity and explain how a parameter affects the stability of the system through different paths of ***,extensive examples are given to demonstrate the effectiveness of the proposed method.

关键词： Interactions multi-terminal HVDC self-/enstabilizing coefficients small-signal stability

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