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IET International Radar Conference 2023, IRC 2023

作者： Li, Zhiwei Zhang, Mengdi Li, Shiyin Lu, Hongyi School of Information and Control Engineering China University of Mining and Technology Jiangsu China

ISBN: (纸本)9781839539954

To achieve the ultra-high-resolution Synthetic Aperture Radar SAR) imaging, it is necessary to obtain ultra-wideband signals. However, it is difficulty for the traditional system to transmit ultra-wideband signal directly, which might increase the requirement of system hardware. In this paper, a method based on multi-frequency pulse diversity (MFPD) mode is proposed to ultra-high-resolution SAR imaging. In MFPD mode, a single channel transmits multiple waveforms from different frequency bands, and several receive channels which satisfy the condition of displaced phase center antenna. Thus, the echoes from different receive channels correspond to different subbands. Then, an ultra-high-resolution imaging can be obtained by spectrum splicing. In addition, a window function is designed for overlapping spectrum to achieve a better imaging performance. Simulation results demonstrate the excellent performance of this method. © The Institution of engineering & technology 2023.

关键词： HIGH-RESOLUTION OVERLAPPING SPECTRUM SPECTRUM SPLICING SYNTHETIC APERTURE RADAR

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An image segmentation method of pulverized coal for particle size analysis

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International Journal of Mining Science and technology 2023年第9期33卷 1181-1192页

作者： Xin Li Shiyin Li Liang Dong Shuxian Su Xiaojuan Hu Zhaolin Lu Affiliated Hospital of China University of Mining and Technology Xuzhou 221002China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Chemical Engineering&Technology China University of Mining and TechnologyXuzhou 221116China Advanced Analysis and Computation Center China University of Mining and TechnologyXuzhou 221116China

An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image ***,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image *** this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal ***,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated ***,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge *** final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison *** addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.

关键词： Pulverized coal Image segmentation Deep learning Particle size analysis

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Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN Yuan CAO Peng LI Guo XIE Tao WEN Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation *** fault diagnosis for railway point machines becomes a hot *** the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature ***,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection ***,support vector machine is utilized for fault *** comparisons show that the proposed method performs *** diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% ***,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： Fault diagnosis Vibrations Support vector machines Diversity reception Switches Feature extraction Rail transportation

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Enhanced Infrared-Visible Image Fusion Method with an ISP Based Degradation Model 13

Enhanced Infrared-Visible Image Fusion Method with an ISP Ba...

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13th International Conference on Intelligent control and information Processing, ICICIP 2025

作者： Guo, Xueyi Liu, Yisha Xue, Weimin Zhang, Zhiwei Information Science and Technology College Dalian Maritime University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

The performance of infrared (IR) and visible (VIS) image fusion tasks depends on the quality of source images, which are usually impacted by various degradation factors in real-world scenarios, leading to poor quality and visual effects of the fused images. By learning the mapping from low-quality (LQ) source images to high-quality (HQ) fused images, neural networks can remove the impacts of degradation factors, generating HQ fused images. However, the lack of paired LQ source images and corresponding HQ fused images poses a significant challenge in training a fusion network. Considering the effectiveness of the image processing pipeline (ISP) in processing IR and VIS images, reversing specific steps of ISP can accurately simulate the image degradation process. Building upon this research, we propose a novel ISP-based degradation model that reverses several ISP steps while incorporating various degradation factors, thereby generating LQ IR and VIS images from source images to train the fusion network. Experimental results on multiple image fusion datasets demonstrate that our method significantly enhances the quality and visual effects of fused images. © 2025 IEEE.

关键词： Image fusion

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Efficient Implementation of Aperture Fill Time Correction for Wideband Array Using the Low-complexity Keystone Transform

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Progress In Electromagnetics Research C 2023年 136卷 101-112页

作者： Wang, Lin Jiang, Yiyang Jiang, Yu Tian, Baoli Shen, Mingwei College of Computer and Information Engineering Hohai University Nanjing China Unit 95438 of PLA Air Force Meishan China Science and Technology on Electronic Information Control Laboratory Chengdu China

In order to remove the influence of the aperture fill time (AFT) for wideband array, the scaling principle of the Keystone (KT) transform is applied to eliminate the linear coupling between spatial domain and frequency domain of wideband array signal. However, the classic KT transform is implemented by interpolation Sinc which is difficult to apply in engineering and leads to the serious problem of insufficient data. To address this, a realization of the low-complexity KT transform is presented, and it is implemented using only the Chirp-z transform (CZT) and fast Fourier transforms (FFT). Additionally, an Autoregressive (AR) model is proposed to compensate the insufficient data for each range, and the order of AR is estimated by the rank of the signal covariance matrix. Simulation results demonstrate that the proposed algorithm significantly reduces computational burden and improves the performance of wideband array beamforming. © 2023, Electromagnetics Academy. All rights reserved.

