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IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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Harmonic detection and filtering of distributed photovoltaic grid-connected system under instantaneous reactive power theory

Harmonic detection and filtering of distributed photovoltaic...

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第34届中国控制与决策会议

作者： Wen Haoyu Ma Hongyan Li Shengyan Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education

To solve the environmental pollution and resources shortage problems of traditional fossil fuels,distributed photovoltaic（PV） grid-connected system are used *** power quality problems caused by harmonics generated during the operation of the system have also received widespread *** and accurate detection and filtering of system harmonics is particularly *** this paper,a brief description of the i-i harmonic detection method based on the instantaneous reactive power（IRP） theory is provided and used to detect the harmonics generated during the operation of the ***,a new LCL-type filter is designed innovatively to filter out the detected harmonics of the ***,the above process is simulated and analyzed by MATLAB/Simulink *** experimental results show that the designed LCL-type filter can reduce the harmonic content of voltage and current on the grid side and effectively improve the power quality of the grid.

关键词： distributed photovoltaic grid-connected system IRP theory harmonic detection LCL-type filter

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Study on Harmonic Elimination after LED Lamp Combination

Study on Harmonic Elimination after LED Lamp Combination

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第34届中国控制与决策会议

作者： Xu Jiechuan Ma Hongyan Dou Jiaming Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education

As LED(Light Emitting Diode) is widely used in distribution grids,the power quality problems caused by their harmonics injected into the grid are becoming more and more prominent,so it is necessary to study the harmonic emission characteristics of new energy-saving ***,it is very important to study the harmonic emission characteristics of new energy-saving ***,three kinds of LED simulation driver circuits are built in this paper,and the simulation results show that the harmonic emission characteristics of different driver circuits exist ***,the harmonic can be effectively reduced by the combination of three LED simulation driver ***,the experimental combination test of energy-saving lamps are carried *** results reveal that different combinations of lamps show different harmonic attenuation levels,and the harmonic currents in lighting system can be attenuated by combining specific types of lamps,thus reducing the fire *** harmonic control method has application value in engineering design.

关键词： LED Driver circuit Harmonic emission characteristics Lighting system

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The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach

The State of Charge Estimation of Lithium-ion Batteries Usin...

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第34届中国控制与决策会议

作者： Wei He Hongyan Ma Yingda Zhang Shuai Wang Jiaming Dou Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education

Accurate state of charge(SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or ***,it is difficult to get an accurate value of SOC since it is an inner state of a battery cell,which cannot be directly *** order to improve the estimation accuracy of SOC,this paper develops a SOC estimation model for a lithium-ion battery using a Particle Swarm Optimization-Extreme Learning Machine(PSO-ELM) *** PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM *** inputs to the PSO-ELM model are the battery voltage,current,and temperature,and the output is the actual SOC *** performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error(MSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and SOC *** results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion Battery State of Charge Particle Swarm Optimization Algorithm Extreme Learning Machine

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Surface crack width measurement based on digital image processing

Surface crack width measurement based on digital image proce...

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第34届中国控制与决策会议

作者： Zongyuan Xie Hongyan Ma Yongxue Ye Jiaming Dou Shengyan Li Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education

Cracks are common and dangerous hazards in *** at the defects of low accuracy and poor feasibility in traditional crack width detection,proposing a crack width measurement method based on an orthogonal skeleton ***,the crack image is processed by ***,the processed image is median filtering and threshold ***,the skeleton and outline of the crack image were extracted by this method,and the measurement and comparison of simulated cracks and actual cracks are *** simulation results show that this method has high accuracy and feasibility and can meet the accuracy of engineering practice.

关键词： OpenCV Crack width measurement Orthogonal skeleton line Image processing

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RECC: A Relationship-Enhanced Content Caching Algorithm Using Deep Reinforcement Learning

RECC: A Relationship-Enhanced Content Caching Algorithm Usin...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jiarui Ren Haiyan Zhang Xiaoping Zhou Menghan Zhu School of Information Engineering Ningxia University Yinchuan China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

Mobile edge caching (MEC) is a promising technology to alleviate traffic congestion in the network. Current studies explored deep reinforcement learning (DRL)-based MEC methods. These methods consider the dynamics of the request size to maximize the cache hit rate. However, they usually ignored the potential request relationships among contents. Two contents with a strong relationship are usually requested sequentially. Inspired by this assumption, this paper proposes a relationship-enhanced content caching algorithm using DRL, named RECC. Our RECC infers user preferences by mining the request relationships among contents. In this work, the relationships are modeled as request sequences, and the request features are learned by using graph embedding. These features will be used as input of state in our DRL-based algorithm. We utilize the Wolpertinger architecture to solve the limitation of large discrete action space. The simulation results indicate that our RECC outperformed the traditional cache policies and state-of-the-art DRL-based method in cache hit rate. Furthermore, the proposed RECC has advantages in long-term stability in the environment where content popularity changes dynamically, and also has a higher cache hit rate when handling the requests with number changes dynamically.

