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A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

Computers, Materials & Continua 2025年第4期83卷 1195-1218页

作者： Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and TechnologyChizhou247000China

Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关键词： Fuzzy rough sets feature selection cross-similarity fuzzy relations

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Optimization design of welding parameters of key components of car body based on finite element 5

Optimization design of welding parameters of key components ...

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2024 5th International Conference on Advanced Materials and intelligent Manufacturing, ICAMIM 2024

作者： Li, Lina Fa, Ping Sun, Hongchang He, Chuan Qiao, Zhiliang Department of Automobile Engineering Tianjin Transportation Technical College Tianjin China Huaiyuan County Vocational and Technical School Anhui Province Bengbu City China School of Mechanical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Intelligent Robot Technology and Application Enterprises Tianjin China

The characteristics of the welding heat source, the morphology of the molten pool, the formation mechanism of the weld, and the amount of heat input have an important and direct influence on the welding quality and performance of the joint. At present, the empirical curve that depends on the experimental results or the calculation formula of statistics cannot comprehensively evaluate the impact of welding on the mechanical properties of the whole structure. Therefore, this research is based on the finite element analysis technique of Simufact Welding software and analyzes the evolution of the temperature field, stress field, and the deformation of the residual stress in the welding process, and provides a powerful tool and a lot of support for welding technology. The aim of the study is to develop an accurate numerical simulation method for the weld temperature field and a dynamic simulation analysis system for the weld stress field and deformation. 2025 Published under licence by IOP Publishing Ltd.

关键词： Residual stresses

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Phase Error Correction Algorithm Based on Complementary Gray Code and Reverse Error Compensation 19th

Phase Error Correction Algorithm Based on Complementary Gray...

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19th Chinese Conference on Image and Graphics Technologies and applications, IGTA 2024

作者： Zhang, Pengjie Kong, Bin Wang, Shaoping Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Anhui Engineering Research Center for Intelligent Driving Technology and Application Hefei230088 China

ISBN: (纸本)9789819799183

This study addresses the periodic phase error problem arising from the nonlinear response of instruments in structured light three-dimensional measurement systems. A novel phase error correction algorithm that integrates complementary Gray codes and reverse error compensation is proposed. The algorithm utilizes complementary Gray codes to assist in phase unwrapping, thereby reducing computational resources and improving the accuracy of the unwrapping process. Furthermore, effective phase error compensation is achieved through two measurements and their phase averaging, significantly enhancing the system's measurement accuracy. Simulation experiments are conducted to analyze the specific impact of nonlinear response on phase measurement, and theoretical derivations validate the effectiveness of the reverse compensation method in eliminating periodic phase errors. The experimental results demonstrate that, compared to traditional methods, the proposed algorithm improves measurement accuracy by more than 80%, confirming its efficacy. This research offers a new solution for enhancing structured light measurement systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Phase measurement

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IsoVISoR: Towards 3D Mesoscale Brain Mapping of Large Mammals at Isotropic Sub-micron Resolution

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Neuroscience Bulletin 2025年第2期41卷 344-348页

作者： Chao-Yu Yang Yan Shen Xiaoyang Qi Lufeng Ding Yanyang Xiao Qingyuan Zhu Hao Wang Cheng Xu Pak-Ming Lau Pengcheng Zhou Fang Xu Guo-Qiang Bi Division of Life Sciences and Medicine CAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of ChinaHefei230026China Interdisciplinary Center for Brain Information Shenzhen-Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Center for Integrative Imaging Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei230027China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application MoE Key Laboratory of Brain-Inspired Intelligent Perception and CognitionSchool of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei230088China Faculty of Life and Health Sciences Shenzhen University of Advanced TechnologyShenzhen518107China CAS Key Laboratory of Brain Connectome and Manipulation The Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China

Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing ***,large brain samples pose major challenges to this approach.

关键词： brain mapping three dimensional fluorescence microscopy mesoscale brain mapping lightsheet based methods block face methods high throughput volumetric imaging tissue clearing techniques

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Local global information aggregation graph convolution for skeleton-based action recognition

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Neurocomputing 2025年 640卷

作者： Xie, Shichong Li, Shengze Chen, Peng Wang, Bing Zhang, Jun School of Artificial Intelligence Anhui University Hefei230601 China School of Electrical Engineering and Automation Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Institute of Health Sciences and Technology & School of Internet Anhui University Hefei230601 China School of Management Science and Engineering Anhui University of Finance & Economics Bengbu233030 China Anhui Rocvision Intelligent Technology Co. Ltd Hefei230001 China

