检索结果-内蒙古大学图书馆

Landslide susceptibility modelling based on AHC-OLID clustering algorithm

ADVANCES IN SPACE RESEARCH 2021年第1期68卷 301-316页

作者： Mao, Yimin Mwakapesa, Deborah S. Wang, Genglong Nanehkaran, Y. A. Zhang, Maosheng Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Jiangxi Peoples R China MLR Key Lab Geohazards Loess Area 438 E Youyi Rd Xian 710054 Shaanxi Peoples R China

The aim of this study is to design a novel machine learning model named Agglomerative Hierarchical clustering algorithm based on Overlapped Interval Divergence distance measure (AHC-OLID), for modelling and assessing landslide susceptibility. The AHC-OLID algorithm is proposed to combat the limitations of many clustering algorithms in modelling and assessing landslide susceptibility, includ-ing: pre-defining the number of clusters;sensitivity to the clusters properties and noisy data;as well as difficulty in processing rainfall data. The proposed algorithm addresses these issues by integrating the traditional Agglomerative Hierarchical clustering (AHC) and Overlapped Interval Divergence distance measure (OLID) methods into landslide susceptibility assessment to enhance its performance. It considers the clusters sizes as well the distances between them and tends to avoid taking into consideration small clusters which are very far from other clusters in the dataset. It is also insensitive to the variation in sizes, variances and shapes of the clusters, which makes the algorithm more advantageous. Besides, the AHC-OLID algorithm makes use of the OLID distance function to process the rainfall data, which takes two factors into consideration: distance between their centers as well as relative size of their overlapped area. Applying this new approach in Baota District, China, produced significant improvement in assessment of landslide susceptibility than previous models. Moreover, the Landslide susceptibility map constructed based on AHC-OLID algorithm can be a useful tool for landslide con-trolling strategies for proper land use and planning. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Landslide Landslide susceptibility Uncertain data clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimization Technology of Combustion Engine Control Based on Swarm Intelligent Optimization algorithm and Improved clustering algorithm

引用

IEEE ACCESS 2024年 12卷 121596-121609页

作者： Wang, Jiadong Zhang, Ming Li, Jinfeng Guoneng Beijing Gas Fired Cogenerat Co Ltd Beijing 100000 Peoples R China

Since control instructions are the fundamental component of thermal power generation, the quality and effectiveness of implementation directly affect the efficiency of the energy system. In order to improve the efficiency of internal combustion engine control, an optimization method of internal combustion engine control based on enhanced clustering algorithm and swarm intelligence optimization algorithm is proposed. The process simplifies the main structure of the gas turbine, divides the combustion engine model into multi-input single-output systems, and introduces the artificial bee colony algorithm to optimize the parameters. A new nectar search formula is constructed by using the global optimal nectar, and the control parameters are calculated by fuzzy logic clustering. The experimental results showed that the modeling error of the load model of internal combustion engine was in the range of -0.47 MW similar to 0.51 MW. When the training iteration speed was tested, the loss value of the research method dropped rapidly in the first 10 iterations. When analyzing the change of the control quantity during load change, if the exhaust flow rate was taken as the control quantity, the control results of the research method was always kept within 5lbm/s error. It demonstrates that this research method can effectively improve the running quality of the internal combustion engine and has a good running efficiency. The research can provide certain technical reference for gas turbine control in thermal power generation.

关键词： Combustion engine control swarm intelligence optimization algorithm clustering algorithm fuzzy logic fireworks algorithm

来源：评论

学校读者我要写书评

暂无评论

Research of energy efficient clustering algorithm for multilayer wireless heterogeneous sensor networks prediction research

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2017年第19期76卷 19345-19361页

作者： Dai Yun-Zhong Luo Ren-Ze Southwest Petr Univ Sch Elect Engn Chengdu 610500 Sichuan Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China

In designing wireless sensor networks of image transmitting, it is important to reduce energy dissipation and prolong network lifetime. This paper presents the research on existing clustering algorithm applied in heterogeneous sensor networks and then puts forward an energy-efficient prediction clustering algorithm, which is adaptive to sensor networks with energy and objects heterogeneous. This algorithm enables the nodes to select the cluster head according to factors such as energy and communication cost, thus the nodes with higher residual energy have higher probability to become a cluster head than those with lower residual energy, so that the network energy can be dissipated uniformly. In order to reduce energy consumption when broadcasting in clustering phase and prolong network lifetime, an energy consumption prediction model is established for regular data acquisition nodes. Simulation results and the application in image clustering show that compared with current clustering algorithms, this algorithm can achieve longer sensor network lifetime, higher energy efficiency, and superior network monitoring quality.

