检索结果-内蒙古大学图书馆

Subleading power corrections for event shape variables in e⁺e^- annihilation

EUROPEAN PHYSICAL JOURNAL C 2024年第4期84卷 437页

作者： Buonocore, Luca Grazzini, Massimiliano Guadagni, Flavio Rottoli, Luca Univ Zurich Phys Inst CH-8057 Zurich Switzerland CERN Theoret Phys Dept CH-1211 Geneva 23 Switzerland

We consider subleading power corrections to event shape variables in e(+)e(-) collisions at the first order in the QCD coupling alpha(S). We start from the jettiness variable tau(2 )and the y(23) resolution variable for the k(T) jet clustering algorithm and we analytically compute the corresponding cumulative cross section. We investigate the origin of the different power suppressed contributions in the two-jet limit and trace it back to their different coverage of the phase space. We extend our analysis to the case of thrust and of the C-parameter, and we finally discuss a class of observables that depend on a continuous parameter giving different weight to central and forward emissions and we evaluate the corresponding subleading power corrections.

关键词： clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Software Module clustering: An In-Depth Literature Analysis

引用

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第6期48卷 1905-1928页

作者： Sarhan, Qusay, I Ahmed, Bestoun S. Bures, Miroslav Zamli, Kamal Z. Univ Duhok Dept Comp Sci Duhok 07006 Kurdistan Regio Iraq Karlstad Univ Dept Math & Comp Sci S-65188 Karlstad Sweden Czech Tech Univ Fac Elect Engn Dept Comp Sci Karlovo Nam 35 Prague 13121 2 Czech Republic Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pekan 26600 Malaysia

Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities' structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field.

关键词： Software Search problems Data mining Software engineering Software algorithms clustering algorithms Systematics Systematic literature study software module clustering clustering applications clustering algorithms clustering evaluation clustering challenges

来源：评论

学校读者我要写书评

暂无评论

Efficient algorithms for Rank-Regret Minimization

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 3801-3816页

作者： Xiao, Xingxing Li, Jianzhong Harbin Inst Technol Dept Comp Sci & Technol Harbin 150001 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Fac Comp Sci & Control Engn Shenzhen 518000 Peoples R China

Multi-criteria decision-making usually requires finding a small representative set from the database. A popular method, the regret minimization set (RMS) query, returns a size r subset S of the full dataset $D$D that minimizes the regret-ratio (the difference between the scores of top-1 in S and top-1 in D, for any utility function). RMS is not shift invariant, causing inconsistency in results. Further, the regret-ratio is often a "made up" number and users may mistake its absolute value. Instead, users do understand the notion of rank. Therefore, in this paper, we consider finding a fixed-size set S to minimize the maximum rank-regret (the rank of top-1 of S in the sorted list of D) over all possible utility functions, called the rank-regret minimization (RRM) problem, which is shift invariant. In 2D space, we design an exact algorithm 2DRRM for RRM. In HD space, we propose an approximate algorithm HDRRM with theoretical guarantees on rank-regret. It combines the ideas of space discretization and clustering. Extensive experiments verify the efficiency and effectiveness of our algorithms. In particular, HDRRM always has the best output quality in experiments.

关键词： Automobiles Databases Approximation algorithms Minimization clustering algorithms Logistics Decision making Multi-criteria decision-making rank-regret regret-ratio skyline top-k query

来源：评论

学校读者我要写书评

暂无评论

Scene Adaptive Building Individual Segmentation Based on Large-Scale Airborne LiDAR Point Clouds

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Yang, Wangshan Zhang, Yongjun Liu, Xinyi Gao, Boyong China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China Wuhan Univ Sch Remote Sensing Informat Engn Wuhan 430079 Peoples R China

