• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.465-470
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• 2015

The curing of epoxy asphalt mixture depends on the chemical reaction of epoxy resin and the curing agent. The curing level of epoxy asphalt mixture needs to be predicted in order to decide traffic opening time and to establish further construction plans. In this study, chemical analysis of the prediction model was executed to expand the applicability of the previous prediction model. Consequently, the curing level prediction model of epoxy asphalt concrete mixture was proposed using the concentration ratio and the acid value ratio. According to the results of outdoor curing experiments, the final prediction model showed that the correlation coefficient is greater than 0.971. Precise prediction results of different composition epoxy asphalt were obtained by reflecting the chemical composition ratios in the curing level prediction model.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.94-95
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• 2020

This paper introduces the mix design and performance evaluation of Ultra-High Performance Concrete (UHPC). The concrete mixture is designed to achieve a densely compacted cementitious matrix via the modified Andreasen & Andersen particle packing model. The compressive strengths of UHPC designed by this method reached 154MPa. The relationship between packing theory and compressive strength of UHPC is discussed in this paper.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.33-36
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• 2004

최근 연속 음성 인식에서의 성능 향상을 위하여 음절을 인식 단위로 사용하고자 하는 노력들이 보고되고 있다. 그러나 음절의 경우 음소에 비해 학습성이 좋지 않고 모델의 수가 많으므로 음절 경계에서의 문맥 종속 모델링이 어렵다는 단점을 갖고 있다. 본 논문에서는 음절의 이러한 단점을 극복하기 위하여 모노폰과 트라이폰을 이용하여 음절 모델을 합성하는 방법을 제안한다. 제안된 모델은 트라이폰에 비하여 평균 $55\%$, PTM에 비하여 평균 $13\%$의 인식 속도 향상을 보이며, 동일한 속도일 경우 PTM, 트라이폰 모델 모두에 대하여 ERR이 약$8\%$ 향상됨을 볼 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.354-356
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• 2000

의사형태소를 디코딩 단위로 한국어 연속 음성 인식에서의 조사, 어미, 접사 및 짧은 용언의 어간등의 단어가 상당수의 인식 오류를 발생시킨다. 이러한 단어들은 발화 지속시간이 매우 짧고 생략이 빈번하며 결합되는 다른 형태소의 형태에 따라서 매우 심한 발음상의 변이를 보인다. 본 논문에서는 이러한 단어들은 한국어 기능어라 정의하고 실제 의사형태소 단위의 인식 실험을 통하여 기능어 집합 1, 2를 규정하였다. 그리고 한국어 기능어에 기능어용 음소를 독립적으로 적용하는 방법을 제안했다. 또한 기능어용 음소가 분리되어 생기는 음향학적 변이들을 처리하기 위해 Gaussian Mixture 수를 증가시켜 보다 견고한 학습을 수행했고, 기능어들의 음향 모델 스코어가 높아짐에 따른 인식에서의 삽입 오류 증가를 낮추기 위해 언어 모델에 fixed penalty를 부여하였다. 기능어 집합1에 대한 음소 모델을 적용한 경우 전체 문장 인식률은 0.8% 향상되었고 기능어 집합2에 대한 기능어 음소 모델을 적용하였을 때 전체 문장 인식률은 1.4% 증가하였다. 위의 실험 결과를 통하여 한국어 기능어에 대해 새로운 음소를 적용하여 독립적으로 학습하여 인식을 수행하는 것이 효과적임을 확인하였다.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.167-170
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• 2002

본 논문에서는 전화망 환경에서의 한국어 숫자음 인식을 위한 특징 파라미터 추출, 음향 모델링 방식을 결정하기 위하여 HTK 툴을 사용한 4 연숫자음 인식실험 결과를 기술한다. 또한, 실험 결과를 토대로 빈번하게 발생하는 숫자음에 대해서 오류율을 분석하였다. 숫자 모델로는 left context biword 모델과 triword 모델을 사용하였으며, 상태수와 mixture 수를 바꾸어 인식 실험을 수행한 결과, triword 모델이 biword 모델보다 인식율이 높은 것으로 나타났으며, substitution 에러율은 " 이<->" 에서 가장 높은 에러가 발생하는 결과를 얻을 수 있다.

