• 대한토목학회논문집
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• 제33권5호
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• pp.1861-1870
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• 2013

최근 기후변화 영향으로 인해 수문변동성이 크게 증가되고 있으며 이러한 변동성을 고려하기 위한 방안으로서 강수량 모의발생 기법에 대한 중요성이 대두되고 있다. 본 연구에서는 복잡한 강수발생 패턴을 인지하고 강수량의 다양한 분포특성을 고려할 수 있는 혼합분포를 이용한 동질성 Hidden Markov Chain(HMM) 모형을 제안하였다. HMM 모형의 개선효과를 검증하기 위해서 기존 Markov Chain 모형과 비교 하였으며 서울관측소 및 전주관측소를 대상으로 연구를 진행하였다. 계절강수량 및 일강수량 등 다양한 시간규모에서 모형의 적합성을 평가하기 위해서 천이확률, 평균, 분산, 왜곡도 및 첨예도 등을 비교하였으며 HMM 모형이 기존 Markov Chain 모형에 비해서 개선된 모의능력을 확인할 수 있었다. 특히, HMM 모형은 극치강수량을 재현하는데 있어서 기존 Markov Chain 모형에 비해서 월등한 모의능력을 보여주었다. 이러한 점에서 장기유출량 및 확률홍수량 등을 산정하기 위한 입력자료로 활용이 충분히 가능할 것으로 판단된다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.259-262
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• 2004

In most of the character recognition systems, the method of template matching or statistical method using hidden Markov model is used to extract and recognize feature shapes. In this paper, we used modified chain-code which has 8-directions but 4-codes, and made the chain-code of hand-written character, after that, converted it into transition chain-code by applying to HMM(Hidden Markov Model). The transition chain code by HMM is analyzed as signal flow graph by Mason's theory which is generally used to calculate forward gain at automatic control system. If the specific forward gain and feedback gain is properly set, the forward gain of transition chain-code using Mason's theory can be distinguished depending on each object for recognition. This data of the gain is reorganized as tree structure, hence making it possible to distinguish different hand-written characters. With this method, $91\%$ recognition rate was acquired.

• 한국수자원학회논문집
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• 제46권10호
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• pp.1029-1040
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• 2013

본 연구에서는 유역의 공간상관성을 고려한 다지점 일단위 강수량을 동시에 모의할 수 있는 일강수량 모의기법을 개발하였다. 기존 Hidden Markov Chain Model(HMM)은 단일지점 강수모의에 적용되어 왔으나 관측지점간의 유역상관성을 충분히 고려하지 못하는 문제점을 가지고 있다. 따라서 본 연구에서는 Chow-Liu Tree (CLT) 모형을 적용하여 다변량(multivariate) 형태로써 유역내에 위치한 강우관측소간의 상호종속성을 고려하기 위하여 기존의 동질성 HMM 강우모의기법과 CLT 알고리즘을 결합한 동질성 CLT-HMM 모형을 개발하였다. 본 연구에서 개발된 동질성 CLT-HMM 모형을 사용하여장기간의수문자료를보유하고있는기상청산하의한강유역강수네트워크에대해서 적합성을 검토하였다. 동질성 CLT-HMM 모형을 적용하여 모의된 결과를 보면 일강수량의 계절적 특성뿐만 아니라 일강수량모의 시 강수시계열의 통계적인 특성들까지 우수하게 모의하였다. 추가적으로 상관행렬(correlation matrix)을 이용하여 기상관측소간의 공간상관 재현성을 검토한 결과 관측지점들 사이의 공간상관성도 비교적 우수하게 재현하는 것을 확인할 수 있었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.499-499
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• 2018

Hydro-meteorological extremes are trivial in these days. Therefore, it is important to identify extreme hydrological events in advance to mitigate the damage due to the extreme events. In this context, exploring temporal distribution of sub-daily extreme rainfall at multiple rain gauges would informative to identify different states to describe severity of the disaster. This study proposehidden Markov chain model (HMM) based rainfall analysis tool to understand the temporal sub-daily rainfall patterns over South Korea. Hourly and daily rainfall data between 1961 and 2017 for 92 stations were used for the study. HMM was applied to daily rainfall series to identify an observed hidden state associated with rainfall frequency and intensity, and further utilized the estimated hidden states to derive a temporal distribution of daily extreme rainfall. Transition between states over time was clearly identified, because HMM obviously identifies the temporal dependence in the daily rainfall states. The proposed HMM was very useful tool to derive the temporal attributes of the daily rainfall in South Korea. Further, daily rainfall series were disaggregated into sub-daily rainfall sequences based on the temporal distribution of hourly rainfall data.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 제13회 신호처리 합동 학술대회 논문집
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• pp.375-378
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• 2000

In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.

