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
  • [21] Two clustering algorithms for data with tolerance based on hard c-means
    Hamasuna, Yukihiro
    Endo, Yasunori
    Hasegawa, Yasushi
    Miyamoto, Sadaaki
    2007 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-4, 2007, : 687 - +
  • [22] On Hard and Fuzzy c-Means Clustering with Conditionally Positive Definite Kernel
    Kanzawa, Yuchi
    Endo, Yasunori
    Miyamoto, Sadaaki
    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011), 2011, : 816 - 820
  • [23] Improved fuzzy C-means clustering for medical image segmentation
    Zhang, Xiaofeng
    Sun, Yujuan
    Gao, Hongjiang
    ICIC Express Letters, 2015, 9 (06): : 1719 - 1725
  • [24] Hyperspectral image clustering with Albedo recovery Fuzzy C-Means
    Azimpour, P.
    Shad, R.
    Ghaemi, M.
    Etemadfard, H.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2020, 41 (16) : 6117 - 6134
  • [25] A novel fuzzy c-means clustering algorithm for image segmentation
    Yang, Yong
    Huang, Shuying
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2006, 13E : 2897 - 2901
  • [26] Application of Fuzzy C-means clustering algorithm in image segmentation
    Guo, Rongchuan
    PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL & ELECTRONICS ENGINEERING AND COMPUTER SCIENCE (ICEEECS 2016), 2016, 50 : 84 - 88
  • [27] A New Image Enhancement Based on the Fuzzy C-Means Clustering
    Liu, Yucheng
    Liu, Yubin
    PRZEGLAD ELEKTROTECHNICZNY, 2012, 88 (3B): : 1 - 4
  • [28] An Image Segmentation Algorithm Based On Fuzzy C-Means Clustering
    Zhang Xinbo
    Jiang Li
    PROCEEDINGS OF 2009 CONFERENCE ON COMMUNICATION FACULTY, 2009, : 123 - 126
  • [29] Image segmentation with anisotropic weighted fuzzy C-means clustering
    Ji, Zexuan
    Chen, Qiang
    Sun, Quansen
    Xia, Deshen
    Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2009, 21 (10): : 1451 - 1459
  • [30] Application of Hard C-means and Fuzzy C-means in data fusion
    Tang Ai-Hong
    Cai Li-An
    Zhang You-Mei
    DIGITAL MANUFACTURING & AUTOMATION III, PTS 1 AND 2, 2012, 190-191 : 265 - 268