Building individual segmentation plays a crucial role in building querying, management, analysis, and attribute addition. Previous research on this topic has primarily concentrated on small-scale scenes and single-type buildings. However, when dealing with complex scenes that contain diverse buildings, existing methods for building individual segmentation often encounter challenges, such as excessive undersegmentation and oversegmentation. To tackle this issue, we propose a scene adaptive building individual segmentation (SABIS) based on large-scale airborne LiDAR point clouds. The method first segments the roof object and then extract elevation feature and area feature of the roof object. Based on these features, the building point cloud is classified into two categories: urban scene buildings and rural residential scene buildings. Finally, for urban scene buildings, the building individual segmentation method based on the cylinder model consistency is used. For rural residential scene buildings, the building individual segmentation method based on bidirectional saliency features is employed. In this article, the proposed SABIS algorithm is quantitatively evaluated by using three large scene datasets at home and abroad and four benchmark methods. All kinds of accuracy are significantly better than the most advanced algorithms.

关键词： Buildings Point cloud compression clustering algorithms Feature extraction Laser radar Windows Object oriented modeling Airborne LiDAR point clouds building individual segmentation building type classification feature extraction object segmentation

来源：评论

学校读者我要写书评

暂无评论

Joint NOMA clustering and Power Allocation in IoRT-Oriented Satellite Terrestrial Relay Networks

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第10期71卷 11078-11088页

作者： Zhao, Bo Ren, Guangliang Dong, Xiaodai Northwestern Ploytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada

In this paper, a joint non-orthogonal multiple access (NOMA) clustering and power allocation problem is studied to maximize the uplink total sum rate in Internet of Remote Things (IoRT)-oriented satellite terrestrial relay networks (STRNs). The joint optimization problem is a mixed-integer programming (MIP) problem, which is non-convex and NP-hard. To solve this problem, we decompose it into two subproblems and propose staged algorithms to solve them. The first subproblem is an optimal NOMA clustering problem, which is still non-convex and NP-hard. To solve the subproblem efficiently, a reinforcement learning-based dynamic clustering algorithm (RL-DCA) is proposed. Using the RL-DCA, the users are able to gradually learn the optimal NOMA clustering policy in a distributed fashion. The RL-DCA has fast convergence and its computational complexity at each user remains fixed for any network size. The second subproblem is an intra-cluster optimal NOMA power allocation problem, which is still non-convex. To solve it, we firstly convert it into a convex problem by constrait approximation, then use the Karush-Kuhn-Tucker (KKT) conditions based algorithm to find the optimal power allocation policy. The second subproblem is solved in an offline fashion, which enables low computational complexity in transmission. Simulation results show that: 1) The RL-DCA has fast convergence and enables good clustering performance;2) The joint NOMA clustering and power allocation scheme greatly outperforms the orthogonal multiple access (OMA) scheme and other NOMA schemes in terms of total sum rate.

关键词： NOMA Relays Uplink Resource management Satellites System performance clustering algorithms Satellite terrestrial relay networks Internet of Remote Things NOMA clustering power allocation joint optimization

来源：评论

学校读者我要写书评

暂无评论

A Novel Location Method for Cable Defects Based on Broadband Impedance-clustering Algorithm 4

A Novel Location Method for Cable Defects Based on Broadband...

引用

4th International Conference on New Energy and Power Engineering, ICNEPE 2024

作者： Wang, Chong Wang, Binjiang Zhao, Xuefeng Yang, Kai Kong, Minru Pu, Lu Sun, Haofei Duan, Wei Yun, Xiahaoyue Deng, Junbo State Grid Shaanxi Electric Power Co. Ltd. Shaanxi Xi'an710048 China Xi'an Jiaotong University School of Electrical Engineering Shaanxi Xi'an710049 China State Grid Shaanxi Electric Power Co. Ltd. Electr. Power Res. Inst. Shaanxi Xi'an710100 China State Grid Shaanxi Electric Power Co. Ltd. Xi'an Electr. Power Supply Co. Shaanxi Xi'an710032 China

ISBN: (纸本)9798331516086

In the process of locating defects by broadband impedance spectrum (BIS) method, interference peaks will be generated on the final location spectrum due to spectral leakage, which will affect the location of the actual defects. The influence of on-site noise will also increase the difficulty of subjective judgment of defect peaks. In this paper, a density-based spatial clustering of applications with noise (DBSCAN) is introduced, and a combination location method for cable defects based on BIS-DBSCAN is proposed. The method automatically clusters the data points of the original location spectrum, and extracts the peaks of defects according to different types of data points after clustering, so as to reduce the misjudgment caused by human judgment and objective factors at the testing site. The proposed method is verified based on simulation and experiments on the 1000m cable. It is indicated that the method effectively improves the identification of cable defects, with a high location accuracy, and the location error is within 0.2%. © 2024 IEEE.

