检索结果-内蒙古大学图书馆

combination Evaluation Method of fuzzy c-Mean clustering Validity Based on Hybrid Weighted Strategy

IEEE AccESS 2021年 9卷 27239-27261页

作者： Wang, H. Y. Wang, J. S. Wang, G. Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function. Research shows that no clustering validity function can handle any data and always perform better than other indexes. Therefore, a hybrid weighted combination evaluation method based on fuzzy c-means (FcM) clustering validity functions was proposed. The weighting method combines expert weighting with information entropy weighting to improve the subjective factor influence of expert weighting and the shortcoming of information entropy weighting in the value judgment of each clustering validity function. Four clustering validity function combination methods of linear, exponential, logarithm and proportion was studied. Finally, the proposed fuzzy clustering validity evaluation method is verified by experiments on artificial data sets and UcI data sets. The experimental results show that the proposed fuzzy clustering validity evaluation method can overcome the shortcoming of single clustering validity function, and can get the optimal clustering number more accurately for different data sets.

关键词： clustering algorithms Indexes Partitioning algorithms Information entropy Licenses Entropy classification algorithms fuzzy c-means clustering algorithm clustering validity function weighted combination expert weighting information entropy weighting

来源：评论

学校读者我要写书评

暂无评论

Experimental study and acoustic emission monitoring on damage mechanism of stud shear connectors

引用

STRUcTURES 2024年 63卷

作者： Yao, Yadong Si, Haopeng Liu, Ke Ni, Tianmeng Gao, Lu Inner Mongolia Univ Inst Transportat Hohhot 010070 Peoples R China Inner Mongolia Univ Inner Mongolia Engn Res Ctr Testing & Strengthenin Hohhot 010020 Peoples R China

Stud shear connectors are popular connectors used in steel-concrete composite structures. Assessing the condition and damage process of studs embedded in concrete is difficult. This paper proposes to assess the condition and damage process of stud shear connectors using an acoustic emission (AE) technique. Nine steel-concrete push-out specimens with stud shear connectors were tested, and AE sensors were used to monitor the damage process and explore the damage mechanism. The test results showed that the specimens failed due to stud fracture, accompanied by concrete cracks. The damage process included three primary stages featuring different characteristics of AE energy. An unsupervised machine learning algorithm called fuzzy c-means clustering was used to analyze the AE signals, and AE events were classified into three clusters, and various damage modes (i.e., concrete cracks and steel-concrete interface debonding, stud fracture, and concrete crushing) were identified based on the parameter characteristics and positions of AE events.

关键词： Acoustic emission (AE) Failure mode fuzzy c-means clustering algorithm Steel-concrete composite structure Stud shear connectors

来源：评论

学校读者我要写书评

暂无评论

Novel model for risk assessment of shield tunnelling in soil-rock mixed strata

引用

AcTA GEOTEcHNIcA 2024年第8期19卷 5111-5123页

作者： Zhou, Xin-Hui Zhou, Annan Shen, Shui-Long Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Dept Civil Engn Shanghai 200240 Peoples R China Shantou Univ Coll Engn Dept Civil Engn & Smart Cites MOEKey Lab Intelligent Mfg Technol Shantou 515063 Guangdong Peoples R China Royal Melbourne Inst Technol RMIT Sch Engn Discipline Civil & Infrastruct Engn Melbourne Vic 3001 Australia

Shield tunnelling presents numerous potential risks particularly in complex geological environments. In this study, we propose a novel fuzzy model for assessing the risk of tunnelling in soil-rock mixed strata. The proposed model incorporates the fuzzy setpair analysis (FSPA) method into fuzzy c-means (FcM) clustering to overcome the limitations of conventional data normalisation. Data pertaining to tunnelling machine, deformation, and vibration are employed to construct an index system using mutual information algorithms for feature selection. The intercriteria importance though intercriteria correlation is employed to weight the indicators, and the FSPA method is adopted to calculate the connection number. Subsequently, the results are classified by the FcM with a modified objective function that considers the importance of risk indicators to derive the risk level of each ring in real time. The proposed model is applied to a case study of a shield tunnelling project in Guangzhou, china. The analysis results indicate a higher risk level from Ring 1572 onwards, which necessitates a judicious regulation of the thrust force and earth pressure. This novel method provides a practical and reliable tool for guiding risk decisions during the tunnel construction.

