Acknowledgement
본 연구는 산업통상자원부 디자인산업기술개발사업('20019083', '비공압 타이어 디자인 기술개발')의 지원으로 이루어졌습니다.
References
- Gent, Alan Neville and Walter, Joseph D., Pneumatic Tire, Mechanical Engineering Faculty Research, 854, 2006
- Karpenko, M., Prentkovskis, O., Skackauskas, P., "Comparison Analysis Between Pneumatic and Airless Tires by Computational Modelling for Avoiding Road Traffic Accidents.", Reliability and Statistics in Transportation and Communicatio, Riga, Latvia, pp. 295-305, October 2022., https://doi.org/10.1007/978-3-031-26655-3_28
- W. Wang, S. Yan, S.G. Zhao, Experimental verification and finite element modeling of radial truck tire under static loading, J. Reinf. Plast. Comp. 32, 490-498., 2013 https://doi.org/10.1177/0731684412474998
- Sardinha, M., Fatima Vaz, M., Ramos, T. R., Reis, L., "Design, properties, and applications of non-pneumatic tires: A review", Proceedings of the Institution of Mechanical Engineers, Vol. 237, No. 11, pp. 2277-2297, May 2023., https://doi:10.1177/14644207231177302
- Xiaochao Jin, Cheng Hou, Xueling Fan, Yongle Sun, Jinan Lv, Chunsheng Lu, "Investigation on the static and dynamic behaviors of non-pneumatic tires with honeycomb spokes", Composite Structures, Vol. 187, pp. 27-35, March 2018., https://doi.org/10.1016/j.compstruct.2017.12.044
- Jackowski, Jerzy, Marcin Wieczorek, Marcin Zmuda, "Energy consumption estimation of nonpneumatic tire and pneumatic tire during rolling." Journal of KONES, Vol. 25, No.1, pp. 159-168, 2018., https://doi.org/10.5604/01.3001.0012.2463
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y., "Generative adversarial nets", Advances in neural information processing systems, Montreal, Quebec, Canada, pp. 2672-2680, Jun 2014., https://doi.org/10.48550/arXiv.1406.2661
- Choi Y, Uh Y, Yoo J, Ha JW., "Stargan v2: Diverse image synthesis for multiple domains." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition., Seattle, WA, USA, pp. 8185-8194, June 2020., https://doi.org/10.48550/arXiv.1912.01865
- Singh, M., Bajpai, U., V, V. and Prasath, S., "Generation of fashionable clothes using generative adversarial networks: A preliminary feasibility study.", International Journal of Clothing Science and Technology, Vol. 32, No. 2, pp.177-187, April 2020, https://doi.org/10.1108/IJCST-12-2018- 0148
- Schrum, Jacob, Vanessa Volz, and Sebastian Risi., "Cppn2gan: Combining compositional pattern producing networks and gans for large-scale pattern generation.", Proceedings of the 2020 Genetic and Evolutionary Computation Conference., pp. 139-147, New YorkNYUnited States, Apr 2020., https://doi.org/10.48550/arXiv.2004.01703
- Baur, Christoph, Shadi Albarqouni, and Nassir Navab., "Generating highly realistic images of skin lesions with GANs.", OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis., Granada, Spain, pp. 260- 267, September 2018.
- Sauer, A., Chitta, K., Muller, J., & Geiger, A., "Projected gans converge faster.", Advances in Neural Information Processing Systems, pp. 17480-17492., Nov 2021., https://doi.org/10.48550/arXiv.2111.01007
- Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. and Uszkoreit, J., "An image is worth 16x16 words: Transformers for image recognition at scale.", arXiv preprint arXiv:2010.11929., Jun 2021., https://doi.org/10.48550/arXiv.2010.11929
- Jackowski, J., Zmuda, M. and Wieczorek, M., "Comparative analysis of small size non-pneumatic tires and pneumatic tires-radial stiffness and hysteresis, selected parameters of the contact patch." Maintenance & Reliability/Eksploatacja i Niezawodnosc, Vol. 25, No. 3, pp. 1-22, July 2023., https://doi.org/10.17531/ein/167362
- Ju, J., Veeramurthy, M., Summers, J.D. and Thompson, L., "Rolling Resistance of a Nonpneumatic Tire Having a Porous Elastomer Composite Shear Band ", Tire Science and Technology, Vol. 41, No. 3, pp. 154-173, July 2013., https://doi.org/10.2346/tire.13.410303
- Deng, Y., Wang, Z., Shen, H., Gong, J. and Xiao, Z., "A comprehensive review on non-pneumatic tyre research." Materials & Design, Vol. 227, pp. 111742, March 2023., https://doi.org/10.1016/j.matdes.2023.111742
- Iglesias, G., Talavera, E. and Diaz-A lvarez, A., 2023., "A survey on GANs for computer vision: Recent research, analysis and taxonomy.", Computer Science Review, Vol. 48, pp. 100553, May 2023., https://doi.org/10.1016/j.cosrev.2023.100553
- Touseef Iqbal, Shaima Qureshi, "The survey: Text generation models in deep learning", Computer and Information Sciences, Vol. 34, No. 6, pp. 2515-2528, May 2023., https://doi.org/10.1016/j.jksuci.2020.04.001
- Zhang, D., Ma, M. & Xia, L. A comprehensive review on GANs for time-series signals. Neural Comput & Applic 34, Vol. 34, pp. 3551-3571, January 2022., https://doi.org/10.1007/s00521-022- 06888-0
