Jihn Chang-Ho
Kyung Hee University · 意思決定科学
研究室紹介
Professor Jihn Chang-Ho's research lab specializes in statistical process control (SPC) and time series modeling, with a focus on developing advanced control charting techniques for autocorrelated processes. The lab investigates optimal linear filtering strategies to enhance the performance of control charts by minimizing out-of-control average run length (ARL) while maintaining in-control ARL constraints. A key research direction involves improving robustness against model errors in time series representations, particularly in industrial and manufacturing settings with in-process measurement data. The lab also explores game-theoretic models for dynamic network behavior, especially in delay-sensitive and impatient user environments.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Many control charts can be viewed as charting the output of a linear filter applied to process data, with an alarm sounded when the filter output falls outside a set of control limits. We generalize this concept by considering a linear filter in its most general time-invariant form. We provide a strategy for optimizing the filter coefficients in order to minimize the out-of-control ARL, while constraining the in-control ARL to some desired value. The optimal linear filters exhibit a number of in
results, mathematical proofs, and concrete analyses.
With the proliferation of in-process measurement technology, autocorrelated data are increasingly common in industrial SPC applications. A number of high performance control charting techniques that take into account the specific characteristics of the autocorrelation through time series modeling have been proposed over the past decade. We present a survey of such methods and analyze and compare their performances for a range of typical autocorrelated process models. One practical concern with t
Statistical Process Control (SPC), and in particular control charting, is widely used to achieve and maintain control of various processes in manufacturing. A control chart is a graphical display that plots quality characteristics versus the sample number or the time line. Interest in effective implementation of control charts for autocorrelated processes has increased in recent years. However, because of the complexities involved, few systematic design approaches have thus far been developed. \
Many control charts are used to determine the output of a linear filter applied to process data. An alarm is sounded when the filter output falls outside a set of control limits. In this study, this concept is generalized by observing the linear filter ..
Most game-theoretic works of Aloha have emphasized investigating Nash equilibria according to the system state represented by the number of network users and their decisions. In contrast, we focus on the possible change of nodes’ utility state represented by delay constraint and decreasing utility over time. These foregone changes of nodes’ state are more likely to instigate selfish behaviors in networking environments. For such environment, in this paper, we propose a repeated Bayesian slotted
본 연구는 온라인 사용자의 연령대와 영화의 장르를기반으로 성공적인 영화 시나리오 구성을 파악하고자하는 것에서 시작되었다. 본 연구의 목적은 사용자의평점 데이터를 기반으로 Random Forest를 활용하여관객이 선호하는 영화의 유형을 규칙으로 정의함으로써 영화 시나리오의 이상적인 조합을 식별하는 것이다. 따라서 본 논문은 관객의 평점에 따라 장르 18개와 연령대 측면에서 영화의 성공과 실패를 좌우하는영화 시나리오 조건들을 규칙으로 해석을 시도하였다. 또한 시나리오 조건과 결과(성공, 실패)로 구성된 규칙의 유용성(frequency)과 신뢰도(error)를 장르와 목표연령대의 바람직한 조합을 선별하는 기준으로 정의했다. 연구 결과 및 내용은 다음과 같다. 첫째, 여러 개의 의사결정나무로 이루어진 Random Forest 알고리즘을 활용하여 많은 수의 규칙을 생성함으로써 신뢰성이높은 규칙들을 산출하였다. 둘째, 규칙 내 불필요한 조건들을 제거하여 규칙을 일반화함으로써 실용적인 규칙을 생
ABSTRACT The Korean guidelines developed by the Ministry of Environment for soil investigations do not seriously take into account statistical characteristics of collected data and statistical assumptions required for the methods applied. In this article, we point out the statistical omissions in the Korean guidelines and propose some supplements to them. Systematic sampling is recommended, since systematic sampling raises sample representativeness and provides a more efficient allocation of res
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Treatment and management of chronic low back pain (CLBP) should be tailored to the patient’s individual context. However, there are limited resources available in which to find and manage the causes and mechanisms for each patient. In this study, we designed and developed a personalized context awareness system that uses machine learning techniques to understand the relationship between a patient’s lower back pain and the surrounding environment. A pilot study was conducted to verify the context
Issued as Progress reports [nos. 1-2], Annual report, and Final report, Project no. E-20-M26
Depression is one of the most important psychiatric disorders worldwide. Most depression-related data mining and machine learning studies have been conducted to predict the presence of depression or to derive individual risk factors. However, since depression is caused by a combination of various factors, it is necessary to identify the complex relationship between the factors in order to establish effective anti-depression and management measures. In this study, we propose a methodology for ide