- Volume 20 Issue 4
Analyses on the Performance of the CNN Reflecting the Cerebral Structure for Prediction of Cybersickness Occurrence
사이버멀미 발생 예측을 위한 대뇌 구조를 반영한 CNN 성능 분석
- Shin, Jeong-Hoon (School of Information Technology, Dae-gu Catholic University)
- 신정훈 (대구가톨릭대학교 IT공학부)
- Received : 2019.12.15
- Accepted : 2019.12.30
- Published : 2019.12.31
In this study, we compared and analyzed the performance of each Convolution Neural Network (CNN) by implementing the CNN that reflected the characteristics of the cerebral structure, in order to analyze the CNN that was used for the prediction of cybersickness, and provided the performance varying depending on characteristics of the brain. Dizziness has many causes, but the most severe symptoms are considered attributable to vestibular dysfunction associated with the brain. Brain waves serve as indicators showing the state of brain activities, and tend to exhibit differences depending on external stimulation and cerebral activities. Changes in brain waves being caused by external stimuli and cerebral activities have been proved by many studies and experiments, including the thesis of Martijn E. Wokke, Tony Ro, published in 2019. Based on such correlation, we analyzed brain wave data collected from dizziness-inducing environments and implemented the dizziness predictive artificial neural network reflecting characteristics of the cerebral structure. The results of this study are expected to provide a basis for achieving optimal performance of the CNN used in the prediction of dizziness, and for predicting and preventing the occurrence of dizziness under various virtual reality (VR) environments.
Supported by : Daegu Catholic University
- G. Xu and H. Wu, "Structural design of convolutional neural networks for steganalysis," IEEE Signal Processing Letters, vol. 23, no. 5, pp. 708-712, 2016. https://doi.org/10.1109/LSP.2016.2548421
- J. Fridrich and J. Kodovsky, "Rich models for steganalysis of digital images," IEEE Trans. Inf. Forensics Security, vol. 7, no. 3, pp. 868-882, Jun. 2012. https://doi.org/10.1109/TIFS.2012.2190402
- C. Chan and L. Cheng, "Hiding data in images by simple LSB substitution," Pattern Recognition, 37(3), 469-474, 2004. https://doi.org/10.1016/j.patcog.2003.08.007
- D. Wu and W. Tsai, "A steganographic method for images by pixel-value differencing," Pattern Recognition, 24, 1613, 2003. https://doi.org/10.1016/S0167-8655(02)00402-6
- V. Holub and J. Fridrich, "Designing steganographic distortion using directional filters," IEEE Workshop on Information Forensic and Security, 2012.
- V. Holub, J. Fridrich, and T. Denemark, "Universal distortion function for steganography in an arbitrary domain," EURASIP Journal on Information Security, 2014.
- S. Kang, H. Park, and J.-I. Park, "CNN-based ternary classification for image steganalysis," Electronics, vol. 8, no. 11, 1225, 2019. https://doi.org/10.3390/electronics8111225
- P. Bas, T. Filler, and T. Pevny, "Break our steganographic system - the ins and outs of organizing BOSS," International Workshop on Information Hiding, pp. 59-70, 2011.