• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1864-1872
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• 2003

Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

• 한국항공운항학회:학술대회논문집
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• 2015.11a
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• pp.191-197
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• 2015

본 논문에서는 공항 지상 감시 장비(ASDE: Airport Surface Detection Equipment) 데이터를 이용하여 항공기의 지상이동 운항모드를 추정하는 방법을 제안하였다. 제안된 방법에서는 항공기의 운항모드와 그에 따라 관측되는 속도 및 가속도를 확률 변수로 정의함으로써, 확률적 추정방법을 통해 운항모드를 추정하였다. 운항모드를 추정하기 위한 모형으로서는 은닉 마르코프 모형(HMM: Hidden Markov Model)을 사용하였으며 실제 ASDE 데이터를 통해 제안된 방법의 성능을 검증해 보았다.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.461-475
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• 2021

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

• Journal of Korea Multimedia Society
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• v.3 no.6
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• pp.566-574
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• 2000

Although a numbed of variants of 2D HMM have been proposed in the literature, they are, in a word, too simple to model the variabilities of images for diverse classes of objects; they do not realize the modeling capability of the 1D HMM in 2D. Thus the author thinks they are poor substitutes for the HMM in 2D. The new model proposed in this paper is a hidden Markov lattice or, we can dare say, a 2D HMM with the causality of top-down and left-right direction. Then with the addition of a lattice constraint, the two algorithms for the evaluation of a model and the maximum likelihood estimation of model parameters are developed in the theoretical perspective. It is a more natural extension of the 1D HMM. The proposed method will provide a useful way of modeling highly variable patterns such as offline cursive characters.

• Journal of KIISE:Software and Applications
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• v.28 no.1
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• pp.46-51
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• 2001

2D로 확장한 HMM은 다수 제안되었지만 엄밀한 의미에 있어서 2D HMM이라고 하기에 부족한 점이 많다. 본 논문에서는 기존의 랜덤 필드 모형이 아닌 새로운 2D HMM을 제안한다. 상하 및 좌우 방향의 causal chain 관계를 가정하고 완전한 격자 형성 조건을 두어 2D HMM의 평가, 매개 변수를 추정하는 알고리즘을 제시하였다. 각각의 알고리즘은 동적 프로그래밍과 최우 추정법에 근거한 것이다. 변수 추정 알고리즘은 반복적으로 이루어지며 국소 최적치에 수렴함을 보였다.

• Journal of KIISE
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• v.44 no.10
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• pp.1026-1033
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• 2017

In this paper a Bayesian modeling and duration-based prediction method is proposed for health clinic time series data using the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). HDP-HMM is a Bayesian extension of HMM which can find the optimal number of health states, a number which is highly uncertain and even difficult to estimate under the context of health dynamics. Test results of HDP-HMM using simulated data and real health clinic data have shown interesting modeling behaviors and promising prediction performance over the span of up to five years. The future of health change is uncertain and its prediction is inherently difficult, but experimental results on health clinic data suggests that practical long-term prediction is possible and can be made useful if we present multiple hypotheses given dynamic contexts as defined by HMM states.

• Annual Conference on Human and Language Technology
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• 1995.10a
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• pp.152-163
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• 1995

본 논문에서는 은닉 마르코프 모형(HMM)을 이용하여 사람이 쓴 필기의 통계적 특징을 갖는 글씨를 생성하는 방법에 대해서 기술코자 한다. 온라인 필기처럼 같이 필기 궤적을 시계열 신호로 표현하고, 그 특징을 통계적 모형의 하나인 HMM으로 표현한다. HMM은 시계열 신호에 대응하는 모형 내부 경로와 심볼열의 확률 분포를 표현하는 함수이다. 따라서 최적 경로에서 볼 수 있는 최적 출력 심볼열은 훈련 필기 데이타의 평균적 필기 특징에 해당한다. HMM이 주어졌을 때 HMM에서 최적의 패턴을 해석적으로 구하는 방법은 알려져 있지 않다. 본 논문에서는 동적 프로그래밍 기법을 적용하여 HMM이나 HMM 네트워크 모형에서 필기를 생성하는 방법을 제시하고, 아울러 HMM의 문제점을 지적한다.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.4
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• pp.189-196
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• 2017

BIS(Bus Information System) provides the different information related to buses including predictions of arriving times at stations. BIS have been deployed almost all cities in our country and played active roles to improve the convenience of public transportation systems. Moving average filters, Kalman filter and regression models have been representative in forecasting the arriving times of buses in current BIS. The accuracy in prediction of arriving times depends largely on the forecasting algorithms and traffic conditions considered when forecasting in BIS. In present BIS, the simple prediction algorithms are used only considering the passage times and distances between stations. The forecasting of arrivals, however, have been influenced by the traffic conditions such as traffic signals, traffic accidents and pedestrians ets., and missing data. To improve the accuracy of bus arriving estimates, there are big troubles in building models including the above problems. Hidden Markov Models have been effective algorithms considering various restrictions above. So, we have built the HMM forecasting models for bus arriving times in the current BIS. When building models, the data collected from Sunchean City at 2015 have been utilized. There are about 2298 stations and 217 routes in Suncheon city. The models are developed differently week days and weekend. And then the models are conformed with the data from different districts and times. We find that our HMM models can provide more accurate forecasting than other existing methods like moving average filters, Kalmam filters, or regression models. In this paper, we propose Hidden Markov Model to obtain more precise and accurate model better than Moving Average Filter, Kalman Filter and regression model. With the help of Hidden Markov Model, two different sections were used to find the pattern and verified using Bootstrap process.

• Journal of KIISE
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• v.42 no.6
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• pp.748-754
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• 2015

This paper proposes a sequential state estimation model consisting of continuous and discrete variables, as a way of generalizing all discrete-state factorial HMM, and gives a design of gait motion model based on the idea. The discrete state variable implements a Markov chain that models the gait dynamics, and for each state of the Markov chain, we created a Gaussian process over the space of the continuous variable. The Markov chain controls the switching among Gaussian processes, each of which models the rotation or various views of a gait state. Then a particle filter-based algorithm is presented to give an approximate filtering solution. Given an input vector sequence presented over time, this finds a trajectory that follows a Gaussian process and occasionally switches to another dynamically. Experimental results show that the proposed model can provide a very intuitive interpretation of video-based gait into a sequence of poses and a sequence of posture states.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.85-100
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• 2023

The stochastic volatility (SV) model is one of the main methods of modeling time-varying volatility. In particular, SV model is actively used in estimation and prediction of financial market volatility and option pricing. This paper attempts to model the time-varying volatility of the bitcoin market price using SV model. Hidden Markov model (HMM) is combined with the SV model to capture characteristics of regime switching of the market. The HMM is useful for recognizing patterns of time series to divide the regime of market volatility. This study estimated the volatility of bitcoin by using data from Upbit, a cryptocurrency trading site, and analyzed it by dividing the volatility regime of the market to improve the performance of the SV model. The MCMC technique is used to estimate the parameters of the SV model, and the performance of the model is verified through evaluation criteria such as MAPE and MSE.