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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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Complete Gait Phase Recognition Based on Muscle Synergy Using PSO-CNN-LSTM Algorithm

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IEEE Sensors Journal 2025年第10期25卷 16775-16786页

作者： Zhang, Kewen Li, Xiaoling Chen, Xin Yu, Longjie Jin, Yinan Fan, Bingfei Du, Mingyu Bao, Guanjun Wu, Xinyu Cai, Shibo Zhejiang University of Technology College of Mechanical Engineering Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education and Zhejiang Province Hangzhou310023 China Zhejiang University of Technology College of Mechanical Engineering Hangzhou310023 China ZJUT Yinhu Research Institute of Innovation and Entrepreneurship Zhejiang University of Technology Fuyang Hangzhou310023 China Shenzhen Institutes of Advanced Technology Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

Accurately recognizing gait phases, by applying proper instrumentation and measurement, is significant in walking rehabilitation training for patients with impaired mobility. In this study, seven phases of complete stand-walk–stand cycle as well as continuous daily walking were recognized based on muscle synergy and particle swarm optimization (PSO)-convolutional neural network (CNN)-long short-term memory (LSTM) model. First, the complete stand-walk–stand walking cycle was divided into starting phase, terminal swing phase, loading response phase, mid-stance phase, terminal stance phase, initial swing phase, and stopping phase, based on the features of surface electromyography (sEMG) collected by portable sEMG acquisition system and measured motion data of lower limb. Second, a muscle weight matrix and an activation sequence matrix were calculated by using nonnegative matrix factorization (NMF). Finally, a PSO-CNN-LSTM network was designed to recognize the complete stand-walk–stand cycle by applying the abovementioned matrices as input. Fourteen subjects volunteered to perform linear walking experiments under procedure to verify the feasibility of the proposed approach by comparing results with other classifiers and features. Experimental results show that the proposed approach was capable of achieving an average recognition accuracy of up to 85.384%. This work will offer a promising gait recognition method for rehabilitation robots to achieve natural and flexible human-robot interaction. © 2001-2012 IEEE.

关键词： Non-negative matrix factorization

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Low Profile, High Aperture Efficiency Magneto-Electric Dipole Circularly Polarized Beam Scanning Antenna Array

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IEEE Antennas and Wireless Propagation Letters 2025年第5期24卷 1094-1098页

作者： Wang, Chao Xu, Guanghui Meng, Yanfeng Yan, Jiaxin Xie, Guoda Chen, Yi Wang, Wei Zhao, Luyu Li, Minquan Li, Yingsong Huang, Zhixiang Anhui University The Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits Information Materials and Intelligent Sensing Laboratory of Anhui Province Center for Big Data and Population Health of lHM Hefei230601 China East China Research Institute of Electronic Engineering Hefei230088 China

In this letter, a low-profile, high-aperture-efficiency millimeter-wave magneto-electric dipole (MED) circularly polarized (CP) beam-scanning antenna array is proposed. Initially, a novel MED antenna element is designed by arranging metallized vias around the radiating patches to serve as beam directors. This novel method not only provides broad axial-ratio (AR) beams and wide half-power beamwidths but also maintains a low-profile and compact structural characteristic. By adjusting the positions of the via directors, the radiation beam direction of the antenna element is effectively controlled. Subsequently, 4 × 4 antenna arrays for the xoz and yoz planes are designed and simulated to verify the CP beam scanning performance. The results demonstrate that the maximum CP beam scanning range can reach from -50° to +50°, with the AR maintained below 3 dB throughout the scanning range. At 29 GHz, the peak aperture efficiencies of the antenna arrays reach 73.8%, while maintaining a low profile of just 0.12λ0. Finally, the measurement results confirm the validity of the simulations. © 2002-2011 IEEE.

关键词： Millimeter waves

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Multivariable financial time series forecasting based on phase space reconstruction compensation

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Neural computing and Applications 2025年第3期37卷 1389-1402页

作者： Li, Jincheng Zhou, Linli Li, Xuefei Wu, Di Xiong, Jianqiao Song, Liangtu School of Big Data and Statistics Anhui University Hefei230039 China Intelligent Agriculture Engineering Laboratory of Anhui Province Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China

