• Journal of Ecology and Environment
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• v.38 no.1
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• pp.95-107
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• 2015

Because an ecological community consists of diverse species that vary nonlinearly with environmental variability, its dynamics are complex and difficult to analyze. To investigate temporal variations of benthic macroinvertebrate community, we used the community data that were collected at the sampling site in Baenae Stream near Busan, Korea, which is a clean stream with minimum pollution, from July 2006 to July 2013. First, we used a self-organizing map (SOM) to heuristically derive the states that characterizes the biotic condition of the benthic macroinvertebrate communities in forms of time series data. Next, we applied the hidden Markov model (HMM) to fine-tune the states objectively and to obtain the transition probabilities between the states and the emission probabilities that show the connection of the states with observable events such as the number of species, the diversity measured by Shannon entropy, and the biological water quality index (BMWP). While the number of species apparently addressed the state of the community, the diversity reflected the state changes after the HMM training along with seasonal variations in cyclic manners. The BMWP showed clear characterization of events that correspond to the different states based on the emission probabilities. The environmental factors such as temperature and precipitation also indicated the seasonal and cyclic changes according to the HMM. Though the usage of the HMM alone can guarantee the convergence of the training or the precision of the derived states based on field data in this study, the derivation of the states by the SOM that followed the fine-tuning by the HMM well elucidated the states of the community and could serve as an alternative reference system to reveal the ecological structures in stream communities.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.10
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• pp.5125-5129
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• 2012

Economic decision models are being increasingly used to assess medical interventions. Advances in this field are mainly due to enhanced processing capacity of computers, availability of specific software to perform the necessary tasks, and refined mathematical techniques. We here estimated the incremental cost-effectiveness of ten strategies for colon cancer screening, as well as no screening, incorporating quality of life, noncompliance and data on the costs and profit of chemotherapy in Iran. We used a Markov model to measure the costs and quality-adjusted life expectancy of a 50-year-old average-risk Iranian without screening and with screening by each test. In this paper, we tested the model with data from the Ministry of Health and published literature. We considered costs from the perspective of a health insurance organization, with inflation to 2011, the Iranian Rial being converted into US dollars. We focused on three tests for the 10 strategies considered currently being used for population screening in some Iranians provinces (Kerman, Golestan Mazandaran, Ardabil, and Tehran): low-sensitivity guaiac fecal occult blood test, performed annually; fecal immunochemical test, performed annually; and colonoscopy, performed every 10 years. These strategies reduced the incidence of colorectal cancer by 39%, 60% and 76%, and mortality by 50%, 69% and 78%, respectively, compared with no screening. These approaches generated ICER (incremental cost-effectiveness ratios) of $9067, $654 and $8700 per QALY (quality-adjusted life year), respectively. Sensitivity analyses were conducted to assess the influence of various scales on the economic evaluation of screening. The results were sensitive to probabilistic sensitivity analysis. Colonoscopy every ten years yielded the greatest net health value. Screening for colon cancer is economical and cost-effective over conventional levels of WTP8.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.29-32
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• 2000

본 논문에서는 음성인식 시스템의 음향모델 개선을 위한 기초적 연구로서, 문맥적인 요소를 필요로 하는 SSS(Successive State Splitting)와 필요로 하지 않는 SSS-free 알고리즘을 이용한 HMnet(Hidden Markov Network) 음향모델 작성방법에 대해 검토하고 작성한 음향모델을 한국어에 적용하여 그 유효성을 확인하였다. HMnet을 이용한 음소모델의 작성방법은 전체 학습 데이터에 대해서 각각 2개의 상태를 가지는 초기 모델을 작성한 후, 이를 시간과 문맥방향으로의 최대 분포를 가지는 상태를 재분할한 후 임의의 상태수가 될 때까지 상태분할을 계속적으로 수행케 하여 각 음소모델을 작성하게 된다. 작성한 HMnet 음향모델의 유효성을 확인하기 위해 ETRI 445 단어의 3인에 대한 화자종속 음소인식 실험을 수행하였다. 인식실험 결과, SSS 알고리즘을 이용한 화자종속실험의 경우 상태수 520에서 평균 $62.8\%$의 인식률을, SSS-free 알고리즘의 경우 상태수 420에서 평균 $64.2\%$의 인식률을 얻었다. 이 결과는 HMM을 이용한 경우(약$43.4\%$)보다 $20\%$이상의 인식률 향상을 보여 이 알고리즘의 유효성을 확인할 수 있었다. SSS와 SSS-free를 비교한 경우, SSS-free가 SSS보다 낮은 상태수에서 평균 $1.4\% 향상된 인식률을 보였다.

