Uncategorized References
[1] P. Zheng et al., "Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives," Frontiers of Mechanical Engineering, vol. 13, no. 2, pp. 137-150, 2018.
[2] C. Prinz, F. Morlock, S. Freith, N. Kreggenfeld, D. Kreimeier, and B. Kuhlenkötter, "Learning factory modules for smart factories in industrie 4.0," Procedia CiRp, vol. 54, pp. 113-118, 2016.
[3] A. F. da Silva, R. L. Ohta, M. N. dos Santos, and A. P. Binotto, "A cloud-based architecture for the internet of things targeting industrial devices remote monitoring and control," IFAC-PapersOnLine, vol. 49, no. 30, pp. 108-113, 2016.
[4] A. A. d. S. Neto, "Technological evolution in machining processes with CNC machines in the context of the concept of Industry 4.0," 2018.
[5] Y. Li, Q. Liu, J. Xiong, and J. Wang, "Research on data-sharing and intelligent CNC machining system," in 2015 IEEE international conference on mechatronics and automation (ICMA), 2015: IEEE, pp. 625-630.
[6] S. S. H. Al-Maeeni, C. Kuhnhen, B. Engel, and M. Schiller, "Smart retrofitting of machine tools in the context of industry 4.0," Procedia CIRP, vol. 88, pp. 369-374, 2020.
[7] T. Gittler, A. Gontarz, L. Weiss, and K. Wegener, "A fundamental approach for data acquisition on machine tools as enabler for analytical Industrie 4.0 applications," Procedia CIRP, vol. 79, pp. 586-591, 2019.
[8] I. Ghionea, A. Ghionea, D. Cioboată, and S. Ćuković, "Lathe machining in the era of Industry 4.0: Remanufactured lathe with integrated measurement system for CNC generation of the rolling surfaces for railway wheels," in IFIP International Conference on Product Lifecycle Management, 2016: Springer, pp. 296-308.
[9] J. Lee, C. Jin, and B. Bagheri, "Cyber physical systems for predictive production systems," Production Engineering, vol. 11, no. 2, pp. 155-165, 2017.
[10] S. Costa, F. Silva, R. Campilho, and T. Pereira, "Guidelines for Machine Tool Sensing and Smart Manufacturing Integration," Procedia Manufacturing, vol. 51, pp. 251-257, 2020.
[11] A. Schütze, N. Helwig, and T. Schneider, "Sensors 4.0–smart sensors and measurement technology enable Industry 4.0," Journal of Sensors and Sensor systems, vol. 7, no. 1, pp. 359-371, 2018.
[12] E. Uhlmann, A. Laghmouchi, C. Geisert, and E. Hohwieler, "Smart wireless sensor network and configuration of algorithms for condition monitoring applications," Journal of Machine Engineering, vol. 17, 2017.
[13] H. A. Taha, S. Yacout, and L. Birglen, "Detection and Monitoring for Anomalies and Degradation of a Robotic Arm Using Machine Learning," in Advances in Automotive Production Technology–Theory and Application: Springer, 2021, pp. 230-237.
[14] W.-Y. Chang, S.-J. Wu, and J.-W. Hsu, "Investigated iterative convergences of neural network for prediction turning tool wear," The International Journal of Advanced Manufacturing Technology, vol. 106, no. 7, pp. 2939-2948, 2020.
[15] H. Yang et al., "Remaining Useful Life Prediction of Ball Screw Using Precision Indicator," IEEE Transactions on Instrumentation and Measurement, 2021.
[16] T. L. Nguyen, S.-K. Ro, and J.-K. Park, "Study of ball screw system preload monitoring during operation based on the motor current and screw-nut vibration," Mechanical Systems and Signal Processing, vol. 131, pp. 18-32, 2019.
[17] N. Riaz, S. I. A. Shah, F. Rehman, S. O. Gilani, and E. Udin, "A novel 2-D current signal-based residual learning with optimized softmax to identify faults in ball screw actuators," IEEE Access, vol. 8, pp. 115299-115313, 2020.
[18] T. L. Nguyen, S.-K. Ro, C. K. Song, and J.-K. Park, "Study on preload monitoring of ball screw feed drive system using natural frequency detection," Journal of the Korean Society for Precision Engineering, vol. 35, no. 2, pp. 135-143, 2018.
