• The Journal of Korea Robotics Society
• /
• v.3 no.2
• /
• pp.117-122
• /
• 2008

This paper presents a multi-robot localization based on multidimensional scaling (MDS) in spite of the existence of incomplete and noisy data. While the traditional algorithms for MDS work on the full-rank distance matrix, there might be many missing data in the real world due to occlusions. Moreover, it has no considerations to dealing with the uncertainty due to noisy observations. We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr$\ddot{o}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• Journal of the Korean Society of Clothing and Textiles
• /
• v.27 no.9_10
• /
• pp.1026-1035
• /
• 2003

The purposes of this study were to analyze the new lifestyle of female consumer of age in the range of 19∼35 and to investigate their clothing-purchasing behavior and preferred sensation by lifestyle group. The questionnaire survey was carried out on 402 subjects with 31 lifestyle questions, 32 questions of clothing purchasing behavior and 18 questions of preferred sensation. Through factor, cluster analysis and anova using SPSS, we found that the female consumers were composed of 6 lifestyle groups; Traditional Appearance Pursuit(19.4%), Personal Life Pursuit(15.7%), Outer Beauty Pursuit(15.9％), Active Practical Pursuit(11.4%), Digital Leisure Pursuit(13.4%) and Unconcern(21.6%). The location of 6 lifestyle group were visualized in 2-D as the horizontal axis of 'Internal↔Appearance' and the vertical axis of 'Personal↔Collective'. Six groups by lifestyle showed different clothing-purchasing behavior and preferred sensations, and had different socio statistical parameters, such as age, income, job and education.

• Health Policy and Management
• /
• v.12 no.3
• /
• pp.54-76
• /
• 2002

This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.11
• /
• pp.3098-3103
• /
• 2009

In this paper, self-similar characteristics over statistical approaches and real-time Ethernet network traffic measurements are estimated. It is also shown that the self-similar traffic reflects real Ethernet traffic chareacteristics by comparing TCP-MT source model which is exactly self-similar model to the traditional Poisson model.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.11 no.1
• /
• pp.67-71
• /
• 2006

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

• Phonetics and Speech Sciences
• /
• v.1 no.3
• /
• pp.155-163
• /
• 2009

This paper introduces an automatic voice classification system for the diagnosis of individual constitution based on Sasang Constitutional Medicine (SCM) in Traditional Korean Medicine (TKM). For the developing of this algorithm, we used the voices of 473 speakers and extracted a total of 144 speech features from the speech data consisting of five sustained vowels and one sentence. The classification system, based on a rule-based algorithm that is derived from a non parametric statistical method, presents binary negative decisions. In conclusion, 55.7% of the speech data were diagnosed by this system, of which 72.8% were correct negative decisions.

• Korean Journal of Child Studies
• /
• v.12 no.2
• /
• pp.94-110
• /
• 1991

This study examined the developmental aspects of sex role stereotypes among children and the impact of stereotypic and reversed stereotypic sex role content on children's sex-stereotypic thinking. In study I, subjects were 181 Korean children ranging from nursery and kindergarten to grade 1. SERLI was used to measure children's sex role stereotypes. In study II, the subjects were 62 six-year-old kindergarten children of each sex. 4 experimental stories were developed haled on Hong(1991). A test-retest design was used to study sex role stereotypes and the impact of stereotypic and reversed-stereotypic sex role content. Statistical analysis of obtained data was by an ANOVA and two-way analysis of co-variance. Results revealed that 6-year-old children's sex role stereotypes were higher than 5-and 7-year-old children. Boys were higher than girls on children's sex role stereotypes. Children exposed to reversed sex role content changed significantly in the direction of reversed stereotyping.

• Proceedings of the Korea Contents Association Conference
• /
• 2016.05a
• /
• pp.11-12
• /
• 2016

SVM (Support Vector machine) is powerful machine-learning method, and obtains better performance than traditional methods in the applications of muti-dimension nonlinear pattern classification. For the case of SVM model training and low efficiency in large samples, this paper proposes a combination of statistical parameters of the GMM-UBM (Universal Background Model) model. It is very effective to solve the problem of the large sample for the SVM training. The experiment is carried on four special dynamic hand gestures using the EPIC sensors. And the results show that the improved dynamic hand gesture recognition system has a high recognition rate up to 96.75%.

• Journal of the Korea Safety Management & Science
• /
• v.7 no.1
• /
• pp.199-210
• /
• 2005

Customer segmentation prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. However, ANN approaches have suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the customer segmentation prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with logistic regression analysis and ANN. The experiment results with real data of insurance company show that SVM superiors to them.

• Journal of the Korean earth science society
• /
• v.25 no.3
• /
• pp.152-159
• /
• 2004

The purpose of this study was to explore characteristics of natural history museum exhibits from the viewpoint of science education. A total of ninety exhibits for this study were examined in national science museums of Korea and Japan. Exhibits of Tokyo national science museum were again divided into two groups: the old and traditional types, and the new and renovated ones. Even though analyzing data was not undertaken through quantitative statistical process, the interpretation of the data was valid enough to fulfill the purpose of the research. While there were clear changes and differences between the old and the new types of exhibits in Tokyo national science museum, the old part of Tokyo museum was similar to one in Korea. Based on analyzing the new types of Tokyo museum, the current movement in the field of natural history museums of Korea explicitly has toward utilizing more science education concepts and ideas.