Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Hoekyung Jung
Pai Chai University
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Sensor data identification based reagent cabinet management system Changsu Kim; Hongyoul Kim; Hoekyung Jung
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (540.67 KB) | DOI: 10.11591/ijece.v9i6.pp5304-5311

Abstract

Recently, a reagent cabinet is used in a laboratory or a laboratory is required to have a system capable of identifying a dangerous situation through sensor data as various sensors are utilized. The existing system identifies the dangerous situation through various sensor data, but there is a problem that the server performs all the operations and the operation of the device is performed manually. In order to solve this problem, this paper proposes a system that can identify the dangerous situation and automatically operate the equipment through the internal environment data of the reagent cabinet. Identification of the hazardous situation is done through the master node used in the reagent cabinet, not the server. The server can continuously update the sensor data through the master node and monitor the real-time status of the reagent cabinet through the application. In this way, it is expected that the risk situation will be promptly addressed by identifying the dangerous situation in the reagent cabinet and operating the device.
Product recommendation system based user purchase criteria and product reviews Jinyoung Kim; Doyeun Hwang; Hoekyung Jung
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (694.117 KB) | DOI: 10.11591/ijece.v9i6.pp5454-5462

Abstract

In this paper, we propose a system that provides customized product recommendation information after crawling product review data of internet shopping mall with unstructured data, morphological analysis using Python. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. And extracts and analyzes only the review including the purchase criterion selected by the user among the product reviews left by other users. The positive and negative evaluations contained in the extracted product review data are quantified and using the average value, we extract the top 10 products with good product evaluation, sort and recommend to users. And provides user-customized information that reflects the user's preference by arranging and providing a center around the criteria that the user occupies the largest portion of the product purchase. This allows users to reduce the time it takes to purchase a product and make more efficient purchasing decisions.
Co-Authors Changsu Kim Doyeun Hwang Hongyoul Kim Jinyoung Kim