检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Jiang, San Ma, Yichen Li, Qingquan Jiang, Wanshou Guo, Bingxuan Li, Lelin Wang, Lizhe The School of Computer Science China University of Geosciences Wuhan430074 China Shenzhen518060 China The Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430078 China The College of Civil and Transportation Engineering Shenzhen University Shenzhen518060 China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430072 China The Provincial Key Laboratory of Geo-information Engineering in Surveying Mapping and Remote Sensing Hunan University of Science and Technology Xiangtan411201 China

SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook. Thus, this paper proposes an efficient match pair retrieval method and implements an integrated workflow for parallel SfM reconstruction. First, an individual codebook is trained online by considering the redundancy of UAV images and local features, which avoids the ambiguity of training codebooks from other datasets. Second, local features of each image are aggregated into a single high-dimension global descriptor through the VLAD (Vector of Locally Aggregated Descriptors) aggregation by using the trained codebook, which remarkably reduces the number of features and the burden of nearest neighbor searching in image indexing. Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable Small World) based graph structure for the nearest neighbor searching. Match pairs are then retrieved by using an adaptive threshold selection strategy and utilized to create a view graph for divide-and-conquer based parallel SfM reconstruction. Finally, the performance of the proposed solution has been verified using three large-scale UAV datasets. The test results demonstrate that the proposed solution accelerates match pair retrieval with a speedup ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction with competitive accuracy in both relative and absolute orientation. © 2023, CC BY.

关键词： Efficiency

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Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs

arXiv

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arXiv 2025年

作者： Zhang, Juan Luo, Junyue Zhang, Fangfang Yue, Xiaoqiang Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

This paper explores the application of kernel learning methods for parameter prediction and evaluation in the Algebraic Multigrid Method (AMG), focusing on several Partial Differential Equation (PDE) problems. AMG is an efficient iterative solver for large-scale sparse linear systems, particularly those derived from elliptic and parabolic PDE discretizations. However, its performance heavily relies on numerous parameters, which are often set empirically and are highly sensitive to AMG’s effectiveness. Traditional parameter optimization methods are either computationally expensive or lack theoretical support. To address this, we propose a Gaussian Process Regression (GPR)-based strategy to optimize AMG parameters and introduce evaluation metrics to assess their effectiveness. Trained on small-scale datasets, GPR predicts nearly optimal parameters, bypassing the time-consuming parameter sweeping process. We also use kernel learning techniques to build a kernel function library and determine the optimal kernel function through linear combination, enhancing prediction accuracy. In numerical experiments, we tested typical PDEs such as the constant-coefficient Poisson equation, variable-coefficient Poisson equation, diffusion equation, and Helmholtz equation. Results show that GPR-predicted parameters match grid search results in iteration counts while significantly reducing computational time. A comprehensive analysis using metrics like mean squared error, prediction interval coverage, and Bayesian information criterion confirms GPR’s efficiency and reliability. These findings validate GPR’s effectiveness in AMG parameter optimization and provide theoretical support for AMG’s practical application. © 2025, CC BY.

关键词： Poisson equation

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Two-Stage Clustering and Independent Competing Based Evolutionary Algorithm for Multimodal Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Zhong, keyu Xiao, Fen Gao, Xieping Hunan Normal University School of Mathematics and Statistics Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education Changsha410081 China Xiangtan University School of Computer Science Xiangtan411105 China Hunan Normal University Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

The multimodal multi-objective optimization problem (MMOP) involves multiple distinct components of the Pareto set (PS), which correspond to the same Pareto front (PF). Most existing multimodal multi-objective evolutionary algorithms (MMOEAs) face challenges in achieving both accuracy and timeliness in solving MMOPs. In this article, a two-stage clustering and independent competing based evolutionary algorithm (TSCICEA) is proposed for solving MMOPs. Firstly, in the early stage of population evolution, the K-means technique is used to coarsely cluster the population into multiple independent subpopulations, which aims to rapidly ascertain the approximate distribution structure of modalities and preliminarily locate their spatial positions. In the later stage, the DBSCAN technique is utilized to conduct fine clustering of the population, which aims to re-cluster misclustered individuals in the coarse clustering into new subpopulations, thus achieving a precise distinction between different modalities. Subsequently, to accurately identify the corresponding modality for each subpopulation, an independent competing strategy is proposed. This strategy can automatically assign different identification approaches for each modality based on their differences, and it simultaneously manages different modalities through parallel execution, thus significantly enhancing both the accuracy and timeliness of modality identification. To evaluate the effectiveness of TSCICEA, we compare it against state-of-the-art algorithms on benchmark test problems with 2–15 objectives and 4–15 variables. Experimental results demonstrate that TSCICEA achieves superior performance in terms of the IEDRX, IMST, and HV indicators © 1997-2012 IEEE.

