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RISS 인기검색어
- 검색키워드
- 저자 : Haiming Ni (검색결과 2 건)
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The Application of RBF Neural Network in the Wood Defect Detection
Hongbo Mu,Mingming Zhang,Dawei Qi,Haiming Ni 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.2
Wood defect is due to the physiological process, genetic factor or affected by the external environment in the growth period. These defects will reduce the utilization value of wood. However, it is very difficult to determine whether there are defects exist, and the degree of defects. Therefore, the effective detection of wood defect information is particularly important. A new wood defect detection method by using RBF neural network was proposed in this paper. The new RBF defect detection method can be divided into the following main steps: (1) Detect wood defects by using X-ray nondestructive testing technology. (2) Deal with defect images by using digital image processing technology. (3) Analyze the information of different defects, and extract the characteristic value of wood defects. (4) Then, the RBF neural network model was constructed. (5) Finally, the RBF neural network is trained with the known samples and simulated with the unknown samples. The experimental results shown that the RBF neural network method was effectively detect the two typical wood defects. This method provides an important theoretical basis to realize the wood defect automatic detection.
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Wood Defects Recognition Based on Fuzzy BP Neural Network
Hongbo Mu,Mingming Zhang,Dawei Qi,Shuyue Guan,Haiming Ni 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.5
Firstly, we applied the X-ray non-destructive testing technology to detect wood defects for getting the images. After graying the images, we calculated their GLCMS(Gray Level Co-occurrence Matrixes), then we normalized GLCMS to obtain the joint probabilities of GLCMS. The feature vectors of images, which included 13 eigenvalues of images were calculated and extracted by the joint probability of GLCMS. The fuzzy BP neural network(abbreviated as FBP) was designed by combining fuzzy mathematics and BP neural network . And the FBP neural network was regarded as the membership function of feature vectors, the outputs of the network was regarded as the degree of membership to the feature vectors in each category. We use the maximum degree of membership method for the pattern recognition of feature vectors, so the automatic identification and classification for feature vectors were achieved , and then the automatic identification of wood defects was realized. By simulated study and training many times, the results shown that the average recognition success rate of the network was more than 90%, and some FBP networks had an extremely high recognition success rate to training samples and test samples.
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