检索结果-内蒙古大学图书馆

Optimizing Cluster Head Selection for E-Commerce-Enabled Wireless Sensor Networks

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1640-1647页

作者： Gupta, Dinesh Ramesh, Janjhyam Venkata Naga Kumar, Mungara Kiran Alghayadh, Faisal Yousef Dodda, Sarath Babu Ahanger, Tariq Ahamed Ilkhamova, Yodgorkhon Karumuri, Srinivasa Rao I K Gujral Punjab Tech Univ Dept CSE Kapurthala 144603 India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522502 India GITAM Univ Sch Technol Dept CSE Hyderabad 502329 India AlMaarefa Univ Coll Appl Sci Comp Sci & Informat Syst Dept Riyadh 7166 Saudi Arabia Cent Michigan Univ Dallas TX 75009 USA Prince Sattam Bin Abdulaziz Univ Dept Management Informat Syst CoBA Al Kharj 11942 Saudi Arabia Tashkent Inst Finance Dept E Commerce & Digital Econ 60A Tashkent Uzbekistan Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn Pilani 333031 Rajasthan India

In wireless sensor networks, the existing clustering head selection protocol is unworkable, leading to uneven network loads and a shorter network lifetime. To improve energy-effective wireless sensor networks (WSN) for e-commerce applications, a cluster head selection technique called CIBOA (Cluster Head Selection using Integrated Butterfly Optimization technique) has been created to address this problem. The current clustering head selection mechanism in wireless sensor networks is ineffective, which results in unequal network loads and a shorter network lifetime. The Butterfly Optimization Algorithm (BOA) has undergone significant modifications to enhance performance. These enhancements significantly boost the optimization speed and accuracy of BOA, thereby strengthening its search capabilities. During the process of selecting cluster heads (CH), a novel fitness function has been developed. This function considers variables such as remaining energy levels, the distance between nodes and base stations, and the average distance between neighboring nodes. The results show that CIBOA comprehensively considers factors like node energy and distance. This holistic approach results in a reduction in overall network operational time, making it particularly suitable for Energy-efficient networks of wireless sensors, especially in the context of e-commerce applications.

关键词： Wireless sensor networks Optimization clustering algorithms Base stations Energy consumption Routing Electronic commerce Cluster based routing system artificial intelligence consumer behavior wireless sensor network

来源：评论

学校读者我要写书评

暂无评论

TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus clustering

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第4期19卷 2512-2522页

作者： Gan, Yanglan Li, Ning Guo, Cheng Zou, Guobing Guan, Jihong Zhou, Shuigeng Donghua Univ Sch Comp Sci & Technol Shanghai 200051 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 200092 Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China

Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages.

关键词： Trajectory clustering algorithms Partitioning algorithms Gene expression Diseases Computer science Manifolds Trajectory inference consensus clustering gene partition single-cell transcriptome

来源：评论

学校读者我要写书评

暂无评论

Improving clustering-Based Forecasting of Aggregated Distribution Transformer Loadings With Gradient Boosting and Feature Selection

引用

IEEE ACCESS 2022年 10卷 443-455页

作者： Rouwhorst, George Duque, Edgar Mauricio Salazar Nguyen, Phuong H. Slootweg, Han Eindhoven Univ Technol Dept Elect Engn NL-5612 AP Eindhoven Netherlands Enexis Netbeheer BV Asset Management NL-5223 MB Shertogenbosch Netherlands

Load forecasting is more important than ever to enable new monitor and control functionalities of distribution networks aiming to mitigate the impact of the energy transition. Load forecasting at medium voltage (MV) level is becoming more challenging, because these load profiles become more stochastic due to the increasing penetration of photovoltaic (PV) generation in distribution networks. This work combines medium to low voltage (MV/LV) transformer loadings measured with advanced metering infrastructure (AMI) and machine learning (ML) algorithms to propose a new clustering based day-ahead aggregated load forecasting approach. This four-step approach improves the day-ahead load forecast of a city. First, MV/LV transformer loadings are clustered based on the shape of their load pattern. Second, a gradient boosting algorithm is used to forecast the load of each cluster and calculate the related feature importance. Third, feature selection is applied to improve the forecast accuracy of each cluster. Finally, the day-ahead load forecast of all clusters are aggregated. The case study presented uses 519 measured MV/LV transformer loadings in a city to perform 30 day-ahead load forecasts. Compared against the day-ahead aggregated load forecast without clustering, the average normalized root mean squared error (NRMSE) reduced 12.7 %, the average mean absolute percentage error (MAPE) reduced 18.2 % and the average Pearson Correlation Coefficient (PCC) increased 0.37 %. The 95 % confidence interval of the difference between the average NRMSE, MAPE and PCC without clustering and with the proposed method indicates a statistically significant improvement in accuracy.

关键词： Load forecasting Transformers Forecasting clustering algorithms Loading Distribution networks Predictive models Aggregated load forecast clustering day-ahead distribution network feature selection gradient boosting

来源：评论

学校读者我要写书评

暂无评论

Spatial Weighted Robust clustering of Multivariate Time Series Based on Quantile Dependence With an Application to Mobility During COVID-19 Pandemic

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第9期30卷 3990-4004页

作者： Lopez-Oriona, Angel D'Urso, Pierpaolo Vilar, Jose A. Lafuente-Rego, Borja Univ A Coruna Res Ctr ICT CITIC Dept Math Res Grp MODES La Coruna Spain Sapienza Univ Rome Dept Social Sci & Econ I-00185 Rome Italy

In this article, a fuzzy clustering model for multivariate time series based on the quantile cross-spectral density and principal component analysis is extended by including: 1) a weighting system which assigns a weight to each principal component in accordance with its importance concerning the underlying clustering structure and 2) a penalization term allowing to take into account the spatial information. The iterative solutions of the new model, which employs the exponential distance in order to gain robustness against outlying series, are derived. A simulation study shows that the weighting system substantially enhances the effectiveness of the former approach. The behavior of the extended model in terms of the spatial penalization term is also analyzed. An application involving multivariate time series of mobility indicators concerning COVID-19 pandemic highlights the usefulness of the proposed technique.

