• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.8
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• pp.62-70
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• 1997

In this paper, we presetn a new method to display 3-D image modelled as a sum of 2-D sliced images by expanding the concept of the conventional 2-D optical correlator based on spatial matched filtr to the 3-D region. It is hsown that a arbitrary image can be constructed by an array of the correlation-peaks between pixel-to-pixel and propose the systhesis precedure of 3-D spatial-matched-fjilter using fresnel diffraction equation to display 3-D image in space. It is also shown that the quantization problem is severe when the systehsised filter function is displayed on the conventional LC-SLM. To overcome this problem, anonlinear quantizaton method using the sigmoid function is suggested, and this method can reduce the bias and the loss of high spatial-frequency information, and improve the diffraction efficiency. Finally, the suggested method is tested by computer simulation and then approved by some optical experiments with the conventional LC-SLM.

• Progress in Superconductivity and Cryogenics
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• v.18 no.3
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• pp.10-20
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• 2016

The Cable-In-Conduit-Conductor (CICC) for the ITER tokamak Central Solenoid (CS) has undergone design change since the first prototype conductor sample was tested in 2010. After tests showed that the performance of initial conductor samples degraded rapidly without stabilization, an alternate design with shorter sub-cable twist pitches was tested and discovered to satisfy performance requirements, namely that the minimum current sharing temperature ($T_{cs}$) remained above a given limit under DC bias. With consistent successful performance of ITER CS conductor CICC samples using the alternate design, an attempt is made here to revisit the internal electromagnetic properties of the CICC cable design to identify any correlation with conductor performance. Results of this study suggest that there may be a simple link between the $Nb_3Sn$ CICC internal self-field and its $T_{cs}$ performance. The study also suggests that an optimization process should exist that can further improve the performance of $Nb_3Sn$ based CICC.

• Journal of Environmental Science International
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• v.12 no.7
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• pp.809-816
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• 2003

The short-term version of Industrial Source Complex Model(ISCST3) was evaluated for estimating short-term concentrations using criteria pollutant(SO$_2$, NO$_2$, CO, PM10) data from emission inventory of Young Tong area in Suwon for the year 2002. The contribution of pollutant concentration from point, line, area sources was found 21.8, 76.5 and 1.6%. Statistical parameters, such as correlation coefficient, index of agreement(IA), normalized mean square error(NMSE) and fractional bias(FB) were calculated for each pollutants. The model performance were found good for PM10(82%) and NO$_2$(69%), but poor for SO$_2$(34%) and CO(13%).

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.69.2-69.2
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• 2013

A galaxy cluster is an important laboratory to study the large scale structure in the Universe and the galaxy evolution. In order to identify candidate galaxy clusters at z~1, we have used deep and wide optical-NIR datasets based on IMS, UKIDSS DXS and CFHTLS wide covering ${\sim}20deg^2$ in the SA22 field. We measure the angular two-point correlation function of the candidate clusters and investigate the star formation activity of the member galaxies. Based on bias factor and halo mass function, candidate clusters have the average halo mass of > $10^{14}h^{-1}M_{\odot}$. At z~1, the star formation rate of cluster galaxies is similar to that of field galaxies, which indicates the environmental quenching is not so significant at z~1 as the local Universe.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• Atmosphere
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• v.15 no.4
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• pp.203-211
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• 2005

One algorithm has been developed for retrieving aerosol optical thickness from GMS-5 visible channel measurements, and then the algorithm was applied for obtaining the geographical distribution of aerosol optical thickness over East Asia during April 2002. Algorithm employs a look-up table based upon radiative transfer calculations with solar geometry, aerosol optical thickness, and surface albedo as inputs. Validation was conducted by comparing retrieved aerosol optical thickness with measured values from ground-based sky radiation measurements at Anmyon Do, Korea. It was found that the correlation coefficient is 0.71 with -0.03 of bias and 0.34 of root mean square error, suggesting that the algorithm developed in this study can be used for estimating aerosol optical thickness in a quantitative sense.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.637-645
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• 2003

Small area estimation had been studied using data-based methods such as Direct, Indirect, Synthetic methods. However recently, model-based such as based on regression or time series estimation methods are applied to the study. In this paper we investigate a model-data based small area estimation which takes into account the spatial relation among the areas. The Economic Active Population Survey in 2001 are used for analysis and the results from the model based and model-data based estimation are compared with using MSE(Mean squared error), MAE(Mean absolute error) and MB(Mean bias).

• Korean Journal of Child Studies
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• v.28 no.4
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• pp.277-287
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• 2007

Antagonism between the Youngnam and Honam regions of Korea as expressed by adolescents and their parents was studied in 167 parent-adolescent pairs (total 334) of Youngnam residents. Adolescents were middle school students 71 (42.5%) and high school students 96 (57.5%), 90 boys (53.9%), 77 girls (46.1%). The instruments were subjects' self-reported social distance and stereotypes. Data were analyzed by t-test, paired t-test, Pearson's Correlation, and hierarchical regression. Major findings were that : (1) adolescents' social distance was positively associated with adolescent and parent negative stereotypes. (2) Adolescent positive/negative stereotypes were positively associated with parent positive/negative stereotypes. (3) Adolescent negative stereotypes exerted an indirect effect on social distance, controlled by parents' negative stereotypes.

• Journal of Environmental Science International
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• v.26 no.9
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• pp.1073-1080
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• 2017

Manufacturing and technology industries produce large amounts of air pollutants. Ulsan Metropolitan City, South Korea, is well-known for its large industrial complexes; in particular, the concentration of $SO_2$ here is the highest in the country. We assessed $SO_2$ monitoring sites based on conditional and joint entropy, because this is a common method for determining an optimal air monitoring network. Monthly $SO_2$ concentrations from 12 air monitoring sites were collected, and the distribution of spatial locations was determined by kriging. Mean absolute error, Root Mean Squared Error (RMSE), bias and correlation coefficients were employed to evaluate the considered algorithms. An optimal air monitoring network for Ulsan was suggested based on the improvement of RMSE.

• Asian-Australasian Journal of Animal Sciences
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• v.10 no.5
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• pp.510-513
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• 1997

This study examined the effects of ignoring inbreeding depression on (co)variance components for weaning weight through the use of Monte Carlo simulation. Weaning weight is of particular interest as a trait for which additive direct and maternal genetic components exist and there then is the potential for a direct-maternal genetic covariance. Ignoring inbreeding depression in the analytical model (.8 kg reduction of phenotypic value per 1% inbreeding) led to biased estimates of all genetic (co) variance components, all estimates being larger than the true values of the parameters. In particular, a negative bias in the direct-maternal genetic covariance was observed in analyses that ignored inbreeding depression. A small spurious sire-by-year interaction variance was also observed.