• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.120-128
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• 2003

When considering the use of rapid prototyping (RP), there are many issues a designer has to address for handling an STL model, the de facto standard fur RP. Today designers can skip all these issues by visiting web-based service bureaus that readily supply needed information for the RP services. Since orders are taken for RP parts through the web page of service providers designers are now asked to upload their STL files to the company server either by direct upload, ftp file transfer, or as an e-mail attachment. If the service bureau, however, fixes or edits an STL filceto optimize the RP process but neglects to tell its customer about the rework in detail, it may cause problems down the line in processing of the original CAD data for other applications. In this paper, we propose a framework for a collaborative virtual environment between CAD designers and RP processes on the internet which directly provides designers with an advanced preprocessor functionality, design visualization, as well as model display, repair, and slicing over the network. This can help smooth data transfer from CAD to RP process with minimum inconsistency in CAD.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1053-1063
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• 2017

Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.18-28
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.74-81
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA(Dropout Genetic Algorithm) which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.2
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• pp.71-80
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• 1988

The primary purpose of this study is to develop an automation tool capable of converting the specification of structured analysis into that of structured design. Structured Analysis and Structured Design Language (SASDL) is a computer-aided description language based on ERA model and particulariged by ISLDM/SEM. The automation tool utilizes the specifications of data flow diagram described in SASDL to produce their corresponding SASDL specification of structure chart. The main idea behind the automatic conversion process is to categorize the bubbles in data flow diagram and to determine the positions of the bubbles in structure chart according to their categories and the relative locations in data flow diagram. To make the problem into manageable size, the whole system is broken down into separate parts called activity units. A great deal of manual jobs, such as checking processes leveling, checking data derivation of processes, deriving structure chart from data flow diagram, checking any inconsistency between data flow diagram and structure chart and so forth, can be automated by using SASDL and conversion tool. The specification of structure chart derived by conversion tool may be used in an initial step of design to be refined by SASDL users.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.2
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• pp.140-145
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• 2016

Objectives : Parental behavior is related to personality development and ego resilience in the childhood. The objective of this study was to identify the influence of parental behavior on ego resilience in Korean middle school student. Methods : Subjects were selected based on stratified multi-stage cluster sampling in Korea youth panel study 2013(Boy : N=1,075, Girl : N=1,033). We used Parental behavior inventory(PBI) to estimate parental behavior and the Ego resilience scale to estimate ego resilience. The data were statistically analyzed using a Pearson correlation analysis and regression analysis with the statistical package for the social sciences(SPSS). We considered differences to be significant when p<0.05. Results : A regression analysis showed that rational explanation, affection, Interest and inconsistency of the parental behavior domains influence ego resilience. Also rational explanation, affection and Interest of the parental behavior domains showed a significant positive correlation with ego resilience(r=0.24, r=0.31, r=0.22, p<0.01). In contrast to early childhood studies, inconsistency showed no significant correlation. Conclusions : Adolescents who had taken more rational explanation, interest and affection from their parents were more likely to have higher ego resilience. However, inconsistency of parental behavior showed no correlation with ego resilience of adolescents, which means that they are affected by several other factors than parental behavior. This study would be a basic research that could be a help to psychosocial approach in pediatric psychiatry.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.406-416
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• 2020

The purpose of this study is to confirm the effect of parents' positive and negative parenting attitudes perceived by children on smartphone dependence. In addition, it is to verify whether aggression and social withdrawal play a mediating role in the relationship between parental attitude and dependence on smartphones. In order to achieve this goal, the data of the "Korean Children and Youth Panel Survey 2018(KCYPS 2018)" were used for analysis. The sample group is 2,399 "elementary school students 4 cohort". The research results of this study are as follows. First, it was found that autonomy support and coercion had a negative effect on aggression of children, but rejection and inconsistency had a positive effect on aggression. Second, it was found that inconsistency and rejection had a positive effect on children's social atrophy, but coercion had a negative effect. Third, it was found that aggression had a positive effect on children's dependence on smartphones, but social withdrawal had no significant effect. Fourth, it was found that autonomy support, rejection, coercion, and inconsistency indirectly affect children's dependence on smartphones through aggression. In this study's conclusion, practical implications for lowering children's dependence on smartphones were suggested.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.99-110
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• 1999

This study deals with the adaptability questions of O-D table estimation models. Its objectives are two-fold; (1) to estimate the characteristics of various O-D table estimation models(i.e. linear regression models. entropy models and statistic models) and (2) to find the model which estimates the O-D table with the best accuracy under the various data conditions. In Pursuing the above, this study gives the particular attentions to the test of the models, using the Sioux Falls network and equilibrium assignment method of MINUTP. The major findings are the followings. Firstly. it finds that the statistic models have the most goodness of fat among all models, if the required data are all Prepared. But it Presents that statistic models are the most sensitive against the underspecification and inconsistency problems of link data. Secondly, It shows that the linear regression models have the worst goodness of fat among all models. But the linear regression models are the most insensitive to the underspecification and inconsistency problems. Thirdly, THE/1 model of entropy model is sensitive against the underspecification and incon-sistency problems, but THE/2 model is insensitive. Finally, other informations like total volume, zonal Production and attraction volumes in 0-D table, help models to gain the better goodness of fit. Especially, in the statistic models. both the zonal production and attraction volume data are helpful to estimate the link volumes. It can be expected that the results dive some implications not only to the selection of optimal model under the various given data, but also to the development or modification of model.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.179-200
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• 2008

We develop a feature selection method that can improve both the efficiency and the effectiveness of classification technique. In this research, we employ case-based reasoning as a classification technique. Basically, this research integrates the two existing feature selection methods, i.e., the univariate analysis and the LVF algorithm. First, we sift some predictive features from the whole set of features using the univariate analysis. Then, we generate all possible subsets of features from these predictive features and measure the inconsistency rate of each subset using the LVF algorithm. Finally, the subset having the lowest inconsistency rate is selected as the best subset of features. We measure the performances of our feature selection method using the data obtained from UCI Machine Learning Repository, and compare them with those of existing methods. The number of selected features and the accuracy of our feature selection method are so satisfactory that the improvements both in efficiency and effectiveness are achieved.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.1
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• pp.75-95
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• 2014

The purpose of this study was to propose the improvements for high quality of National Library of Korea (NLK) Subject Headings List which was developed as a subject access tool in 2002. For this study, literature review, case study for subject headings lists of national libraries, and data analysis of headings and relationships were performed. Several problems were found as follows: inconsistency of subject headings descriptions, unclear and unnecessary relationships among headings, inconsistency of hierarchical relationships, lack of currency, incorrectness of classification numbers, duplication of newly requested terms, and assignment of non-preferred and unregistered terms in bibliographic records. Focusing on these problems, modifications of subject headings and bibliographic records, installation and operation of an review committee on subject headings, and supplementation of a manual were suggested to improve NLK Subject Headings List.