关键词： Fast Fourier transforms

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Study on Spatial Distribution and Network Evolution Characteristics of Logistics Enterprises in Yangtze River Delta 16th

Study on Spatial Distribution and Network Evolution Characte...

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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Shen, Lei Tang, Gennian Zhang, Jianhua Zhijiang College of Zhejiang University of Technology Shaoxing312030 China School of Information and Control Engineering Qingdao University of Technology Qingdao266525 China

ISBN: (纸本)9789819617760

China's economic model is undergoing a transition from extensive growth to high-quality development, a transformation that has been significantly reinforced within the context of regional integration strategies. Logistics enterprises play a pivotal role in driving high-quality economic development, particularly in the Yangtze River Delta region. This research, framed within the context of the Yangtze River Delta regional integration strategy, analyzes the dynamic changes in the spatial layout, organizational networks, and driving mechanisms of logistics enterprises. The findings aim to assist national and local governments in formulating development plans for logistics enterprises that align with the requirements of regional integration, thereby achieving healthy, sustainable regional logistics development and contributing to overall economic growth. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistics Enterprises Organizational Networks Spatial Distribution Yangtze River Delta Integration

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Improving graph collaborative filtering with network motifs

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Neural Computing and Applications 2025年第16期37卷 9413-9432页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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An adaptive 3D reconstruction method for asymmetric dual-angle multispectral stereo imaging system on UAV platform

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Science China(information Sciences) 2024年第8期67卷 285-299页

作者： Chen WANG Xian LI Yanfeng GU Zixu WANG School of Electronics and Information Engineering Harbin Institute of Technology Beijing Institute of Control and Electronic Technology

A multispectral imaging system often cannot capture 3D spatial information owing to hardware limitations, which diminishes the effectiveness across various domains. To address this problem, we have developed a multispectral stereo imaging system along with an adaptive 3D reconstruction algorithm. Unlike existing unmanned aerial vehicle stereo imaging systems, our multispectral stereo imaging system uses two multispectral cameras with asymmetric spectral bands positioned at different angles. This design enables the acquisition of a higher number of bands and lateral spatial information while maintaining a lightweight structure. This system introduces challenges such as large geometric distortions and intensity differences between multiple bands. To accurately recover 3D spatial information, we propose an adaptive 3D reconstruction method. This method employs a position and orientation system-assisted projection transformation and a normalized threshold adjustment strategy. Finally, mutual information is used to reconstruct the multispectral images densely, effectively addressing nonlinear differences and generating a comprehensive multispectral point cloud. Our stereo system was used for two real data collections in different regions, and the efficacy of the proposed 3D reconstruction method was validated by comparing it with existing methods and commercial software.

关键词： multispectral images 3D reconstruction stereo imaging system unmanned aerial vehicle feature extraction

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Application of Data Fusion Algorithms in the Data Processing of Intelligent Greenhouses 1

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13th International Conference on Frontier Computing, FC 2023

作者： Li, Jinguang Li, Dan College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin City China Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin Jilin City China

ISBN: (数字)9789819992997

ISBN: (纸本)9789819992980

Greenhouses can ensure the normal growth of crops in extreme environments. With the popularization of social intelligence, how to build an efficient and accurate environmental monitoring system to ensure that crops have suitable growth conditions is a top priority. In order to effectively solve the problem of data missing and large data errors caused by sensor collection in intelligent greenhouses under extreme environments, this paper uses data preprocessing and multi-sensor fusion algorithms to ensure the accuracy of data. The effectiveness of the data fusion algorithm is verified through data transmission experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Crops

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A graph neural network and multi-task learning-based decoding algorithm for enhancing XZZX code stability in biased noise

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Chinese Physics B 2025年第5期34卷 250-257页

作者： Bo Xiao Zai-Xu Fan Hui-Qian Sun Hong-Yang Ma Xing-Kui Fan School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China School of Sciences Qingdao University of TechnologyQingdao 266033China

Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum *** XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased ***,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale ***,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding *** the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM *** high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional *** demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code.

关键词： quantum error correction XZZX code biased noise graph neural network

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