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: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/3027126 |
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| _version_ | 1832561490619531264 |
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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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