检索结果-内蒙古大学图书馆

A deep learning object detection method to improve cluster analysis of two-dimensional data

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第28期83卷 71171-71187页

作者： Couturier, Raphael Gregori, Pablo Noura, Hassan Salman, Ola Sider, Abderrahmane Univ Franche Comte UFC FEMTO ST Inst Besancon France Univ Jaume I Castellon Dept Matemat Inst Matemat & Aplicac Castellon E-12071 Castellon de La Plana Spain Amer Univ Beirut Elect & Comp Engn Dept Beirut Lebanon Univ Bejaia Fac Exact Sci LIMED Lab Bejaia 06000 Algeria

clustering is an unsupervised machine learning method grouping data samples into clusters of similar objects, used as a system support tool in numerous applications such as banking customers profiling, document retrieval, image segmentation, and e-commerce recommendation engines. The effectiveness of several clustering techniques is sensible to the initialization parameters, and different solutions have been proposed in the literature to overcome this limitation. They require high computational memory consumption when dealing with big data. In this paper, we propose the application of a recent object detection Deep Learning model (YOLO-v5) for assisting the initialization of classical techniques and improving their effectiveness on two-variate datasets, leveraging the accuracy and reducing dramatically the memory and time consumption of classical clustering methods.

关键词： clustering algorithms clustering initialization methods clustering initialization metrics Deep learning object detection model

来源：评论

学校读者我要写书评

暂无评论

Application of Image Segmenting Technology Based on Fuzzy C-Means Algorithm in Competition Video Referee

引用

IEEE ACCESS 2024年 12卷 34378-34389页

作者： Yan, Xu North China Elect Power Univ Dept Phys Educ Baoding 071003 Peoples R China

As sports developing and popularizing, people have put forward increasing demands for fairness and transparency in competitions. However, traditional competition referee systems rely too heavily on human subjective judgment, which often leads to significant subjectivity and in-accuracy issues. To overcome these difficulties, some improvements have been made on the basis of the fuzzy C-means algorithm to preserve more information, while also suppressing membership to improve the convergence speed of the algorithm. These results confirm that the accuracy of the model converged approximately in the 600th round, with an accuracy of 96.1%. Compared with the standard fuzzy C-means algorithm, the convergence speed has been improved by 33% and the accuracy has been improved by 2.1%. This experiment verifies the effectiveness and practicality of the video referee in this competition. This provides new ideas for the development of image segmenting technology and has certain application potential in sports competition refereeing, judicial notarization refereeing, and other fields.

关键词： Image segmentation Fuzzy sets clustering algorithms Mathematical models Object segmentation Fuzzy logic Biomedical imaging Visual analytics Fuzzy theory C-means algorithm image segmenting technology video image recognition video referee

来源：评论

学校读者我要写书评

暂无评论

Research on improvement of Fuzzy K-Modes clustering algorithm for categorical data

Research on improvement of Fuzzy K-Modes clustering algorith...

引用

2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Wang, Jing Yu, Liqin Si, Pinyin Wu, Guangyue Yang, Zibo School of Information Engineering Beijing Institute of Graphic Communication Beijing China

ISBN: (纸本)9781510681514

Cluster analysis is an important method in data mining and has been widely used in various aspects of real life. The Fuzzy K-Modes algorithm is one of the most commonly used algorithms for clustering categorical data due to its simplicity and effectiveness. However, this algorithm has three main problems: sensitivity of initial cluster centers, vulnerability of distance measurement methods to noise, and failure to consider different contributions of attributes during the clustering process. In this paper, we overview some existing improvement algorithms based on the three problems in detail and conduct experimental analysis of these algorithms on real data sets. Furthermore, we summarize the challenges faced in the process of improving the Fuzzy K-Modes algorithm and give possible research directions in future. © 2024 SPIE.

