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Jinlong Zhu,Fanhua Yu,Mingyu Sun,Dong Zhao,Qingtian Geng 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.1
A method for detecting foreign substances in mould based on scatter grams was presented to protect mouldsautomatically during moulding production. This paper proposes a wavelet transform foreign detection methodbased on Monte Carlo analysis mechanism to identify foreign objects in the tube. We use the Monte Carlomethod to evaluate the image, and obtain the width of the confidence interval by the deviation statistical grayhistogram to divide the image type. In order to stabilize the performance of the algorithm, the high-frequencyimage and the low-frequency image are respectively drawn. By analyzing the position distribution of the pixelgray in the two images, the suspected foreign object region is obtained. The experiments demonstrate theeffectiveness of our approach by evaluating the labeled data.
Zhu, Jinlong,Yu, Fanhua,Sun, Mingyu,Zhao, Dong,Geng, Qingtian Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.1
A method for detecting foreign substances in mould based on scatter grams was presented to protect moulds automatically during moulding production. This paper proposes a wavelet transform foreign detection method based on Monte Carlo analysis mechanism to identify foreign objects in the tube. We use the Monte Carlo method to evaluate the image, and obtain the width of the confidence interval by the deviation statistical gray histogram to divide the image type. In order to stabilize the performance of the algorithm, the high-frequency image and the low-frequency image are respectively drawn. By analyzing the position distribution of the pixel gray in the two images, the suspected foreign object region is obtained. The experiments demonstrate the effectiveness of our approach by evaluating the labeled data.
Classroom Roll-Call System Based on ResNet Networks
Jinlong Zhu,Fanhua Yu,Guangjie Liu,Mingyu Sun,Dong Zhao,Qingtian Geng,Jinbo Su 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.5
A convolution neural networks (CNNs) has demonstrated outstanding performance compared to otheralgorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers haveproposed a residual network to ease the training for recognition accuracy improvement. In this study, a novelface recognition model based on game theory for call-over in the classroom was proposed. In the proposedscheme, an image with multiple faces was used as input, and the residual network identified each face with aconfidence score to form a list of student identities. Face tracking of the same identity or low confidence weredetermined to be the optimisation objective, with the game participants set formed from the student identitylist. Game theory optimises the authentication strategy according to the confidence value and identity set toimprove recognition accuracy. We observed that there exists an optimal mapping relation between face andidentity to avoid multiple faces associated with one identity in the proposed scheme and that the proposedgame-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.