关键词： clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Pavement Crack Classification and Recognition Algorithm Combined With Tensor Voting and RANSAC

引用

IEEE ACCESS 2024年 12卷 72117-72130页

作者： Wang, Haiying Tao, Jianyu Li, Hansheng Changan Univ Key Lab Rd Construct Technol & Equipment MOE Xian 710064 Peoples R China Shaanxi Transportat Holding Grp Co Ltd Xian 710075 Peoples R China

The existing methods for pavement crack classification and identification solely offer information about the crack type, neglecting size and direction details, which are essential for guiding repair efforts and forming the engineer digital information data. In response to the challenges posed by insufficient crack information, prolonged training time and intricate parameter adjustment inherent in employing deep learning algorithms for pavement crack classification and recognition, we propose an integrated approach combining tensor voting with the random sample consensus for pavement crack classification and recognition. The method involves pre-processing road images using gray value transformation and the K-Means clustering algorithm. Subsequently, the tensor voting algorithm is applied to enhance the linear features, resulting in the generation of linear saliency maps of cracks along with crack junction information. Furthermore, a non-maximum suppression method and the RANSAC algorithm are employed to refine and fit the crack skeleton curves respectively, accomplishing the crack classification and recognition. The outcomes demonstrate that the proposed integrated approach in the crack skeleton segmentation algorithm yields an average F1-score of 0.7879, outperforming traditional non-maximum suppression methods. The accuracy of crack classification and recognition reaches 96%, outperforming other crack classification and recognition algorithms grounded in digital image processing methods. Compared with the neural networks employed for classification and recognition, the proposed algorithm is able to capture direction and size details of cracks, which can provide guidance for intelligent crack repair. This additional information can offer valuable guidance for intelligent crack repair processes.

关键词： Classification algorithms clustering algorithms Tensors Image segmentation Roads Interference Surface cracks Random sequences Crack classification and recognition non-maximum suppression random sample consensus (RANSAC) tensor voting

来源：评论

学校读者我要写书评

暂无评论

Channel Pruning of Transfer Learning Models Using Novel Techniques

引用

IEEE ACCESS 2024年 12卷 94914-94925页

作者： Thaker, Pragnesh Mohan, Biju R. Natl Inst Technol Karnataka Srinivasnagar Surathkal 575025 Karnataka India

This research paper delves into the challenges associated with deep learning models, specifically focusing on transfer learning. Despite the effectiveness of widely used models such as VGGNet, ResNet, and GoogLeNet, their deployment on resource-constrained devices is impeded by high memory bandwidth and computational costs, and to overcome these limitations, the study proposes pruning as a viable solution. Numerous parameters, particularly in fully connected layers, contribute minimally to computational costs, so we focus on convolution layers' pruning. The research explores and evaluates three innovative pruning methods: the Max3 Saliency pruning method, the K-Means clustering algorithm, and the Singular Value Decomposition (SVD) approach. The Max3 Saliency pruning method introduces a slight variation by using the three maximum values of the kernel instead of all nine to compute the saliency score. This method is the most effective, substantially reducing parameter and Floating Point Operations (FLOPs) for both VGG16 and ResNet56 models. Notably, VGG16 demonstrates a remarkable 46.19% reduction in parameters and a 61.91% reduction in FLOPs. Using the Max3 Saliency pruning method, ResNet56 shows a 35.15% reduction in parameters and FLOPs. The K-Means pruning algorithm is also successful, resulting in a 40.00% reduction in parameters for VGG16 and a 49.20% reduction in FLOPs. In the case of ResNet56, the K-Means algorithm achieved a 31.01% reduction in both parameters and FLOPs. While the Singular Value Decomposition (SVD) approach provides a new set of values for condensed channels, its overall pruning ratio is smaller than the Max3 Saliency and K-Means methods. The SVD pruning method prunes 20.07% parameter reduction and a 24.64% reduction in FLOPs achieved for VGG16, along with a 16.94% reduction in both FLOPs and parameters for ResNet56. Compared with the state-of-the-art methods, the Max3 Saliency and K-Means pruning methods performed better in Flops reduction m