关键词： Time series analysis clustering algorithms COVID-19 Time measurement Principal component analysis Linear programming Stochastic processes clustering COVID-19 multivariate time series PCA quantile cross-spectral density spatial statistics weighting

来源：评论

学校读者我要写书评

暂无评论

A Deep Embedded clustering Algorithm for the Binning of Metagenomic Sequences

引用

IEEE ACCESS 2022年 10卷 54348-54357页

作者： Bao, Huynh Quang Vinh, Le Van Hoai, Tran Van Ho Chi Minh City Univ Technol HCMUT Fac Comp Sci & Engn Ho Chi Minh City 70000 Vietnam Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City 70000 Vietnam HCMC Univ Technol & Educ Fac Informat Technol Ho Chi Minh City 70000 Vietnam

The study of metagenomic sequences brings a deep understanding of microbial communities. One of the crucial steps in metagenomic projects is to classify sequences into different organisms, named the binning problem. In the emerging methods for classification, deep learning is a potential technology to be applicable with high accuracy. However, it is well-known that reference databases, which are highly required by deep learning based methods, are not always available. As a result, some existing binning solutions have applied unsupervised learning processes, but utilizing the strength of deep learning in an unsupervised model is still a challenging problem. This work proposes a binning algorithm for metagenomic sequences, called MetaDEC, which applies a deep unsupervised learning approach. By following the two-phase paradigm, the algorithm firstly divides sequences into groups of overlapping sequences. The groups are then classified into clusters using an adversarial deep embedded clustering technique. Experimental results show that MetaDEC achieves competitive performance compared to existing methods on both simulated and real metagenomic data.

关键词： Genomics clustering algorithms Classification algorithms Bioinformatics Feature extraction Sequential analysis Organisms Algorithm clustering deep learning metagenomics DNA sequence

来源：评论

学校读者我要写书评

暂无评论

Web Services clustering via Exploring Unified Content and Structural Semantic Representation

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第4期19卷 4082-4096页

作者： Kang, Guosheng Liu, Jianxun Xiao, Yong Cao, Yingcheng Cao, Buqing Shi, Min Hunan Univ Sci & Technol Hunan Prov Key Lab Serv Comp & Novel Software Tech Xiangtan 411199 Peoples R China Hunan Univ Sci & Technol Dept Comp Sci & Engn Xiangtan 411199 Peoples R China Washington Univ Sch Med Dept Genet St Louis MO 63110 USA

clustering Web services can improve the quality and efficiency of service discovery and management within a service repository. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex and heterogeneous service relationship network. The rich network relations inherently reflect either positive or negative clustering association between Web services, which can be a strong supplement to service semantics for characterizing functional affinities between Web services. In this paper, we propose to cluster Web services by utilizing both description documents and the structural information from the service relationship network. We first learn the content semantic information from service description documents based on the widely used Doc2vec model, and meanwhile, learn the structural semantic information from the service relationship network based on a network representation learning algorithm. Then, we propose to pretrain the content and structural semantic information to obtain the most relevant and unified features through training a service classification model with partially labeled data. Finally, a spectral clustering algorithm is utilized for Web services clustering based on the above unified features with preserved content and structural semantics. Therefore, the proposed services clustering approach takes advantage of both service content semantic and service network structure semantic based similarity between services. Extensive experiments are conducted on a real-world dataset from ProgrammableWeb, composed of 12919 Web API services. Experimental results demonstrate that our approach yields an improvement of 4.78% in precision and 5.4% in recall over the state-of-the-art method.

关键词： Web services Semantics clustering algorithms Task analysis Tagging Quality of service Mashups Web service services clustering service network network embedding semantic mining

来源：评论

学校读者我要写书评

暂无评论

User clustering and Resource Allocation in Hybrid NOMA-OMA Systems Under Nakagami-m Fading

引用

IEEE ACCESS 2022年 10卷 38709-38728页

作者： Mahmoudi, Ali Abolhassani, Bahman Razavizadeh, S. Mohammad Nguyen, Ha H. Iran Univ Sci & Technol Sch Elect Engn Tehran *** Iran Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada

In this paper, we tackle the problem of optimizing user clustering, power, and resource (time slot or bandwidth) allocation in the downlink of a hybrid non-orthogonal multiple access (NOMA)-orthogonal multiple access (OMA) system. In such a system, users are organized into several clusters under one of the following scenarios: (1) fixed cluster size, (2) fixed number of clusters, and (3) variable number of clusters and variable cluster size. A power domain NOMA (PD-NOMA) scheme is used in each cluster, while OMA is employed for allocating resources to different clusters. The goal is to maximize the minimum success probability (which is equivalent to minimizing the maximum outage probability) among all users to guarantee fairness. We prove that at the optimal solution, all users have the same success probability, which is called the common success probability (CSP). Then, we propose an efficient algorithm for finding the optimal CSP and cluster resource allocation factors simultaneously. The optimal power allocation factors and the optimal decoding order of users in each cluster are then derived in closed-form expressions based on the obtained optimal CSP. Simulation results show considerable performance gains by the proposed scheme, compared to existing schemes in terms of fairness, the minimum success probability of users, and the sum throughput.

关键词： Resource management NOMA clustering algorithms Heuristic algorithms Decoding Closed-form solutions Throughput Hybrid NOMA-OMA user clustering power allocation resource allocation fairness

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

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

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

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：