关键词： construction safety fuzzy c-means clustering algorithm fuzzy set pair analysis Risk assessment Shield tunnel

来源：评论

学校读者我要写书评

暂无评论

Exponent and Logarithm component-Wise construction Method of FcM clustering Validity Function Based on Subjective and Objective Weighting

引用

INTERNATIONAL JOURNAL OF fuzzy SYSTEMS 2023年第2期25卷 647-669页

作者： Liu, Jia-Xu Wang, Jie-Sheng Wang, Guan Zhao, Xiao-Rui Wang, Hong-Yu Jin, Di Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Inspect & Certificat Ctr Liaoning Anshan 114051 Peoples R China

The cluster validity function is used to evaluate the quality of the cluster results, and giving the exact number of initial cluster categories will rationalize the cluster results. Most single cluster validity functions and combined cluster validity functions generally have strong subjective problems, which also increases the burden on decision analysts and have great limitations in applications. To overcome the shortcomings of these clustering validity functions and improve the accuracy of the optimal cluster category classification for the datasets, based on the clustering performance evaluation components, a validity functional component construction method based on the exponential and log form was proposed. The weighting method adopts the combination of expert empowerment and standard separation method to combine the five weights so as to obtain 52 different fuzzy clustering validity functions. Then, based on the fuzzy c-mean (FcM) clustering algorithm, the performance analysis are carried out by using multiple data sets. Experimental simulation of these functions are proceeded on six commonly used UcI datasets. A clustering validity function with the simplest structure and the best classification effect was selected by comparison. Finally, this function is compared with 8 typical single clustering validity functions and four common clustering validity combination evaluation methods on 8 UcI data sets. Through experimental simulation, the proposed validity function is compared in processing data sets, but also has strong scientific theoretical basis. Thus, the feasibility and effectiveness of the proposed clustering validity function construction method are proved.

关键词： fuzzy c-means clustering algorithm clustering validity function Subjective and objective weighting component-wise design

来源：评论

学校读者我要写书评

暂无评论

fuzzy c-means clustering Interval Type-2 cerebellar Model Articulation Neural Network for Medical Data classification

引用

IEEE AccESS 2019年 7卷 20967-20973页

作者： Le, Tien-Loc Yuan Ze Univ Dept Elect Engn Taoyuan 32003 Taiwan Lac Hong Univ Dept Elect Elect & Mech Engn Bien Hoa 810000 Vietnam

This paper presents a fuzzy c-means clustering interval type-2 cerebellar model articulation neural network (FcM-IT2cMANN) method to help physicians improve diagnostic accuracy. The proposed method combines two classifiers, in which the IT2cMANN is the primary classifier and the fuzzy c-means algorithm is the pre-classifier. First, the data are divided into nc groups using the pre-classifier, and then, the main classifier is applied to determine whether the sample is in a healthy or diseased state. Implementing the gradient descent method, the adaptive laws for updating the FcM-IT2cMANN parameters are derived. Furthermore, the system convergence is proven by the Lyapunov stability theory. Finally, the classification of breast cancer and liver disease datasets from the University of california at Irvine is conducted to illustrate the effectiveness of the proposed classifier.

关键词： classification problem cerebellar model articulation neural network fuzzy c-means clustering algorithm medical diagnosis

来源：评论

学校读者我要写书评

暂无评论

Design of optimal power supply parameters for industrial expansion

引用

JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2023年第3期44卷 3751-3762页

作者： Wu, Guanghua Li, Hongsheng Li, Hongyu Guo, Shiping Ma, Wenjian Dong, Jing State Grid Hebei Elect Power Co Ltd Mkt Serv Ctr Shijiazhuang 050000 Hebei Peoples R China

The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal power supply parameters of the business expansion installation based on grey correlation degree and fuzzy c-means clustering algorithm is proposed. Firstly, the grey correlation degree is used to process the optimal power supply parameter data of industrial expansion and installation, and the parameters of fuzzy c-means clustering algorithm are set. On this basis, an intelligent management system for the optimal power supply process of industrial expansion and installation is constructed, and the system development conditions are set up;According to the four business links of project reserve, business acceptance, collaborative operation and performance evaluation, the customer business expansion and installation function module is constructed, so as to realize the calculation of the optimal power supply line of the business expansion and installation and complete the research on the optimal power supply parameters. The experimental results show that the output stability, output throughput performance and parameter optimization ability of this method for the line impedance characteristic control of the power supply of the industrial expansion device are good and are always on the rise. At 3 cm, the output throughput reaches 1.9%, and the parameter analysis ability can reach 350 pixels, which has certain application value.

关键词： Grey correlation degree fuzzy c-means clustering algorithm business expansion newspaper installation Optimal power supply parameters

来源：评论

学校读者我要写书评

暂无评论

Employment Quality EvaluationModel Based on Hybrid Intelligent algorithm

引用

computers, Materials & continua 2023年第1期74卷 131-139页

作者： Xianhui Gu Xiaokan Wang Shuang Liang Henan Mechanical Electrical Vocational College Xinzheng Hennan451191China University of Florence Firenze50041Italy

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy c-means(FcM)is ***,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation *** test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison *** comparison test verifies the superiority of the model.

