과제정보
본 논문은 화랑대연구소의 2021년도(21-군학-5) 저술활동비 지원을 받아 연구되었음.
참고문헌
- 대한민국 국방부, "2020 국방백서", 대한민국 국방부, 2020
- 정춘일, "4차 산업혁명과 군사혁신 4.0", 전략연구, 제24권, 제2호, pp. 183-211, 2017.
- Kim, Young-Jin, and Eun-Gyung Kim, "Image based fire detection using convolutional neural network", Journal of the Korea Institute of Information and Communication Engineering, Vol. 20, No. 9, pp. 1649-1656, 2016. https://doi.org/10.6109/JKIICE.2016.20.9.1649
- F Rosenblatt, "The perceptron: a probabilistic model for information storage and organization in the brain.", Psychological review, Vol. 65, No. 6, pp. 386-408, 1958. https://doi.org/10.1037/h0042519
- GE Hinton, S Osindero, YW Teh, "A fast learning algorithm for deep belief nets", Neural computation, Vol. 18, No. 7, pp. 1527-1554, 2006. https://doi.org/10.1162/neco.2006.18.7.1527
- SM Ahn, "Deep Learning Architectures and Applications", Journal of Intelligence and Information Systems, Vol. 22, No. 2, pp. 127-142, 2016 https://doi.org/10.13088/JIIS.2016.22.2.127
- Inoue, H., "Image-based smoke detection with k-Subspaces clustering", Proc. of 2009 RISP International Workshop on Nonlinear Circuits and Signal Processing (NCSP'09), pp. 321-324, 2009.
- Wu, Pin, et al., "Human smoking event detection using visual interaction clues", 2010 International Conference on Pattern Recognition IEEE, pp. 4344-4347, 2010.
- Odetallah, Amjad D., and Sos S. Agaian., "Human visual system-based smoking event detection", Mobile Multimedia/Image Processing, Security, and Applications 2012, Vol. 8406, 2012.
- Bien, Tse-Lun, and Chang Hong Lin., "Detection and recognition of indoor smoking events", Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, Vol. 8784, 2013.
- Agarap, Abien Fred., "Deep learning using rectified linear units (relu)", arXiv preprint arXiv:1803.08375, 2018.
- Marreiros, A. C., Daunizeau, J., Kiebel, S. J., & Friston, K. J., "Population dynamics: variance and the sigmoid activation function", Neuroimage, Vol. 42, No. 1, pp. 147-157, 2008. https://doi.org/10.1016/j.neuroimage.2008.04.239
- Joulin, Armand, et al., "Efficient softmax approximation for gpus", Proceedings of the 34th International Conference on Machine Learning, PMLR, Vol. 70, pp. 1302-1310, 2017.
- Qin, Zhenyue, Dongwoo Kim, and Tom Gedeon, "Rethinking softmax with cross-entropy: Neural network classifier as mutual information estimator" arXiv preprint arXiv:1911.10688. 2019.
- Du, Simon, et al., "Gradient descent finds global minima of deep neural networks" Proceedings of the 36th International Conference on Machine Learning, PMLR, Vol. 97, pp. 1675-1685, 2019.
- Kalchbrenner, Nal, Edward Grefenstette, and Phil Blunsom, "A convolutional neural network for modelling sentences" arXiv preprint arXiv:1404.2188, 2014.
- Sandler, Mark, et al., "MobileNetV2: Inverted residuals and linear bottlenecks" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 4510-4520, 2018.
- Kingma, Diederik P., and Jimmy Ba., "Adam: A method for stochastic optimization" arXiv preprint arXiv:1412.6980, 2014.
- Kwon, Hyun, et al. "Optimal cluster expansion-based intrusion tolerant system to prevent denial of service attacks." Applied Sciences 7.11 (2017): 1186. https://doi.org/10.3390/app7111186
- Kwon, Hyun, et al. "Classification score approach for detecting adversarial example in deep neural network." Multimedia Tools and Applications (2020): 1-22.
- Kwon, Hyun, and Jun Lee. "AdvGuard: Fortifying Deep Neural Networks against Optimized Adversarial Example Attack." IEEE Access (2020).
- Kwon, Hyun, Hyunsoo Yoon, and Ki-Woong Park. "Acoustic-decoy: Detection of adversarial examples through audio modification on speech recognition system." Neurocomputing 417 (2020): 357-370. https://doi.org/10.1016/j.neucom.2020.07.101