• Journal of the HCI Society of Korea
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• v.8 no.1
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• pp.1-9
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• 2013

This is to develop a new way of testing self-esteem by adopting DAP(Draw a Person) test and to make a platform to digitalize it for young people in the adolescent stage. This approach is to get high effectiveness of the self-esteem measurement using DAP test, including some personal inner situations which can be easily missed in the large statistical analysis. The other objective of this study is digitalize to recover limits of DAP test in the subjective rating standard. It is based on the distribution of the figure drawing expressed numerically by the anxiety index of Handler. For these two examinations, we made experiment through 4 stages with second grade middle school 73 students from July 30th to October 31th in 2009 during 4 months. Firstly, we executed 'Self Values Test' for all 73 people, and divided them into two groups; one is high self-esteem group of 36 people, the other is low self-esteem group of 37 people. Secondly, we regrouped them following D (Depression), Pd (Psychopathic Deviate), Sc (Schizophrenia) scales of MMPI; one is high self-esteem group of 7 people, the other is low self-esteem group of 13 people. Thirdly, we conducted DAP test separately for these 20 people. We intended to verify necessity and appropriateness of direction of 'Digitalizing Measurement System' by comparing and analyzing relation between DAP and Self-esteem following evaluation criteria which has similarity in 3 tests, after executing DAP to reflect peculiarity of adolescents sufficiently. We compared and analyzed result abstracted by sampling DAP test of two groups; One is high self-esteem group of 2 people, the other is low self-esteem group of 2 people; to confirm whether we can improve limitation that original psychological testing has by comparing mutual reliance of measurement test. Finally, with DAP test gained from correlations between self-esteem and melancholia following as above-mentioned steps, we discovered possibility of realization to get a concrete and individual criteria of evaluation based on Expert System as a way of enhancing accessibility in quantitative manner. 'Digitalizing Measurement Program' of DAP test suggested in this study promote results' reliability based on existing tests and measurement.

• Journal of Korean Home Economics Education Association
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• v.31 no.1
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• pp.1-19
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• 2019

The purposes of this study were to concretely reveal the effect of the parents' child-rearing attitude perceived by adolescents and the school life adaptation on the problem behavior, and to verify the moderating effect of the ego-resilience on the relationship between the parents' child-rearing attitude and the school life adaptation and the adolescent problem behavior. This study analyzed a total of 2,107 students in the first year of high school, which was the 4th year data(2013) of Korea Children Youth Panel Survey(KCYPS) 2010. The reliability, descriptive statistics, t-test, and hierarchical regression analysis were conducted using SPSS WIN 22.0. The results were as follows. First, the effect of the parents' child-rearing attitude(supervision, affection, reasonable explanation, excessive interference, excessive expectation, and inconsistency), school life adaptation(relationship with teacher, relationship with friend, school regulation, and learning activity), and ego-resilience on the adolescent problem behavior was analyzed. As a result, the relationship with friend(-) had the highest influence on the adolescent problem behavior, followed by learning activity(-), inconsistency(+), ego-resilience(-), excessive interference(+), and supervision(-). However, the remaining sub-variables did not have any significant influence on the adolescent problem behavior. Second, the moderating effect of the ego-resilience on the relationship among the parents' child-rearing attitude, adaptation to school life, and adolescent problem behavior. The ego-resilience was found to moderate the effects of parents' positive child-rearing attitude, interpersonal relationships, and school adaptation on the adolescent problem behavior. However, the moderating effect was not significant for the effect of negative child-rearing attitude on the adolescent problem behavior. Therefore, various ego-resilience enhancement programs need to be developed and researched as a part of the safety education through the home economics class.

• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.63-72
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• 2015

The importance of the appropriate use and procurement of Safety and Health Expense has been increasing along with the recent increase of construction projects in height, size and complexity. However, the current standards for deducting the Safety and Health Expense have shown limitations in applying the properties and environment of the construction project due to its Safety and Health Expense Rate's classification method. Therefore, the purpose of this study is to develop a prediction model for the Safety and Health Expense that enables the consideration of different environment and properties of construction projects. The study uses multiple regression analysis to analyze the Safety and Health Expense of Ordinary(A) of less than 0.5 billion WON. The research results have shown that the use of multiple regression analysis reduces the error rate to 4.38% which the current standard calculation method have shown 18.48%. Therefore, the use of the suggested model provides reliable Safety and Health Expense prediction values that considers the properties of the project. It is expected that the results of this study contributes to the effective safety management by providing the appropriate amount of Safety and Health Expense to the project. In this study, only projects of less than 5 billion WON have been considered in the analysis. Therefore, more data is required for future studies to suggest an overall Safety and Health Expense predict ion model that covers the whole construction industry.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.6
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• pp.421-428
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• 2014

For the analysis of water supply network, demand-driven and pressure-driven analysis methods have been proposed. Of the two methods, demand-driven analysis (DDA) can only be used in a normal operation condition to evaluate hydraulic status of a pipe network. Under abnormal conditions, i.e., unexpected pipe destruction, or abnormal low pressure conditions, pressure-driven analysis (PDA) method should be used to estimate the suppliable flowrate at each node in a network. In order to carry out the pressure-driven analysis, head-outflow relationship (HOR), which estimates flowrate at a certain pressure at each node, should be first determined. Most previous studies empirically suggested that each node possesses its own characteristic head-outflow relationship, which, therefore, requires verification by using actual field data for proper application in PDA modeling. In this study, a model pipe network was constructed, and various operation scenarios of normal and abnormal conditions, which cannot be realized in real pipe networks, were established. Using the model network, data on pressure and flowrate at each node were obtained at each operation condition. Using the data obtained, previously proposed HOR equations were evaluated. In addition, head-outflow relationship at each node was analyzed especially under multiple pipe destruction events. By analyzing the experimental data obtained from the model network, it was found that flowrate reduction corresponding to a certain pressure drop (by pipe destruction at one or multiple points on the network) followed intrinsic head-outflow relationship of each node. By comparing the experimentally obtained head-outflow relationship with various HOR equations proposed by previous studies, the one proposed by Wagner et al. showed the best agreement with the exponential parameter, m of 3.0.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.381-396
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• 2003

Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11S
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• pp.3651-3667
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• 2000

Recently, in the distributed multimedia environments based on internet, as radical growing technologies, the most of researchers focus on both streaming technology and distributed object thchnology, Specially, the studies which are tried to integrate the streaming services on the distributed object technology have been progressing. These technologies are applied to various stream service mamgements and protocols. However, the stream service management mexlels which are being proposed by the existing researches are insufficient for suporting the QoS of stream services. Besides, the existing models have the problems that cannot support the extensibility and the reusability, when the QoS-reiatedfunctions are being developed as a sub-module which is suited on the specific-purpose application services. For solving these problems, in this paper. we suggested a QoS Integrated platform which can extend and reuse using the distributed object technologies, and guarantee the QoS of the stream services. A structure of platform we suggested consists of three components such as User Control Module(UCM), QoS Management Module(QoSM) and Stream Object. Stream Object has Send/Receive operations for transmitting the RTP packets over TCP/IP. User Control ModuleI(UCM) controls Stream Objects via the COREA service objects. QoS Management Modulel(QoSM) has the functions which maintain the QoS of stream service between the UCMs in client and server. As QoS control methexlologies, procedures of resource monitoring, negotiation, and resource adaptation are executed via the interactions among these comiXments mentioned above. For constmcting this QoS integrated platform, we first implemented the modules mentioned above independently, and then, used IDL for defining interfaces among these mexlules so that can support platform independence, interoperability and portability base on COREA. This platform is constructed using OrbixWeb 3.1c following CORBA specification on Solaris 2.5/2.7, Java language, Java, Java Media Framework API 2.0, Mini-SQL1.0.16 and multimedia equipments. As results for verifying this platform functionally, we showed executing results of each module we mentioned above, and a numerical data obtained from QoS control procedures on client and server's GUI, while stream service is executing on our platform.

• Journal of radiological science and technology
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• v.40 no.4
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• pp.647-655
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• 2017

This study was conducted to review the mediating effects of retirement preparation in how the degree of requirements for retirement education and the degree of preparation for retirement education affects quality of life, and how the degree of participation in retirement education which is a moderating variable is moderated. The study findings show that first, in terms of the difference in quality of life across different general characteristics, those who live in a city rather than a Gun, those who had received education of graduate school or higher rather than those with an education of undergraduate university programs or lower, those who were public officers or employees of corporations rather than those who were self-employed had a higher quality of life. The group satisfied with their economic status and health status were found to be more satisfied with their quality of life. Second, a correlation analysis showed that there was a positive correlation between retirement preparation, quality of life, and degree of requirements for retirement preparation. Moreover, there was also a positive correlation between quality of life, retirement education and the degree of requirements for retirement education. There was a positive correlation between retirement education and the degree of requirements for retirement preparation. Third, participation in retirement education moderated the indirect effect that the degree of preparation for retirement education affected quality of life through the degree of retirement preparation. In other words, the degree of requirements for retirement education affects retirement preparation and affects quality of life through the indirect effects of retirement education. As such, the moderated mediating effects of retirement education on retirement preparation was found to be greater. This indicates that quality of life may also vary in accordance with the requirements for retirement education.

• Review of Culture and Economy
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• v.21 no.2
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• pp.141-173
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• 2018

In this article, we explore how artistic fame is formed by analyzing antecedents of fame the extent to which the name of an actor or his/her work is positively known by his/her audiences among Korean handicraft artists. Drawing on prior literature on reputation and fame, we clarify the differences between the concept of reputation and the concept of fame and further distinguish three types of reputation among individual artists, depending on its sources expert reputation, market reputation, and peer reputation. We employ the mixed method in this study, in which we first conducted open-end interviews with three kinds of constituents (i.e., critics, market intermediaries, and artists) and then developed and tested the hypotheses derived from the insights we had obtained from the interviews. We further considered the impact of reputational work, defined as the level of effort devoted and activities performed by an artist him(her)self geared toward promoting his(her) work, on artistic fame. We find that there are large differences in factors associated with artistic fame between non elite and elite Korean handicraft artist groups, where elite status is captured by artists' educational background (i.e., Seoul National University and Hongik University, which are considered elite schools in accordance with prior research). Specifically, findings suggest that among non elite status artists, recognition by experts, or what we call expert reputation, acquired through national awards and invitations from prominent exhibitions as well as artists' own reputational work that incurs high cost, such as self-financed exhibition openings, were shown to be highly significant factors associated with artistic fame, which was measured as the number of media exposures related to her/his art work. By contrast, among elite status artists, peer reputation acquired through an artist's institutional affiliations and relatively low cost artists' own reputational work, such as self listing on a highly publicized magazine, were shown to be significant factors associated with fame. Taken together, this paper contributes to research on cultural industries and markets by highlighting the importance of understanding artistic fame not just as the outcome of her/his talent but as the social product that arises at the intersection of actors (artists) and her/his audiences in the social evaluation process.