[19] D. Hong, S. Bang, and B. Kim, "Unsupervised Condition Diagnosis of Linear Motion Guide using Generative Model based on Images," IEEE Access, 2021.
[20] Y.-C. Huang, C.-H. Kao, and S.-J. Chen, "Diagnosis of the hollow ball screw preload classification using machine learning," Applied Sciences, vol. 8, no. 7, p. 1072, 2018.
[21] P. Li et al., "Prognosability study of ball screw degradation using systematic methodology," Mechanical Systems and Signal Processing, vol. 109, pp. 45-57, 2018.
[22] B. Denkena, M.-A. Dittrich, H. Noske, D. Stoppel, and D. Lange, "Data-based ensemble approach for semi-supervised anomaly detection in machine tool condition monitoring," CIRP Journal of Manufacturing Science and Technology, vol. 35, pp. 795-802, 2021.
[23] J. K. Suhr, "Kanade-lucas-tomasi (klt) feature tracker," Computer Vision (EEE6503), pp. 9-18, 2009.
[24] A. Dosovitskiy et al., "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020.
[25] P. Rosin, "Thresholding for change detection," in Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271), 1998: IEEE, pp. 274-279.
[26] D. C. Luvizon, B. T. Nassu, and R. Minetto, "Vehicle speed estimation by license plate detection and tracking," in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014: IEEE, pp. 6563-6567.
[27] X. Cao, J. Lan, P. Yan, and X. Li, "KLT feature based vehicle detection and tracking in airborne videos," in 2011 Sixth International Conference on Image and Graphics, 2011: IEEE, pp. 673-678.
[28] B. D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," 1981: Vancouver, British Columbia.
[29] T. Tommasini, A. Fusiello, E. Trucco, and V. Roberto, "Making good features track better," in Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No. 98CB36231), 1998: IEEE, pp. 178-183.
[30] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[31] A. Mahajan and S. Chaudhary, "Categorical image classification based on representational deep network (RESNET)," in 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), 2019: IEEE, pp. 327-330.
[32] K. Han et al., "A survey on visual transformer," arXiv preprint arXiv:2012.12556, 2020.
[33] K. Huang, H. Lei, Z. Jiao, and Z. Zhong, "Recycling Waste Classification Using Vision Transformer on Portable Device," Sustainability, vol. 13, no. 21, p. 11572, 2021.
[34] J. Y.-Y. Lin, S.-M. Liao, H.-J. Huang, W.-T. Kuo, and O. H.-M. Ou, "Galaxy Morphological Classification with Efficient Vision Transformer," arXiv preprint arXiv:2110.01024, 2021.
[35] M. Soleymani, M. Bonyani, H. Mahami, and F. Nasirzadeh, "Construction material classification on imbalanced datasets for construction monitoring automation using Vision Transformer (ViT) architecture," arXiv preprint arXiv:2108.09527, 2021.
[36] L. Li, W. Huang, I. Y.-H. Gu, and Q. Tian, "Statistical modeling of complex backgrounds for foreground object detection," IEEE Transactions on Image Processing, vol. 13, no. 11, pp. 1459-1472, 2004.
[37] S. B. Tambe, D. Kulhare, M. Nirmal, and G. Prajapati, "Image processing (IP) through erosion and dilation methods," 2013.
[38] S. N. Srihari, "Reliability analysis of biased majority-vote systems," IEEE Transactions on Reliability, vol. 31, no. 1, pp. 117-118, 1982.
[39] D. A. Van Dyk and X.-L. Meng, "The art of data augmentation," Journal of Computational and Graphical Statistics, vol. 10, no. 1, pp. 1-50, 2001.
[40] L. Taylor and G. Nitschke, "Improving deep learning with generic data augmentation," in 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018: IEEE, pp. 1542-1547.
[41] P. Trajdos and M. Kurzynski, "Weighting scheme for a pairwise multi-label classifier based on the fuzzy confusion matrix," Pattern Recognition Letters, vol. 103, pp. 60-67, 2018.
[42] A. Gupta, N. Tatbul, R. Marcus, S. Zhou, I. Lee, and J. Gottschlich, "Class-Weighted Evaluation Metrics for Imbalanced Data Classification," arXiv preprint arXiv:2010.05995, 2020.