The basal theory of Gauss-MRF is expounded and 2-5 order Gauss MRF models are established. Parameters of the 2-5 order Gauss-MRF models for 300 wood samples＇ surface texture are also estimated by using LMS. The data ...The basal theory of Gauss-MRF is expounded and 2-5 order Gauss MRF models are established. Parameters of the 2-5 order Gauss-MRF models for 300 wood samples＇ surface texture are also estimated by using LMS. The data analysis shows that： 1） different rexture parameters have a clear scattered distribution, 2） the main direction of texture is the direction represented by the maximum parameter of Gauss-MRF parameters, and 3） for those samples having the same main direction, the finer the texture is, the greater the corresponding parameter is, and the smaller the other parameters are; and the higher the order of Gauss-MRF is, the more clearly the texture is described. On the condition of the second order Gauss MRF model, parameter B1, B2 of tangential texture are smaller than that of radial texture, while B3 and B4 of tangential texture are greater than that of radial texture. According to the value of separated criterion, the parameter of the fifth order Gauss-MRF is used as feature vector for Hamming neural network classification. As a result, the ratio of correctness reaches 88%.展开更多
Mining method selection is the first and the most critical problem in mine design and depends on some parameters such as geotechnical and geological features and economic and geographic factors. In this paper, the fac...Mining method selection is the first and the most critical problem in mine design and depends on some parameters such as geotechnical and geological features and economic and geographic factors. In this paper, the factors affecting mining method selection are determined. These factors include shape, thick- ness, depth, slope, RMR and RSS of the orebody, RMR and RSS of the hanging wall and footwall. Then, the priorities of these factors are calculated. In order to calculate the priorities of factors and select the best mining method for Qapiliq salt mine, Iran, based on these priorities, fuzzy analytical hierarchy process （AHP） technique is used. For this purpose, a questionnaire was prepared and was given to the associated experts. Finally, after a comparison carried out based on the effective factors, between the four mining methods including area mining, room and pillar, cut and fill and stope and pillar methods, the stope and nillar mining method was selected as the most suitable method to this mine.展开更多
This paper describes a new method to identify the type of fabric weave by using a neural network classifier. The characteristic parameters of the input layer, derived from fabric image, are composed of the Markov rand...This paper describes a new method to identify the type of fabric weave by using a neural network classifier. The characteristic parameters of the input layer, derived from fabric image, are composed of the Markov random field character, the difference between the maximum and the minimum of gray level projections in weft and warp directions, the area ratio of the brightness region to the total area in image, the weft and the warp yarn count. The experimental results show that the neural network classifier can effectively classify fabric weave with 98.33% of accuracy, which is helpful in the recognition of fabric weave parameters.展开更多
基金This paper is supported by the Municipal Natural Science Foundation of Harbin （2004AFX X J 0 20） and Provincial Natural Science Foundation of Heilongjiang （C2004-03）.
文摘The basal theory of Gauss-MRF is expounded and 2-5 order Gauss MRF models are established. Parameters of the 2-5 order Gauss-MRF models for 300 wood samples＇ surface texture are also estimated by using LMS. The data analysis shows that： 1） different rexture parameters have a clear scattered distribution, 2） the main direction of texture is the direction represented by the maximum parameter of Gauss-MRF parameters, and 3） for those samples having the same main direction, the finer the texture is, the greater the corresponding parameter is, and the smaller the other parameters are; and the higher the order of Gauss-MRF is, the more clearly the texture is described. On the condition of the second order Gauss MRF model, parameter B1, B2 of tangential texture are smaller than that of radial texture, while B3 and B4 of tangential texture are greater than that of radial texture. According to the value of separated criterion, the parameter of the fifth order Gauss-MRF is used as feature vector for Hamming neural network classification. As a result, the ratio of correctness reaches 88%.
文摘Mining method selection is the first and the most critical problem in mine design and depends on some parameters such as geotechnical and geological features and economic and geographic factors. In this paper, the factors affecting mining method selection are determined. These factors include shape, thick- ness, depth, slope, RMR and RSS of the orebody, RMR and RSS of the hanging wall and footwall. Then, the priorities of these factors are calculated. In order to calculate the priorities of factors and select the best mining method for Qapiliq salt mine, Iran, based on these priorities, fuzzy analytical hierarchy process （AHP） technique is used. For this purpose, a questionnaire was prepared and was given to the associated experts. Finally, after a comparison carried out based on the effective factors, between the four mining methods including area mining, room and pillar, cut and fill and stope and pillar methods, the stope and nillar mining method was selected as the most suitable method to this mine.
文摘This paper describes a new method to identify the type of fabric weave by using a neural network classifier. The characteristic parameters of the input layer, derived from fabric image, are composed of the Markov random field character, the difference between the maximum and the minimum of gray level projections in weft and warp directions, the area ratio of the brightness region to the total area in image, the weft and the warp yarn count. The experimental results show that the neural network classifier can effectively classify fabric weave with 98.33% of accuracy, which is helpful in the recognition of fabric weave parameters.
