• Korean Journal of Food Science and Technology
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• v.18 no.2
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• pp.82-87
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• 1986

The water sorption isotherms of binary mixtures, prepared by corn starch and isolated soybean protein (ISP) or casein, were measured and analyzed. Simple equations to predict moisture content from knowledge of composition and water activity of the mixture were derived by applying Response Surface Analysis. Comparison between predicted and experimental moisture content for 13 combinations of corn starch-lSP mixture at the range of $a_w$ 0.25-0.87 resulted in a maximum error of only 6.06% and an absolute mean error of 2.60%, and for the mixture of corn starch-casein the error was -4.39% and 2.12%, respectively. The agreement between experimental and predicted water sorption isotherms was shown to be 'highly acceptable' for the binary mixtures of 50% corn starch-50% ISP and 50% corn starch -50% casein.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1401-1410
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• 1999

In this paper, we propose a fast block matching algorithm using the lower and upper bound of mean absolute difference (MAD) which is calculated at the search region overlapped with neighbor blocks. At first, we calculate the lower bound of MAD and reduce the search point by using this lower bound. In this method, we can get good prediction error performance close to full search block matching algorithm (FSBMA), but there exists some computational complexity that has to be reduced. Therefore, we further reduce the computational complexity by using pixel subsampling besides the lower and upper bound of MAD. Experimental results show that we can remarkably reduce the computational complexity with good prediction error performance close to FSBMA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.128-140
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• 2000

In this paper, we propose a fast block matching algorithm using the mean absolute error (MAE) of neighbor search point and search region reduction. The proposed algorithm is composed of two stages. At the first stage,the search region is divided into nonoverlapped 3$\times$3 areas and MAE of the center point of each area iscalculated. The minimum MAE value of all the calculated MAE's is determined as reference MAE. At thesecond stage, because the possibility that final motion vector exist near the position of reference MAE is veryhigh, we use smaller search region than first stage, And, using the MAE of center point of each area, the lowerbound of rest search point of each area is calculated and block matching process is performed only at the searchpoints that the lower bound is smaller than reference MAE. By doing so, we can significantly reduce thecomputational complexity while keep the increasement of motion estimation error small.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.41-56
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• 2000

In this paper, we propose a two-stage fast block matching algorithm using the mean absolute error (MAE) of neighbor search point that can reduce the computational complexity to estimate motion vector while the motion estimation error performance is nearly the same as full search algorithm (FSA) In the proposed method, the lower bound of MAE 6at current search point IS calculated using the MAE of neighbor search point And we reduce the computational complexity by performing the block matching process only at the search point that has to be block matched using the lower bound of MAE The proposed algorithm is composed of two stages The experimental results show that the proposed method drastically reduces the computational complexity while the motion compensated error performance is nearly kept same as that of FSA.

• Journal of Korean Society of Forest Science
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• v.88 no.3
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• pp.285-291
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• 1999

Loblolly(Pinus taeda L.) and slash(Pinus elliottii Engelm.) pines are the most important timber producing species in the Southern United States. Site index equations to estimate site index curves(base age 25 years) for loblolly pine and slash pine plantations have been developed based on long-term repeated measurement data sets. To check magnitude of errors in estimating site index, each cumulative measurement cycle data sets and all combined data sets were used to recalculate site index values. The Chapman-Richards' growth function was selected for stand height prediction. Anamorphic base age invariant site index curves were presented based on this height prediction equation. Statistics used in the evaluation were mean of the differences and mean of the absolute differences. For plantation ages less than 5 years, site index values showed very sensitive fur both species based on the evaluation test.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.73-82
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• 2004