• Korean Chemical Engineering Research
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• v.56 no.1
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• pp.29-41
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• 2018

Separating a mixture having an azeotrope or low relative volatility with single distillation column is difficult. Separating water-acetic acid mixture and water-ethanol mixture with a distillation column consumes a lot of energy. Pervaporation membrane can be used to separate the mixture in the concentration region where separation is difficult with distillation. We simulated a distillation-membrane hybrid process where membrane is located on the head of the distillation column for efficient separation of water-acetic acid and water-ethanol mixture. Permeability data were obtained from experiments and literature. We formulated an optimization problem for the process with total annual cost (TAC) as an objective function and major design variables as optimization variables. Major optimization variable affecting TAC of the hybrid process was shown to be distillate concentration. We also suggested a simplified optimization procedure to get a close-to-optimal solution.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.411-419
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• 2003

An area corresponding to the spatial resolution of optical remote sensor imagery often includes more than one pure surface material. In such case, a pixel value represents a mixture of spectral reflectance of several materials within it. This study attempts to apply the spectral mixture analysis on forest and to evaluate the information content of endmember fractions resulted from the spectral unmixing. Landsat-7 ETM+ image obtained over the study area in the Kwangneung Experimental Forest was initially geo-referenced and radiometrically corrected to reduce the atmospheric and topographic attenuations. Linear mixture model was applied to separate each pixel by the fraction of six endmember: deciduous, coniferous, soil, built-up, shadow, and rice/grass. The fractional values of six endmember could be used to separate forest cover in more detailed spatial scale. In addition, the soil fraction can be further used to extract the information related to the canopy closure. We also found that the shadow effect is more distinctive at coniferous stands.

• The Journal of the Acoustical Society of Korea
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• v.24 no.3
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• pp.141-149
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• 2005

Tn general. Gaussian mixture model (GMM) is used to estimate the speaker model from the speech for speaker identification. The parameter estimates of the GMM are obtained by using the Expectation-Maximization (EM) algorithm for the maximum likelihood (ML) estimation. However the EM algorithm has such drawbacks that it depends heavily on the initialization and it needs the number of mixtures to be known. In this paper, to solve the above problems of the EM algorithm. we propose an EM algorithm with the initialization based on incremental ${\cal}k-means$ for GMM. The proposed method dynamically increases the number of mixtures one by one until finding the optimum number of mixtures. Whenever adding one mixture, we calculate the mutual relationship between it and one of other mixtures respectively. Finally. based on these mutual relationships. we can estimate the optimal number of mixtures which are statistically independent. The effectiveness of the proposed method is shown by the experiment for artificial data. Also. we performed the speaker identification by applying the proposed method comparing with other approaches.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2012.04a
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• pp.202-205
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• 2012

• Journal of Energy Engineering
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• v.6 no.2
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• pp.203-211
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• 1997

This study is on the separation of Global warming effect gas, CO$_2$by chemical absorption from mixture of CO$_2$-N$_2$which was modeled after flue gas of fire power plant. Investigation of optimum condition for absorbent was carried out by using sparged vessel apparatus. Through packed tower experiments, applicabilities of two absorption models were tested by comparing experimental results with theoretical values. Absorbent used in the experiments was Monoethanolamine (MEA) and gas mixture was made in the mole composition of 15% CO$_2$and 85% N$_2$. Through estimations of CO$_2$loading and CO$_2$removal efficiency, optimum concentration of absorbent was found in the range of 4-5 M. To find a rate of absorption, an enhancement factor was introduced. Values of rate of absorption were calculated by Film model and Higbie model, respectively. Higbie model showed good agreement with experimental results. Therefore, this models is considered to be applicable to the CO$_2$separation process for flue gas from fire power plant.