• 한국통신학회논문지
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• 제37권1B호
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• pp.50-58
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• 2012

본 연구의 주된 목적은 사용자 관점에서 본 통신 시스템 서비스 가용도의 이론적 모델 개발이다. 이를 위하여 호(Call) 도착은 non-homogeneous 포아손 과정의 가정, 그리고 시스템 상태는 CTMC 모델의 가정을 토대로 서비스 가용도의 추계적 모델을 개발하였다. 제시한 모델은 시간에 따라 변하는 호 도착률을 포함하여 사용자 관점에서 본 서비스 신뢰도 모형의 사용자 모델을 효율적으로 나타냈다. 아울러 시스템 자원의 고장 없이도 사용자가 서비스를 받지 못하는 시스템 상태인 운영 고장 상태를 모델에 포함하여 제공자 입장이 아니라 사용자 관점에서 모델을 구축하였다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권1호
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• 정보과학회 논문지
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• 제42권6호
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• pp.748-754
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• 2015

본 연구에서는 다중 상태 변수의 인수 HMM을 일반화하여 연속 은닉 변수와 이산 은닉 변수가 결합된 순차 상태 추정 모형을 제안하고 이에 기반한 보행 동작 모형을 설계한다. 유한 상태의 이산변수는 마르코프 연쇄 구조로 보행의 동역학적 특성을 표현하고 각 이산 상태에 대해 연속 변수를 독립변수로 한 가우스 과정을 정의한다. 마르코프 상태 천이는 여러 가우스 과정 사이의 스위칭을 제어하며 각 가우스 과정은 동일한 자세의 회전 또는 다양한 시각을 표현한다. 온라인 필터링 추론을 위해 입자 필터 방식의 추론 알고리듬도 제시한다. 이 알고리듬은 입력 벡터 열이 주어졌을 때 이들 병렬적 가우스 과정을 동적으로 갈아타는 스위칭 궤적을 디코딩 해준다. 실험 결과 비선형적 보행자 비디오 영상을 보행방향과 보행 상태의 열로 분리하며 매우 직관적인 해석을 할 수 있음을 보였다.

• 한국수학교육학회지시리즈A:수학교육
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• 제25권1호
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• pp.1-8
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• 1986

It is a common saying that markov-chain is a special case of probability course. That is to say, It means an unchangeable markov-chain process of the transition-probability of discontinuous time. There are two kinds of ways to show transition probability parade matrix theory. The first is the way by arrangement of a rightangled tetragon. The second part is a vertical measurement and direction sing by transition-circle. In this essay, I try to find out existence of procession for transition-probability applied markov-chain. And it is possible for me to know not only, what it is basic on a study of chain but also being applied to abnormal problems following a flow change and statistic facts expecting to use as a model of air expansion in physics.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1043-1050
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• 2022

Markov chains and Monte Carlo Simulation were applied to account for the probabilistic nature of pavement deterioration over time using data collected in the field. The primary purpose of this study was to evaluate pavement network performance of Western Australia (WA) by applying the existing pavement management tools relevant to WA road construction networks. Two approaches were used to analyze the pavement networks: evaluating current pavement performance data to assess WA State Road networks and predicting the future states using past and current pavement data. The Markov chains process and Monte Carlo Simulation methods were used to predicting future conditions. The results indicated that Markov chains and Monte Carlo Simulation prediction models perform well compared to pavement performance data from the last four decades. The results also revealed the impact of design, traffic demand, and climate and construction standards on urban pavement performance. This study recommends an appropriate and effective pavement engineering management system for proper pavement design and analysis, preliminary planning, future pavement maintenance and rehabilitation, service life, and sustainable pavement construction functionality.

• 한국정밀공학회지
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• 제27권2호
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• pp.78-85
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• 2010

The remanufacturing system is a series of industrial process in which worn-out products are restored to like-new condition. The remanufacturing system is differ from the repair system not only process characteristics but also product characteristics. So, it is required to design another model for the remanufacturing system which is distinct from the repair system and also performance index is required for the remanufacturing system. Therefore, in this paper we suggest the availability model for remanufacturing system by using Markov Process. This model represents each of the states of the remanufacturing system. Also performance indexes of remanufacturing system are introduced. Performance indexes are consisting of part reuse frequency and time, part disposal frequency and time. As a result, we can have a choice and control the proper part and offer useful information during the remanufacturing by using these availability model and performance indexes.

