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• W4376648745 endingPage "113173" @default.
• W4376648745 startingPage "113173" @default.
• W4376648745 abstract "Defrost control using digital image processing is a potential, economical, and energy-saving solution for air-source heat pump or refrigeration system, comparing with currently studied or used direct/indirect measuring methods. However, under complex operating condition of different shooting angles, lighting conditions, and pixel level, which are common in refrigeration system, the accuracy of frost state recognition with existing digital image processing methods decrease dramatically. Therefore, a new method based on optimized gray level co-occurrence matrix and new characterization parameter combination to extract image texture characteristics, combining extreme learning machine algorithm (OGLCM-ELM), is proposed for the first time. An experimental rig for evaporator frosting image under different lighting intensity, shooting angle and pixel level is set up. The collected experimental image data are divided into 3 classifications (Frostless, light frost, and heavy frost) or 2 classifications ((Frostless + light frost, and heavy frost)). The results show for ternary classification, OGLCM-ELM reveals significantly higher recognition accuracy then existing methods, average accuracy is as higher as 98.14%. It is also 5.28% and 3.14% higher than those of OGLCM-SVM, OGLCM-BP. Other performance parameters, precision, recall, F1-score, and calculating time are also totally better than other methods. For binary classification. the average accuracy of OGLCM-ELM even reaches 99% under complex operating condition, indicating it is a practical and potential technology for defrost control." @default.
• W4376648745 created "2023-05-17" @default.
• W4376648745 creator A5019486950 @default.
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• W4376648745 date "2023-08-01" @default.
• W4376648745 modified "2023-10-17" @default.
• W4376648745 title "Study on a novel defrost control method based on the surface texture of evaporator image with gray-level cooccurrence matrix, new characterization parameter combination and machine learning" @default.
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• W4376648745 doi "https://doi.org/10.1016/j.enbuild.2023.113173" @default.
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