• 한국정보처리학회논문지
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• 제7권6호
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• pp.1852-1860
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• 2000

Distinct from the Markov random field or pseudo 2D HMM models for image analysis, this paper proposes a new model of 2D hidden Markov mesh chain(HMMM) model which subsumes the definitions of and the assumptions underlying the conventional HMM. The proposed model is a new theoretical realization of 2D HMM with the causality of top-down and left-right progression and the complete lattice constraint. These two conditions enable an efficient mesh decoding for model estimation and a recursive maximum likelihood estimation of model parameters. Those algorithms are developed in theoretical perspective and, in particular, the training algorithm, it is proved, attains the optimal set of parameters.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• 한국컴퓨터정보학회논문지
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• 제9권3호
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• pp.121-125
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• 2004

대부분의 수기 숫자 인식 시스템에서는 자모 형태를 이용한 특징 점 추출과, 형태소 적 분석기법을 많이 사용하였다. 본 연구에서는 체인코드를 사용하고, 생성된 체인코드를 미분하여 최소 값이 되는 미분코드를 만들었다. 이 미분코드는 대부분의 수기 숫자에 적용해 본 결과 숫자 변별력이 매우 뛰어남을 알 수 있었다. 처리 순서는 몇 개의 수기숫자를 전 처리하고, 체인코드와 미분코드를 생성 한 후, HMM 인식 네트워크를 사용하여 숫자 인식하였다. 처리 결과 96.1%의 수기숫자를 인식하였으며, 매우 심하게 왜곡된 숫자는 인식하지 못하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2016년도 학술발표회
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• pp.197-197
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• 2016

Forecasting future drought events in a region plays a major role in water management and risk assessment of drought occurrences. The creeping characteristics of drought make it possible to mitigate drought's effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, a new probabilistic scheme is proposed to forecast droughts, in which a discrete-time finite state-space hidden Markov model (HMM) is used aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The 3-month standardized precipitation index (SPI) is employed to assess the drought severity over the selected five stations in South Kore. A reversible jump Markov chain Monte Carlo algorithm is used for inference on the model parameters which includes several hidden states and the state specific parameters. We perform an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to derive a probabilistic forecast that considers uncertainties. Results showed that the HMM-RCP forecast mean values, as measured by forecasting skill scores, are much more accurate than those from conventional models and a climatology reference model at various lead times over the study sites. In addition, the probabilistic forecast verification technique, which includes the ranked probability skill score and the relative operating characteristic, is performed on the proposed model to check the performance. It is found that the HMM-RCP provides a probabilistic forecast with satisfactory evaluation for different drought severity categories, even with a long lead time. The overall results indicate that the proposed HMM-RCP shows a powerful skill for probabilistic drought forecasting.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.390-390
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• 2019

Application of Hidden Markov Model (HMM) to the hydrological time series would be an innovative way to identify extreme rainfall events in a series. Even though the optimum number of hidden states can be identify based on maximizing the log-likelihood or minimizing Bayesian information criterion. However, occasionally value for the log-likelihood keep increasing with the state which gives false identification of the optimum hidden state. Therefore, this study attempts to identify optimum number of hidden states for Colombo station, Sri Lanka as fundamental approach to identify frequency and percentage of extreme rainfall events for the station. Colombo station consisted of daily rainfall values between 1961 and 2015. The representative station is located at the wet zone of Sri Lanka where the major rainfall season falls on May to September. Therefore, HMM was ran for the season of May to September between 1961 and 2015. Results showed more or less similar log-likelihood which could be identified as maximum for states between 4 to 7. Therefore, measure of central tendency (i.e. mean, median, mode, standard deviation, variance and auto-correlation) for observed and simulated daily rainfall series was carried to each state to identify optimum state which could give statistically compatible results. Further, the method was applied for the second major rainfall season (i.e. October to February) for the same station as a comparison.