Jae‐Yoon Jung
경희대학교 산업경영공학과 · 컴퓨터과학
Jae-Yoon Jung 교수의 연구실은 스마트 팩토리와 기업의 디지털 전환을 견인하는 핵심 기술인 엣지 컴퓨팅, 비즈니스 프로세스 분석, 그리고 데이터 기반 품질 제어 기법을 중심으로 연구를 진행하고 있습니다. 특히, 실시간 데이터 처리와 이상 탐지, 불균형한 품질 데이터에 대한 고비용 분류 기법, 비즈니스 프로세스 모델의 유사도 기반 클러스터링을 통해 신규 프로세스 설계 및 재설계를 지원하는 기술적 접근을 개발하고 있습니다. 이는 제조업과 도시 교통 분석 등 다양한 산업 분야에서의 응용 가능성을 지닌 융합 연구입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Monitoring the status of the facilities and detecting any faults are considered an important technology in a smart factory. Although the faults of machine can be analyzed in real time using collected data, it requires a large amount of computing resources to handle the massive data. A cloud server can be used to analyze the collected data, but it is more efficient to adopt the edge computing concept that employs edge devices located close to the facilities. Edge devices can improve data processi
We describe a proposed methodology for business process choreography. We focus on two types of business processes (contract and executable) and provide an interface protocol to represent interoperability patterns between them. The approach is designed to let existing processes, usually managed by an enterprise's own internal workflow management system, collaborate.
Data-driven quality control techniques are being actively developed for implementation in smart factories. Quality prediction during manufacturing processes is a good example of how big data analytics can influence advanced manufacturing environments. In this paper, the problem of classifying manufacturing process conditions into normal and defective products according to defect types is dealt with. Such a quality analysis data set is generally unbalanced because the defective rate is quite low
Abstract. Business process is collection of standardized and structured tasks inducing value creation of a company. Nowadays, it is recognized as one of significant intangi-ble business assets to achieve competitive advantages. We introduce a novel approach to business process analysis, which has more and more significance as process-aware in-formation systems that are spreading widely over a lot of companies. In this paper, a methodology of business process clustering based on process similarit
Identifying zones and movement patterns of people is crucial to understanding adjacent regions and the relationship in urban areas. Most previous studies addressed zones or movement patterns separately without analysing simultaneously the two issues. In this article, we propose an integrated approach to discover directly both zones and movement patterns among the zones, referred to as movement patterns between zones (MZPs), from historical boarding behaviours of passengers in subway networks by