关键词： Computational modeling Convolutional neural networks Kernel clustering algorithms Accuracy Transfer learning Filtering algorithms Deep compression of CNN channel pruning structured pruning neural network compression transfer learning

来源：评论

学校读者我要写书评

暂无评论

Recovering Static and Time-Varying Communities Using Persistent Edges

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 2087-2099页

作者： Avrachenkov, Konstantin Dreveton, Maximilien Leskela, Lasse Inria Sophia Antipolis F-06902 Valbonne France Ecole Polytech Fed Lausanne Sch Comp & Commun Sci CH-1015 Lausanne Switzerland Aalto Univ Dept Math & Syst Anal Espoo 02150 Finland

This article focuses on spectral methods for recovering communities in temporal networks. In the case of fixed communities, spectral clustering on the simple time-aggregated graph (i.e., the weighted graph formed by the sum of the interactions over all temporal snapshots) does not always produce satisfying results. To utilise information carried by temporal correlations, we propose to employ different weights on freshly appearing and persistent edges. We show that spectral clustering on such weighted graphs can be explained as a relaxation of the maximum likelihood estimator of an extension of the degree-corrected stochastic block model with Markov interactions. We also study the setting of evolving communities, for which we use the prediction at time t-1 as an oracle for inferring the community labels at time t. We demonstrate the accuracy of the proposed methods on synthetic and real data sets.

关键词： Markov processes Maximum likelihood estimation clustering algorithms Biological system modeling Image edge detection Partitioning algorithms Optimization Graph clustering temporal networks spectral methods stochastic block model

来源：评论

学校读者我要写书评

暂无评论

An Improved Evolutionary Method for Social Internet of Things Service Provisioning Based on Community Detection

引用

IEEE ACCESS 2024年 12卷 132939-132963页

作者： Allakaram Tawfeeq, Bahar Masoud Rahmani, Amir Koochari, Abbas Jafari Navimipour, Nima Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran *** Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz *** Iran Western Caspian Univ Res Ctr High Technol & Innovat Engn Baku 1001 Azerbaijan

Social IoT (SIoT) refers to socializing in the Internet of Things (IoT), where things generate social relationships. Due to the development of objects and issues such as delayed response, slow search, and composite service process, distributed object service discovery, selection, and composition based on the social structure have become essential challenges in the SIoT. Therefore, it is necessary to provide an efficient method for evaluating the effectiveness of service discovery in identifying suitable devices to offer requested services and the best composition strategy for combining requested services. This paper presents a new community detection algorithm that detects IoT devices with social connections in the SIoT network to facilitate service discovery and composition by reducing search space. Additionally, it introduced a new service provisioning algorithm to optimize service discovery and composition, called An Improved Genetic Algorithm based on Community Detection (IGA-CD). Its effectiveness in detected communities is better than other methods in computation modularity, execution time, and cluster assignment quality methods by determining the network's ideal devices. Experimental results demonstrate the efficacy of the proposed algorithms, which outperform other approaches in terms of scalability, efficiency, and flexibility. The IGA-CD average execution time is 0.129 seconds, which proves its efficiency and faster composition.

关键词： Social Internet of Things Object recognition Search problems Optimization clustering algorithms Scalability Performance evaluation Genetic algorithms service discovery service composition community detection improved genetic algorithm

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：