• Korean Journal of School Psychology
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• v.11 no.1
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• pp.253-274
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• 2014

The present study classified groups based on latent profile of self-determination motivation(amotivation, external motivation, intrinsic motivation), and examined the determinants for each group. The data was collected through panel data of Korea Education Longitudinal Study(KELS), total 5,459 participants who answered questionnaires of self-determination motivation of two times both second grade of middle school and second grade of high school. To identify the change motivational type, standardized residual was conducted using SPSS 17.0., and the latent classes for the change of motivational type was investigated using M-Plus in the frame work of Latent Profile Analysis(LPA). The results indicated that five groups(increase of self-determination, self-determination maintenance, self-determination developmental delay, elf-determination confusion, decrease of self-determination group) were classified based on latent profile. In addition, parental control, academic self-concept, teacher-student relationship, test anxiety, avoidance orientation, gender, father's education, and income were significantly related to each group. Lastly, the implications for directions of the adolescent counseling, limitations and future research are discussed.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.491-502
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• 2010

When human beings acquire and develop knowledge through education, their prior knowledge influences the next learning process. As this is a fact that should be considered in machine learning, we need to examine the effects of controlling the order of training sequence on machine learning. In this research, the role of the supervisor is extended to control the order of training samples, in addition to just instructing the target values for classification problems. The supervisor sequences the training examples categorized by SOM to the learning model which in this case is MLP. The proposed method is distinguished in that it selects the most instructive example from categories formed by SOM to assist the learning progress, while others use SOM only as a preprocessing method for training samples. The result shows that the method is effective in terms of the number of samples used and time taken in training.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5534-5541
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• 2012

This paper classifies cities in the metropolitan area based on natural hazard vulnerability. The procedure of our empirical analysis is divided into three parts as follows: First, it summarizes variables related to natural hazard vulnerability to significant factors, carrying out principal component analysis. Second, it classifies cities in the metropolitan area, conducting cluster analysis using factor scores. Lastly, it proposes differential measures for natural hazard mitigation for classified cities in the metropolitan area, based on natural hazard vulnerability.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.135-141
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• 2008

We propose a new method for recognizing the activities of daily living(ADL) based on the state-dependent motion analysis using 3-axial accelerometers and a glove type RFID reader. Two accelerometers are used for the classification of 5 body states based on the decision tree. Classification of the instrumental activities is performed based on the hand interaction with an object ID using an accelerometer and a RFID reader. Object-dependent hand movements are classified into 5 categories in advance and final decision combines the body state and the instrumental activities. Experiment shows that the suggested hierarchical motion analysis provides accuracy rate of over 90% for all 20 ADLs.

• Journal of KIISE:Software and Applications
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• v.37 no.10
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• pp.723-735
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• 2010

Evaluating the level of achievement and providing the knowledge which is appropriate at the evaluated level have great influence in studying of the human beings. This shows the importance of the order of training and the training order should be considered in machine learning. In this research, to assess the influence of the order of training, we propose a method of controlling the order of training samples utilizing the experience of supervisor in the training of MLP. The supervisor finds out the current state of MLP using teaching experience and student evaluation, and then selects the most instructive sample for MLP in that state. We use CRF to represent and utilize the experience of supervisor. While the proposed method is similar to active learning in selecting samples, it is basically different in that selection is not to reduce the number of samples to be used but to assist the learning progress. The result from classification problem shows that the method is usually effective in terms of time taken in training in contrast to random selection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1199-1206
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• 2015

This paper introduces an algorithm for computing similarity between two poses in the motion capture data with different scale of skeleton, different number of joints and different joint names. The proposed algorithm first performs the topological analysis on the skeleton hierarchy for classifying the joints into more meaningful groups. The global joints positions of each joint group then are aggregated into a point cloud. The number of joints and their positions are automatically adjusted in this process. Once we have two point clouds, the algorithm finds an optimal 2D transform matrix that transforms one point cloud to the other as closely as possible. Then, the similarity can be obtained by summing up all distance values between two points clouds after applying the 2D transform matrix. After some experiment, we found that the proposed algorithm is able to compute the similarity between two poses regardless of their scale, joint name and the number of joints.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1125-1137
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• 2014

This paper studies what affects the private education cost for the elementary, middle, and high school students. It is a big issue now because there can be a problem in the equal opportunity for education if the portion of private education cost is very high in the total education cost. If we spend more time and money on the private education than the school education, it can cause the polarization among the classes and regions. The excessive private education also can deteriorate the school system. we use various regression and classification methods to analyze the cost of private education and find the important variables in the models. we found that large cities spend more money on the private education than small cities. We also found that high school students spend more than middle school students and the elementary students and the household with more income spend more money on the private education.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.259-270
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• 2005

The primary research objective of this study is to evaluate a weight of purchase decision making factors according to the purchasing style. In this study, the purchasing style is classified into two categories-online shopping and offline shopping group. This study adopts the AHP method to calculate a weight of factors. For this purpose, 22 purchasing factors which affect on consumer purchasing decision making are classified into four factors - a product related factor, a convenience related factor, a purchasing risk related factor and a shopping enjoyment related factor. In this study, the weights of purchasing factors are evaluated according to 1)the purchasing style-online and offline Purchasing group, 2)the frequency of online shopping-high and low group, 3)the media used for online shopping-the TV home shopping and Internet home shopping group. The result shows that there are difference the weight of factors according to the purchasing style.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.43-53
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• 2014

In this paper, we propose an ontology-based approach to measuring degree-of-interest in country by analyzing wikipedia usage data. First, we developed the degree-of-interest ontology called DOI ontology by extracting concept hierarchies from wikipedia categories. Second, we map the title of frequently edited articles into DOI ontology, and we measure degree-of-interest based on DOI ontology by analyzing wikipedia page views. Finally, we perform chi-square test of independence to figure out if interesting fields are independent or not by country. This approach shows interesting fields are closely related to each country, and provides degree of interests by country timely and flexibly as compared with conventional questionnaire survey analysis.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.211-219
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• 2005

A selection of the analysis section for traffic accidents is used to analyze definitely the cause of accidents sorting similar accidents by a group and to raise the effect of improvement projects deciding the priority of accidents. In the existing method, an uniformly dividing method based on road mileages has been used, which has no consideration for similarities among accidents. Consequently, in recent, a slider-length method considering accident types rather than road mileages is widely used. In this study, using K-mean method, a non-hierarchical grouping technique used in the Cluster Analysis ai a applicatory method for the slider length method, a method classifies accidents that occurred the most nearby mileages into one group is proposed. To verify the proposed method, a comparison between the f-mean method and the dividing method at regular intervals on the data of a total of 25.6km lengths along Kyung-bu freeway in Pusan direction was made so that the K-mean method was proved to an effective method considering the similarities and adjacencies of accidents.