• Journal of Korea Water Resources Association
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• v.32 no.1
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• pp.3-13
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• 1999

In this study, an alternative spatial analysis method against conventional methods such as Thiessen method, Inverse Distance method, and Kriging method, named Spatial-Analysis Neural-Network (SANN) is presented. It is based on neural network modeling and provides a nonparametric mean estimator and also estimators of high order statistics such as standard deviation and skewness. In addition, it provides a decision-making tool including an estimator of posterior probability that a spatial variable at a given point will belong to various classes representing the severity of the problem of interest and a Bayesian classifier to define the boundaries of subregions belonging to the classes. In this paper, the SANN is implemented to be used for analyzing a mean annual precipitation filed and classifying the field into dry, normal, and wet subregions. For an example, the whole area of South Korea with 39 precipitation sites is applied. Then, several useful results related with the spatial variability of mean annual precipitation on South Korea were obtained such as interpolated field, standard deviation field, and probability maps. In addition, the whole South Korea was classified with dry, normal, and wet regions.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.7-14
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• 2006

This paper presents an efficient face recognition method using both first moment of image and basis images. First moment which is a method for finding centroid of image, is applied to exclude the needless backgrounds in the face recognitions by shifting to the centroid of face image. Basis images which are the face features, are respectively extracted by principal component analysis(PCA) and fixed-point independent component analysis(FP-ICA). This is to improve the recognition performance by excluding the redundancy considering to second- and higher-order statistics of face image. The proposed methods has been applied to the problem for recognizing the 48 face images(12 persons*4 scenes) of 64*64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed methods has a superior recognition performances(speed, rate) than conventional PCA and FP-ICA without preprocessing, the proposed FP-ICA has also better performance than the proposed PCA. The city-block has been relatively achieved more an accurate similarity than Euclidean or negative angle.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.1
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• pp.110-120
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• 1986

45.deg. corss jet flow, at the mixing of two jet flows, was experimentally studied. For this study, only the statistical turbulent characteristics and high order moments will be analysed by on-line computer system (hot-wire anemometer system, dynamic analyser and computer system, plotting and printing system). Since mean velocity distributions, intensities of turbulence, Reynolds stresses, correlation coefficients, and other general results were already studied and presented. One dimensional probability density distributions of u', v', and w' were analysed comparing with Gaussian curve, which showed skew and flat tendency according to the Y and Z directions. For the analysis of the joint flow of turublent components, the joint probability density distributions were examined. The fagures were drawn so as to be read joint probabilities, joint probability densities, fluctuating velocities u', v', and w'. For further detailed examination of the variations of skewness and flatness phenomena, iso-joint probability density contours obtained from the profiles of the joint probability density distributions were studied. According to the displacement of positions from the center of the mixing flow and the directions, the flatness and skewness factors were increased.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.55-60
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• 2021

This paper relates with the performance evaluation of QE-MMA (Quantized Error-MMA) adaptive equalization algorithm based on the stepsize and quantizer bit number in order to reduce the intersymbol interference due to nonlinear distortion occurred in the time dispersive channel. The QE-MMA was proposed using the power-of-two arithmetic for the H/W implementation easiness substitutes the multiplication and addition into the shift and addition in the tap coefficient updates process that modifies the SE-MMA which use the high-order statistics of transmitted signal and sign of error signal. But it has different adaptive equalization performance by the step size and quantizer bit number for obtain the sign of error in the generation of error signal in QE-MMA, and it was confirmed by computer simulation. As a simulation, it was confirmed that the convergence speed for reaching steady state depend on stepsize and the residual quantities after steady state depend on the quantizer bit number in the QE-MMA adaptive equalization algorithm performance.

• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.165-188
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• 2009

This paper has suggested the methodology for the frontier portfolios and the optimal portfolio under the mean-VaR framework, not assuming the normal distribution and considering the investor's preferences for the higher moments of return distributions. It suggested the grid and rank approach which did not need an assumption about return distributions to find the frontier portfolios. And the optimal portfolio was selected using the utility function that considered the 3rd and the 4th moments. For the application of the methodology, weekly returns of the developed countries index, the emerging market index and the KOSPI index were used. After the frontier portfolios of the mean-variance framework and the mean-VaR framework were selected, the optimal portfolios of each framework were compared. This application compared not only the difference of the standard deviation but also the difference of the utility level and the certainty equivalent expressed by weekly expected returns. In order to verify statistical significances about the differences between the mean-VaR and the mean-variance, this paper presented the statistics which were obtained by the historical simulation method using the bootstrapping. The results showed that an investor under the mean-VaR framework had a tendency to select the optimal portfolio which has bigger standard deviation, comparing to an investor under the mean-variance framework. In addition, the more risk averse an investor is, the bigger utility level and certainty equivalent he achieves under the mean-VaR framework. However, the difference between the two frameworks were not significant in statistical as well as economic criterion.

