KJCKorea
Journal Central

@article{ART002708955},
author={Jong-Hyun Kim},
title={Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2021},
volume={26},
number={4},
pages={47-53},
doi={10.9708/jksci.2021.26.04.047}

TY - JOUR
AU - Jong-Hyun Kim
TI - Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method
JO - Journal of The Korea Society of Computer and Information
PY - 2021
VL - 26
IS - 4
PB - The Korean Society Of Computer And Information
SP - 47
EP - 53
SN - 1598-849X
AB - In this paper, we propose a quadtree-based optimization technique that enables fast Super-resolution(SR) computation by efficiently classifying and dividing physics-based simulation data required to calculate SR. The proposed method reduces the time required for quadtree computation by downscaling the smoke simulation data used as input data. By binarizing the density of the smoke in this process, a quadtree is constructed while mitigating the problem of numerical loss of density in the downscaling process. The data used for training is the COCO 2017 Dataset, and the artificial neural network uses a VGG19-based network. In order to prevent data loss when passing through the convolutional layer, similar to the residual method, the output value of the previous layer is added and learned. In the case of smoke, the proposed method achieved a speed improvement of about 15 to 18 times compared to the previous approach.
KW - Quadtree;Binarization;Downscaling;Convolutional neural network;Super-resolution;Fluid simulations
DO - 10.9708/jksci.2021.26.04.047
ER -

Jong-Hyun Kim. (2021). Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method. Journal of The Korea Society of Computer and Information, 26(4), 47-53.

Jong-Hyun Kim. 2021, "Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method", Journal of The Korea Society of Computer and Information, vol.26, no.4 pp.47-53. Available from: doi:10.9708/jksci.2021.26.04.047

Jong-Hyun Kim "Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method" Journal of The Korea Society of Computer and Information 26.4 pp.47-53 (2021) : 47.

Jong-Hyun Kim. Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method. 2021; 26(4), 47-53. Available from: doi:10.9708/jksci.2021.26.04.047

Jong-Hyun Kim. "Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method" Journal of The Korea Society of Computer and Information 26, no.4 (2021) : 47-53.doi: 10.9708/jksci.2021.26.04.047

Jong-Hyun Kim. Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method. Journal of The Korea Society of Computer and Information, 26(4), 47-53. doi: 10.9708/jksci.2021.26.04.047

Jong-Hyun Kim. Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method. Journal of The Korea Society of Computer and Information. 2021; 26(4) 47-53. doi: 10.9708/jksci.2021.26.04.047

Jong-Hyun Kim. Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method. 2021; 26(4), 47-53. Available from: doi:10.9708/jksci.2021.26.04.047

Jong-Hyun Kim. "Multiple Binarization Quadtree Framework for Optimizing Deep Learning-Based Smoke Synthesis Method" Journal of The Korea Society of Computer and Information 26, no.4 (2021) : 47-53.doi: 10.9708/jksci.2021.26.04.047