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A multi-modal clustering method for traditonal Chinese medicine clinical data via media convergence

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 390-400页

作者： Jingna Si Ziwei Tian Dongmei Li Lei Zhang Lei Yao Wenjuan Jiang Jia Liu Runshun Zhang Xiaoping Zhang State Key Laboratory of Tree Genetics and Breeding College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina Department of Comparative Plant and Fungal Biology Wellcome Trust Millennium BuildingArdinglyUK School of Information Science and Technology Beijing Forestry UniversityBeijingChina Engineering Research Center for Forestry‐oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijingChina National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical SciencesBeijingChina University of Wisconsin‐Milwaukee MilwaukeeWisconsinUSA Experimental Research Center China Academy of Chinese Medical SciencesBeijingChina Guang'anmen Hospital China Academy of Chinese Medical SciencesBeijingChina

Media convergence is a media change led by technological *** media convergence technology to the study of clustering in Chinese medicine can significantly exploit the advantages of media *** consistent and complementary information among multiple modalities through media convergence can provide technical support for *** article presents an approach based on Media Convergence and Graph convolution Encoder Clustering(MCGEC)for traditonal Chinese medicine(TCM)clinical *** feeds modal information and graph structure from media information into a multi-modal graph convolution encoder to obtain the media feature representation learnt from multiple *** captures latent information from various modalities by fusion and optimises the feature representations and network architecture with learnt clustering *** experiment is conducted on real-world multimodal TCM clinical data,including information like images and *** has improved clustering results compared to the generic single-modal clustering methods and the current more advanced multi-modal clustering *** applied to TCM clinical datasets can achieve better *** multimedia features into clustering algorithms offers significant benefits compared to single-modal clustering approaches that simply concatenate features from different *** provides practical technical support for multi-modal clustering in the TCM field incorporating multimedia features.

关键词： graph convolutional encoder media convergence multi-modal clustering traditional Chinese medicine

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Surface crack width measurement based on digital image processing

Surface crack width measurement based on digital image proce...

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Chinese Control and Decision Conference, CCDC

作者： Zongyuan Xie Hongyan Ma Yongxue Ye Jiaming Dou Shengyan Li Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China National Virtual Simulation Experimental Center for Smart City Education Beijing China

ISBN: (纸本)9781665478977

Cracks are common and dangerous hazards in buildings. Aiming at the defects of low accuracy and poor feasibility in traditional crack width detection, proposing a crack width measurement method based on an orthogonal skeleton line. Firstly, the crack image is processed by grayscale. Secondly, the processed image is median filtering and threshold segmentation. Finally, the skeleton and outline of the crack image were extracted by this method, and the measurement and comparison of simulated cracks and actual cracks are completed. The simulation results show that this method has high accuracy and feasibility and can meet the accuracy of engineering practice.

关键词： Image segmentation Simulation buildings Process control Position measurement Gray-scale Skeleton

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Research on partitioning guide strategy of evacuation in public buildings

Research on partitioning guide strategy of evacuation in pub...

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第33届中国控制与决策会议

作者： Tang Jian Hu Yuling Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data

In order to decrease the damage in emergencies and improve the efficiency of evacuation in the space of public buildings,an evacuation strategy with guiders is *** guiders are set within equal areas in evacuation *** force model is used to describe *** on the multi-agent simulation platform Anylogic,plenty of simulation experiments are ***-guided evacuation processes and equal area partitioning guiding processes are compared with double and three *** research results show the guider strategy with equal area partitioning can improve efficiency of evacuation *** addition,the relationship between the number of guiders and evacuation areas are also studied in order to optimize the evacuation *** also compared and analyzed the evacuation density map of double exits and triple exits to observe the evacuation and congestion analysis.

关键词： evacuation partitioning guide strategy equal area zone Anylogic

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