• Human Ecology Research
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• v.52 no.5
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• pp.443-456
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• 2014

The purpose of this study was to explore the patterns of private education, investigate the characteristics of private education patterns, and analyze the differences in study habits and academic achievement of youth on the basis of private education patterns. In this study, we used the data from the 2012 Panel of the Korea Children and Youth Panel Study by the National Youth Policy Institute. The subjects of this study were ninth-grade students and their parents. The statistical methods used for the analysis were two-step clustering, Chi-squared test, analysis of variance, and multiple regression. The major findings were as follows: first, private education was classified into three patterns, namely financial investment, time investment, and reduction of investment; and four categories, namely; private education methodology, private education time, private education expenses, and number of youth with access to private education. Second, the statistically significant socio-demographic characteristics of private education patterns were parents' education, parents' job type, father's working hours, sex of children, housing form, and income. Third, the study found that financial investment and a reduce of investment led to better study habits and academic achievement than time investment and no investment. Fourth, private education and study habits showed statistically meaningful effects on academic achievement; in particular, study habits had strong effects on academic achievement. Based on the results, a variety of educational programs for the improvement of the study habits of the youth were suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1519-1534
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• 2016

In this paper, we study how to optimize the data shuffle phase by leveraging the flow relationship in datacenter networks (DCNs). In most of the clustering computer frameworks, the completion of a transfer (a group of flows that can enable a computation stage to start or complete) is determined by the flow completing last, so that limiting the rate of other flows (not the last one) appropriately can save bandwidth without impacting the performance of any transfer. Furthermore, for the flows enter network late, more bandwidth can be assigned to them to accelerate the completion of the entire transfer. Based on these characteristics, we propose the flow chasing algorithm (FCA) to optimize the completion of the entire transfer. We implement FCA on a real testbed. By evaluation, we find that FCA can not only reduce the completion time of data transfer by 6.24% on average, but also accelerate the completion of data shuffle phase and entire job.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.788-793
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• 2015

This paper proposes a path planning algorithm to reduce the inspection time of AOI (Automatic Optical Inspection) machines for SMT (Surface Mount Technology) in-line system. Since the field-of-view of the camera attached at the machine is much less than the entire inspection region of board, the inspection region should be clustered to many groups. The image acquisition time depends on the number of groups, and camera moving time depends on the sequence of visiting the groups. The acquired image is processed while the camera moves to the next position, but it may be delayed if the group includes many components to be inspected. The inspection delay has influence on the overall job time of the machine. In this paper, we newly considers the inspection delay time for path planning of the inspection machine. The unified approach using genetic algorithm is applied to generates the groups and visiting sequence simultaneously. The chromosome, crossover operator, and mutation operator is proposed to develop the genetic algorithm. The experimental results are presented to verify the usefulness of the proposed method.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.281-296
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• 2009

This study examines the job and life satisfactions of the self-employed. It uses the Korean Labour and Income Panel Study(KLIPS, hereafter) data for 1998 and 2004. We examine the phases of satisfaction and what variables influence satisfaction for both years and compare the results in order to see what changed between the two regimes. We make use of k-means clustering to divide self-employed into similar degrees of satisfaction. As a result, we are able to classify the self-employed into three groups(low, medium and high) both for the two regimes. High groups consists of relatively younger, well-educated, low working dates, higher proportion of woman than other groups. As a result of regression analysis, we have some evidence that women are more satisfied than men for job satisfaction and that the existence of income is more important than the amount of income for life satisfaction. The age, education, satisfaction for working place, and health are significant to both satisfactions.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.541-551
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• 2021

Among the numerous duties of a librarian in a library, the work of arranging books is a job that the librarian has to do one by one. Thus, the cost of labor and time is large. In order to solve this problem, the interest in book-arranging robots based on artificial intelligence has recently increased. In this paper, we propose the K-ACO algorithm, which is the shortest path algorithm for multi-stops that can be applied to the library book arrangement robots. The proposed K-ACO algorithm assumes multiple robots rather than one robot. In addition, the K-ACO improves the ANT algorithm to create K clusters and provides the shortest path for each cluster. In this paper, the performance analysis of the proposed algorithm was carried out from the perspective of book arrangement time. The proposed algorithm, the K-ACO algorithm, was applied to a university library and compared with the current book arrangement algorithm. Through the simulation, we found that the proposed algorithm can allocate fairly, without biasing the work of arranging books, and ultimately significantly reduce the time to complete the entire work. Through the results of this study, we expect to improve quality services in the library by reducing the labor and time costs required for arranging books.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.79-86
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• 2020

This study aimed to identify the convergence factor where social support, self-esteem, and job stress affect the type of anger expressions in dental hygienists. The study involved 402 hygienists from different dental institutions. Clustering analysis was carried out to classify the types, and logistics regression analysis to find the related factors. Based on the types of anger expressions found in dental hygienists, they were divided into an anger control group and anger out-in group, with the former comprising 233 participants (58%). The results show that those with higher self-esteem (OR=5.592) and enjoying greater social support from their colleagues (OR=1.172) tend to belong to the anger control group. In other words, the study suggests that dental hygienists can control anger better with higher self-esteem and stronger social support from colleagues.

• Journal of Digital Contents Society
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• v.10 no.1
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• pp.169-175
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• 2009

Grid computing is an application to obtain the most efficient performance from computing resources in terms of cost and convenience. It is also considered as a good method to solve a problem that cannot be settled by conventional computing technologies such as clustering or is requiring supercomputing capability due to its complex and long-running task. In order to run grid computing effectively, it needs to connect high-performance computing resources in real-time which are distributed geographically. Answering to the needs of this grid application, researchers in several universities with Argonne National Laboratory in the USA (ANL) as the main axis have developed Globus. It is noticed, however, that the quality of service (QoS) is not guaranteed when certain jobs are exchanged through networks in the context of Globus. To tackle with this problem, the ANL has invented Globus Architecture for Reservation and Allocation (GARA). The researchers of this paper constructed a testbed for evaluating the ability to reserve resource in the GARA system and implemented the GARA code for it. We analyzed the applied results and discussed future research plans.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.