关键词： WSN Energy-efficient Heterogeneous sensor networks clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Plane segmentation and fitting method of point clouds based on improved density clustering algorithm for laser radar

引用

INFRARED PHYSICS & TECHNOLOGY 2019年 96卷 133-140页

作者： Xu, Xiaobin Luo, Minzhou Tan, Zhiying Zhang, Min Yang, Hao Hohai Univ Coll Mech & Elect Engn Changzhou 213022 Peoples R China Hohai Univ Jiangsu Key Lab Special Robot Technol Changzhou 213022 Peoples R China

Plane segmentation and fitting method of point clouds based on improved density clustering algorithm is put forward. We proposed the plane segmentation and fitting framework, which comprises of four steps: coordinate transformation, filtering, coarse segmentation, fine segmentation, plane fitting. The global coordinates of laser radar are deduced. Abnormal points are removed using statistical filtering based on Gaussian distribution. After filtering, Point clouds are segmented roughly adopting improved density clustering algorithm with proposed threshold, which is originally related to the resolution of laser radar. The point clouds are segmented furthermore with normal vector, which could make up for shortcomings, which are over-segmentation and under segmentation. Finally planes are fitted with normal vector and centroid point. The laser radar was designed, and plane segmentations and fitting were carried out. The experimental results show that it is effective and automatic for plane segmentation with proposed method.

关键词： Segmentation Laser radar SLAM clustering algorithm Variable threshold

来源：评论

学校读者我要写书评

暂无评论

Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation

引用

OPTIK 2015年第24期126卷 4817-4822页

作者： Li, Haiyang He, Hongzhou Wen, Yongge Mianyang Normal Univ Sch Math & Comp Sci Mianyang 621000 Sichuan Provinc Peoples R China

K-means clustering is usually used in image segmentation due to its simplicity and rapidity. However, K-means is heavily dependent on the initial number of clusters and easily falls into local falls into local optimum. As a result, it is often difficult to obtain satisfactory visual effects. As an evolutionary computation technique, particle swarm optimization (PSO) has good global optimization capability. Combined with PSO, K-means clustering can enhance its global optimization capability. But PSO also has the shortcoming of easily falling into local optima. This study proposes a new image segmentation algorithm called dynamic particle swarm optimization and K-means clustering algorithm (DPSOK), which is based on dynamic particle swarm optimization (DPSO) and K-means clustering. The calculation methods of its inertia weight and learning factors have been improved to ensure DPSOK algorithm keeping an equilibrium optimization capability. Experimental results show that DPSOK algorithm can effectively improve the global search capability of K-means clustering. It has much better visual effect than K-means clustering in image segmentation. Compared with classic particle swarm optimization K-means clustering algorithm (PSOK), DPSOK algorithm has obvious superiority in improving image segmentation quality and efficiency. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Dynamic particle swarm optimization (DPSO) K-means Image segmentation clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

K-mean clustering algorithm for processing signals from compound semiconductor detectors

引用

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2011年第1期659卷 242-246页

作者： Tada, Tsutomu Hitomi, Keitaro Wu, Yan Kim, Seong-Yun Yamazaki, Hiromichi Ishii, Keizo Tohoku Univ Ctr Cyclotron & Radioisotope Aoba Ku Sendai Miyagi 9808579 Japan Tohoku Univ Grad Sch Engn Dept Quantum Sci & Energy Engn Aoba Ku Sendai Miyagi 9808579 Japan

The K-mean clustering algorithm was employed for processing signal waveforms from TIBr detectors. The signal waveforms were classified based on its shape reflecting the charge collection process in the detector. The classified signal waveforms were processed individually to suppress the pulse height variation of signals due to the charge collection loss. The obtained energy resolution of a Cs-137 spectrum measured with a 0.5 mm thick TIBr detector was 1.3% FWHM by employing 500 clusters. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Thallium bromide Digital signal processing clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on Enterprise Fixed Assets Management Based on K-MEANS clustering algorithm

引用

AGRO FOOD INDUSTRY HI-TECH 2017年第3期28卷 2540-2544页

作者： Ye, Linliang Zhejiang Tech Inst Econ Hangzhou Zhejiang Peoples R China

At present, China's market economy reform is in the continuous development stage, and the market demand faced by Chinese enterprises is becoming more and more complicated. Only the enterprises reasonably integrate and absorb the available effective resources, the operation cost of the enterprise can be reduced to the greatest extent, and then, a position in a difficult market can be obtained. And as a solid support for the corporate assets, the fixed assets have a very important significance. Based on this, in this paper, the enterprise fixed assets management based on K-MEANS clustering algorithm was researched. Firstly, the design of K-MEANS clustering algorithm was introduced, and then, the problems of fixed asset management were introduced. Finally, the specific process of the research of enterprise fixed assets management based on K-MEANS clustering algorithm was introduced in detail. The test results showed that the improved clustering algorithm could meet the higher requirements of clustering results.

