參考文獻 |
[1]A. Belhadj, P. Baudouin, F. Breaban, A. Deffontaine, M. Dewulf and Y. Houbaert, “Effect of laser cutting on microstructure and on magnetic properties of grain non-oriented electrical steels,” J. Magn. Magn. Mater., vol. 256, no. 1-3, pp. 20-31, 2003.
[2]G. Loisos and A.J. Moses, “Effect of mechanical and Nd:YAG laser cutting on magnetic flux distribution near the cut edge of non-oriented steels,” J. Mater. Process. Technol., vol. 161, no. 1-2, pp. 151-155, 2005.
[3]S. Russell and P. Norvig, “Artificial intelligence: a modern approach,” New York, USA: Prentice Hall, 2009.
[4]M. Radovanović and M. Madić, “Experimental investigations of CO2 laser cut quality : a review,” Nonconvent. Technol. Rev., vol. 15, no. 4, pp. 35-42, 2011.
[5]黃立仁與羅慶璋,「利用二氧化碳雷射切割S304品質評估」,銲接與切割,11:1卷,頁38-48,2001。
[6]N. Rajaram, J. Sheikh-Ahmad and S.H. Cheraghi, “CO2 laser cut quality of 4130 steel,” Int. J. of Mach. Tool Manuf., vol. 43, no. 4, pp. 354-358, 2003.
[7]B.S. Yilbas, “Laser cutting quality assessment and thermal efficiency analysis.” J. Mater. Process. Technol., vol. 155-156, pp. 2106-2115, 2004.
[8]K. AbdelGhany and M. Newishy, “Cutting of 1.2 mm thick austenitic stainless steel sheet using pulsed and CW Nd:YAG laser,” J. Mater. Process. Technol., vol. 168, no. 3, pp. 438-447, 2005.
[9]S. Bayraktar and Y. Turgut, “Effects of different cutting methods for electrical steel sheets on performance of induction motors,” Proc. Inst. Mech. Eng., Part B, vol. 232, no. 7, pp. 1287-1294, 2016.
[10]T.H. Nguyen, C.K. Lin, P.C. Tung, N.V. Cuong and J.R. Ho, “An extreme learning machine for predicting kerf waviness and heat affected zone in pulsed laser cutting of thin non-oriented silicon steel,” Opt. Lasers Eng., vol. 134, no. 106244, 2020.
[11]C.L. Liu, W.H Hsaio and Y.C. Tu, “Time series classification with multivariate convolutional neural network,” IEEE Trans. Ind. Electron., 2019.
[12]Z. Wang and T. Oates, “Encoding time series as images for visual inspection and classification using tiled convolutional neural networks,” Twenty-Ninth AAAI Conf. Artif. Intell., 2015.
[13]N. Hatami, Y. Gavet and J. Debayle, “Classification of time-series images using deep convolutional neural networks,” France, 2017.
[14]R. Ke, W. Li, Z. Cui and Y. Wang, “Two-stream multi-channel convolutional neural network (TM-CNN) for multi-lane traffic speed prediction considering traffic volume impact,” J. Transp. Res. Rec., vol. 2674, no. 4, 2020.
[15]T. Zan, H. Wang, M Wang, Z.H. Liu and X.S. Gao, “Application of multi-dimension input convolutional neural network in fault diagnosis of rolling bearings,” Appl. Sci., vol. 9, no. 13, pp. 2690, 2019.
[16]呂助增,「雷射原理及應用」,聯經出版社,台北,1987。
[17]吳成柯,「數位影像處理」,儒林圖書,台北,1995。
[18]M. Stokes, M. Anderson, S. Chandrasekar and R. Motta, “A standard default color space for the internet – sRGB,” IEC, 1996.
[19]鐘國亮,「影像處理與電腦視覺」,東華出版社,台北,2015。
[20]G. Bradski and A. Keahler, “Learning OpenCV:computer vision with the OpenCV library,” Sebastopol, California, USA: O’Reilly, 2008.
[21]曾竣煌,「熔融沉積成型技術之路徑規劃與提升製造效率研究」,碩士論文,國立中央大學機械工程學系,2018。
[22]D.H. Douglas and T. Peucker, “Algorithms for the reduction of the number of points required to represent a digitized line or its caricature,” Cartographica: The International Journal for Geographic Information and Geovisualization, vol. 10, pp.112-122, 1973.
[23]Z. Wang and T. Oates, “Imaging time-series to improve classification and imputation,” IJCAI, 2015.
[24]J.P. Eckmann, S.O. Kamphorst and D. Ruelle, “Recurrence plots of dynamical systems,” World Scientific Series on Nonlinear Science Series A, vol. 16, pp. 441-446, 1995.
[25]C. Nwankpa, W. Ijomah, A. Gachagan and S. Marshall, “Activation functions: comparison of trends in practice and research for deep learning,” Proc. IEEE Conf. Comput. Vis. Pattern. Recognit., 2018.
[26]E. Jang, S. Gu and B. Poole, “Categorical reparameterization with gumbel-softmax,” In Proc. Int. Conf. Learn. Rep, 2017.
[27]G.E. Hinton and R.R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, 2006.
[28]Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[29]A. Krizhevsky, I. Sutskever and G.E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp.84-90, 2017.
[30]K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” In Int. Conf. Learn. Rep., 2014.
[31]C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, “Going deeper with convolutions,” Proc. IEEE Conf. Comput. Vis. Pattern. Recognit., pp.1-9, 2014.
[32]K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Conf. Comput. Vis. Pattern. Recognit., pp. 770-778, 2016.
[33]PyTorch官方網站,取自https://pytorch.org
[34]IPG Photonics官方網站,取自https://www.ipgphotonics.com/en
[35]H. Tuthill, “Comprehensive information about metallurgy of stainless steel,” Food and Environmental Sanitation, 2005.
[36]SciPy官方網站,取自https://www.scipy.org/
[37]Pyts官方網站,取自https://pyts.readthedocs.io/
[38]ZEISS官方網站,取自https://www.micro-shop.zeiss.com/en/de/shop/
objectives/421031-9910-000/Objective-A-Plan-5x-0.12-Ph0-M27。
[39]OpenCV官方網站,取自http://opencv.org |