检索结果-内蒙古大学图书馆

Wireless Charging Scheduling for Long-term Utility Optimization

ACM Transactions on Sensor Networks 2025年第1期21卷 1-31页

作者： Xu, Jia Chen, Wenbin Dai, Haipeng Xu, Lijie Xiao, Fu Liu, Linfeng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have investigated the problem of long-term charging scheduling with random variable. This article designs an optimization model for the long-term scheduling of chargers to maximize the time-averaged charging utility while ensuring its time-averaged constraints of budget and response rate. The Lyapunov optimization technique is adopted to transform the stochastic optimization problem into a deterministic optimization problem, which remains NP-hard. Thus, an approximation algorithm following greedy approach is proposed to solve the deterministic optimization problem. We further provide the theoretical analysis of feasibility and performance guarantee of the proposed scheduling algorithm. The simulation results show that our algorithm outperforms three comparison algorithms by 6.53%, 20.04%, and 19.97% in terms of time-averaged charging utility, as well as by 11.25%, 4.42%, and 3.73% in terms of time-averaged response rate on average. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

ACFM: Adaptive Channel Feature Matching for Pedestrian Re-Identification

引用

IEEE Access 2025年 13卷 82278-82290页

作者： Lu, Zhengcai Tian, Zhengwei Luzhou Vocational and Technical College School of Artificial Intelligence and Big Data Sichuan Luzhou646000 China Luzhou Key Laboratory of Intelligent Data Analysis and Processing Luzhou646000 China

Image misalignment is a significant challenge in the field of pedestrian re-identification. Previous studies typically align pedestrian features using additional models or by leveraging auxiliary information. However, these methods are data-dependent and can fail when dealing with substantial scene variations. To address these challenges, this paper proposes an efficient adaptive channel feature matching (ACFM) strategy. This method adaptively aligns channel feature maps according to image content, enables accurate matching of local features across images, and mitigates the need for supplementary data. Building on the ACFM approach, this paper develops a multi-branch adaptive matching network model. The model incorporates a channel attention mechanism to enhance the representation capacity of individual channels and integrates the ACFM algorithm within local branches to optimize the distance metric computation, effectively capturing and aligning local details. The multi-branch structure is designed to handle both global and local features, enabling the network to comprehensively capture and integrate image information. Evaluation experiments conducted on two widely-used benchmark datasets, Market-1501 and DukeMTMC-ReID, demonstrate that the proposed method offers significant advantages over current state-of-the-art approaches. © 2013 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

引用

Computers, Materials & Continua 2025年第4期83卷 157-175页

作者： Xiaoyin Yi Long Chen Jiacheng Huang Ning Yu Qian Huang School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of EducationChongqing400065China Artificial Intelligence and Big Data College Chongqing Polytechnic University of Electronic TechnologyChongqing401331China

Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.

关键词： Adversarial examples black-box transferability regularized constrained transfer-based adversarial attacks

来源：评论

学校读者我要写书评

暂无评论

A data representation method using distance correlation

引用

Frontiers of Computer Science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

来源：评论

学校读者我要写书评

暂无评论

LeapGNN: Accelerating Distributed GNN Training Leveraging Feature-Centric Model Migration 23

LeapGNN: Accelerating Distributed GNN Training Leveraging Fe...

引用

23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Chen, Weijian He, Shuibing Qu, Haoyang Zhang, Xuechen The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang Lab China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver United States

ISBN: (纸本)9781939133458

Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the transfer of massive graph vertex features to GNN models, which leads to a significant communication bottleneck. Recognizing that the model size is often significantly smaller than the feature size, we propose LeapGNN, a feature-centric framework that reverses this paradigm by bringing GNN models to vertex features. To make it truly effective, we first propose a micrograph-based training strategy that leverages a refined structure to enhance locality, combined with the model migration technique, to minimize remote feature retrieval. Then, we devise a feature pre-gathering approach that merges multiple fetch operations into a single one to eliminate redundant feature transmissions. Finally, we employ a micrograph-based merging method that adjusts the number of micrographs for each worker to minimize kernel switches and synchronization overhead. Our experimental results demonstrate that LeapGNN achieves a performance speedup of up to 4.2× compared to the state-of-the-art method, namely P3 © 2025 FAST. All Rights Reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Granular Sequential Three-Way Decision for Specific Decision Classes

引用

IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1650-1663页

作者： Cheng, Yunlong Yang, Xiuhua Zhang, Qinghua Shao, Yabin Wang, Guoyin Chongqing University of Posts and Telecommunications School of Science Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism Key Laboratory of Big Data Intelligence Computing Chongqing Key Laboratory of Computational Intelligence Chongqing400065 China

Sequential three-way decision (S3WD) is an efficient granular computing paradigm for dealing with uncertain problems. However, it is primarily oriented to all decision classes, which contradicts the fact that decisions are typically for the specific decision classes. Meanwhile, most S3WD models hide the topological structure of the granules, leading to difficulties in semantic interpretation. To address the issues, integrating model construction, attribute reduction and knowledge extraction, a general framework of granular sequential three-way decision (GrS3WD) for the specific decision classes is proposed to improve semantic interpretation and computational efficiency. First, a two-stage trisecting strategy and a GrS3WD model are proposed to integrate model construction with attribute reduction. Its main advantage is that it retains the topological structure of granules, which not only enhances semantic interpretation but also avoids unnecessary double counting. Second, three acceleration strategies and a novel granular sequential three-way reduction (GrS3WR) algorithm are proposed to fast obtain a classification-based reduct or a class-specific reduct. Finally, the decision rules with multi-granularity can be directly extracted from the concept tree generated by GrS3WR. Experimental results demonstrate that a class-specific reduct usually has fewer attributes and better classification performance than a classification-based reduct. Moreover, GrS3WR can significantly improve the computational efficiency of attribute reduction. © 1993-2012 IEEE.

关键词： Granular computing

来源：评论

学校读者我要写书评

暂无评论

Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

引用

Energy Engineering 2025年第1期122卷 243-264页

作者： Liyao Yang Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Institute of Distributed Energy Storage Safety Big Data Beijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China

Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is *** address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and ***,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance ***,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution *** Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction ***,the proposed models are validated using NASA and CALCE lithium-ion battery *** results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.

关键词： State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Frequency and Polarization Reconfigurable Transparent Water Antenna

引用

Progress in Electromagnetics Research C 2025年 152卷 177-186页

作者： Li, Lei Gao, Jing Nan, Jingchang School of Electronics and Information Engineering Liaoning Technical University Huludao125105 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China

A novel frequency and polarization reconfigurable water patch antenna is proposed for radio communication in the UHF band. Based on theoretical analysis and simulation results, water is an ideal material for designing transparent liquid ground. Water enhances outstanding transparency, excellent aesthetics, and high optical stealth performance for a wider range of application scenarios. The entire structure is made with polyvinyl chloride material and distilled water, except for the feed structure. By filling different cavities with liquid water, five different operating states are obtained in 1.924–2.5 GHz (26.1%), 1.67–2.33 GHz (33%), 0.644–2.288 GHz (112.1%), 1.975–2.54 GHz (25%), and 1.748–2.108 GHz (18.7%), achieving frequency reconfigurability. The antenna can be flexibly switched between linear polarization (LP) and two right-handed circular polarization (RHCP) states. The results show that the 3 dB axial ratio (AR) bandwidth covers 1.93–2.08 GHz (7.5%) and 2.06–2.132 GHz (3.5%). The antenna achieves high optical transparency of 100% and a peak gain of 7.97 dBi. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Circular polarization

来源：评论

学校读者我要写书评

暂无评论

An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：