• Nuclear Engineering and Technology
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• 제25권1호
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• pp.154-165
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• 1993

연속시간 마코프프로세스를 이용한 한 통계적 방법에 의한 일차원 지하 핵종이동 모델이 제시되었다. 지하매질은 보편적으로 지하수속도, 분산계수 또는 지연계수 등 물리화학적 변수 등의 비균질성을 보여 일반적인 결정론적 이류분산모델로는 잘 기술되지 않는다. 통계적 모델에서의 최종결과는 시간에 따른 함수로서의 기대값과 그 기대값의 분산도를 보여주는 분산치다. 매질이 균질하다고 생각될 정도로 나뉘어진 구획에 대한 핵종의 농도 분포를 구하여 결정론적인 해석해에 의한 농도분포와 비교하여 비균질 매질, 또는 현저하게 구분되는 다층매질의 경우에 대해서 유용 할 것이라는 결론을 얻었다. 매질을 나눈 구획수가 수치적 분산에 민감한 것으로 나타났지만 해석적 모델에 의해 분산계수가 보정될 수 있었다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2009년도 추계학술대회 논문집
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• pp.99-105
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• 2009

Crack detection method of a rotating blade was suggested in this paper. A rotating blade was modeled with a cantilever beam connected to a hub undergoing rotating motion. The existence and the location of crack were able to be recognized from the vertical response of end tip of a rotating cantilever beam by employing Discrete Hidden Markov Model (DHMM) and Empirical Mode Decomposition (EMD). DHMM is a famous stochastic method in the field of speech recognition. However, in recent researches, it has been proved that DHMM can also be used in machine health monitoring. EMD is the method suggested by Huang et al. that decompose a random signal into several mono component signals. EMD was used in this paper as the process of extraction of feature vectors which is the important process to developing DHMM. It was found that developed DHMMs for crack detection of a rotating blade have shown good crack detection ability.

• 제어로봇시스템학회논문지
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• 제21권11호
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• pp.1034-1037
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• 2015

In recent years in the field of robotics, various methods have been developed to create an intimate relationship between people and robots. These methods include speech, vision, and biometrics recognition as well as gesture-based interaction. These recognition technologies are used in various wearable devices, smartphones and other electric devices for convenience. Among these technologies, gesture recognition is the most commonly used and appropriate technology for wearable devices. Gesture recognition can be classified as contact or noncontact gesture recognition. This paper proposes contact gesture recognition with IMU and EMG sensors by using the hidden Markov model (HMM) twice. Several simple behaviors make main gestures through the one-stage HMM. It is equal to the Hidden Markov model process, which is well known for pattern recognition. Additionally, the sequence of the main gestures, which comes from the one-stage HMM, creates some higher-order gestures through the two-stage HMM. In this way, more natural and intelligent gestures can be implemented through simple gestures. This advanced process can play a larger role in gesture recognition-based UX for many wearable and smart devices.

• Asian Pacific Journal of Cancer Prevention
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• 제15권1호
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• pp.441-447
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• 2014

Background: Multi-state models are appropriate for cancer studies such as gastrectomy which have high mortality statistics. These models can be used to better describe the natural disease process. But reaching that goal requires making assumptions like Markov and homogeneity with time. The present study aims to investigate these hypotheses. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. To assess Markov assumption and time homogeneity in modeling transition rates among states of multi-state model, Cox-Snell residuals, Akaikie information criteria and Schoenfeld residuals were used, respectively. Results: The assessment of Markov assumption based on Cox-Snell residuals and Akaikie information criterion showed that Markov assumption was not held just for transition rate of relapse (state 1 ${\rightarrow}$ state 2) and for other transition rates - death hazard without relapse (state 1 ${\rightarrow}$ state 3) and death hazard with relapse (state 2 ${\rightarrow}$ state 3) - this assumption could also be made. Moreover, the assessment of time homogeneity assumption based on Schoenfeld residuals revealed that this assumption - regarding the general test and each of the variables in the model- was held just for relapse (state 1 ${\rightarrow}$ state 2) and death hazard with a relapse (state 2 ${\rightarrow}$ state 3). Conclusions: Most researchers take account of assumptions such as Markov and time homogeneity in modeling transition rates. These assumptions can make the multi-state model simpler but if these assumptions are not made, they will lead to incorrect inferences and improper fitting.