• Journal of Digital Convergence
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• v.11 no.11
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• pp.397-407
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• 2013

The purpose of this study was to develop comprehensive model which could represent service quality, satisfaction and loyalty in ubiquitous living English experience learning center, and to analyze an influence of service quality of elementary school students attending that center on satisfaction. The variables were extracted in connection with service quality, satisfaction and loyalty in ubiquitous living English experience learning center, and relations among those variables were examined. The study verified causality and influences between variables using feasibility of variables and structural equation thru confirmatory factor analysis, based on questionnaires of 262 students who attended ubiquitous living English experience learning center. The suggestion of the study on ubiquitous living English experience learning center of elementary school students are as follows. Programs in relation with living English should run more efficiently to expand ubiquitous living English experience learning center. More important is that guidelines or orientation for students to recognize how to use the programs be needed. Also, this study shows that the educational performance and satisfaction are found to be very large, and participation in the program of that center needs to be encouraged in terms of schools.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1436-1442
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• 2020

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.101-112
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• 1997

The purpose of this study was to examine usefulness of KEDI-Individual Basic Learning Skills Test as a diagnostic tool of learning disorders(LD). Learning disorder group consisted of two subgroups, verbal learning disorder group(VLD, n＝34) and nonverbal learning disorder group(NVLD, n＝14). Comparison group consisted of Dysthymia Disorder subgroup(n＝11) and Normal subgroup(n＝20). Performance of intelligence test and achievement test was examined in all 4 subgroups. In KEDI-WISC, VLD subgroup revealed primary problems in vocabulary, information and verbal-auditory attention test. NVLD group revealed primary problems in almost all performance tests such as visual acuity, psycho-motor coordination speed and visual-spatial organizations ability subtest. In KEDI-Individual Basic Learning Test, VLD group revealed primary problems in phonological coding process, word recognition and mathematics. For successful classification of LD children, the importance of achievement test and intelligence test was discussed by discriminant analysis and factor analysis. The results indicate that KEDI-Individual Basic Learning Skills is of considerable usefulness in diagnosing LD, but must be used in subtests, and additional tests must be conducted for thorough exploration of LD.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.4
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• pp.474-484
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• 2024

Reinforcement learning, which are also studied in the field of defense, face the problem of sample efficiency, which requires a large amount of data to train. Transfer learning has been introduced to address this problem, but its effectiveness is sometimes marginal because the model does not effectively leverage prior knowledge. In this study, we propose a stochastic initial state randomization(SISR) method to enable robust knowledge transfer that promote generalized and sufficient knowledge transfer. We developed a simulation environment involving a cooperative robot transportation task. Experimental results show that successful tasks are achieved when SISR is applied, while tasks fail when SISR is not applied. We also analyzed how the amount of state information collected by the agents changes with the application of SISR.

• Journal of IKEEE
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• v.20 no.1
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• pp.94-102
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• 2016

This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.

• ETRI Journal
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• v.39 no.3
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• pp.364-372
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• 2017

For the reliability of controllers in a software defined network (SDN), a dynamic and self-learning schedule method (DSL) is proposed. This method is original and easy to deploy, and optimizes the combination of multiple controllers. First, we summarize multiple controllers' combinations and schedule problems in an SDN and analyze its reliability. Then, we introduce the architecture of the schedule method and evaluate multi-controller reliability, the DSL method, and its optimized solution. By continually and statistically learning the information about controller reliability, this method treats it as a metric to schedule controllers. Finally, we compare and test the method using a given testing scenario based on an SDN network simulator. The experiment results show that the DSL method can significantly improve the total reliability of an SDN compared with a random schedule, and the proposed optimization algorithm has higher efficiency than an exhaustive search.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.936-941
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• 2005

KORAIL Human Resources Development Center' educational system is changed according to new start KORAIL. So, New master plan for will perform new vision POWER KORAIL 2010 is presented. 'C' Learning, 'W' Learning and 'E' Learning form the basis of these new methods and their principles have been applied ever since. These three independent yet interconnected learning axis were formed in order to achieve balance and harmony, realized in an integrated educational community. This model of education has become the foundation of our educational ideal at KORAIL's HRD Center. Innovative thinking and diligent scrutiny of educational applications and methods at the KORAIL HRD Center will bring about winning 'the war for talent'.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.230-232
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• 2023

Federated Learning (FL) is a technique that excels in training a global model using numerous clients while only sharing the parameters of their local models, which were trained on their private training datasets. As a result, clients can obtain a high-performing deep learning (DL) model without having to disclose their private data. This setup is based on the understanding that all clients share the common goal of developing a global model with high accuracy. However, recent studies indicate that the security of gradient sharing may not be as reliable as previously thought. This paper introduces the latest research on various attacks that threaten the privacy of federated learning.

• Journal of Oriental Neuropsychiatry
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• v.24 no.2
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• pp.145-154
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• 2013

Objectives : The purpose of this study is to examine the effects of treatment with scalp acupunctures for children with learning disorders. Methods : For this study, we evaluated Korea standard progressive matrices test (K-SPM) on 24 children with learning disorders who visited Korean medical center neuropsychiatry outpatient clinic from July 2012 to January 2013. Scalp acupuncture, cognitive enhancement therapy and speech-language therapy were applied. All children were treated 2 times a week for 4 months and we compared K-SPM test scores before treatment and 30 times after the treatment. Results : 1) After the treatment, K-SPM test scores have increased significantly (p<0.05) and the number of children in grade 5 (<5%) have decreased from 14 to 6. 2) Comparing K-SPM test scores between two groups: one with medical history and the other without medical history, the scores in both groups have increased significantly (p<0.05). 3) We also divided the children into two groups according to age: under the age of 13 and over the age of 13, and compared K-SPM test scores. Although the scores in both groups have increased respectively, it is the scores of the former group (under the age of 13) that have increased significantly (p<0.05). Conclusions : The treatments with scalp acupunctures were shown to be an effective intervention when improving K-SPM test scores of children with learning disorders.

• Korean Journal of Environment and Ecology
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• v.32 no.3
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• pp.332-341
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• 2018

This study aims to establish operating methods for value-oriented and competitive operation of Dalseong Wetland Ecological Learning Center in Daegu, Korea, which will open in 2018. For this purpose, we surveyed 77 Ecological learning facility managers nationwide using 22 questions on the subjects such as the operating method, volunteer, budget, and profit. The managers of 50 ecological learning facilities (Type A) responded, and we extracted the responses by the managers of 12 ecological learning facilities (Type B) that had the similar size as the Dalseong Wetland Ecological Learning Center and conducted an additional analysis. The results of the survey indicated that it was advisable for Daegu Metropolitan City to operate the Dalseong Wetland Ecological Learning Center and that the staff at least 3-5 managers were necessary while the number of volunteers to regularly work at the center was estimated to be 25-30. Excluding labor costs, the annual operating budget was estimated to be between 150 million won and 200 million won if Daegu Metropolitan City operates the facility directly. This study is meaningful in that it provides reference data to establish realistic and detailed management plan of the Dalseong Wetland Ecological Learning Center based on the opinions of the surveyed managers of ecological learning facilities in Korea.