Accurate financial time series forecasting is an important challenge in the financial field due to varying levels of interaction among multiple financial time series, complicating the extraction of valid information from these variables. This study introduces an effective and efficient multivariable financial time series forecasting model based on phase space reconstruction compensation: Phase Space Reconstruction Compensation Long Short-Term Memory (PSR-LSTM). The PSR-LSTM model leverages the long short-term memory (LSTM) network to analyze short-term data behaviors, reconstructs multiple long-term variables by phase space reconstruction method, and utilizes multivariable trend attention to capture trend information in correlated variables. This trend information is finally used to correct the LSTM network’s predictive result. Experimental results demonstrate that the PSR-LSTM outperforms existing multivariable forecasting models by effectively mitigating noise interference while achieving optimal forecasting performance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Phase space methods

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Graph contrastive learning via coarsening: A time and memory efficient approach

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KNOWLEDGE-BASED SYSTEMS 2025年 317卷

作者： Du, Ziwei Wang, Tao Yang, Zhen Chen, Jie Duan, Zhen Zhao, Shu Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Anhui Peoples R China Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Anhui Peoples R China

Graph Contrastive Learning (GCL), training the graph neural networks encoder by contrasting different views in a self-supervised way, has demonstrated remarkable efficacy in graph representation learning. However, most existing GCL approaches tend to be time-and memory-consuming because they require extensive node contrasts across the entire original graph. To address this, we present CGCL, a fast graph contrastive learning approach based on graph coarsening. It aims to train an encoder on coarse graphs with lower time and memory costs while performing comparably to one trained on the original graph. Specifically, we coarsened the original graph into a series of highly-informative, smaller-tractable coarse graphs to reduce their scale. We then designed a multi-scale contrastive learning paradigm in the multi-granularity space, incorporating coarse-coarse and coarse-fine contrast to efficiently capture global and hierarchical information. CGCL accelerates model training while ensuring that the learned node representations are comprehensive. Extensive experiments towards node classification on seven real world datasets demonstrate that CGCL can achieve competitive performance with lower time and memory costs. In particular, on the ogbn-mag dataset, compared to state-of-the-art methods, CGCL reduces time consumption by up to 89.06% and memory usage by up to 50.56%, while maintaining comparable performance.

关键词： Contrastive learning Graph coarsening Self-supervised learning Graph neural networks Node classification

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BEDiff: denoising diffusion probabilistic models for building extraction

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Optoelectronics Letters 2025年第5期21卷 298-305页

作者： LEI Yanjing WANG Yuan CHAN Sixian HU Jie ZHOU Xiaolong ZHANG Hongkai College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information China Three Gorges University Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University College of Electrical and Information Engineering Quzhou University School of Electronic and Information Engineering Anhui Jianzhu University

Accurately identifying building distribution from remote sensing images with complex background information is challenging. The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds. Building on this concept, we propose a novel framework, building extraction diffusion model(BEDiff), which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion. Our approach begins with the design of booster guidance, a mechanism that extracts structural and semantic features from remote sensing images to serve as priors, thereby providing targeted guidance for the diffusion process. Additionally, we introduce a cross-feature fusion module(CFM) that bridges the semantic gap between different types of features, facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively. Our proposed BEDiff marks the first application of diffusion models to the task of building extraction. Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff, affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.

关键词： A

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UAV Video Vehicle Detection: Benchmark and Baseline

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Xiao, Yun Wang, Jinfa Zhao, Zhicheng Jiang, Bo Li, Chenglong Tang, Jin Anhui Univ Sch Artificial Intelligence Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Anhui Prov Key Lab Secur Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Computat Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China

With the increasing application of unmanned aerial vehicles (UAVs) in intelligent transportation systems, vehicle object detection in UAV videos has received increasing attention. Precise categorization and detection for vehicles in UAVs is important in many practical applications. However, existing object detection methods, tailored for natural images, often fall short of accurately identifying vehicle objects. Additionally, high-altitude UAV imaging mainly employs horizontal bounding box annotation, frequently leading to significant obstruction and overlapping. Hence, we propose a new task called UAV video vehicle detection (VVD) to achieve precise detection and categorization of vehicles in high-altitude UAV imaging environments. To facilitate the research and development of UAV VVD, we construct the first large-scale well-annotated benchmark UAV VVD dataset, which includes 70 UAV videos captured at a 500-m altitude, with 361489 vehicle instances annotated by the oriented bounding boxes and vehicle categories. Moreover, we introduce a novel category refinement network (CRNet) approach that extracts and refines vehicle object features from the bounding box of the detection results to classify vehicle categories. This approach effectively eliminates the interference of the background and other vehicle objects in candidate boxes. Notably, the vehicle object features are projected into subspace, enabling the category refinement module (CRM) to focus more on the distinctive characteristics of the vehicle object itself through normalization operations. We conduct extensive experiments on the proposed VVD dataset. Experimental results demonstrate the superiority and effectiveness of the proposed CRNet method. The relevant code and dataset are available at https://***/mmic-lcl.

