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Soo-Min Kang

Korea University · Business, Management and Accounting

About the Lab

Professor Soo-Min Kang's research lab specializes in intelligent transportation systems and advanced security technologies, with a strong focus on enhancing mobility for people with disabilities and improving aviation security through AI-driven solutions. The lab conducts innovative research in smart mobility patent analysis, leveraging data clustering and technology mapping to identify underdeveloped technological opportunities in assistive mobility. It also pioneers the application of computer vision and deep learning to enhance object detection in X-ray imaging, particularly for identifying high-risk items in baggage screening. The lab’s work bridges technological innovation with real-world societal impact, emphasizing accessibility and safety in transportation and security infrastructure.

smart mobilityaviation securitycomputer visiondeep learningpatent analysis

Research Overview

Papers
2
Total Citations
25
Papers (5y)
2
Primary Field
Business, Management and Accounting

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
2total
2020
Citations per year (5y)
25total
2020

Selected Papers

2
1
Article|21 citations·2020
Identification of Vacant and Emerging Technologies in Smart Mobility Through the GTM-Based Patent Map Development
Jiwon Yu, Jong-Gyu Hwang, Jong-Gyu Hwang, Jumi Hwang, Jumi Hwang, Sungchan Jun, Sumin Kang, Chulung Lee, Hyundong Kim
SJR Q1SustainabilityOA

With the development of the online platforms and the Internet of Things (IoT), various transportation services have been provided, and the lifestyle of the general public has changed significantly. However, the speed of development of technologies and services for the mobility handicapped has been relatively slow. Accordingly, in this paper, the smart mobility patent data for the mobility handicapped is subdivided through clustering to derive the mobility handicapped-related vacant technologies,

Management of Technology and InnovationBusiness, Management and Accounting
2
Article|4 citations·2020
O-Net: Dangerous Goods Detection in Aviation Security Based on U-Net
Woong Kim, Sungchan Jun, Sumin Kang, Chulung Lee
SJR Q1IEEE AccessOA

Aviation security X-ray equipment currently searches objects through primary screening, in which the screener has to re-search a baggage/person to detect the target object from overlapping objects. The advancements of computer vision and deep learning technology can be applied to improve the accuracy of identifying the most dangerous goods, guns and knives, from X-ray images of baggage. Artificial intelligence-based aviation security X-rays can facilitate the high-speed detection of target objec

Computer Vision and Pattern RecognitionComputer Science

Research Areas

Management of Technology and InnovationComputer Vision and Pattern Recognition

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