J
Jongpil Jeong
Researcher at Sungkyunkwan University
Publications - 208
Citations - 1046
Jongpil Jeong is an academic researcher from Sungkyunkwan University. The author has contributed to research in topics: Mobility management & Handover. The author has an hindex of 10, co-authored 203 publications receiving 682 citations.
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Proceedings ArticleDOI
Integrated OTP-Based User Authentication Scheme Using Smart Cards in Home Networks
TL;DR: The proposed authentication protocol is designed to accept the existing home networks based on the one-time password protocol and is quite satisfactory in terms of the security requirements of home networks, because of requiring low computation by performing simple operations using one-way hash functions.
Journal ArticleDOI
Time-Series Data Augmentation based on Interpolation
TL;DR: A time-series data augmentation method based on interpolation that is robust against the impairment of trend information of the original time- series and has the advantage of not high complexity is proposed.
Journal ArticleDOI
Components for Smart Autonomous Ship Architecture Based on Intelligent Information Technology
TL;DR: A smart autonomous ship architecture that enables Unmanned Ship by using Intelligence Information Technology (ICBMS + AI), which is the core technology of the fourth industrial revolution, and remote ship operation and management system that can operate it safely, economically and efficiently.
Journal ArticleDOI
A Novel Architecture of Air Pollution Measurement Platform Using 5G and Blockchain for Industrial IoT Applications
TL;DR: The architecture of the proposed platform, which breaks the existing passive method and collects data in real time through IoT sensor based on 5G wireless network and blockchain, is described and use cases are introduced.
Journal ArticleDOI
Mobile Robot Path Optimization Technique Based on Reinforcement Learning Algorithm in Warehouse Environment
HyeokSoo Lee,Jongpil Jeong +1 more
TL;DR: The results of experiments conducted using two basic algorithms were compared to identify the fundamentals required for planning the path of a mobile robot and utilizing reinforcement learning techniques for path optimization.