• Journal of Welding and Joining
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• 제20권6호
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• pp.769-775
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• 2002

This study is a middle report on the development of intelligent spot welding monitoring technology applicable to the production line. An intelligent algorithm has been developed to predict the quality of welding in real time. We examined whether it is effective or not through the In-Line and the Off-Line tests. The purpose of the present study is to provide a reliable solution which can prevent welding defects in production site. In this study, the process variables, which were monitored in the primary circuit of the welding, are used to estimate the weld quality by Hidden Markov Model(HMM). The primary dynamic resistance patterns are recognized and the quality is estimated in probability method during the welding. We expect that the algorithm proposed in the present study is feasible to the applied in the production sites for the purpose of in-process real time quality monitoring of spot welding.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.833-834
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• 2014

In recent years, a pattern recognition method has been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov model has recently been used as pattern recognition methods in various fields. In this study, a HMM method for the fault diagnosis of a mechanical system is introduced, and a rotating machine with mass unbalance is selected for fault diagnosis. Moreover, a diagnosis procedure to identity the size of a defect is proposed in this study.

• 한국재난정보학회:학술대회논문집
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• 한국재난정보학회 2016년 정기학술대회
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• pp.388-390
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• 2016

본 연구에서는 금강유역의 가뭄과 한반도 주변 지역의 기후 인자들과의 상관관계를 파악하고 이를 바탕으로 기후변화 시나리오를 이용하여 미래의 가뭄을 예측하였다. 1974 - 2015년 동안 11 - 5월에 발생한 강우 자료와 NOAA에서 제공하는 NCEP-NCAR 자료를 이용하여 한반도 주변 기후인자와 금강유역의 강우가 과거 발생한 가뭄과 어떠한 상관관계를 갖는지를 분석하였다. 금강유역의 강우 패턴을 4개의 스테이지로 구분한 후 이를 상태층으로 참고하였으며, 관측 자료는 학습단계에 활용하였다. 이러한 기후인자와 강우 관계의 학습 결과를 바탕으로 기후변화 시나리오를 적용하고 미래의 기후요소를 예측하였으며 이를 통해 미래 금강유역의 가뭄을 예측하였다. 본 연구의 결과는 금강권역 수자원 공급 계획 및 설계의 기초자료로 제공될 수 있으며, 가뭄 대비 대책 사업의 우선순위 결정에 대한 근거 자료로 활용될 수 있을 것으로 기대된다.

• 대한전자공학회논문지
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• 제27권4호
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• pp.636-644
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• 1990

Hidden Markov models(HMM's) have been known to be useful representation for speech signal and are used in a wide variety of speech systems. For speech recognition applications, it is desirable to incorporate durational information of states in model which correspond to phonetic duration of speech segments. In this paper we propose duration-dependent HMM's that include durational information of states appropriately for the left-to-right model. Reestimation formulae for the parameters of the proposed model are derived and their convergence is verified. Finally, the performance of the proposed models is verified by applying to an isolated word, speaker independent speech recognition system.

• 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.

• Journal of Information Processing Systems
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• 제8권2호
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• Genomics & Informatics
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• 제12권4호
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• pp.145-150
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• 2014

Recent technical advances, such as chromatin immunoprecipitation combined with DNA microarrays (ChIp-chip) and chromatin immunoprecipitation-sequencing (ChIP-seq), have generated large quantities of high-throughput data. Considering that epigenomic datasets are arranged over chromosomes, their analysis must account for spatial or temporal characteristics. In that sense, simple clustering or classification methodologies are inadequate for the analysis of multi-track ChIP-chip or ChIP-seq data. Approaches that are based on hidden Markov models (HMMs) can integrate dependencies between directly adjacent measurements in the genome. Here, we review three HMM-based studies that have contributed to epigenetic research, from a computational perspective. We also give a brief tutorial on HMM modelling-targeted at bioinformaticians who are new to the field.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2014년도 제50차 하계학술대회논문집 22권2호
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• pp.327-328
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• 2014

클라우드 컴퓨팅 환경에서는 시스템의 성능 및 비용적인 측면에서 정보 공유의 용이성, 장소의 제약성 최소화, 저장 공간의 효율적 사용을 위해 분산 파일시스템을 이용하고 있다. 하지만 현재 HDFS의 복제 정책은 모든 데이터에 3개의 복제복을 유지하도록 하고 있다. 하지만 이러한 정책은 데이터의 중요도, 이용빈도수를 반영하지 못한 정책으로 상이한 서비스 품질 및 신뢰성 수준을 반영하지 못한다. 본 논문에서는 Hidden Markov Model을 이용하여 데이터의 이용 빈도수에 따라 복사본의 개수를 조절하는 알고리즘을 제안한다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2014년도 제49차 동계학술대회논문집 22권1호
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• pp.347-348
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• 2014

최근 급증하는 교통 혼잡으로 인해 시간적/물질적 손실이 크게 발생하고 있다. 이러한 교통난 해소는 시설투자만으로는 근본적인 해결책이 될 수 없다는 판단 하에 지난 수년간 보다 정확한 교통량을 예측하기 위해 시계열 기반의 다양한 교통량 예측 모델들이 개발 되어 왔다. 그러나 시계열 기반의 모델들은 회귀분석을 통해 과거 교통량을 분석하고 과거의 교통패턴이 미래에도 지속적으로 연장된다는 가정 하에 연구되었기 때문에 실시간으로 급변하는 불규칙한 교통 패턴에 대한 예측의 신뢰성을 떨어트린다. 또한 시계열 기반의 예측 기법은 어떠한 회귀분석 모델을 사용하는지에 따라 성능의 차이가 많이 나타나기 때문에 회귀분석 모델 선택이 중요하다. 이러한 제약을 극복하기 위해 본 논문에서는 은닉 마르코프 모델(Hidden Markov model)을 이용해 동적인 교통 패턴에 따라 현재 상황에 맞는 회귀분석 모델을 선택하는 신뢰도 높은 교통량 예측 시스템을 제안한다.

• 음성과학
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• 제7권4호
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• pp.11-26
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• 2000

In this paper, we propose the HMM with the MIN module. Because initial and re-estimated variance vectors are important elements for performance in HMM recognition systems, we propose a method which compensates for the mismatched statistical feature of training and test data. The MIN module function is a differentiable function similar to the sigmoid function. Unlike a continuous density function, it does not include variance vectors of the data set. The proposed hybrid HMM/MIN module is a unified network in which the observation probability in the HMM is replaced by the MIN module neural network. The parameters in the unified network are re-estimated by the gradient descent method for the Maximum Likelihood (ML) criterion. In estimating parameters, the variance vector is not estimated because there is no variance element in the MIN module function. The experiment was performed to compare the performance of the proposed HMM and the conventional HMM. The experiment measured an isolated number for speaker independent recognition.