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This paper presents the development of user-friendly design and training tool for convolutional neural networks (CNNs) and support vector machines (SVMs) as an application development environment based on MATLAB. As the first test trial, an application of deep CNN (DCNN) for anomaly detection is developed and trained using a large number of images to distinguish undesirable small defects such as crack, burr, protrusion, chipping, spot, and fracture phenomena that occur in the production process of resin molded articles. Then, as the second test trial, a SVM incorporated with the AlexNet and another SVM incorporated with our original sssNet are, respectively, designed and trained to classify sample images into accepting as OK or rejecting as NG categories with high categorization rate. In the case of these SVMs, the training can be conducted by using only images of OK category. The AlexNet and the sssNet are different types of DCNNs, whose compressed feature vectors have 4096 and 32 elements, respectively. The two lengths of compressed feature vectors are used as the inputs for the two types of SVMs, respectively. The usability and operability of the developed design and training tool for DCNNs and SVMs are demonstrated and evaluated through training and classification experiments.
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Cengil, E., Cnar, A., & Ozbay, E. (2017). Image classification with caffe deep learning frame-work. In Proceedings of 2017 International Conference on Computer Science and Engineering (UBMK), Antalya (pp. 440–444).
Yuan, L., Qu, Z., Zhao, Y., Zhang, H., & Nian, Q. (2017). A convolutional neural network based on tensorflow for face recognition. In Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing (pp. 525–529).
Nagata, F., Tokuno, K., Tamano, H., Nakamura, H, Tamura, M., Kato, K., et al. (2018). Basic application of deep convolutional neural network to visual inspection. In Proceedings of International Conference on Industrial Application Engineering (ICIAE2018), Okinawa (pp. 4–8).
Nagata, F., Tokuno, K., Otsuka, A., Ikeda, T., Ochi, H., Tamano, H., et al. (2018) Design tool of deep convolutional neural network for visual inspection. In Proceedings of The Third International Conference on Data Mining and Big Data (DMBD2018), Springer-Nature LNCS Conference Proceedings 10943, Shanghai (pp. 604–613).
Cristianini, N., & Shawe-Taylor, J. (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press. CrossRef
Flores-Fuentes, W., Rivas-Lopez, M., Sergiyenko, O., Gonzalez-Navarro, F. F., Rivera-Castillo, J., Hernandez-Balbuena, D., et al. (2014). Combined application of power spectrum centroid and support vector machines for measurement improvement in optical scanning systems. Signal Processing, 98, 37–51. CrossRef
Flores-Fuentes, W., Sergiyenko, O., Gonzalez-Navarro, F. F., Rivas-Lopez, M., Rodriguez-Quinonez, J. C., Hernandez-Balbuena, D., et al. (2016). Multivariate outlier mining and regression feedback for 3D measurement improvement in opto-mechanical system. Optical and Quantum Electronics, 48(8), 403. CrossRef
Rodriguez-Quinonez, J. C., Sergiyenko, O., Hernandez-Balbuena, D., Rivas-Lopez, M., Flores-Fuentes, W., & Basaca-Preciado, L. C. (2014). Improve 3D laser scanner measurements accuracy using a FFBP neural network with Widrow-Hoff weight/bias learning function. Opto-Electronics Review, 22(4), 224–235. CrossRef
Real, O. R., Castro-Toscano, M. J., Rodriguez-Quinonez, J. C., Serginyenko, O., Hernandez-Balbuena, D., Rivas-Lopez, M., et al. (2019). Surface measurement techniques in machine vision: Operation, applications, and trends. In Optoelectronics in machine vision-based theories and applications (pp. 79–104). Hershey: IGI Global. CrossRef
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV (pp. 1097–1105).
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. CrossRef
Nagata, F., & Watanabe, K. (2002). Learning of contact motion using a neural network and its application for force control. In Proceedings of the 4th Asian Control Conference (ASCC2002) (pp. 420–424).
Nagata, F., Mizobuchi, T., Tani, S., Watanabe, K., Hase, T., & Haga, Z. (2009). Impedance model force control using neural networks-based effective stiffness estimator for a desktop NC machine tool. Journal of Manufacturing Systems, 28(2/3), 78–87. CrossRef
Nagata, F., Mizobuchi, T., Hase, T., Haga, Z., Watanabe, K., & Habib, M. K. (2010). CAD/CAM-based force controller using a neural network-based effective stiffness estimator. Artificial Life and Robotics, 15(1), 101–105. CrossRef
Nagata, F., & Watanabe, K. (2011). Adaptive learning with large variability of teaching signals for neural networks and its application to motion control of an industrial robot. International Journal of Automation and Computing, 8(1), 54–61. CrossRef
Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines. Technical Report MSR-TR-98-14 (pp. 1–24).
- Development of Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines
Maki K. Habib
- Chapter 25