Application of Curvelet Transform and PLC Controllers in Automation as Elements of Ensuring High-Quality Control of an Industrial Gas Burner
DOI:
https://doi.org/10.24136/jaeee.2025.005Keywords:
curvelet transform, gas burner, image processing, industrial control, PLC controllerAbstract
The paper describes and proposes a multidimensional curvelet transformation for flame image analysis and describes the use of a PLC controller in an original program for controlling an industrial gas burner mounted on metallurgical furnaces for burning metal molds. Both the use of the curvelet transform and automation using PLC controllers for controlling the gas burner are elements that ensure the accuracy and safety of the entire industrial process.
References
Akansu A.N. (2001). Multiresolution signal decomposition. Academic Press, Second Edition. ISBN 0-12-047141-8, London.
Dettori L., Semler L. (2007). A comparison of walvelet, ridgelet and curvelet-based texture classification algorithms in computed tomography. Computers in Biology and Medicine 37(2007), p. 486-498.
Starck J.L, Candes E.J., Donoho D.L. (2002). The curvelet transform for image denoising. IEEE Transactions on Image Processing ( Volume: 11, Issue: 6, June 2002), DOI: 10.1109/TIP.2002.1014998, p. 670 – 684.
Lu G., Stasiak A., Shao J., Yan Y. (2007). Digital imaging based measurement of combustion flame characteristics. Proceedings of the 2007 IEEE International Workshop on Imaging Systems and Techniques, IST’07. Cracovia, Poland
Candes E.J. (2003). What is a curvelet? Notices of The American Mathematical Society, 50 (2003), p. 1402-1403.
Demanet L., Ying L. (2007). Curvelets and Wave Atoms for Mirror-Extended Images, Curvelet.org. https://curvelet.org/paper (access date: 29/12/2024).
Ławicki T. (2013), Application of curvelet transform to flame image analysis. Doctoral thesis. Lublin University of Technology, Lublin, Poland (Published in Polish).
Dnoho D.L. Duncan M.R. (1999). Digital curvelet transform: strategy, implementations and experiments. Stanford University, Stanford.
Jianwei Ma, Plonka G. (2010). The Curvelet Transform: IEEE Signal Processing Magazine, 27 (2),
p. 118-133.
Sayed U., Moffaddel M.A., Abd-Elhafiez W.M. (2013). Image object extraction based on curvelet transform. Applied Mathematics & Information Science. 7(2013), p. 133-138.
Bhutada G.G., Anand R.S., Saxena S.C. (2012). Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform. Digital Signal Processing 1(2012), p.118-130.
Bolton W. (2015). Programmable Logic Controllers (6th, revised ed.). Newnes. ISBN 9780081003534.
Parr, E.A. (1998). Computers and industrial control. Industrial Control Handbook. Industrial Press Inc. ISBN 0-8311-3085-7.
Kandray D. (2010). Programmable Automation Technologies, Industrial Press, 2010, Chapter 8 Introduction to Programmable Logic Controllers, ISBN 978-0-8311-3346-7.
Candes E.J., Donoho D.L. (2005). Continuous curvelet transform. II. Discretization and frames. Applied and Computational Harmonic Analysis 19(2005), p. 198-222.
https://mall.industry.siemens.com/mall/pl/pl/Catalog/Product/6ES7215-1AG40-0XB0 (access date: 01/09/2024).
https://assets.omron.eu/downloads/datasheet/en/v5/h06e_e5cn-h_advanced_digital_temperature_controller_datasheet_en.pdf (access date: 01/09/2024).
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Automation, Electronics and Electrical Engineering

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.