关键词： Employment quality fuzzy c-means clustering algorithm grey correlation analysis method evaluation model index system comparative test

来源：评论

学校读者我要写书评

暂无评论

Research on chinese Audio and Text Alignment algorithm Based on AIc-FcM and Doc2Vec

引用

AcM TRANSAcTIONS ON ASIAN AND LOW-RESOURcE LANGUAGE INFORMATION PROcESSING 2023年第3期22卷 1-22页

作者： chen, Keliang Huang, Jianming cui, Yansong Ren, Weizheng Beijing Univ Posts & Telecommun Sch Elect Engn 10 Xitucheng Rd Beijing 100876 Peoples R China

"Audiobook" is a multimedia-based reading technology that has emerged in recent years. Realizing the alignment of e-book text and book audio is the most important part of its processing. This article describes an audio and text alignment algorithm using deep learning and neural network technology to improve the efficiency and quality of audiobook production. The algorithm first uses dual-threshold endpoint detection technology to segment long audio into short audio with sentence dimensions and recognizes it as short text. The threshold is calculated by AIc-FcM optimized based on simulated annealing genetic algorithm. Then the algorithm uses Doc2vec optimized by the threshold prediction method based on the average length of the short text to calculate the text similarity. Finally, proofread and output the text sequence and audio segment aligned in the time dimension to meet the needs of audiobook production. Experiments show that compared to traditional audio and text alignment algorithms, the proposed algorithm is closer to the ideal segmentation result in long audio segmentation, and the alignment effect is basically the same as Doc2vec and the time complexity is reduced by about 35%.

关键词： Audio and text alignment fuzzy c-means clustering algorithm akaike information criterion Doc2vec dual threshold endpoint detection

来源：评论

学校读者我要写书评

暂无评论

An enhanced distributed differential evolution algorithm for portfolio optimization problems

引用

ENGINEERING APPLIcATIONS OF ARTIFIcIAL INTELLIGENcE 2023年第1期121卷

作者： Song, Yingjie Zhao, Gaoyang Zhang, Bin chen, Huayue Deng, Wuquan Deng, Wu Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Shandong Technol & Business Univ Sch Stat Yantai 264005 Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China Chongqing Univ Cent Hosp Dept Endocrinol Key Lab Biorheol Sci & TechnolMinist Educ Chongqing 400014 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

The population structure of differential evolution (DE) algorithm cannot maintain the diversity of the population to the greatest extent and help the population avoid to fall into the local optima in time. In this paper, a co-evolutionary multi-swarm adaptive differential evolution algorithm, namely EcMADE is proposed to solve the premature convergence and search stagnation. First of all, in terms of population structure, based on the parallel distributed framework, EcMADE randomly and evenly divides the population into exploration subpopulation, development subpopulation, and auxiliary subpopulation, and introduces an adaptive information exchange mechanism so that subpopulations can escape local optima in time. Then, a multi-operator parallel search strategy is proposed to keep population diversity and meet the optimization needs of different problems. Finally, an adaptive adjustment mechanism of control parameters is developed, through recent elite parameter archive and weight distribution to fully mine successful parameter information, and generate control parameters with a high success rate for the current evolutionary stage. In order to prove the effectiveness of the EcMADE, 10 test functions and portfolio optimization problem are selected in here. The experiment results show that the EcMADE can effectively solve these test functions, the accuracy and efficiency is superior to those of two classical DE algorithms. The actual application results show that the EcMADE can significantly improve the ability of portfolio to resist extreme losses, which proves the effectiveness and feasibility of the EcMADE once again. The EcMADE has better optimization performance by comparing with some well-known algorithms in term of the solution quality, robustness and space distribution. It provides a new algorithm for solving complex optimization problems.

关键词： Adaptive differential evolution algorithm Multi-population parallel distribution structure Parallel search Portfolio optimization fuzzy c-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Short-term forecasting method for lighting energy consumption of large buildings based on time series analysis

引用

INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2023年第3期45卷 220-232页

作者： Li, Yanpeng Zhang, Guofeng Univ Sci & Technol Liaoning Dept Environm Design Anshan Liaoning Peoples R China Univ Sci & Technol Liaoning Dept Architecture Anshan Liaoning Peoples R China

In order to overcome the problems of high data noise, low prediction accuracy and long prediction time in the traditional short-term prediction method of lighting energy consumption of large buildings, a short-term prediction method of lighting energy consumption of large buildings based on time series analysis is proposed in this paper. The improved threshold function is used to denoise the data, and the fuzzy c-means clustering algorithm is used to cluster the denoised data. The time series analysis method is used to construct the self-excitation threshold autoregressive model. When the model parameters are optimal, the clustered data are input into the model to output the short-term prediction results of lighting energy consumption of large buildings. The experimental results show that compared with the traditional method, the average data noise of this method is 12.3 dB, the prediction accuracy remains above 94% and the average prediction time is only 57 ms.

关键词： time series analysis large buildings lighting energy consumption short-term forecast fuzzy c-means clustering algorithm self-excitation threshold autoregressive model particle swarm optimisation 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：