关键词： clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Depiction of Subsurface Leak Areas Based on Adaptive Sensitive Frequency Attribute Analysis

引用

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Cheng, Qi Cui, Fan Dong, Guoqi Zhang, Guixin Wang, Ran Zhang, Mengli China Univ Min & Technol Beijing Sch Geosci & Surveying Engn Beijing 100083 Peoples R China Gen Prospecting Inst China Natl Adm Coal Geol Beijing 100039 Peoples R China

Ground penetrating radar (GPR) frequency attributes are commonly used to describe subsurface structures and characterize anomalous media. Single-frequency slices face challenges in capturing the broadband characteristics of GPR data, so the fusion of extracted multi-frequency components using a fusion algorithm can be effective. However, selecting appropriate attributes and mapping them to media characterization remain unresolved challenges. In this study, we propose a workflow based on adaptive sensitive frequency attribute analysis (ASFAA) to address these issues. First, the generalized S-transform (GST) is used to calculate the multi-frequency attributes of GPR data. Then, a sensitive feature analysis method combining hierarchical clustering and correlation analysis is employed to reduce redundancy in frequency attributes. Multi-frequency data are fused using the potential of heat-diffusion for affinity-based transition embedding (PHATE), which performs affinity-based diffusion embedding. The workflow is tested with synthetic and field data, yielding characterization results consistent with both the forward model results and actual leak extents. Therefore, the proposed workflow effectively integrates multi-frequency components, demonstrating its capability to delineate leak extents.

关键词： Data mining Time-frequency analysis Correlation Probability Principal component analysis Media Geology clustering algorithms Signal to noise ratio Radar Data fusion ground penetrating radar (GPR) multi-frequency attributes time-frequency analysis

来源：评论

学校读者我要写书评

暂无评论

PCSP: A Novel Class Discovery Algorithm for Radio Signal Classification

引用

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第4期10卷 1190-1203页

作者： Chen, Jie Zhao, Zhijin Zheng, Shilian Zhang, Luxin Qiu, Kunfeng Yang, Xiaoniu Hangzhou Dianzi Univ Telecommun Sch Hangzhou 310018 Peoples R China Natl Key Lab Electromagnet Space Secur Innovat Studio Academician Yang Jiaxing 314033 Peoples R China

Novel class discovery (NCD) aims to cluster novel signals in an unlabeled set by using prior knowledge of the labeled signal sets. In order to improve the recognition ability of radio signals, in this paper, we propose a prototype classifier and similarity prediction method (PCSP) for NCD of radio signals. Firstly, focusing on the different recognition abilities of the network for labeled and unlabeled signals, PCSP adopts different similarity prediction strategies and comparative learning, so that the network can obtain a better cluster representation. Secondly, in view of the limitation of the closed set assumption of the traditional deep learning network on the expression ability of unlabeled data, PCSP uses prototype encoding for each type of signal to obtain a prototype classifier, so that the network has a more compact intra-class feature representation. Finally, simulation experiments are carried out on the interference signal dataset and the modulation signal dataset respectively. The results show that the performance of PCSP is much better than the existing algorithms.

关键词： Classification algorithms Feature extraction clustering algorithms Self-supervised learning Prototypes Prediction algorithms Modulation Novel class discovery interference recognition automatic modulation classification deep learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Multidimensional Statistics Denoising Algorithm Based on Gaussian Mixture Model for Photon-Counting LiDAR Data

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 13308-13323页

作者： Chen, Sitong Nie, Sheng Xi, Xiaohuan Xia, Shaobo Zhu, Feng Wang, Cheng Zhu, Xiaoxiao Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Changsha Univ Sci & Technol Dept Geomat Engn Changsha 410114 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Hubei Luojia Lab Wuhan 430079 Peoples R China China Earthquake Adm Inst Geol State Key Lab Earthquake Dynam Beijing 100029 Peoples R China