关键词： Autonomous aerial vehicles Object detection Vehicle detection Feature extraction Annotations Benchmark testing Accuracy Training Data mining Automobiles Category refinement network (CRNet) object detection unmanned aerial vehicle (UAV) video vehicle detection (VVD) vehicle categories

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Enhancing Structural Stability and Electrochemical Performance of Ultra-high Ni-rich Co-free Cathode via MgHPO_(4) Dual-functional Modification

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Chemical Research in Chinese Universities 2025年第2期41卷 333-342页

作者： LIN Huahui SHEN Yu WEI Li SONG Ran WU Fan WEI Peng YANG Zhenzhong REN Yurong QU Ke DING Zhengping Changzhou Key Laboratory of Intelligent Manufacturing and Advanced Technology for Power Battery Jiangsu Province Engineering Research Center of Intelligent Manufacturing Technology for the New Energy Vehicle Power BatterySchool of Materials Science and EngineeringChangzhou UniversityChangzhou 213164P.R.China Key Laboratory of Polar Materials and Devices(MOE) Shanghai Center of Brain-inspired Intelligent Materials and DevicesDepartment of ElectronicsEast China Normal UniversityShanghai 200241P.R.China

Ultra-high nickel layered cathodes(Ni≥95%)have emerged as prospective candidates for next-generation lithium-ion batteries(LIBs)due to their exceptional specific capacity and ***,the commercial application of these cathodes has been hindered by several challenges,including structural instability during cycling,high sensitivity to air,and slow Li+*** this research,a one-step modification strategy was developed to simultaneously achieve Mg doping and Li_(3)PO_(4)layer coating for the ultra-high nickel *** results demonstrated that Mg doping not only alleviates lattice strain changes during the H2–H3 phase transition(H2:the second hexagonal phase;H3:the third hexagonal phase)but also serves as a structural anchor,preventing Ni^(2+)migration and occupation within the Li *** Li_(3)PO_(4)surface coating layer acts as an electrochemical shield,protecting against interfacial side reactions and enhancing the Li+diffusion *** a result,the LiNi_(0.95)Mn_(0.05)O_(2) cathode,with both internal and external modifications,demonstrates significant improvement in cycling stability(85.7%capacity retention after 100 cycles)and Li^(+)transport performance(130.6 mA·h·g^(−1) at 10 C,1 C=189.6 mA·h·g^(−1)),providing a solid foundation for the further development and application of ultra-high nickel cathodes.

关键词： Lithium-ion battery Ni-rich Co-free cathode MgHPO_(4) Surface modification

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Efficient Physical Truncation of Low-Frequency ATEM Problems in Specific Geometries by Using Random Forest Regression Based PMM Model

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2025年 10卷 1-7页

作者： Feng, Naixing Zeng, Shuiqing Wang, Huan Zhang, Yuxian Huang, Zhixiang Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Anhui Univ Anhui Higher Educ Inst Anhui Lab Informat Mat & Intelligent Sensing Key Lab Electromagnet Environm Sensing Hefei Peoples R China

In addressing the challenges posed by low-frequency airborne transient electromagnetics (ATEM), it is necessary to take into account the considerations of accuracy, computational efficiency, and the scale and intricacy of the physical domain. This becomes particularly crucial when dealing with large-scale, complex issues, with the aim of mitigating the computational resource burden associated with managing such complexities. In order to further meet the aforementioned criteria, a Perfectly Matched Monolayer (PMM) model has been introduced into the Random Forest Regression (RFR) framework. The RFR-based PMM model has demonstrated exceptional accuracy through the utilization of Bagging's integrated learning methodology, while also reducing the computational resource requirements for processing time. In comparison to traditional machine learning models, our model has exhibited significant advantages in terms of training stability, model efficiency, and parallelization capabilities. To verify and establish the reliability of this approach, three-dimensional numerical simulations of the ATEM problem were conducted. The proposed model in this study has exhibited superior accuracy, efficiency, and versatility in addressing the low-frequency ATEM problem, integrating with the FDTD method.

关键词： Computational modeling Accuracy Finite difference methods Decision trees Solid modeling Random forests Time-domain analysis Training Three-dimensional displays Electromagnetics Airborne transient electromagnetics (ATEMs) finite-difference time-domain (FDTD) perfectly matched monolayer (PMM) random forest Regression (RFR)

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