Thermographic image processing analysis in a solar concentrator with hard C-means clustering

被引:0
|
作者
Flores, Marco A. [1 ]
Serrano, Fernando E. [1 ]
Cadena, Carlos [2 ]
Alvarez, Jose C. [3 ]
机构
[1] Univ Nacl Autonoma Honduras UNAH, Inst Invest Energia IIE, Tegucigalpa, Honduras
[2] Univ Salta, Inst Energia No Convenc, Salta, Argentina
[3] Univ Peruana Ciencias Aplicadas, Lima, Peru
关键词
Digital image processing; Thermographic image; Analysis; Renewable energies; Solar energy; Clustering;
D O I
10.1016/j.egyr.2023.05.261
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Thermographic measurements are used to determine the temperatures reached by the focus of a modified Fresnel solar concentrator, where a container is placed to take advantage of this energy. The three steps of this investigation are: (i) the edges of each thermographic image are obtained by means of a Butterworth low-pass filter, (ii) the temperature grid in the solar concentrator is obtained by means of a feature extraction algorithm, and finally: (iii) the classification will be done through a C-means hard clustering algorithm selecting the center of each cluster to accurately find the temperature region to generate the isotherms and extract the temperatures with this algorithm. With the hard C-means algorithm, isotherm level curves and temperature graphs are obtained. Subsequently, two analyzes are carried out to validate that the original unprocessed thermographic images correspond spatially and in their spectrum with the processed images, with the aim of corroborating the acuteness of the digital image processing methodology implemented in this research. Finally, a correlation analysis is performed to validate the temperature matches of the original thermographic images. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:312 / 321
页数:10
相关论文
共 50 条
  • [1] A genetic hard c-means clustering algorithm
    Meng, L
    Wu, QH
    Yong, ZZ
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2002, 9 (03): : 421 - 438
  • [2] Fuzzy Approaches To Hard c-Means Clustering
    Runkler, Thomas A.
    Keller, James M.
    2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2012,
  • [3] Hard C-means clustering for voice activity detection
    Gorriz, J. M.
    Ramirez, J.
    Lang, E. W.
    Puntonet, C. G.
    SPEECH COMMUNICATION, 2006, 48 (12) : 1638 - 1649
  • [4] Diverse fuzzy c-means for image clustering
    Zhang, Lingling
    Luo, Minnan
    Liu, Jun
    Li, Zhihui
    Zheng, Qinghua
    PATTERN RECOGNITION LETTERS, 2020, 130 (130) : 275 - 283
  • [5] A Modified Brainstorm Optimization for Clustering Using Hard c-Means
    Roy, Reetika
    Anuradha, J.
    2015 IEEE INTERNATIONAL CONFERENCE ON RESEARCH IN COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (ICRCICN), 2015, : 202 - 207
  • [6] Hard versus fuzzy c-means clustering for color quantization
    Wen, Quan
    Celebi, M. Emre
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2011,
  • [7] Hard versus fuzzy c-means clustering for color quantization
    Quan Wen
    M Emre Celebi
    EURASIP Journal on Advances in Signal Processing, 2011
  • [8] Multi-view alternative hard c-means clustering
    Liu, Zhe
    Zhu, Sijia
    Lyu, Shen
    Letchmunan, Sukumar
    INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, 2024,
  • [9] A Fast Fuzzy C-means Clustering Algorithm Based on Soft and Hard Clustering
    Ji NaiHua
    Yao Huiping
    Wang Yingjie
    PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON INFORMATION, ELECTRONIC AND COMPUTER SCIENCE, VOLS I AND II, 2009, : 638 - 641
  • [10] Performance Analysis of Fuzzy C-Means Clustering Methods for MRI Image Segmentation
    Choudhry, Mahipal Singh
    Kapoor, Rajiv
    TWELFTH INTERNATIONAL CONFERENCE ON COMMUNICATION NETWORKS, ICCN 2016 / TWELFTH INTERNATIONAL CONFERENCE ON DATA MINING AND WAREHOUSING, ICDMW 2016 / TWELFTH INTERNATIONAL CONFERENCE ON IMAGE AND SIGNAL PROCESSING, ICISP 2016, 2016, 89 : 749 - 758