The micropulse multibeam photon-counting laser altimeter significantly improves the sampling density along the orbit while introducing abundant noise. Therefore, noise removal is crucial for the subsequent applications of the photon-counting laser altimeter. This study proposes a new multidimensional statistics algorithm based on the Gaussian mixture model for photon noise removal. The method can simultaneously utilize multiple point cloud statistics and avoid manually selecting denoising thresholds, dividing signals and noise adaptively. To evaluate the performance of the algorithm, it is applied to both simulated ICESat-2 data and real ICESat-2 data, and evaluation indicators including recall, precision, and the harmonic means of recall and precision are used for analysis. Experimental results indicate that compared to denoising using a single statistic, denoising with multidimensional statistics (specifically, the sum of the distance of k-nearest neighbors, the photon density, and the standard deviation of the height difference within an elliptic neighborhood of the target photon) generally yields better results. Multidimensional statistics can influence and constrain each other, leading to improved decision-making outcomes. After comparing the algorithm with mainstream DBSCAN and KNN methods, the results indicate that the GMM method achieves the best denoising results, with average F-values 5.22% and 7.26% higher than those of DBSCAN and KNN methods, respectively, across all datasets. In conclusion, this algorithm can comprehensively assess and make decisions based on different statistical measures, demonstrating robustness and adaptability in extracting signal photons, and is worth promoting in the photon denoising applications.

关键词： Photonics Noise Noise reduction Classification algorithms Point cloud compression clustering algorithms Training And land elevation satellite-2 (ICESat-2) cloud gaussian mixture model (GMM) ice multidimensional statistics photon denoising photon-counting lidar

来源：评论

学校读者我要写书评

暂无评论

Mining Both Commonality and Specificity From Multiple Documents for Multi-Document Summarization

引用

IEEE ACCESS 2024年 12卷 54371-54381页

作者： Ma, Bing Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China

The multi-document summarization task requires the designed summarizer to generate a short text that covers the important information of original multiple documents and satisfies content diversity. To fulfill the dual requirements of coverage and diversity in multi-document summarization, this study introduces a novel method. Initially, a class tree is constructed through hierarchical clustering of documents. Subsequently, a sentence selection method based on class tree is proposed for generating a summary. Specifically, a top-down traversal is performed on the class tree, during which sentences are selected from each node based on their similarity to the centroid of the documents within the node and their dissimilarity to the centroid of documents not belonging to the node. Sentences selected from the root node reflect the commonality of all document, and sentences selected from the sub nodes reflect the distinct specificity of the respective subclasses. Experimental results on standard text summarization datasets DUC'2002, DUC'2003, and DUC'2004 demonstrate that the proposed method significantly outperforms the variant method that considers only commonality of all documents, achieving average improvements of up to 1.54 and 1.42 in ROUGE-1 and ROUGE-L scores, respectively. Additionally, the method demonstrates significant superiority over another variant method that considers only the specificity of subclasses, achieving average improvements of up to 2.16 and 2.01 in ROUGE-1 and ROUGE-L scores, respectively. Furthermore, extensive experiments on DUC'2004 and Multi-News datasets show that the proposed method outperforms lots of competitive supervised and unsupervised multi-document summarization methods and yields considerable results.

关键词： Vectors Task analysis Feature extraction Natural languages clustering algorithms Symmetric matrices Standards Class tree commonality and specificity hierarchical clustering of documents multi-document summarization pre-trained embedding representation

来源：评论

学校读者我要写书评

暂无评论

An Energy-Efficient Multihop Routing Protocol for 3-D Bridge Wireless Sensor Network Based on Secretary Bird Optimization Algorithm

引用

IEEE SENSORS JOURNAL 2024年第22期24卷 38045-38060页

作者： Zheng, Shannong Huo, Jiuyuan Yang, Jiguang Cao, Fang Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China

A multihop routing protocol based on the secretary bird optimization algorithm (SBOA) is proposed to address the issues of limited node energy and unbalanced energy consumption in a 3-D bridge wireless sensor network (BWSN). This protocol offers a comprehensive solution that optimizes network lifetime and energy distribution. The method for calculating the optimal number of cluster heads (CHs) in 3-D BWSN is presented. Second, the network is clustered using the fuzzy C-means (FCM) algorithm, and suitable CHs are selected via the entropy weight method. Third, during the intercluster routing phase, SBOA is utilized to find the optimal path between CHs and base stations (BSs). Finally, a reclustering mechanism is proposed to control the frequency of updating the cluster structure, enhancing the energy efficiency of the proposed protocol. The experimental results demonstrate that the proposed protocol improves network lifetime, energy efficiency, and energy consumption balance by 135%, 7%, and 59.3%, respectively, compared with current mainstream methods. Therefore, the proposed protocol exhibits good adaptability in 3-D BWSN.