In the field of human action recognition based on skeletal data, how to capture the complex features of skeletal sequences in both spatial and temporal dimensions is a significant challenge. Existing Graph Convolutional Network (GCN) methods typically focus on extracting spatial features using various approaches, while neglecting the temporal characteristics inherent in human actions. In this paper, we propose the L(ocal) G(lobal)-GCN, a novel approach that emphasizes the under-explored temporal dimension by segmenting time to capture both local and global information. We also introduce a relative position embedding method tailored for skeletal data to enhance the representation capability of graph convolution. Additionally, we propose a partitioning method that reduces computational complexity while retaining all information, leveraging the sequential nature of skeletal data. Our LG-GCN achieves and surpasses the state-of-the-art accuracy across three widely employed datasets: NTU RGB+D with 97.3% (X-View), NTU RGB+D 120 with 91.1% (X-Set), and NW-UCLA with 96.6%. © 2025

关键词： Network theory (graphs)

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LSReg-Net: An End-to-end Registration Network for Large-Scale LiDAR Point Cloud in Autonomous driving

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IEEE Sensors Journal 2025年第11期25卷 20675-20686页

作者： Tao, Yucheng Yang, Xiuqing Wang, Hanqi Wang, Jian Li, Zhiyuan Liang, Huawei University of Science and Technology of China Hefei230026 China The Second Research Institute of CAAC Civil Aviation Logistics Technology Company Limited Chengdu610043 China Chinese Academy of Sciences Hefei Institutes of Physical Science Hefei230031 China Anhui Engineering Research Center for Intelligent Driving Technology and Application Hefei230031 China Chinese Academy of Sciences Innovation Research Institute of Robotics and Intelligent Manufacturing Hefei230031 China

Large-scale point cloud registration is a foundamental problem for autonomous driving. To achieve alignment, most existing methods focus on local point cloud features for matching. However, these approaches fail to account for the sparse distribution of features in large-scale driving scenarios, limiting their effectiveness. This article introduces an end-to-end registration network, named LSReg-Net, specifically designed to address this challenge, offering a more reliable solution for large-scale LiDAR point cloud. The LSReg-Net performs registration by following sequential modules. Firstly, this study develops a sampling strategy, Distance- Weighted Farthest Points Sampling (DW-FPS), which effectively enhances sparse feature robustness. Then a Scale Attention Fusion (SAF) network is proposed to capture both local and geometric features. By considering both spatial sparsity and geometric context of the keypoints, the LSReg-Net obtains accurate correspondences after matching. Finally, a coarse-to-fine registration module is conducted with multilevel correspondences to acquire a precise rigid transformation. Extensive experiments on two large-scale LiDAR datasets demonstrate that the proposed approach outperforms existing registration methods. In addition, real-world experiments based on an experimental platform further validate the high efficiency. © 2001-2012 IEEE.

关键词： Geometry

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Modeling Fine-Grained Relations in Dynamic Space-Time Graphs for Video-Based Facial Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Huang, Changqin Jiang, Fan Han, Zhongmei Huang, Xiaodi Wang, Shijin Zhu, Yanlai Jiang, Yunliang Hu, Bin Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Jinhua321004 China Charles Sturt University School of Computing Mathematics and Engineering AlburyNSW2640 Australia State Key Laboratory of Cognitive Intelligence Anhui Hefei230088 China Hangzhou Hikvision Digital Technology Co. Ltd Education Business Department Hangzhou310051 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Beijing Institute of Technology School of Medical Technology Beijing10081 China

Facial expressions in videos inherently mirror the dynamic nature of real-world facial events. Consequently, facial expression recognition (FER) should employ a dynamic graph-based representation to effectively capture the relational structure of facial expressions rather than relying on conventional grid or sequence methods. However, existing graph-based approaches have their limitations. Frame-level graph methods provide a coarse representation of the facial graph across time and space, while landmark-based graph methods need to introduce additional facial landmarks, resulting in a static graph structure. To address these challenges, we propose spatial-temporal relation-aware dynamic graph convolutional networks (ST-RDGCN). This fine-grained relation modeling approach enables the dynamic modeling of evolving facial expressions in videos through dynamic space-time graphs, eliminating the need for facial landmarks. ST-RDGCN encompasses three graph construction paradigms: dynamic independent space graph, dynamic joint space-time graph, and dynamic cross space-time graph. Furthermore, we propose a relation-aware space-time graph convolution (RSTG-Conv) operator to learn informative spatiotemporal correlations in dynamic space-time graphs. In extensive experimental evaluations, our ST-RDGCN demonstrates state-of-the-art performance on the five popular video-based FER datasets, achieving overall accuracy scores of 99.69%, 91.67%, 56.51%, 69.37%, and 49.03% on the CK+, Oulu-CASIA, AFEW, DFEW, and FERV39 k datasets, respectively. In particular, our ST-RDGCN outperforms the current best method by 3.6% in UAR on the most challenging FERV39 k dataset. Furthermore, our analysis reveals that the dynamic cross space-time graph scheme is the most effective among the three dynamic graph construction schemes. © 2010-2012 IEEE.