• Journal of The Korean Association For Science Education
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• v.19 no.1
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• pp.19-28
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• 1999

The major purposes of this study are to examine the effects of portfolio assessment on elementary school student's science knowledge, inquiry ability, science attitudes and to investigate students' perceptions on portfolio assessment. Control group consists of 45 fourth-graders at M-Elementary school located at Miwon, Chongwon-gun, Chung-buk and experimental group 36 fourth-graders of G-Elementary school located in Daejeon-si. The inventories of scientific knowledge I, inquiry ability, and science attitudes were administered to both groups as a pre-test. The experimental group was given portfolio assessment instruction and control group traditional instruction for about six weeks. Inventories about scientific knowledge 2, inquiry ability, and science attitudes were administered to both groups as a post-test. A questionnaire on the perception on portfolio assessment was given to experimental group after the treatment. The results were statistically analyzed with SPSS. Control group showed higher score on scientific knowledge than that of experimental group (p<0.5). No statistically meaningful difference was identified in inquiry ability and scientific attitude. More in-depth analysis revealed that scientific attitudes were improved statistically meaningfully by portfolio assessment. The students' perceptions on portfolio assessment is very positive. Students have positive responses on interests in portfolio assessment, feelings of involvement in learning, self-regulated learning, higher levels of thinking, intentions of participation in portfolio assessment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.549-556
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• 2017

This study investigated the importance of social cognitive intervention and the cognitive rehabilitation intervention by comparing the difference and examining the relationship between neurological cognitive function and social cognitive function of stroke patients in the acute phase and chronic stroke before returning to the community. LOTCA, cartoon intention inference task, and social behavior sequence task were performed on 30 acute stroke inpatients and 30 chronic stroke patients from May 2015 to June 2016. A two sample t test was conducted to examine the differences between the groups. The Pearson's correlations test was performed to examine the correlation among the variables in each group. As a result, there were statistically significant differences between the neurological cognitive function and social cognitive function of acute stroke patients and chronic stroke patients who were undergoing rehabilitation training before returning to the community (p<0.05). A linear relationship was found between the thinking operation and social behavior sequence task in the acute stroke group (r=0.539, p<0.05). In the chronic stroke group, visual perception (r=0.530, p<0.05), visual motor organization (r=0.655, p<0.05) and thinking operation (r=0.534, p<0.05) were correlated with the cartoon intention inference task. In addition, the social behavior sequence task were correlated with visual organization (r=0.534, p<0.05) and thinking operation (r=0.764, p<0.05). As a result of multiple regression analysis, the neurological cognitive functions influencing the social cognitive function in the cartoon task was found to be the thinking operation (B = 0.431) in acute stroke patients and the thinking operation (B=0.272) and visuomotor organization (B = 0.218) in the case of chronic stroke. In addition, the results of the social behavior sequence task revealed the thinking operation (B=0.417) in the acute stroke patients, and thinking operation (B=0.267), visual motor organization(B=0.274) and visual perception(B=151) in chronic stroke patients to be significant. According to this result, there is a difference in the neurological and social cognitive levels between the two groups. Therefore, the social cognition is strongly related to the high level cognitive function as thinking operation of the neurological cognitive function. Therefore, in further research, it would be necessary to determine if there is a change in higher cognitive function in neurological cognitive function after applying a social cognition intervention program for stroke.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.102-109
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• 2008

In this paper, the robust hybrid modulation type classifier which use both HOS and WT key features and can recognize 10 digitally modulated signals without a priori information in multipath fading channel conditions is proposed. The proposed classifier developed using data taken field measurements in various propagation model (i,e., rural area, small town and urban area) for real world scenarios. The 9 channel data are used for supervised training and the 6 channel data are used for testing among total 15 channel data(i.e., holdout-like method). The Proposed classifier is based on HOS key features because they are relatively robust to signal distortion in AWGN and multipath environments, and combined WT key features for classifying MQAM(M=16, 64, 256) signals which are difficult to classify without equalization scheme such as AMA(Alphabet Matched Algorithm) or MMA(Multi-modulus Algorithm. To investigate the performance of proposed classifier, these selected key features are applied in SVM(Support Vector Machine) which is known to having good capability of classifying because of mapping input space to hyperspace for margin maximization. The Pcc(Probability of correct classification) of the proposed classifier shows higher than those of classifiers using only HOS or WT key features in both training channels and testing channels. Especially, the Pccs of MQAM 3re almost perfect in various SNR levels.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.397-408
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• 2009

In this study, a stochastic precipitation generation framework for simultaneous simulation of daily precipitation at multiple sites is presented. The precipitation occurrence at individual sites is generated using hybrid-order Markov chain model which allows higher-order dependence for dry sequences. The precipitation amounts are reproduced using Anscombe residuals and gamma distributions. Multisite spatial correlations in the precipitation occurrence and amount series are represented with spatially correlated random numbers. The proposed model is applied for a network of 17 locations in the middle of Korean peninsular. Evaluation statistics are reported by generating 50 realizations of the precipitation of length equal to the observed record. The analysis of results show that the model reproduces wet day number, wet and dry day spell, and mean and standard deviation of wet day amount fairly well. However, mean values of 50 realizations of generated precipitation series yield around 23% Root Mean Square Errors (RMSE) of the average value of observed maximum numbers of consecutive wet and dry days and 17% RMSE of the average value of observed annual maximum precipitations for return periods of 100 and 200 years. The provided model also reproduces spatial correlations in observed precipitation occurrence and amount series accurately.