신호 교차로에서 포화차두시간에 영향을 미치는 영향인자는 도로조건, 교통조건, 환경조건으로 분류된다. 이러한 요인들의 복합적인 관계가 포화차두시간에 영향을 미친다. 현재 포화교통류율은 이상적인 조건일 때의 포화차두시간을 산출하고, 이를 이용해서 기본 포화교통류율을 구하고, 여기에 좌 우회전, 차로폭, 경사, 중차량 보정계수을 고려함으로써 특정 차로군의 포화교통류율을 산정하고 있다. 포화차두시간에 영향을 미치는 인자들 중에서 정량적으로 나타내기 어려운 인자 즉, 퍼지적 성격을 가진 인자들은 고려하지 않고 있다. 따라서 본 연구에서는 퍼지 근사추론 방법을 이용하여 정성적 인자의 영향을 고려한 모형을 구축하였다. 모형의 입력자료는 강우조건과 주변밝기의 정도, 중차량 구성비의 언어적 표현를 사용하였다. 이러한 변수들에 대하여 설문조사를 통해서 퍼지집합의 멤버쉽함수를 설정하였으며. 이에 기초하여 교차로에서 각 조건별로 포화차두시간을 관측하였다. 이러한 현장 관측치를 바탕으로 퍼지 제어규칙을 설정하고 모형을 구축하였다. 모형의 평가는 추론치와 실측치를 비교함으로써 이루어 졌으며, 결정계수인 $R^2$와 평균절대오차(MAE)와 평균제곱오차(MSE)를 사용하여 분석한 결과 본 모형의 설명력이 높은 것으로 평가되었다. 본 연구의 과정에서 강우에 의한 교통용량 감소는 중차량 구성비가 클수록 주변밝기의 정도가 나쁠수록 더욱 큰 것으로 나타났으며 그 감소율은 5.3%에서 21.8%에 이르는 넓은 범위의 값을 보였고. 주변밝기 정도에 따른 교통용량 감소는 4.7$\sim$7.5% 수준으로 나타났다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1535-1545
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• 2014

This study aims at improving the Reliability of DSRC Traffic information considering Traffic and Road Characteristics. First of all, this study analyzed the characteristics of DSRC data on urban expressway and problems of outlier data occurrence. After then, this study produced reliable traffic information by using an optimal method of the Outlier-Filtering. After Outlier-Filtering, this study performed accuracy evaluation and appropriateness check for the number of samples per confidence level. As a result, it showed that the MAPE was between 2.2% and 9.7% and RSME was between 2.2 and 7.5 which are very similar figures to the actual average traffic speed. Also, The samples of both Am peak and Pm peak periods were analyzed to be appropriate at the confidence level of 95%, and 90% within the allowable error range of 5kph.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.16-17
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• 2015

본 논문에서는 실내 환경 중 TV 시청환경에서 마이크로폰 어레이를 이용하여 다양한 다중 음원 방향을 추정하는 기법을 제안한다. 제안된 기법은 기존의 하나의 음원에 특화되어 있는 GCC-PHAT 기반의 방법을 GCC-PHAT 버퍼와 NMF를 도입하여 다중음원의 방향 추정을 가능하게 만들었다. 제안된 기법의 성능을 평가하기 위해서 실 거주 환경에서 발생하는 소음원과 TV 소리 방향 추정 결과에 대한 실측치와 추정치 간의 오차인 절대 평균오차를 측정하였으며, 실험 결과 제안한 기법이 기존의 방법인 GCC-PHAT보다 우수한 추정 성능을 보임을 확인하였다.

• Smart Media Journal
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• v.9 no.3
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• pp.122-129
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• 2020

The possibility that an asymptotic coronavirus-19 infected person around the world is not aware of his infection and can spread it to people around him is still a very important issue in that the public is not free from anxiety and fear over the spread of the epidemic. In this paper, the K-means clustering algorithm and deep learning-based crowd aggregation were proposed to determine the possibility of contact with confirmed cases of infectious diseases. As a result of 300 iterations of all input learning images, the PSNR value was 21.51, and the final MAE value for the entire data set was 67.984. This means the average absolute error between observations and the average absolute error of fewer than 4,000 people in each CCTV scene, including the calculation of the distance and infection rate from the confirmed patient and the surrounding persons, the net group of potential patient movements, and the prediction of the infection rate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2B
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• pp.155-163
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• 2011

In this study, a soil moisture estimation model was developed using a decision tree model, an artificial neural networks (ANN) model, remotely sensed data, and ground network data of daily precipitation, soil moisture and surface temperature. Soil moisture data of the Yongdam dam basin (5 sites) were used for model validation. Satellite remote sensing data and geographical data and meteorological data were used in the classification and regression tree (CART) model for data classification and the ANNs model was applied for clustered data to estimate soil moisture. Soil moisture data of Jucheon, Bugui, Sangjeon, Ahncheon sites were used for training and the correlation coefficient between soil moisture estimates and observations was between 0.92 to 0.96, root mean square error was between 1.00 to 1.88%, and mean absolute error was between 0.75 to 1.45%. Cheoncheon2 site was used for validation. Test statistics showed that the correlation coefficient, the root mean square error, the mean absolute error were 0.91, 3.19%, and 2.72% respectively. Results demonstrated that the developed soil moisture model using CART and ANN was able to apply for the estimation of soil moisture distribution.