과도유동 해석을 위한 CNN에서의 이미지 변환방법
Image Conversion Methods in CNN for Transient Flow Analysis
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초록
To apply CNN to a fluid problem, we need a method to effectively convert the physical quantities of fluid into an image. The performance of CNN was evaluated using the image transformation method using the minimum and maximum values of the pressure distribution data and the image transformation methods using the normal distribution of the pressure distribution data. Through the performance evaluation of the learned CNN, the image transformation methods of Method 4 and Method 5, which applied the normal distribution of representative pressure distribution data, were very effective. In particular, Method 5 includes the initial and final pressure distribution data to include overall pressure distribution data, thereby improving the resolution of the color map to improve classification performance.
Abstract
To apply CNN to a fluid problem, we need a method to effectively convert the physical quantities of fluid into an image. The performance of CNN was evaluated using the image transformation method using the minimum and maximum values of the pressure distribution data and the image transformation methods using the normal distribution of the pressure distribution data. Through the performance evaluation of the learned CNN, the image transformation methods of Method 4 and Method 5, which applied the normal distribution of representative pressure distribution data, were very effective. In particular, Method 5 includes the initial and final pressure distribution data to include overall pressure distribution data, thereby improving the resolution of the color map to improve classification performance.
참고문헌 (12)
- Y. LeCun, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86(11), pp.2278~2324 (1998)
- W. Shang, "Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear units", Proceedings of the 33rd International Conference on Machine Learning, pp.2217~2225 (2016)
- A. Krizhevsky, "ImageNet Classification with Deep Convolutional Neural Networks", Advances in Neural Information Processing Systems, pp.1097~1105 (2012)
- G. E. Dahl, "Improving deep neural networks for LVCSR using rectified linear units and dropout", Conference of Acoustics, Speech and Signal Processing, pp.8609~8613 (2013)
- Y. Bengio, "Deep Learning of Representations for Unsupervised and Transfer Learning", Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp.17~37 (2012)
- S. C. Park, "Application of CNN for steering control of autonomous vehicle", Conference of the Korea Institute of Information and Communication Engineering, 22(1), pp.234~235 (2018)
- E. Rezende, "Malicious Software Classification Using VGG16 Deep Neural Network’s Bottleneck Features", Advances in Intelligent Systems and Computing, pp.51~59 (2018)
- C. Liu, "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)", pp.37~48 (2016)
- C. Szegedy, "Inception-v4, inception-resnet and the impact of residual connections on learning", Thirty-first AAAI conference on artificial intelligence (2017)
- 이태환, "증기표의 과열, 포화 및 압축영역의 신경회로망 모델링", 한국기계기술학회지, 20(6), pp.872~878 (2018), 한국기계기술학회[DOI]
- 이태환, "딥러닝 기법을 사용한 이차원 과도유동 해석", 한국기계기술학회지, 22(1), pp.12~18 (2020), 한국기계기술학회[DOI]
- J. H. Park, "Prediction and control of near-wall turbulent flow with deep learning", 2018 Conference of The Korean Society of Mechanical Engineers, pp.518~520 (2018)