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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Yu, Binhua Liu, Ping-An Liu, Yichen Wang, Hongyu Zhang, Liang Wang, Yanhua Liu, Kun Xi'an Modern Control Technology Research Institute Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP), which contain fine geometric structure information of targets, plays animportant role in the field of Radar automatic target recognition (RATR). In the actual application of RATR, radar may often capture targets that are non-cooperative, making it difficult to establish a comprehensive database, and traditional recognition algorithms are prone to misjudgment when facing unknown targets. Therefore, Open set recognition (OSR) is proposed to simultaneously recognize in-database and out-of-database targets. Considering the pose sensitivity of HRRP, minor changes in the target's pose can cause significant changes in the HRRP images, leading to a potentially dispersed distribution of samples from the same category in the feature space, which makes it difficult for traditional fixed decision boundaries in OSR to adapt. To address this, this paper proposes an HRRP open set recognition method capable of adaptively learning closed decision boundaries. Based on Hierarchical dirichlet processes (HDP), the method achieves adaptive clustering of subclasses under different categories according to the data distribution characteristics of each category, determining multiple small decision boundaries within each category, and allowing the decision boundaries to dynamically adjust according to the sample features. Finally, experiments with real HRRP data have verified the effectiveness of the proposed method. © 2024 IEEE.

关键词： Radar imaging

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Lightweight CNN for Radar HRRP Recognition Using NAS-Based Pruning and Multi-Knowledge Distillation 2

Lightweight CNN for Radar HRRP Recognition Using NAS-Based P...

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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Zhang, Zhilong Wu, Heke Xu, Yinhui Zhang, Liang Wang, Yanhua Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Xi'an Modern Control Technology Research Institute Xi'an710065 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

Deep convolutional neural network (CNN) has been widely studied in radar target high resolution range profile (HRRP) recognition. However, the CNN with deep structure requires high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we propose a differentiable neural architecture search (NAS)-based channel pruning and multi-knowledge distillation (MKD) to prune and fine-tune the CNN for lightweight. In the pruning stage, NAS method is used to automatically find the good-performance pruning structure by optimizing channel preserve scores. After the search process, these scores are used for global pruning to derive the pruned model. In the fine-tuning stage, MKD method combine the physical scattering knowledge with the category knowledge from pretrained model to restore the pruned model performance. Experimental results on VGG-16 with HRRP dataset inversed from the MSTAR show that the recognition accuracy of the proposed method decreases 1.52% by using 5% model parameters. © 2024 IEEE.

关键词： Convolutional neural networks

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A Track Association Algorithm Based on Transitive Closure Fuzzy Clustering

A Track Association Algorithm Based on Transitive Closure Fu...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Huang, Yinghao Zhang, Kaihua Wang, Jing Cai, Yunze Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Shanghai Academy of Spaceflight Technology Shanghai201100 China

ISBN: (纸本)9789811581540

Aiming at track association before and after multi-target track interruption, a track associating method on the basis of transitive closure fuzzy clustering is proposed. Firstly, the method converts the traditional track fuzzy similarity matrix into a fuzzy equivalent matrix by squaring method, and split sub-blocks of the equivalent matrix. Then the method calculates final track correlation matrix based on correlating threshold function related to track interruption duration, and obtains correct track association by the correlation matrix. The simulation proves that, the proposed method improves association accuracy comparing with other association algorithms based on fuzzy theory, and presents a more significant advantage in the case that targets maneuver and tracks cross. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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Sparse View HRRP Recognition Based on Dual-Task of Generation and Recognition Method 2

Sparse View HRRP Recognition Based on Dual-Task of Generatio...

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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Meng, Yuanhao Wang, Lue Zhou, Qiang Zhang, Xin Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Modern Control Technology Research institute Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

In recent years, high-resolution range profiles (HRRPs) have played a significant role in radar target recognition. The pose sensitivity of HRRPs results in unsatisfactory target recognition capabilities under sparse viewing angles. However, for non-cooperative targets, obtaining full-view HRRPs is often challenging, leading to suboptimal performance in HRRP-based target recognition. To address the issue of target recognition under sparse viewing angles, we propose an end-to-end model DGR for sparse view recognition that integrates combined generation and recognition tasks. The DGR model extracts deep features of HRRPs through an attention mechanism and utilizes these features for both target recognition and the generation of adjacent-view HRRPs, with both tasks mutually reinforcing each other for improved performance in recognition and data generation. Furthermore, by employing domain adaptation methods, the model learns the variability of target HRRPs in the source domain and applies this knowledge to the target domain, enhancing its generalization ability. Experimental results demonstrate that the DGR model achieves optimal recognition and generation capabilities simultaneously. © 2024 IEEE.

