• Environmental and Resource Economics Review
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• v.12 no.4
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• pp.545-557
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• 2003

A new semiparametric estimator of a dichotomous choice contingent valuation model is proposed by adapting the well-known density weighted average derivative of the regression function. A small sample behavior of the estimator is demonstrated very briefly by a simulation and the estimator is applied to estimate the WTP for preserving the Dong River area in Korea.

• Geomechanics and Engineering
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• v.21 no.1
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• pp.11-21
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• 2020

The traditional formulations for estimation of bearing capacity in rock mechanics assume a homogeneous and isotropic rock mass. However, it is common that the rock mass consists of different layers of different rock properties or of the same rock matrix with distinct geotechnical quality levels. The bearing capacity of a heterogeneous rock is estimated traditionally through the weighted average. In this paper, the solution of the weighted average is compared to the finite difference method applied to a bilayer rock mass. The influence of different parameters such as the thickness of the layers, the rock type, the uniaxial compressive strength and the overall geotechnical quality of the rock mass on the bearing capacity of a bilayer rock mass is analyzed. A parametric study by finite difference method is carried out to develop a bearing capacity factor in function of the layer thickness and the rock mass quality expressed in terms of the geological strength index, which is presented in a form of a chart. Therefore, this correlation factor allows estimating the bearing capacity of a rock mass that is formed by two layers with distinct GSI, depending on the bearing capacity of the rock mass formed only by the upper layer and considered by that way as homogenous and isotropic rock mass.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.315-316
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• 2011

일반적으로 영상에서 얼굴 영상을 검출하고 인식하는 알고리즘은 패턴 인식 연구에 있어서 인간과 컴퓨터의 상호작용의 연구라는 면에서 아주 중요한 문제로 연구되어 왔다. 본 논문에서는 고유얼굴을 이용하여 유클리디언 거리법과 퍼지기법의 인식률을 비교해보고자 한다. PCA(Principal Component Analysis) 방식은 우수한 인식 결과를 보장하는 얼굴인식 기법중의 하나이며, 얼굴 영상을 이용하여 공분산 행렬을 계산하고, 공분산 행렬을 통해 생성된 저차원의 벡터, 즉 고유얼굴(Eigenface)을 이용하여 가중치를 계산하고, 이 가중치를 기준으로 인식을 수행하는 기법이다. 이를 기반으로 하여, 본 논문에서는 전처리 과정, 고유얼굴 과정, 유클리디언 거리법 및 퍼지 소속도 함수 설계 과정, 신경망 학습과정, 인식과정으로 구성된 5단계의 얼굴 인식 알고리즘을 제안한다.

• Journal of Integrative Natural Science
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• v.7 no.3
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• pp.200-207
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• 2014

Average number of samples to signal (ANSS) and average time to signal (ATS) are the most widely used criterion for comparing the efficiencies of the quality control charts. In this study the method of evaluating ANSS and ATS values of the multivariate exponentially weighted moving average (EWMA) control charts with Markov chain approach was presented when the production process is in control state or out of control state. Through numerical results, it is found that when the number of transient state r is less than 50, the calculated ANSS and ATS values are unstable; and ATS(r) tends to be stabilized when r is greater than 100; in addition, when the properties of multivariate EWMA control chart is evaluated using Markov chain method, the number of transient state r requires bigger values when the smoothing constatnt ${\lambda}$ becomes smaller.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.131-144
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• 2019

The purpose of study was to determine the hygrophyte index of each plant(HIP) considering the moisture environment condition (MEC) of the native plants on the downstream slope of the fill dam and evaluate its applicability which to develop a method to search for leaks and saturated zones of the fill dam for status evaluation of precision safety diagnosis. The HIP was weighted average and consisted of 19 ranks. The weighted average was calculated according to the following three procedures: First, the linear assumption was made according to the actual habitat environmental conditions, the second one was weighted to 10% of the optimal habitat condition, and finally the average value of the distribution range values. The Hygrophyte index of vegetation at each plot (HIV) was obtained from the Sinheung reservoir (Yesan-gun, Chungcheongnam-do) using the results of vegetation survey of the Sinheung reservoir with precision safety diagnosis and suggested the use of the hygrophyte index of the cultivated vegetation. The average HIP range of plant species that emerged in 50 survey sites on the downstream slope of the Sinheung reservoir is 2.99 to 3.56. The coefficient of variation showed a large difference depending on the appearance of the leakage indicator plant(LIP) species. The range of HIV is 2.80 to 4.26, the mean value is 3.37, standard deviation is 0.37 and the coefficient of variation is 9.7%. As a result, the value of the coefficient of variation showed a large difference depending on the appearance of the plant species.

