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@article{ART002597292},
author={HAN EIGSEUB},
title={A Study on the Application of TensorFlow to Determine the Correctional Distance},
journal={Journal of Knowledge Information Technology and Systems},
issn={1975-7700},
year={2020},
volume={15},
number={3},
pages={323-329},
doi={10.34163/jkits.2020.15.3.002}

TY - JOUR
AU - HAN EIGSEUB
TI - A Study on the Application of TensorFlow to Determine the Correctional Distance
JO - Journal of Knowledge Information Technology and Systems
PY - 2020
VL - 15
IS - 3
PB - Korea Knowledge Information Technology Society
SP - 323
EP - 329
SN - 1975-7700
AB - Environmental pollution is getting serious around the world recently. Economic losses from air pollution and threats from ultra fine dust are becoming social problems. Currently, measurements are made through wood and optical measuring equipment, but there is a problem where measurements and human sense of corrective action do not match. This paper aimed to implement an algorithm of judgment to match the measured value with the human sense of visibility. Using IoT-based cameras in buildings, measured photo information is sent to the server to make corrective distance measurements, and real-time transmitted photos and existing measured photo information are processed in high-speed operation through Tensorflow, requiring high-reliability corrective distance. An algorithm that is supplemented with a SVM nonlinear regression model algorithm for existing corrective distance determination algorithms has been implemented to automate with algorithms similar to those that are directly judged by humans. In this study, a support vector machine (SVM) nonlinear regression model algorithm is used to perform high-speed computation using Tensorflow, and a system implementation model is proposed to improve reliability of the corrective judgment algorithm model.
KW - Tensorflow;Smart factory;IoT;LwM2M(Lightweight M2M);Intelligent IoT systems;Deep learning
DO - 10.34163/jkits.2020.15.3.002
ER -

HAN EIGSEUB. (2020). A Study on the Application of TensorFlow to Determine the Correctional Distance. Journal of Knowledge Information Technology and Systems, 15(3), 323-329.

HAN EIGSEUB. 2020, "A Study on the Application of TensorFlow to Determine the Correctional Distance", Journal of Knowledge Information Technology and Systems, vol.15, no.3 pp.323-329. Available from: doi:10.34163/jkits.2020.15.3.002

HAN EIGSEUB "A Study on the Application of TensorFlow to Determine the Correctional Distance" Journal of Knowledge Information Technology and Systems 15.3 pp.323-329 (2020) : 323.

HAN EIGSEUB. A Study on the Application of TensorFlow to Determine the Correctional Distance. 2020; 15(3), 323-329. Available from: doi:10.34163/jkits.2020.15.3.002

HAN EIGSEUB. "A Study on the Application of TensorFlow to Determine the Correctional Distance" Journal of Knowledge Information Technology and Systems 15, no.3 (2020) : 323-329.doi: 10.34163/jkits.2020.15.3.002

HAN EIGSEUB. A Study on the Application of TensorFlow to Determine the Correctional Distance. Journal of Knowledge Information Technology and Systems, 15(3), 323-329. doi: 10.34163/jkits.2020.15.3.002

HAN EIGSEUB. A Study on the Application of TensorFlow to Determine the Correctional Distance. Journal of Knowledge Information Technology and Systems. 2020; 15(3) 323-329. doi: 10.34163/jkits.2020.15.3.002

HAN EIGSEUB. A Study on the Application of TensorFlow to Determine the Correctional Distance. 2020; 15(3), 323-329. Available from: doi:10.34163/jkits.2020.15.3.002

HAN EIGSEUB. "A Study on the Application of TensorFlow to Determine the Correctional Distance" Journal of Knowledge Information Technology and Systems 15, no.3 (2020) : 323-329.doi: 10.34163/jkits.2020.15.3.002