Based on the "Grayscales average distribution" method which equally distributes the input gray levels to output gray levels, three improved methods named: "Reduce the gray range expressed by the less si...Based on the "Grayscales average distribution" method which equally distributes the input gray levels to output gray levels, three improved methods named: "Reduce the gray range expressed by the less significant subfields", "Low levels preset" and "Modify the exponent of inverse-gamma function" are proposed in this paper. Using these methods, the inverse-gamma relation subfields code can be obtained easily which can improve the low level expressions of AC-PDP. And a program, "gray scales distribution validate program", which can enhance the expressions of the demanded gray levels range, is also proposed in this paper.展开更多
A comprehensive evaluation method is proposed to analyze dust pollution generated in the production process of mines.The method employs an optimized image-processing and deep learning framework to characterize the gra...A comprehensive evaluation method is proposed to analyze dust pollution generated in the production process of mines.The method employs an optimized image-processing and deep learning framework to characterize the gray and fractal features in dust images.The research reveals both linear and logarithmic correlations between the gray features,fractal dimension,and dust mass,while employing Chauvenel criteria and arithmetic averaging to minimize data discreteness.An integrated hazardous index is developed,including a logarithmic correlation between the index and dust mass,and a four-category dataset is subsequently prepared for the deep learning framework.Based on the range of the hazardous index,the dust images are divided into four categories.Subsequently,a dust risk classifcation system is established using the deep learning model,which exhibits a high degree of performance after the training process.Notably,the model achieves a testing accuracy of 95.3%,indicating its efectiveness in classifying diferent levels of dust pollution,and the precision,recall,and F1-score of the system confrm its reliability in analyzing dust pollution.Overall,the proposed method provides a reliable and efcient way to monitor and analyze dust pollution in mines.展开更多
文摘Based on the "Grayscales average distribution" method which equally distributes the input gray levels to output gray levels, three improved methods named: "Reduce the gray range expressed by the less significant subfields", "Low levels preset" and "Modify the exponent of inverse-gamma function" are proposed in this paper. Using these methods, the inverse-gamma relation subfields code can be obtained easily which can improve the low level expressions of AC-PDP. And a program, "gray scales distribution validate program", which can enhance the expressions of the demanded gray levels range, is also proposed in this paper.
基金supported by the National Natural Science Foundation of China(52174099)the Natural Science Foundation of Liaoning Province(2021-KF-23-01)the Fundamental Research Funds for the Central Universities of Central South University(2022ZZTS0510).
文摘A comprehensive evaluation method is proposed to analyze dust pollution generated in the production process of mines.The method employs an optimized image-processing and deep learning framework to characterize the gray and fractal features in dust images.The research reveals both linear and logarithmic correlations between the gray features,fractal dimension,and dust mass,while employing Chauvenel criteria and arithmetic averaging to minimize data discreteness.An integrated hazardous index is developed,including a logarithmic correlation between the index and dust mass,and a four-category dataset is subsequently prepared for the deep learning framework.Based on the range of the hazardous index,the dust images are divided into four categories.Subsequently,a dust risk classifcation system is established using the deep learning model,which exhibits a high degree of performance after the training process.Notably,the model achieves a testing accuracy of 95.3%,indicating its efectiveness in classifying diferent levels of dust pollution,and the precision,recall,and F1-score of the system confrm its reliability in analyzing dust pollution.Overall,the proposed method provides a reliable and efcient way to monitor and analyze dust pollution in mines.
