• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.986-994
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• 2005

This Paper proposes the likelihood-based nonlinear dimension reduction method of the speech feature parameters in order to construct the voice activity detecter adaptable in noisy environment. The proposed method uses the nonlinear values of the Gaussian probability density function with the new parameters for the speec/nonspeech class. We adapted Likelihood Ratio Test to find speech part and compared its performance with that of Linear Discriminant Analysis technique. In experiments we found that the proposed method has the similar results to that of Gaussian Mixture Models.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.146-148
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• 1999

문서분류의 문제점 중의 하나는 사용하는 데이터의 차원이 매우 크다는 것이다. 그러므로 문서에서 필요한 단어만을 자동적으로 추출하여 문서데이터의 차원을 축소하는 작업이 문서분류에서는 필수적이다. DF(Document Frequency)는 문서의 차원축소의 대표적인 통계적 방법 중 하나인데, 본 논문에서는 문서의 차원축소에 DF와 주성분 분석(PCA)을 비교하여 주성분 분석이 문서의 차원축소에 적합함을 실험적으로 보인다. 그리고 비선형 주성분 분석(nonlinear PCA) 방법 중 locally linear PCA와 kenel PCA를 적용하여 비선형 주성분 분석을 이용하여 문서의 차원을 줄이는 것이 선형 주성분 분석을 이용하는 것 보다 문서분류에 더 적합함을 실험적으로 보인다.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.53-56
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• 2004

본 논문은 잡음 환경하에서 특징 벡터의 차원 축소를 통한 음성 구간 검출에 관한 연구이다. 음성/비음성 분류는 통계적 모델을 이용한 분류-기반 방법을 사용한다. 검출기에서 실시간 적응화를 위해 우도-기반의 특징 벡터에 대한 차원 축소 방법을 제안한다. 이 방법은 음성/비음성 클래스에 대한 가우시안 확률 밀도 함수에 의한 비선형적 우도값을 새로운 특징으로 취하는 방법이다. 음성/비음성 결정은 우도비 검증(Likelihood Ratio Test)의 방법을 이용하며, LDA(Linear Discriminant Analys)에 의한 축소 결과와 성능을 비교한다. 실험 결과 제안된 차원 축소 방법을 통하여 2차원으로 축소된 특징 벡터가 고차원에서의 결과와 대등함을 확인하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1250-1253
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• 2009

기후변화가 수자원에 미치는 영향을 평가하는데 있어서 주로 기후모형인 Global Climate Model (GCM)이 사용되고 있다. 그러나 이러한 기후모형의 공간적 해상도는 $3^{\circ}{\sim}4^{\circ}$ 정도로 한반도의 경우 바다로 묘사되기도 한다. 따라서 GCM을 이용해서 기후변화가 유역단위 수자원에 미치는 영향을 평가하기 위해서는 일반적으로 축소기법이 사용되고 있다. 현재까지 다양한 축소기법이 개발되었으며, 대표적인 모형으로는 SDSM(Statistical Down-Scaling Model)과 LARS-WG(The Long Ashton Research Station Weather Generator)이 있다. 이에 본 연구에서는 SDSM, LARS-WG와 함께 최근에 축소기법으로 사용되고 있는 인공신경망 기법을 이용해서 CCCMA(Canadian Centre for Climate Modeling and Analysis)에서 일 단위로 모의한 CGCM3 A2 시나리오를 기반으로 우포늪의 강우 및 온도시나리오를 구축하였다. 대상 지점인 우포늪은 경상남도 창녕군 우포늪(위도 $35^{\circ}$33', 경도 $128^{\circ}$25')에 위치하고 있으며, 모의 기간은 CASE1의 경우 현재, CASE2는 2050$^{\sim}$ 2080년, CASE3는 2080년$^{\sim}$2100년으로 각각 구분하여 축소기법을 적용하였다. 축소결과 축소기법에 따라 일정정도 차이를 보이기는 하였으나 강우와 온도 모두 증가하게 됨을 확인하였다.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.107-112
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• 2011

