Intro

Education

  • M.S. in Dept. of Industrial Engineering, Seoul National Unviersity, South Korea, 2019.7 - present
  • B.S. in Dept. of Computer Science, University of Wisconsin - Madison, USA, 2015.9 - 2019.5

Interest Areas

  • User experience
  • Data analysis
  • Usability

Publications

International Journal

  • Applied Science(2nd Author) - Identifying the Risk Factors in the Context-of-Use of Electric Kick Scooters Based on a Latent Dirichlet Allocation
  • Safety Science(2nd Author) - Contextual risk factors in the use of electric kick scooters: An episode sampling inquiry

International Conference

  • 2019 IEEE IEEM(Conference poster) - Developing User Evaluation Technology Based on New Product Concepts

Domestic Conference

  • 2019 KIIE (Conference oral presentation) - Ergonomic Design for South Korean Military Backpack using Anthropometric dimensions
  • 2020 ESK (Conference oral presentation) - Evaluating User Experience Using Physiological Data from Conversational Interfaces for Interactive TV

Work

Professional Experience

Research Assistant, Postech, 2015.7 - 2015.8

  • EEG 센서를 활용한 영화 흥행률 예측

Project Experience

Samsung Electronics, 2019.7 - 2021.2

  • 공기청정기, 에어콘, 청소기, 세탁기, 의류관리기, 건조기, 전자레인지, 오븐, 식기세척기, 가스레인지 인간공학적 설계 가이드라인 및 체크리스트 작성

Ministry of Culture, Sports and Tourism, 2019.7 - 2019.12

  • 몰입형 관광 경험 극대화를 위한 다중 감각 경험을 접목해 인터렉티브 콘텐츠 개발

KATECH, 2019.7 - present

  • 미래형 이동 수단 사용자 경험 평가 맥락 및 단계별 평가 방법론 개발
  • 인간공학적 실험 및 평가 체계 검증
  • 미래형 이동 수단의 사용자 경험 평가 표준 실행 가이드 개발

Coway, 2020.7 - present

  • 안마의자 인간공학적 실험 및 평가 설계 제안

Coumputer Related Skills

  • Programming Language: C, Java, Ruby, JavaScript, Python
  • OS: MS Windows, MacOS, Linux, Android, iOS
  • Tools: VIM, eclipse, Android Studio

Project

Fantasy Football

  • Our motivation for this project was sparked by a shared interest in sports. We had built up a dataset as a side project prior to this course by scraping data from www.pro-football-reference.com1 and www.footballoutsiders.com2 using python scripts, so we decided to use that dataset. Fantasy football is a very large industry. It is estimated that each season costs employers in the USA $17 billion dollars in productivity3. As such, there is a lot of interest in the field and people are always looking for an edge. Normally it is expensive to purchase tools (ex. ESPN API) to gain an advantage on evaluating the data. Therefore it is hard to try to interpret large amounts of data without investing the capital. We began the project with the intention to create a tool that can perform searches within our extracted data to allow a user to try to identify trends in the data.
  • by fantasy coders: Chan Hyeok Yun, Oliver DeBarros, Joseluis Garcia

    • License Plate Recognition

  • Incoming Image
  • Convert to Binary
  • Find properties of connected Components
  • Filter out undesired bounding boxes
  • Compare Binary picture of letter to alphanumeric database
  • Output best comparison values

    • Contact

    윤찬혁(Chan Hyeok Yun)
    ych8일팔4 at snu.ac.kr
    2021년 7월 졸업 예정인 전문연구요원 입사를 희망하는 윤찬혁입니다. 잘 부탁드립니다.

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