关键词： Graphic methods

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Charging Out Rectangle: Quadrangle IoU for Scene Text Detection under Balanced Supervision 25

Charging Out Rectangle: Quadrangle IoU for Scene Text Detect...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control engineering

作者： Min Ma Xiangnan Zhang Wang Bin Qiuli Wang Kexing Che School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Key Laboratory of Intelligent Information Processing Corporation Key Laboratory of Aperture Array and Space Application The 38th Research Institute of China Electronics Technology Group Hefei Anhui China Government and Enterprise Division China Unicom Shandong Branch Jinan Shandong China School of Electronic Engineering Xidian University Xi' an Shaanxi China

ISBN: (纸本)9798400712647

In the past few years, text detection approaches have been able to cope with more and more challenging complex scenes. In this field, the arbitrary shape and various scale of text instances are the two most frequently mentioned topics. Among the existing methods, the bounding box prediction methods based on Intersection over Union (IoU) are efficient, but can only handle multi-oriented rectangles. Although arbitrary shape text detection methods have been proposed, these methods are often aimed at curved text. Besides, the post-processing is usually cumbersome. Considering that the affine transformation phenomenon caused by out-plane rotation is more frequent than curved text in real scenario, we design a quadrangle IoU (QIoU) loss to efficiently predict arbitrary convex quadrangle text. Furthermore, we analyze the challenge of variable scale in text detection from the perspective of unbalanced supervision information, and propose a cross-scale knowledge distillation (CSKD) method to improve the detection accuracy of large-scale text. An extensive ablation study demonstrates the effectiveness and superiority of the proposed strategies. During inferring, our method does not consume extra time. Using the basic feature pyramid network for feature extraction, our method achieves state-of-the-art results on several popular datasets for scene text detection.

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Performance Enhancement of Nanogenerator by Piezoelectric Filler Microstructure Design Towards Electrical Transmission Power Line Monitoring

SSRN

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SSRN 2025年

作者： Lu, Haowei Hu, Zhenghua Wang, Xin Chen, Gaoru Zheng, Xiaogan Xia, Jintao Huang, Mingju Yang, Wenlong Liu, Junhui Hu, Benlin Henan Key Laboratory of High Efficiency Energy Conversion Science and Technology Henan International Joint Laboratory of New Energy Materials and Devices School of Physics and Electronics Henan University Kaifeng475004 China School of Information and Artificial Intelligence Anhui Agricultural University Hefei230036 China School of Intelligent Manufacturing Wenzhou Polytechnic Wenzhou325035 China State Grid Fuzhou Electric Power Supply Company Fuzhou350009 China School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin150080 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China

Flexible composites-based piezoelectric nanogenerator (PENG) with low cost, stable properties and sensitivity to mechanical deformation is highly suitable for constructing a new self-powered perception layer in distributed electrical transmission power lines, which has the potential to reduce labor maintenance costs and improve sensing recognition accuracy. However, the lower output performance of PENG makes it difficult to integrate with energy management circuit and signal recognition system. Herein, we propose a enhanced output performance of composites-based PENG by designing piezoelectric fillers, where BCZT@Ag nanoparticles grow on the surface of BCZT fibers to form a Bracnch heterostructure. The PENG prepared by this new filler exhibits significantly enhanced output of 96.4 V and 15.52 µA, which are about 3.2 and 6.5 times higher than those of PENG without specific design, benefiting from improved charge transport mechanism and induced-polarization. Moreover, a intelligent Power IoTs system through the synergistic integration of this high-performance PENG and learning-assisted data analytics has been constructed, which enables accurate self-powered real-time monitoring of abnormal vibration states in transmission power lines to avoid rupture accident. This work not only provides an effective strategy to enhance the performance of PENG but also offers a new solution for the safe operation in power grid. © 2025, The Authors. All rights reserved.

关键词： Piezoelectricity

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A communication-free leader-follower formation method for UGVs via sensors fusion

A communication-free leader-follower formation method for UG...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Lin, Linglong Wang, Zhiling Liang, Huawei Xu, Fengyu Huang, Junjie Institute of Applied Technology Hefei Institute of Physical Science Chinese Academy of Sciences Hefei Anhui230088 China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei Anhui230088 China Anhui Province Key Laboratory of Intelligent Connected Technology Hefei Anhui230601 China

ISBN: (纸本)9781538680698

This paper proposes a leader-follower formation method for unmanned ground vehicles under which there is no need to exchange information by communication between leader and following vehicles. Based on the measurement of relative information, such as distances, orientations and velocity, by monocular camera and laser scanner mounted on the following vehicle, the formation is formed by recognizing the leader vehicle and tracking its trajectory normally. If the detection of the leader vehicle in monocular camera fails, an on-line estimator based on kinematical constraint and Kalman filters are designed to guaranteed the measurement of the leader continuously in a hundred of milliseconds time scale. The experimental results under complex traffic environment verify the feasibility and validity of the proposed method. © 2018 IEEE.

关键词： Ground vehicles

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