A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO

In recent years, the combustion furnace has been widely applied in many different fields of industrial technology, and the accurate detection of combustion states can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation. However, the com...

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Main Authors: Pinggai Zhang, Minrui Fei, Ling Wang, Xian Wu, Chen Peng, Kai Chen
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2021/3027126
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author Pinggai Zhang
Minrui Fei
Ling Wang
Xian Wu
Chen Peng
Kai Chen
author_facet Pinggai Zhang
Minrui Fei
Ling Wang
Xian Wu
Chen Peng
Kai Chen
author_sort Pinggai Zhang
collection DOAJ
description In recent years, the combustion furnace has been widely applied in many different fields of industrial technology, and the accurate detection of combustion states can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation. However, the combustion states inside the industrial furnace change according to the production needs, which further challenges the optimal set of model parameters. To effectively segment the flame pixels, a novel segmentation method for furnace flame using adaptive color model and hybrid-coded human learning optimization (AHcHLO) is proposed. A new adaptive color model with mixed variables (NACMM) is designed for adapting to different combustion states, and the AHcHLO is developed to search for the optimal parameters of NACMM. Then, the best NACMM with optimal parameters is adopted to segment the combustion flame image more precisely and effectively. Finally, the experiment results show that the developed AHcHLO obtains the best-known overall results so far on benchmark functions and the proposed NACMM outperforms state-of-the-art flame segmentation approaches, providing a high detection accuracy and a low false detection rate.
format Article
id doaj-art-41a44a447b6d4c039680df01a78fd401
institution Kabale University
issn 1076-2787
1099-0526
language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-41a44a447b6d4c039680df01a78fd4012025-02-03T01:24:49ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/30271263027126A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLOPinggai Zhang0Minrui Fei1Ling Wang2Xian Wu3Chen Peng4Kai Chen5Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaIn recent years, the combustion furnace has been widely applied in many different fields of industrial technology, and the accurate detection of combustion states can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation. However, the combustion states inside the industrial furnace change according to the production needs, which further challenges the optimal set of model parameters. To effectively segment the flame pixels, a novel segmentation method for furnace flame using adaptive color model and hybrid-coded human learning optimization (AHcHLO) is proposed. A new adaptive color model with mixed variables (NACMM) is designed for adapting to different combustion states, and the AHcHLO is developed to search for the optimal parameters of NACMM. Then, the best NACMM with optimal parameters is adopted to segment the combustion flame image more precisely and effectively. Finally, the experiment results show that the developed AHcHLO obtains the best-known overall results so far on benchmark functions and the proposed NACMM outperforms state-of-the-art flame segmentation approaches, providing a high detection accuracy and a low false detection rate.http://dx.doi.org/10.1155/2021/3027126
spellingShingle Pinggai Zhang
Minrui Fei
Ling Wang
Xian Wu
Chen Peng
Kai Chen
A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO
Complexity
title A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO
title_full A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO
title_fullStr A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO
title_full_unstemmed A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO
title_short A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO
title_sort novel segmentation method for furnace flame using adaptive color model and hybrid coded hlo
url http://dx.doi.org/10.1155/2021/3027126
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