• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.381-388
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• 2023

This thesis is purposed to develop a regional-customized education program selection model using big data analysis. Based on the literature review, the concepts and characteristics of big data and lifelong education are analyzed. In addition, this thesis presents how to collect the data for lifelong education and to use big data suitable for the characteristics of lifelong education. Based on these results, a regional- customized lifelong education program selection model is developed. The regional customized lifelong education program model is developed by the following six steps. The customized education program model proposed in this study has a high degree of flexibility in terms of practical use, as it can be utilized in real-time data provision methods such as the nationally approved Lifelong Learning Personal Status Survey without the need for analysis one year later, allowing for selective analysis and future predictions. It is clear that there is a significant need and value for big data in the education field. Furthermore, all programs used in the sample model are provided free of charge, and due to the programming nature, the community is actively engaged in exchanges, making it very easy to modify and improve for the development of a more complete education program model in the future.

• The Korean Journal of Elementary Counseling
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• v.11 no.3
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• pp.371-390
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• 2012

This study examined the reliability and validity of the Korean short version of the Big-Five Questionnaire for children, a instrument designed to measure Big-Five personality domains of elementary school students. The short Big-Five Questionnaire for children was composed of 15 items based on exploratory factor analyses on th data from 5th and 6th grade elementary school students(N=278). Confirmatory factor analyses revealed evidence of structural validity of the Korean short version BFQ-C. The correlations of K-BFQC-SF with the criteria of depression, academic achievement, career maturity were assessed to verify criterion-related validity. The correlation coefficients were correspondent to the results of previous studies. This study is meaningful in that it is sufficient to assess five factor personality domains in school settings.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.253-253
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• 2015

This research evaluated the effect of two main sources on the results of the ocean regional circulation model (ORCMs) during downscaling and nesting the results from the coarse data. The two sources should be the domain size, and temporal and spatial resolution different between driving and driven data. The Big-Brother Experiment is applied to examine the impact of them on the results of the ORCMs separately. Within resolution of 3km grid point ORCMs applying in the Big-Brother Experiment framework, it showed that the simulation results of the ORCMs depend on the domain size and specially the spatial and temporal resolution of lateral boundary conditions (LBCs). The domain size can be selected at 9.5 times larger than the interest area, and the spatial resolution between driving data and driven model can be up to 3 of ratio resolution and updating frequency of the LBCs can be up to every 6 hours per day.

• Management & Information Systems Review
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• v.36 no.5
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• pp.85-98
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• 2017

The purpose of this study is to exploratory examine the structural relationships among meaningfulness of work, personality(Big 5 character-types) and job stress. To conduct such examination, the author (i) designated meaningfulness of work, personality(Big 5 character-types) and job stress as variables and (ii) designed a research model by conducting preceding studies on the variables. To examine the research model the author collected the survey data from the residents in Kyoungsangbuk-do, 332 copies of questionnaire. Collected data were analyzed using SPSS and AMOS programs. The analysis results are as follows. Especially, (1) the meaningfulness of work had a positive effect on agreeableness, conscientiousness, and extraversion. (2) the meaningfulness of work had a negative effect on neuroticism. (3) the meaningfulness of work had no effect on openness to experience. (4) the neuroticism factor had a positive effect on psychological job stress and physical job stress. (5) the openness to experience had a negative effect on psychological job stress and physical job stress. (6) the meaningfulness of work had no effect on psychological job stress and physical job stress. The implications and limitation which this study are as follows. First, this study has discovered that there was statistically significant relationship between the meaningfulness of work and Big 5 character-types. Second, Big 5 character-types(neuroticism, openness to experience) had statistically effect on psychological job stress and physical job stress. This study have limitation in that was conducted based on cross-sectional design of research. Because, the mechanism of job stress is a dynamic process.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.684-690
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• 2018

In society of the big data is being recognized as one of the core technologies witch is analysis of the collected information, the intelligent evolution of society seems to be more oriented society through an optimized value creation based on a prediction technique. If we take advantage of technologies based on big data about various data and a large amount of data generated during system operation, it will be possible to support stable operation and prevention of faults and failures. In this paper, we suggested an environment using the collection and analysis of big data, and proposed an derive time series prediction model for predicting failure through server performance monitoring for data collected and analyzed. It can be capable of supporting stable operation of the IT systems through failure prediction model for the server operator.

• IE interfaces
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• v.13 no.2
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• pp.171-177
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• 2000

The Six-Sigma is now common word as a management innovation in every business. Many companies have practiced to the TPM in Korea. These companies have regarded the Six-Sigma as a new approach or redundant strategy for management. This paper suggests a model which contains how to unite TPM and Six-sigma to make a big synergy effect for the company which is on developing of TPM. This model based upon SAMSUNG CORNING as a process industry.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• Korean Journal of Computational Design and Engineering
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• v.14 no.6
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• pp.374-381
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• 2009

To meet the soaring demand recently, South Korea big shipbuilders are examining two things. One is new investment in plant and equipment. The other is replacement of production resources. Considering plant & equipment investment and replacement of production resources, even if actual production ability would be enough, the real output could be affected by limitation of logistics with lack of analysis. As we set up big shipyard in virtual space, we could perform actual production by using confirm production plan in virtual space. We've analyzed the load of block stock, load of road and load of transporter for logistics effects are followed by production increase. This research is to determine the possible problems of those analyzed results and to present the resolution using the current layout. And then modified yard layout, we reanalyzed previous three logistics effects. This simulation model could help administrator to make rational decision for changing yard layout.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.81-90
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• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.