• The Journal of Engineering Geology
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• v.2 no.2
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• pp.147-154
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• 1992

A computer simulation of earthquake sequence in the time and magnitude space was done using random number generation. The theory of the simulation are based on the two statistical models of earthquake events. Those models are Stationary Poisson Process for independent earthquakes and Branching Markov Process for aftershocks. The generated earthquake sequnces resemble the actual earthquake catalogs.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.357-360
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• 2005

In this paper we consider the well-known semiparametric proportional hazards (PH) models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n. For a given vector of response values which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enable us to estimate the survival curve when n < < p. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma (DLBCL) complementary DNA(cDNA) data.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2007.05a
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• pp.121-124
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• 2007

최근 각종 질환의 원인이 되고 있는 비만은 심각한 사회문제로 대두되고 있으며, 이를 해결하기 위해 비만관리를 위한 측정 시스템의 필요성이 증가하고 있다. 본 논문은 비만관리를 위해 복부의 근전도 신호를 분석해서 언제 어디서든 본인의 건강상태를 체크하여 적절한 의료 서비스를 받을 수 있는 측정 시스템에 관한 연구이다. 복부 근전도 신호 분석을 위해서 에너지 검출, 신호 특징 추출, 상태 분류 및 인식 등을 위한 알고리즘을 제안한다. 이 신호 분석 알고리즘을 측정 시스템에 적용하여 복부의 비만도 및 복부의 근력을 평가하여 건강상태에 대한 적절한 평가를 제공하는 시스템을 제안한다.

• The Journal of the Acoustical Society of Korea
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• v.25 no.3E
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• pp.89-94
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• 2006

For audio indexing and targeted search of specific audio or corresponding visual contents, the MPEG-7 standard has adopted a sound classification framework, in which dimension-reduced Audio Spectrum Projection (ASP) features are used to train continuous hidden Markov models (HMMs) for classification of various sounds. The MPEG-7 employs Principal Component Analysis (PCA) or Independent Component Analysis (ICA) for the dimensional reduction. Other well-established techniques include Non-negative Matrix Factorization (NMF), Linear Discriminant Analysis (LDA) and Discrete Cosine Transformation (DCT). In this paper we compare the performance of different dimensional reduction methods with Gaussian mixture models (GMMs) and HMMs in the classifying video sound clips.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3E
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• pp.85-90
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• 2004

This paper presents two kinds of new approaches, one of which is concerned with recognition of emotional speech such as anger, happiness, normal, sadness, or surprise. The other is concerned with emotion recognition in speech. For the proposed speech recognition system handling human speech with emotional states, total nine kinds of prosodic features were first extracted and then given to prosodic identifier. In evaluation, the recognition results on emotional speech showed that the rates using proposed method increased more greatly than the existing speech recognizer. For recognition of emotion, on the other hands, four kinds of prosodic parameters such as pitch, energy, and their derivatives were proposed, that were then trained by discrete duration continuous hidden Markov models(DDCHMM) for recognition. In this approach, the emotional models were adapted by specific speaker's speech, using maximum a posteriori(MAP) estimation. In evaluation, the recognition results on emotional states showed that the rates on the vocal emotions gradually increased with an increase of adaptation sample number.

• MALSORI
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• no.45
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• pp.117-126
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• 2003

Decision tree-based state tying has been proposed in recent years as the most popular approach for clustering the states of context-dependent hidden Markov model-based speech recognition. The aims of state tying is to reduce the number of free parameters and predict state probability distributions of unseen models. But, when doing state tying, the size of a decision tree is very important for word independent recognition. In this paper, we try to construct optimized decision tree based on the average of feature vectors in state pool and the number of seen modes. We observed that the proposed optimal decision tree is effective in predicting the state probability distribution of unseen models.