An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

Hu, Fankui; Chen, Haibing; Wang, Xiaofei

doi:10.1109/access.2019.2963444

Cited by 32 publications

(19 citation statements)

References 37 publications

(50 reference statements)

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“…By consulting relevant literature in China and abroad, combined with the actual situation of power grid construction projects, the risk factors affecting the cost of power grid construction projects are comprehensively analyzed and the cost risk of power grid construction projects is obtained, which mainly includes two types of risk factors: planned development risk and engineering construction risk [14]. Among them, planned development risks include infrastructure project planning risks, infrastructure project research risks, infrastructure project approval risks, and infrastructure project change risks.…”

Section: Identification Of Cost Risk Of Power Grid Constructionmentioning

confidence: 99%

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

Zhong

Yin

2022

Journal of Control Science and Engineering

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In order to effectively control the cost risk of power grid construction projects, the author proposes a cost risk management system for power grid construction projects based on the fuzzy clustering algorithm. The system introduces the fuzzy clustering maximum tree algorithm, and by constructing a mathematical model, combined with the empirical analysis, the key risk factors in the cost risk of power grid construction projects are identified. Through the analysis, it can be concluded that the key risks in the cost risk of power grid construction projects are the planning risks of infrastructure projects, the research risks of infrastructure projects, and the cost risks of infrastructure projects. Experimental results show that combined with expert experience and the actual situation of power grid engineering, the classification result at threshold λ = 0.785 becomes more realistic. At this λ level, 6 risk factors are grouped into 4 categories as follows: class I x 1 , x 2 , x 4 , class II x 3 , class III x 5 , and class IV x 6 . Through research, the identification of key risks can enable project managers to control the cost of power grid construction projects, targeted, so that risks can be minimized and investment returns can be improved.

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Section: Identification Of Cost Risk Of Power Grid Constructionmentioning

confidence: 99%

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

Zhong

Yin

2022

Journal of Control Science and Engineering

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“…Based on the introduction of the DCLGMS algorithm, the time complexity of DCLGMS relies on five parts: (1) The time for detecting noise is O (n), and the number of points is n. (2) The time for finding the natural neighbor structure is O (n * logn) by utilizing KD-Tree. (3) There are three steps to extract local core points: find the best value of k by using natural neighbors, calculate the LGMS of each point, and assign noncore points.…”

Section: The Complexity Analysismentioning

confidence: 99%

“…Cluster analysis has been widely applied in many fields, such as pattern recognition [1], image segmentation [2], document clustering [3], intrusion detection [4], and market research [5]. As a kind of data mining technology [6], cluster analysis has become the focus of discussion and research, whose heat is on the rise.…”

Section: Introductionmentioning

confidence: 99%

A novel clustering algorithm based on the gravity-mass-square ratio and density core with a dynamic denoising radius

Zhang

Yu-qin

et al. 2021

Appl Intell

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Cluster analysis has been widely used in pattern recognition, image segmentation, document clustering, intrusion detection, market research, and so on. There have been many clustering algorithms for cluster analysis now. However, most of them are unsuitable for complex patterns with large variations in density and manifold structure. Also, they are not robust to noises. To overcome the above deficiencies, we propose a novel clustering algorithm (called DCLGMS) based on the gravity-masssquare ratio (LGMS) and density core (DCore) with a dynamic denoising radius. Our algorithm can obtain the correct clustering result in arbitrarily shaped datasets excluding noises without parameter settings. In this algorithm, DCore can maintain the shapes of clusters, and LGMS can help to extract local core points. In addition, a dynamic denoising radius is beneficial to detect noise points caused by arbitrary reasons and avoid interference at the beginning of the algorithm. The results of experiments on both synthetic datasets and real datasets show that our algorithm has excellent performance.

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“…For the FCM algorithm [9][10][11][12], the rate of convergence is always decided by the distribution characteristics of the data. The FCM is noise sensitive, because the distribution of the image data is affected by the noise, which leads to two problems.…”

Section: Step 2 Morphological Operations and Fcmmentioning

confidence: 99%

An Efficient Segmentation Technique for Different Medical Image Modalities

Mahmoud

El‐Shafai

Taha

et al. 2020

Menoufia Journal of Electronic Engineering Research

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In this paper, a study of the segmentation of medical images is presented. The paper provides a solid introduction to image enhancement along with image segmentation fundamentals. Firstly, the local spatial information of the image is enhanced with morphological operations to ensure noise-immunity and image detailprotection. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, fuzzy c-means (FCM) clustering is used with modification of membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed technique is very simple to implement and significantly fast, since it is not necessary to compute the distance between the neighboring pixels and the cluster centers. It is also efficient when dealing with noisy images because of its ability to improve membership partition matrix efficiently. Experimental results performed on different medical image modalities illustrate that the proposed technique can achieve good results, as well as short time and efficient image segmentation.

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An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

Cited by 32 publications

References 37 publications

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

A novel clustering algorithm based on the gravity-mass-square ratio and density core with a dynamic denoising radius

An Efficient Segmentation Technique for Different Medical Image Modalities

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