关键词： Three-dimensional displays clustering algorithms Wireless sensor networks Energy consumption Routing Routing protocols Sensors Bridge wireless sensor network (BWSN) fuzzy C-means (FCM) algorithm multihop routing secretary bird optimization algorithm (SBOA)

来源：评论

学校读者我要写书评

暂无评论

Soil Moisture Retrieval Using BuFeng-1 A/B Based on Land Surface clustering Algorithm

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 4680-4689页

作者： Guo, Zhizhou Liu, Baojian Wan, Wei Lu, Feng Niu, Xinliang Ji, Rui Jing, Cheng Li, Weiqiang Chen, Xiuwan Yang, Jun Bai, Zhaoguang Peking Univ Sch Earth & Space Sci Inst Remote Sensing & GIS Beijing 100871 Peoples R China China Meteorol Adm Key Lab Radiometr Calibrat & Validat Environm Sat Natl Satellite Meteorol Ctr Natl Ctr Space Weather Beijing Peoples R China Innovat Ctr FengYun Meteorol Satellite FYSIC Beijing 100081 Peoples R China China Acad Space Technol Xian Branch CAST XIAN Xian 710100 Peoples R China CSIC Inst Space Sci ICE Earth Observat Res Grp Barcelona 08193 Spain DFH Satellite Co Ltd Beijing 100094 Peoples R China

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm(3)center dot cm(-3). The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm(3)center dot cm(-3). This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.

关键词： Land surface Sea surface Surface roughness Rough surfaces Satellite broadcasting clustering algorithms Vegetation mapping Bufeng-1 (BF-1) global navigation satellite system reflectometry (GNSS-R) land surface clustering soil moisture (SM)

来源：评论

学校读者我要写书评

暂无评论

Cost-Effective Deployment for Fully-Decoupled RAN: A Techno-Economic Approach

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第11期73卷 17007-17023页

作者： Zhao, Jiwei Chen, Jiacheng Cheng, Bo Qian, Bo Xu, Yunting Zhou, Haibo Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310058 Peoples R China Peng Cheng Lab Dept Strateg & Adv Interdisciplinary Res Shenzhen 518000 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China China United Network Commun Corp Guangzhou Branch Guangzhou 510630 Peoples R China Natl Inst Informat Informat Syst Architecture Sci Res Div Tokyo 1018430 Japan

With the advent of the Internet of Everything, the forthcoming 6G networks will necessitate an extensive deployment of base stations to achieve comprehensive network coverage. This development presents a formidable challenge for operators due to the surging expenses associated with each base station and the exponential increase in their quantity. Consequently, formulating strategies to mitigate network deployment costs has become an imperative challenge that operators must address urgently. The fully-decoupled radio access network (FD-RAN), through its innovative separation of uplink and downlink base stations, provides the flexibility for deployment in response to asymmetrical service demands, offering a promising solution to the deployment cost dilemma. In this study, we first propose a techno-economic cost model (TECM) for FD-RAN deployment based on the techno-economic approach and then formulate a cost-minimization problem for decoupled network deployment. To tackle the complexity of massive terminals, we present an adjacent gravity-based optimal link clustering algorithm (AGOC) to transform the significant 0-1 programming problem into a mixed integer program. Leveraging FD-RAN's uplink-downlink decoupling, we decompose the original problem into separate subproblems for uplink and downlink network deployments. For the decoupled problem, we further propose a branch-and-cut based network deployment (BCND) algorithm to solve the two decoupled deployment subproblems, respectively. Simulation results indicate that FD-RANs can offer significant cost benefits when facing differentiated service demands. Furthermore, the analysis reveals that power consumption and rental costs are the primary factors influencing network expenses.

关键词： Costs Base stations Uplink Downlink Wireless networks Investment clustering algorithms Branch and cut clustering FD-RAN network deployment techno-economic

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：