关键词： Evolutionary algorithms

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A Video Axle Counting and Type Recognition Method Based on Improved YOLOv5S 1

引用

6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Li, Liubin Wu, Jiawei Luo, Haibo Wu, Rongteng Li, Zuoyong College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

The number of axles of the vehicle and the type of tires can reflect the information of the vehicle to a certain extent, and the load capacity can be calculated according to the number of axles of the truck and the type of axles. Therefore, the identification of the axle is of great significance for judging whether the truck is overweight. At present, the method of calculating the axles is carried out by the method of Laser Radar or grating for axle counting. In the prior art, the method of axle counting is complicated to deploy and the cost is high. Some computer vision-based axle statistics methods have emerged in recent years, but complete vehicle sideways pictures are required. However, due to the long body of the truck and the limited space factor, it is difficult to obtain the complete vehicle in an original image. Although image stitching can solve this problem, the current video image stitching methods have a relatively high time cost. To solve this issue, we propose an object detection and tracking method based on YOLOv5s for axle counting and tire type identification. Experimental results show that the proposed method has extremely high accuracy and can meet real-time requirements even without GPU. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

SpreadFGL: Edge-Client Collaborative Federated Graph Learnin...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Luying Zhong Yueyang Pi Zheyi Chen Zhengxin Yu Wang Miao Xing Chen Geyong Min College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster UK School of Engineering Computing and Mathematics University of Plymouth UK Department of Computer Science University of Exeter UK

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

关键词： Training Accuracy Collaboration Benchmark testing Imputation Generators Topology

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A GENERAL ALTERNATING-DIRECTION IMPLICIT FRAMEWORK WITH GAUSSIAN PROCESS REGRESSION PARAMETER PREDICTION FOR LARGE SPARSE LINEAR SYSTEMS

arXiv

引用

arXiv 2021年

作者： Jiang, Kai Su, Xuehong Zhang, Juan Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

This paper proposes an efficient general alternating-direction implicit (GADI) framework for solving large sparse linear systems. The convergence property of the GADI framework is discussed. Most of existing ADI methods can be unified in the developed framework. Meanwhile the GADI framework can derive new ADI methods. Moreover, as the algorithm efficiency is sensitive to the splitting parameters, we offer a data-driven approach, the Gaussian process regression (GPR) method based on the Bayesian inference, to predict the GADI framework’s relatively optimal parameters. The GPR method only requires a small training data set to learn the regression prediction mapping, can predict accurate splitting parameters, and has high generalization capability. It allows us to efficiently solve linear systems with a one-shot computation, and does not require any repeated computations. Finally, we use the three-dimensional convection-diffusion equation, two-dimensional parabolic equation, and continuous Sylvester matrix equation to examine the performance of our proposed methods. Numerical results demonstrate that the proposed framework is faster tens to thousands of times than the existing ADI methods, such as (inexact) Hermitian and skew-Hermitian splitting type methods in which the consumption of obtaining relatively optimal splitting parameters is ignored. As a result, our proposed methods can solve much larger linear systems which these existing ADI methods have not reached. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear systems

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Hierarchical Porous Cellulose Monolith Prepared by Thermally Induced Phase Separation for High-Efficiency Uranium Adsorption

SSRN

引用

SSRN 2023年

作者： Sun, Peng Ma, Lin Liao, Jian Wang, Jiejun Huang, Chen Xu, Lu Wang, Jie Fu, Mengtao Zhou, Qingzhi Ma, Hongjuan Hunan Provincial Key Laboratory of Radon Institute of Nuclear Science and Technology University of South China Hengyang421001 China Shanghai Applied Radiation Institute School of Environmental and Chemical Engineering Shanghai University Shanghai200444 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China College of Ocean Engineering Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Beihai536000 China