关键词： Radar target recognition

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Iterative-Reweighting-Based Robust Iterative-Closest-Point Method

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 739-746页

作者： ZHANG Jianlin ZHOU Xuejun YANG Ming Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Noblelift Intelligent Equipment Co. Ltd.Huzhou 313100ZhejiangChina

In point cloud registration applications,noise and poor initial conditions lead to many false *** matches significantly degrade registration accuracy and speed.A penalty function is adopted in many robust point-to-point registration methods to suppress the influence of false ***,after applying a penalty function,problems cannot be solved in their analytical forms based on the introduction of ***,most existing methods adopt the descending *** this paper,a novel iterative-reweighting-based method is proposed to overcome the limitations of existing *** proposed method iteratively solves the eigenvectors of a four-dimensional matrix,whereas the calculation of the descending method relies on solving an eight-dimensional ***,the proposed method can achieve increased computational *** proposed method was validated on simulated noise corruption data,and the results reveal that it obtains higher efficiency and precision than existing methods,particularly under very noisy *** results for the KITTI dataset demonstrate that the proposed method can be used in real-time localization processes with high accuracy and good efficiency.

关键词： point cloud registration iterative reweighting iterative closest-point(ICP) robust localization

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M3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection

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Computers in Biology and Medicine 2024年 173卷 108291-108291页

作者： Zhou, Tao Wang, Hongwei Du, Yuhu Liu, Fengzhen Guo, Yujie Lu, Huiling School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Medical Information and Engineering Ningxia Medical University Yinchuan750004 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

Background: It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. Methods: To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. Results: The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. Conclusion: The M3YOLOv5 model can effectiv

关键词： Fracture

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ICP algorithm point cloud registration method based on a new improved Harris Hawk algorithm

ICP algorithm point cloud registration method based on a new...

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2021 China Automation Congress, CAC 2021

作者： Wang, Chen Jiao, Shangbin Gao, Rui Li, Yuxing School of Automation and Information Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China School of Electronic and Electrical Engineering Baoji University of Arts and Sciences Baoji China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Baoji China

ISBN: (纸本)9781665426473

To address shortcomings of ICP algorithm, which is sensitive to the initial position and easy to fall into local optimum, we propose a Harris Hawks Optimizer (HHO) point cloud registration method combining PCA preprocessing and parallel population initialization. The PCA method is used to align the point clouds with the main axes and perform the main axes correction. Since the six input variables in the population initialization stage of HHO point cloud registration are different and independent, the initial population of HHO algorithm is initialized by the parallel initialization method. The results show that the improved algorithm is able to successfully overwhelm the fault of ICP algorithm, and its global seek performance and optimization accuracy are more advantageous, and the algorithm operates efficiently, with high registration accuracy and robustness. © 2021 IEEE

关键词： PCA HHO Parallel Computing ICP

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Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity

arXiv

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arXiv 2024年

作者： Shen, Tianqi Liu, Shaohua Feng, Jiaqi Ma, Ziye An, Ning Department of Computer Science City University of Hong Kong Hong Kong Image Processing Center Beihang University China Shen Yuan Honors College Beihang University China Research Institute of Mine Artificial Intelligence China Coal Research Institute China State Key Laboratory of Intelligent Coal Mining and Strata Control China

Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-level structural integrity due to incomplete initial geometric coverage, and inadequate feature-level integrity from insufficient topological constraints during optimization. To overcome these limitations, Topology-GS incorporates a novel interpolation strategy, Local Persistent Voronoi Interpolation (LPVI), and a topology-focused regularization term based on persistent barcodes, named PersLoss. LPVI utilizes persistent homology to guide adaptive interpolation, enhancing point coverage in low-curvature areas while preserving topological structure. PersLoss aligns the visual perceptual similarity of rendered images with ground truth by constraining distances between their topological features. Comprehensive experiments on three novel-view synthesis benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics, while maintaining efficient memory usage. This study pioneers the integration of topology with 3D-GS, laying the groundwork for future research in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

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State-of-Charge Dependent Battery Model Identification Using Gaussian Process Regression

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IFAC-PapersOnLine 2021年第3期54卷 647-652页

作者： Kesen Fan Yiming Wan Key Laboratory of Image Processing and Intelligent Control Ministry of Education China and School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China

This paper investigates the parameter identification of a state-of-charge dependent equivalent circuit model (ECM) for Lithium-ion batteries. Different from most existing ECM identification methods, we focus on identifying the functional relations between ECM parameters and state-of-charge (SOC). By transforming the ECM into an ARX model, a Gaussian process regression (GPR) approach is proposed, without using parametric functions to describe the SOC dependence of ARX coefficients. The proposed approach derives the posterior distributions of ECM parameters, thus is capable to quantify the estimation uncertainties. Another advantage lies in the flexibility of incorporating the knowledge of batteries into the prior distributions used in GPR, which enhances the estimation performance in the presence of noises. The effectiveness of the proposed GPR approach is illustrated by simulation examples under both low and high noise levels.

关键词： Gaussian process regression non-parametric approach Lithium-ion battery equivalent circuit model state-of-charge dependent circuit parameter

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Federated Motor imagery Classification for Privacy-Preserving Brain-Computer Interfaces

arXiv

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arXiv 2024年

作者： Jia, Tianwang Meng, Lubin Li, Siyang Liu, Jiajing Wu, Dongrui The Key Laboratory The Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University Science and Technology Research Institute Shenzhen518063 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy. © 2024, CC BY.

关键词： Federated learning

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