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RECIPROCAL BEHAVIOR OF THE INBOUND GROUP TUORISTS IN TIBET
Li Hui,Yang Zhenzhi,Li Taohong,Shi Hong 글로벌지식마케팅경영학회 2016 Global Marketing Conference Vol.2016 No.7
Based on the reciprocity theory, a dynamic reciprocal behavior model is verified by sampling the US and French group tourists in Tibet. Result shows that the US group tourists have only negative reciprocity, and French ones have both positive and negative reciprocity, indicating that tourists are more willing to sacrifice their interests to revenge hostile persons.
FFWR-Net: A feature fusion wear particle recognition network for wear particle classification
Suli Fan,Taohong Zhang,Xuxu Guo,Aziguli Wulamu 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.4
Wear particles produced by machines in the process of wear carry valuable information including wear mechanism and wear severity. Wear particle classification based on wear particle images provides predictive analysis for wear condition of machines. A novel wear particle recognition network based on feature fusion, FFWR-Net, is proposed in this research paper for wear particle images classification. In FFWR-Net, traditional feature extraction method by image processing technique (i.e. manually feature extracting) and deep learning convolutional neural network method (i.e. automatically feature extracting) is paralleled to extract the features of wear particle image. Then the features obtained by two different methods are fused together for building a wear particle classifier. In order to verify the effectiveness of the proposed classifier, it is compared with the previous convolutional neural network models on the same wear particle dataset. The comparison results show the accuracy and effectiveness of the proposed FFWR-Net classifier is better than the previous models.