- Jeong JJ, Tariq A, Adejumo T, Trivedi H, Gichoya JW, Banerjee I., "Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.", J Digit Imaging. Journal of Digital Imaging.,Vol. 35, No. 2, pp. 137-152, April 2022., https://doi.org/10.1007/s10278-021-00556-w
- Nash, John. "Non-cooperative games." Annals of mathematics, Vol. 54, No. 2, pp. 286-295, September 1951., https://doi.org/10.2307/1969529
- Fedorova, Stanislava., "GANs for urban design.", arXiv preprint arXiv:2105.01727, May 2021., https://doi.org/10.48550/arXiv.2105.01727
- Niroshan, L. and Carswell, J.D., "Poly-GAN: Regularizing Polygons with Generative Adversarial Networks.", In International Symposium on Web and Wireless Geographical Information Systems, Vol. 13912, pp. 179-193, Jun 2023., https://doi.org/10.1007/978-3-031-34612-5_13
- OpenStreetMap, Available: https://www.openstreetmap.org
- Niroshan, Lasith, Carswell, James D., "OSM-GAN: Using Generative Adversarial Networks for Detecting Change in High-Resolution Spatial Images", Springer International Publishing, Vol. 143, pp. 95-105, June 2022., https://doi.org/10.1007/978-3-031-08017-3_9
- Zorzi, S., Bittner, K. and Fraundorfer, F., "Machine-learned regularization and polygonization of building segmentation masks." International Conference on Pattern Recognition, MiCo Milano Congress Center, ITALY, pp. 3098-3105, January 2021, https://doi.org/10.48550/arXiv.2007.12587
- Kato, N., Osone, H., Oomori, K., Ooi, C.W. and Ochiai, Y., "Gans-based clothes design: Pattern maker is all you need to design clothing." In Proceedings of the 10th Augmented Human International Conference, New York, NY, United States, March 2019, pp. 1-7, https://doi.org/10.1145/3311823.3311863
- OpenCV, available: https://opencv.org/
- Suarez, Gloria Bueno Garcia Oscar Deniz. Learning image processing with OpenCV. 2013.
- Chandan, G., Jain, A. and Jain, H., "Real time object detection and tracking using Deep Learning and OpenCV.", In 2018 International Conference on inventive research in computing applications (ICIRCA), Coimbatore, India, pp. 1305-1308, July 2018, https://doi.org/10.1109/ICIRCA.2018.8597266
- N. Boyko, O. Basystiuk and N. Shakhovska, "Performance Evaluation and Comparison of Software for Face Recognition, Based on Dlib and Opencv Library," 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, Aug 2018, pp. 478-482, https://doi.org/10.1109/DSMP.2018.8478556.
- Montatore, M., "Real-time object recognition in industrial automation processes", Diss. Politecnico di Torino, pp. 101, 2022.
- Sedgwick, Philip. "Pearson's correlation coefficient.", pp. 345, July 2012, https://doi.org/10.1136/bmj.e4483
- Zhou Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," in IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004, https://doi.org/10.1109/TIP.2003.819861.
- Kieffer, B., Babaie, M., Kalra, S. and Tizhoosh, H.R., 2017, November. "Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks.", In 2017 seventh international conference on image processing theory, tools and applications (IPTA), Montreal, Canada, pp. 1-6, Oct 2017, https://doi.org/10.48550/arXiv.1710.05726
- Ronneberger, O., Fischer, P. and Brox, T., "U-net: Convolutional networks for biomedical image segmentation.", In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 2015, https://doi.org/10.48550/arXiv.1505.04597
- ImageNet Dataset, Available: https://www.image-net.org/
- Tan, M., Le, Q., "Efficientnet: Rethinking model scaling for convolutional neural networks.", In International conference on machine learning, Long Beach Convention Center, Long Beach, pp. 6105-6114, May 2019, https://doi.org/10.48550/arXiv.1905.11946
- K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770- 778, June 2016, https://doi.org/10.1109/CVPR.2016.90.
- Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jegou, H., "Training data-efficient image transformers & distillation through attention.", In International conference on machine learning, pp. 10347-10357, Jan 2021, https://doi.org/10.48550/arXiv.2012.12877
- Pyinstaller, Available: https://pyinstaller.org/en/stable/
- Tkinter, Available: https://docs.python.org/3/library/tkinter.html
- O'Shea, K. and Nash, R., "An introduction to convolutional neural networks.", arXiv preprint arXiv:1511.0845, Nov 2015., https://doi.org/10.48550/arXiv.1511.08458
- Visa, S., Ramsay, B., Ralescu, A.L. and Van Der Knaap, E., 2011. "Confusion matrix-based feature selection.", Maics, Vol. 710, No. 1, pp. 120-127, January 2011,
- Bradley, A.P., "The use of the area under the ROC curve in the evaluation of machine learning algorithms.", Pattern recognition, Vol. 30, No. 7, pp. 1145-1159, July 1997, https://doi.org/10.1016/S0031-3203(96)00142-2
- Karras, T., Aittala, M., Laine, S., Harkonen, E., Hellsten, J., Lehtinen, J. and Aila, T., 2021. Aliasfree generative adversarial networks., Advances in Neural Information Processing Systems 34, May 2022, pp.852-863.