关键词： K-MEANS clustering algorithm fixed assets asset management

来源：评论

学校读者我要写书评

暂无评论

New clustering algorithm-based fault diagnosis using compensation distance evaluation technique

引用

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2008年第2期22卷 419-435页

作者： Lei, Yaguo He, Zhengjia Zi, Yanyang Chen, Xuefeng Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

This paper presents a fault diagnosis method of rotating machinery based on a new clustering algorithm using a compensation distance evaluation technique (CDET). A two-stage feature selection and weighting technique is adopted in this algorithm. Feature weights are computed via CDET according to the sensitivity of features and assigned to the corresponding features to indicate their different importance in clustering. Feature weighting highlights the importance of sensitive features and simultaneously weakens the interference of insensitive features. The new clustering algorithm is described and applied to incipient fault and compound fault diagnosis of locomotive roller bearings. The diagnosis result shows the algorithm is able to reliably recognise not only different fault categories and severities but also the compound faults, and demonstrates the superior effectiveness and practicability of the algorithm. Therefore, it is a promising approach to fault diagnosis of rotating machinery.

关键词： feature weighting compensation distance evaluation technique clustering algorithm fault diagnosis

来源：评论

学校读者我要写书评

暂无评论

Linear Diophantine Fuzzy clustering algorithm Based on Correlation Coefficient and Analysis on Logistic Efficiency of Food Products

引用

IEEE ACCESS 2024年 12卷 34889-34902页

作者： Kannan, Jeevitha Jayakumar, Vimala Saeed, Muhammad Alballa, Tmader Khalifa, Hamiden Abd El-Wahed Gomaa, Heba Ghareeb Alagappa Univ Dept Math Karaikkudi 630003 India Univ Management & Technol Dept Math Lahore 54000 Punjab Pakistan Princess Nourah bint Abdulrahman Univ Coll Sci Dept Math POB 84428 Riyadh 11671 Saudi Arabia Qassim Univ Coll Sci Dept Math Buraydah 51452 Saudi Arabia Cairo Univ Fac Grad Studies Stat Res Dept Operat & Management Res Giza 12613 Egypt Inst Management Informat Syst Suez 43332 Egypt

The significance of clustering algorithms lies in their ability to distinguish problems and devise customized solutions. In the broader context of clustering, fuzzy clustering is one of the crucial aspects. In response to the real-world clustering problems, this research suggests a new fuzzy cluster scheme of data under the linear diophantine fuzzy set(LDFS) framework. More precisely, LDF clustering is initiated with the aid of the correlation coefficient( $\mathcal {CC}$ ) and weighted correlation coefficient( $\mathcal {WCC}$ ) for LDFS. Due to their ability to quantify the degree of similarity between two elements, $\mathcal {CC}$ are valuable in clustering problems. The LDF- clustering algorithm comprises a well-integrated algorithm for managing uncertainty and $\mathcal {CC}$ among LDFS. Also, our approach to LDF clustering is compared to existing fuzzy clustering studies to assess its effectiveness. Since LDFS broadens the score space, the experimental evaluation of our proposed scheme enables Decision makers(DM) to freely select their score values. The theme of this study is to impart the commencement of LDF-clustering analysis and attempt to apply $\mathcal {CC}$ to the clustering problem. An interpretative example provides the analysis of the logistic efficiency of food products by employing an LDF-clustering algorithm.

关键词： LDFS clustering algorithm correlation coefficient logistics food products optimization decision making algorithms

来源：评论

学校读者我要写书评

暂无评论

Distributed Soft clustering algorithm for IoT Based on Finite Time Average Consensus

引用

IEEE INTERNET OF THINGS JOURNAL 2021年第21期8卷 16096-16107页

作者： Yu, Hao Chen, Hui Zhao, Shengjie Shi, Qingjiang Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Tongji Univ Sch Math Sci Shanghai 200092 Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China

clustering is a common technique for statistical data analysis and it has been widely used in many fields. This article investigates data clustering over the Internet-of-Things (IoT) network. Facing the IoT network challenges, including data volume, communication latency, and information security, we here propose a distributed soft clustering algorithm for the IoT environments where each IoT node may have data from multiple clusters. Considering that the main task of soft clustering is to compute each cluster center in a weighted averaging fashion, our distributed clustering method resorts to an efficient finite-time average-consensus algorithm. Moreover, to make the distributed clustering algorithm more stable and be able to escape from some bad local optimum, we propose a distributed deterministic initialization method based on data variance partitioning. Experiments show that the proposed distributed soft clustering algorithm can offer the same performance as its centralized counterpart in terms of both convergence and clustering quality. Besides, unlike most clustering methods relying on probabilistic initialization, our algorithm could provide stable clustering quality which makes it more suitable for IoT networks. A real-world case study about the clustering analysis for distributed data sets collected by environmental monitoring stations is offered, which shows the potential of our algorithms in practical applications.

关键词： Average consensus clustering algorithm distributed algorithm fuzzy C-means Internet of Things (IoT) K-means

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：