• Ecology and Resilient Infrastructure
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• v.11 no.2
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• pp.55-64
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• 2024

The disaster impact assessment system was introduced in 2005 as a disaster prevention procedure for comprehensive and systematic developmental projects. However, according to the 'Practical Guidelines for Disaster Impact Assessment', Jeju Island's unique hydrogeological features necessitate the calculation of isohyetal-based probabilistic rainfall, which can reflect altitude, when estimating probabilistic rainfall for flood volume determination, rather than using conventional methods. Despite Jeju Island being centered around Hallasan, there are three Automatic Weather Stations (AWS) located at the summit of Hallasan, making weather stations denser than in other cities and provinces. Therefore, it is judged that there would be no difficulty in applying conventional methods, such as utilizing the probabilistic rainfall data from the weather stations or employing the Thiessen method, to estimate flood volumes for small-scale project areas. Accordingly, this study conducts a comparative analysis of the impact of applying general probabilistic rainfall from weather stations and isohyetal-based probabilistic rainfall in site in the context of Jeju Island's disaster impact assessment system.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.53-66
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• 1993

A Bayesian approach is suggested to the multi-proportions randomized response model. O'Hagan's (1987) Bayes linear estimator is extended to the inference of unrelated question-type randomized response model. Also some numerical comparisons are provided to show the performance of the Bayes linear estimator under the Dirichlet prior.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.157-164
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• 1994

본 연구에서는 확률화응답기법을 이용하여 모집단내의 민감집단의 비율을 추정함에 있어 조사의 효율성을 높이기 위한 층화표본의 최적할당방법을 제안한다. 확률화응답기법은 Warner(1965)에 의하여 제안된 방법으로 민감한 사안에 대한 조사시 무응답이나 거짓응답으로 인한 비표본오차를 줄일수 있는 기법으로 간접질문에 의한 조사방법이다. 여기에서 최적할당이란 베이즈위험을 최소로 하는 할당법을 의미하며, 이 과정에서 민감집단의 모비율에 대한 사전분포로는 베타분포를 취하였다.

• Journal of Korea Water Resources Association
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• v.49 no.6
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• pp.481-493
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• 2016

The statistical analysis to the torrential rainfall data that is defined as a rainfall amount more than 80 mm/day is performed with Daegu and Busan rainfall data which is collected during 384 months. The number of occurrence of the torrential rainfall events can be simulated usually using Poisson distribution. However, the Poisson distribution can be frequently failed to simulate the statistical characteristics of the observed value when the observed data is zero-inflated. Therefore, in this study, Generalized Poisson distribution (GPD), Zero-Inflated Poisson distribution (ZIP), Zero-Inflated Generalized Poisson distribution (ZIGP), and Bayesian ZIGP model were used to resolve the zero-inflated problem in the torrential rainfall data. Especially, in Bayesian ZIGP model, a informative prior distribution was used to increase the accuracy of that model. Finally, it was suggested that POI and GPD model should be discouraged to fit the frequency of the torrential rainfall data. Also, Bayesian ZIGP model using informative prior provided the most accurate results. Additionally, it was recommended that ZIP model could be alternative choice on the practical aspect since the Bayesian approach of this study was considerably complex.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.607-613
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• 2019

This paper proposes LF-MMI (Lattice Free Maximum Mutual Information)-based acoustic modeling using external knowledge for speech recognition. Note that an external knowledge refers to text data other than training data used in acoustic model. LF-MMI, objective function for optimization of training DNN (Deep Neural Network), has high performances in discriminative training. In LF-MMI, a phoneme probability as prior probability is used for predicting posterior probability of the DNN-based acoustic model. We propose using external knowledges for training the prior probability model to improve acoustic model based on DNN. It is measured to relative improvement 14 % as compared with the conventional LF-MMI-based model.

• Journal of KIISE:Software and Applications
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• v.41 no.8
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• pp.580-585
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• 2014

This paper conducts a variety of experiments for "the extraction of Korean parallel sentences using Wikipedia data". We refer to various methods that were previously proposed for other languages. We use two approaches. The first one is to use translation probabilities that are extracted from the existing resources such as Sejong parallel corpus, and the second one is to use dictionaries such as Wiki dictionary consisting of Wikipedia titles and MRDs (machine readable dictionaries). Experimental results show that we obtained a significant improvement in system using Wikipedia data in comparison to one using only the existing resources. We finally achieve an outstanding performance, an F1-score of 57.6%. We additionally conduct experiments using a topic model. Although this experiment shows a relatively lower performance, an F1-score of 51.6%, it is expected to be worthy of further studies.

• Bulletin of the Korean Mathematical Society
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• v.5 no.1
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• pp.1-12
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• 1968

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.67-70
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• 2007

본 논문은 클러스터링 문제에서 사전 정보에 대한 활용의 효율을 개선시킬 수 있는 방법을 제안한다. 클러스터링에서 사전 정보의 존재 시 이의 활용은 성능을 개선시킬 수 있는 계기가 될 수 있으므로 그의 활용 폭을 늘리기 위한 방법으로 다양한 사용 방법의 적용인 semi-supervised 클러스터링 앙상블을 제안한다. 사전 정보의 활용 방법의 방안으로써 association-rule의 개념을 접목하였다. 클러스터 수를 다르게 적용하더라도 패턴간의 유사도가 높으면 같은 그룹에 속할 확률은 높아진다. 다양한 초기화에 따른 클러스터의 동작은 사전 정보의 활용을 다양화 시키게 되며, 사전 정보에 충족하는 각각의 클러스터 결과를 제시한다. 결과를 총 취합하여 association-matrix를 형성하면 패턴간의 유사도를 얻을 수 있으며 결국 association-matrix를 통해 클러스터링 할 수 있는 방법을 제시한다.

• Journal of Korea Water Resources Association
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• v.48 no.1
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• pp.37-44
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• 2015

A Bayesian nonstationary probability rainfall estimation model using the Grid method is developed. A hierarchical Bayesian framework is consisted with prior and hyper-prior distributions associated with parameters of the Gumbel distribution which is selected for rainfall extreme data. In this study, the Grid method is adopted instead of the Matropolis Hastings algorithm for random number generation since it has advantage that it can provide a thorough sampling of parameter space. This method is good for situations where the best-fit parameter values are not easily inferred a priori, and where there is a high probability of false minima. The developed model was applied to estimated target year probability rainfall using hourly rainfall data of Seoul station from 1973 to 2012. Results demonstrated that the target year estimate using nonstationary assumption is about 5~8% larger than the estimate using stationary assumption.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.77-83
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• 2009

This paper proposes a new noise-biased compensation of minimum statistics(MS) method using a nonlinear function and a priori speech absence probability(SAP) for speech enhancement in non-stationary noisy environments. The minimum statistics(MS) method is well known technique for noise power estimation in non-stationary noisy environments. It tends to bias the noise estimate below that of true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function and a priori speech absence probability (SAP) for biased compensation. Specifically. we apply the adaptive parameter according to the a posteriori SNR. In addition, when the a priori SAP equals unity, the adaptive biased compensation factor separately increases ${\delta}_{max}$ each frequency bin, and vice versa. We evaluate the estimation of noise power capability in highly non-stationary and various noise environments, the improvement in the segmental signal-to-noise ratio (SNR), and the Itakura-Saito Distortion Measure (ISDM) integrated into a spectral subtraction (SS). The results shows that our proposed method is superior to the conventional MS approach.