Recently, climate change around the world due to global warming has became an important issue and damages by climate change have a bad effect on human life. Changes of Sea Surface Temperature(SST) is associated with natural disaster such as Typhoon and El Nino. So we predicted daily future SST using Statistical Downscaling Method and CGCM 3.1 A1B scenario. 9 points of around Korea peninsular were selected to predict future SST and built up a regression model using Multiple Linear Regression. CGCM 3.1 was simulated with regression model, and that comparing Probability Density Function, Box-Plot, and statistical data to evaluate suitability of regression models, it was validated that regression models were built up properly.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.103-110
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• 2005

Speech Database is one of the most important part of Text-to-speech(TTS) system Especially, the embedded TTS system needs more small size of database than that of the server TTS system So, the compression and statistical reduction or database is a very important factor in the embedded TTS system But this compression and statistical reduction of database always rise a loss of quality of the synthesised speech. In this paper, we propose a method of constructing database for high quality embedded TTS system and verify the quality of synthesised speech with MOS(Mean Opinion Score) test.

• Journal of the Korean association of regional geographers
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• v.19 no.4
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• pp.567-579
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• 2013

The purpose of this paper is illuminating positively the perception and compliance rate of local food principles in Korea. The results are as follows. First, in terms of the perception rate, social trust and local production/consumption are the most important components rather than eco-friendliness. This means the importance of both spatial and social aspects of local food concept. Second, in terms of the compliance rate, social trust and shortened food chain are well complied, rather than eco-friendliness and shortened food miles. Third, in terms of the difference between perception and compliance rate, the social aspects such as eco-friendliness, social trust, and shortened food chain has shown relatively smaller difference than the spatial aspects such as shortened food miles and local production/consumption.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.659-669
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• 2021

Multivariate regression often appears in longitudinal or functional data analysis. Since multivariate regression involves multi-dimensional response variables, it is more strongly affected by the so-called curse of dimension that univariate regression. To overcome this issue, Yoo (2018) and Yoo (2019a) proposed three model-based response dimension reduction methodologies. According to various numerical studies in Yoo (2019a), the default method suggested in Yoo (2019a) is least sensitive to the simulated models, but it is not the best one. To release this issue, the paper proposes an selection algorithm by comparing the other two methods with the default one. This approach is called principal selected response reduction. Various simulation studies show that the proposed method provides more accurate estimation results than the default one by Yoo (2019a), and it confirms practical and empirical usefulness of the propose method over the default one by Yoo (2019a).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1111-1116
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• 2009

In this paper, we discuss distortion-tolerant pattern recognition using computational integral imaging reconstruction. Three-dimensional object information is captured by the integral imaging pick-up process. The captured information is numerically reconstructed at arbitrary depth-levels by averaging the corresponding pixels. We apply Fisher linear discriminant analysis combined with principal component analysis to computationally reconstructed images for the distortion-tolerant recognition. Fisher linear discriminant analysis maximizes the discrimination capability between classes and principal component analysis reduces the dimensionality with the minimum mean squared errors between the original and the restored images. The presented methods provide the promising results for the classification of out-of-plane rotated objects.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.259-271
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• 2019

A statistical downscaling method was adopted in order to establish the high-resolution wave prediction system in the East Sea coastal area. This system used forecast data from the Global Wave Watch (GWW) model, and the East Sea and Busan Coastal Wave Watch (CWW) model operated by the Korea Meteorological Administration (KMA). We used the CWW forecast data until three days and the GWW forecast data from three to seven days to implement the statistical downscaling method (inverse distance weight interpolation and conditional merge). The two-dimensional and station wave heights as well as sea surface wind speed from the high-resolution coastal prediction system were verified with statistical analysis, using an initial analysis field and oceanic observation with buoys carried out by the KMA and the Korea Hydrographic and Oceanographic Agency (KHOA). Similar to the predictive performance of the GWW and the CWW data, the system has a high predictive performance at the initial stages that decreased gradually with forecast time. As a result, during the entire prediction period, the correlation coefficient and root mean square error of the predicted wave heights improved from 0.46 and 0.34 m to 0.6 and 0.28 m before and after applying the statistical downscaling method.