The development and application of new and efficient adsorption materials are key issues in the treatment of nuclear wastewater. This paper introduces a novel hierarchical porous amidoxime cellulose monolith synthesized through thermally induced phase separation (TIPS) using low-cost, biodegradable, and renewable natural cellulose. This monolith demonstrates remarkable efficacy in adsorbing uranyl ion. The inherent hydrophilicity of natural cellulose, coupled with the hierarchical porous structure achieved through TIPS, imparts strong mass transfer capabilities to the material. The adsorption properties of this amidoxime cellulose monolith for uranyl ions were evaluated in both batch and flow-through column adsorption scenarios. Remarkably, the adsorption saturation was achieved within a concise period of 13 h in batch adsorption. The hierarchical porous structure of the amidoxime cellulose monolith exhibited exceptional adsorption performance for uranyl ion, achieving a removal rate of 99.99%. Moreover, this removal rate was sustained for 200 min, and the monolith proved recyclable for at least 5 cycles in flow-through column adsorption tests involving nuclear wastewater. When used for adsorption in real wastewater environments, the material achieved the removal of over 99.96% of total α and β radioactivity. These findings underscore the high efficiency of the newly developed hierarchical porous amidoxime cellulose monolith in uranyl ion adsorption. They emphasize its substantial potential for the efficient adsorption and removal of uranyl ion from nuclear wastewater. © 2023, The Authors. All rights reserved.

关键词： Cellulose

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Large AI Model-Based Semantic Communications

arXiv

引用

arXiv 2023年

作者： Jiang, Feibo Peng, Yubo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed reality, and the Internet of Everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model (LAM) provides new solutions to overcome the above issues. Here, we propose a LAM-based SC framework (LAM-SC) specifically designed for image data, where we first apply the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semanticaware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the possibility of applying the LAM-based KB in future SC paradigms. Copyright © 2023, The Authors. All rights reserved.

关键词： Latent semantic analysis

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Invariant-preserving schemes with arbitrarily high-order accuracy for the two-component Camassa–Holm system via Hamiltonian boundary value methods

引用

Communications in Nonlinear Science and Numerical Simulation 2025年 150卷

作者： Qian Luo Aiguo Xiao Min Li Xiaoqiang Yan School of Mathematics and Computational Science & Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan 411105 China School of Mathematics and Physics China University of Geosciences Wuhan 430074 China National Center for Applied Mathematics in Hunan Xiangtan University Hunan 411105 China

In this paper, we present an arbitrarily high-order numerical scheme for the two-component Camassa–Holm system, ensuring the preservation of three invariants: energy and two Casimir functions. The spatial discretization is achieved using Fourier–Galerkin methods, resulting in a semi-discrete system which retains a Hamiltonian structure and approximates the invariants of the original continuous system. Subsequently, an energy-preserving Runge–Kutta method, such as Hamiltonian boundary value methods, is applied to the semi-discrete system, yielding a fully discrete scheme of arbitrarily high order. The proposed scheme is validated through numerical simulations, demonstrating its accuracy and capability in capturing different types of solutions of the two-component Camassa–Holm equation.

关键词： Blended iteration Fourier–Galerkin methods Hamiltonian boundary value methods Invariant-preserving scheme Two-component Camassa–Holm system

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COMPOSITION OPERATORS ON BLOCH AND HARDY TYPE SPACES

arXiv

引用

arXiv 2022年

作者： Chen, Shaolin Hamada, Hidetaka Zhu, Jian-Feng College of Mathematics and Statistics Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application 421002 China Faculty of Science and Engineering Kyushu Sangyo University 3-1 Matsukadai 2-Chome Higashi-Ku Fukuoka813-8503 Japan School of Mathematical Sciences Huaqiao University Quanzhou362021 China

The main purpose of this paper is to discuss Hardy type spaces, Bloch type spaces and the composition operators of complex-valued harmonic functions. We first establish a sharp estimate of the Lipschitz continuity of complex-valued harmonic functions in Bloch type spaces with respect to the pseudo-hyperbolic metric, which gives an answer to an open problem. Then some classes of composition operators on Bloch and Hardy type spaces will be investigated. The obtained results improve and extend some corresponding known *** Codes 31A05, 47B33, 30H10, 30H30 Copyright © 2022, The Authors. All rights reserved.

关键词： Harmonic functions

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