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.489-496
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• 2015

In this paper, a new weighted prediction scheme is proposed to improve the coding efficiency for video scenes containing brightness variations. Conventional weighted prediction is applied by the reference picture and use only one weighted parameter set. Thus, it is only useful for GBV(Glabal Brightness Variation). In order to solve this problem, the proposed algorithm use three kind of schemes depending on situation. Experimental results show that maximum coding efficiency gain of the proposed method is up to 10.2% in luminance. Average computional time complexity is increased about 163% in encoder and about 101% in decoder.

• Journal of Nutrition and Health
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• v.38 no.10
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• pp.847-855
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• 2005

This study investigated and compared the daily activities and energy expenditure of normally-weighted and obese elementary school children. The participants were 115 boys and 115 girls (6th grade) at ten elementary schools in Busan. Time spent on each activity was evaluated using the twenty-four hour recall method. 1) The general characteristics of the normally-weighted and obese children did not differ, although the normally-weighted children exercised and used stairs more than the obese children.2) Among their classified activities, the children spent most of their time resting, study, leisure, physiology and hygiene, commuting, and housework in that decreasing order. Normally-weighted children spent less time tying down and in conversation with family and friends than obese ones. 3) The children's average activity factors were 1.47 - 1.50. The normally-weighted children's rest energy expenditure (REE) (1391.4 kcal,1264.7 kcal) was less than that of the obese children (1711.4 kcal. 1461.0 kcal) . The normally-weighted children's daily energy expenditure was less than that of the obese, but energy expenditure per body weight was less in obese children than in the normally-weighted. In conclusion, sedentary activities and energy expenditure per body weight was less in the obese compared to the normally-weighted children. Programs to help children perceive and practice desirable physical activities should be suggested for prevention of obesity in children. (Korean J Nutrition 38(10): 847$\sim$855,2005)

• ETRI Journal
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• v.36 no.5
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• pp.714-720
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• 2014

In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.366-373
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• 2017

In this paper, we propose a variable weighted average (VWA) learning method in order to improve the performance of the fuzzy ART neural network that has been developed by Grossberg. In a conventional method, the Fast Commit Slow Recode (FCSR), when an input pattern falls in a category, the representative pattern of the category is updated at a fixed learning rate regardless of the degree of similarity of the input pattern. To resolve this issue, a variable learning method proposes reflecting the distance between the input pattern and the representative pattern to reduce the FCSR's category proliferation issue and improve the pattern recognition rate. However, these methods still suffer from the category proliferation issue and limited pattern recognition rate due to inevitable excessive learning created by use of fuzzy AND. The proposed method applies a weighted average learning scheme that reflects the distance between the input pattern and the representative pattern when updating the representative pattern of a category suppressing excessive learning for a representative pattern. Our simulation results show that the newly proposed variable weighted average learning method (VWA) mitigates the category proliferation problem of a fuzzy ART neural network by suppressing excessive learning of a representative pattern in a noisy environment and significantly improves the pattern recognition rates.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1960-1969
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• 2016

In this paper, we propose an automatic hierarchical organ segmentation method on abdominal CT images. First, similar atlases are selected using bone-based similarity registration and similarity of liver, kidney, and pancreas area. Second, each abdominal organ is roughly segmented using image-based similarity registration and intensity-based locally weighted voting. Finally, the segmented abdominal organ is refined using mask-based affine registration and intensity-based locally weighted voting. Especially, gallbladder and pancreas are hierarchically refined using location information of neighbor organs such as liver, left kidney and spleen. Our method was tested on a dataset of 12 portal-venous phase CT data. The average DSC of total organs was $90.47{\pm}1.70%$. Our method can be used for patient-specific abdominal organ segmentation for rehearsal of laparoscopic surgery.