Automatic Intelligent Asymmetry Detection Using Digital Infrared Imaging with K-Means Clustering

Kim, Kwang-Baek; Song, Doo Hoen

doi:10.5391/ijfis.2015.15.3.180

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The use of optimization algorithms reduced the cost of the objective function over that resulting from the employment of standalone clustering techniques. Such an improvement has been noted in other areas as well [45].…”

Section: Resultssupporting

confidence: 68%

Meta-Heuristic Algorithms to Improve Fuzzy C-Means and K-Means Clustering for Location Allocation of Telecenters Under E-Governance in Developing Nations

Gupta

Muttoo

Pal

2019

IJFIS

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The telecenter, popularly known as the rural kiosk or common service center, is an important building block for the improvement of e-governance in developing nations as they help in better citizen engagement. Setting up of these centers at appropriate locations is a challenging task; inappropriate locations can lead to a huge loss to the government and allied stakeholders. This study proposes the use of various meta-heuristic algorithms (particle swarm optimization, bat algorithm, and ant colony optimization) for the improvement of traditional clustering approaches (K-means and fuzzy C-means) used in the facility location allocation problem and maps them for the betterment of telecenter location allocation. A dataset from the Indian region was considered for the purpose of this experiment. The performance of the algorithms when applied to traditional facility location allocation problems such as set-cover, P-median, and the P-center problem was investigated, and it was found that their efficiency improved by 20%-25% over that of existing algorithms.

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Section: Resultssupporting

confidence: 68%

Meta-Heuristic Algorithms to Improve Fuzzy C-Means and K-Means Clustering for Location Allocation of Telecenters Under E-Governance in Developing Nations

Gupta

Muttoo

Pal

2019

IJFIS

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“…The k-means algorithm [16,17] partitions n given vectors x j , j = 1,..., n, into c groups (also called as clusters) G i , i = 1,..., c, and finds cluster centers c i , i = 1,..., c, that minimize the objective function defined as follows:…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

A Clustering-Based Fault Detection Method for Steam Boiler Tube in Thermal Power Plant

Yu¹,

Jang²,

Yoo³

et al. 2016

Journal of Electrical Engineering and Technology

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-System failures in thermal power plants (TPPs) can lead to serious losses because the equipment is operated under very high pressure and temperature. Therefore, it is indispensable for alarm systems to inform field workers in advance of any abnormal operating conditions in the equipment. In this paper, we propose a clustering-based fault detection method for steam boiler tubes in TPPs. For data clustering, k-means algorithm is employed and the number of clusters are systematically determined by slope statistic. In the clustering-based method, it is assumed that normal data samples are close to the centers of clusters and those of abnormal are far from the centers. After partitioning training samples collected from normal target systems, fault scores (FSs) are assigned to unseen samples according to the distances between the samples and their closest cluster centroids. Alarm signals are generated if the FSs exceed predefined threshold values. The validity of exponentially weighted moving average to reduce false alarms is also investigated. To verify the performance, the proposed method is applied to failure cases due to boiler tube leakage. The experiment results show that the proposed method can detect the abnormal conditions of the target system successfully.

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Autofocus alignment of long distance Near-ınfrared laser units for smoke sensing in wide closed area

2021

J Supercomput

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Automatic Intelligent Asymmetry Detection Using Digital Infrared Imaging with K-Means Clustering

Cited by 3 publications

References 17 publications

Meta-Heuristic Algorithms to Improve Fuzzy C-Means and K-Means Clustering for Location Allocation of Telecenters Under E-Governance in Developing Nations

Meta-Heuristic Algorithms to Improve Fuzzy C-Means and K-Means Clustering for Location Allocation of Telecenters Under E-Governance in Developing Nations

A Clustering-Based Fault Detection Method for Steam Boiler Tube in Thermal Power Plant

Autofocus alignment of long distance Near-ınfrared laser units for smoke sensing in wide closed area

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