• 디지털융복합연구
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• 제11권11호
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• pp.397-407
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• 2013

본 연구의 목적은 유비쿼터스 생활영어체험학습장의 서비스품질, 만족도 및 충성도를 설명할 수 있는 종합모델을 구안하여 유비쿼터스 생활영어체험학습장에 참여한 초등학교 학생들의 서비스품질이 만족도에 미치는 영향을 분석하는 데 있었다. 선행연구들에서 유비쿼터스 생활영어체험학습장의 서비스품질, 만족도, 충성도와 관련된 변인들을 추출하고, 각 변인들간의 관련성을 검토하였다. 본 연구는 유비쿼터스 생활영어체험학습장에 참여한 학생 262명에 대한 설문조사를 바탕으로 확인적 요인분석을 통한 변인의 타당성과 구조방정식을 사용하여 변인들간의 인과관계 및 영향력 정도를 검증하였다. 초등학교 학생들의 유비쿼터스 생활영어체험학습장 관련 변인들간의 인과관계를 경험적으로 밝혀낸 이 연구의 결과를 토대로 시사점을 정리하면, 첫째, 유비쿼터스 생활영어체험학습장을 보다 확대하고 효율적으로 진행하기 위해서는 생활영어와 관련된 프로그램을 효율적으로 운영을 하여야 한다. 유비쿼터스 생활영어체험학습장은 생활영어와 관련된 프로그램의 발굴과 개발, 그리고 프로그램의 사용방법을 충분히 학생들에게 안내하는 교육도 중요함을 알 수 있었다. 둘째, 유비쿼터스 생활영어체험학습장에 대한 교육적 성과와 만족도는 매우 큰 것으로 나타났으며, 학교차원에서도 유비쿼터스 생활영어체험학습장 프로그램 참여를 적극 권장할 필요가 있음을 알 수 있었다.

• Nuclear Engineering and Technology
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• 제52권7호
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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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• 제8권1호
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• pp.101-112
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• 1997

본 연구의 목적은 학습 장애를 진단하는데 있어 성취도 검사인 기초학습기능검사의 유용성을 살펴보는 것이다. 학습 장애 집단은 두 유형으로 나누어 언어적 학습 장애 집단(VLD：Verbal Learning Disorder) 34명, 비언어적 학습 장애 집단(NVLD：Nonverbal Learning Disorder) 14명으로 총 48명으로 구성되었으며, 비교 집단으로는 Dysthymia 집단 11명, 정상아 20명을 대상으로 지능 검사 및 기초학습 기능검사의 수행을 비교하였다. 지능 검사에서 VLD집단은 어휘력 및 언어를 통한 학습 과제, 언어-청각적인 주의과제에서 의미있는 저하를 나타내었고, NVLD 집단은 시-지각의 정확도, 정신-운동성 기능의 협응 속도, 시각-공각적인 조직력 등 동작성 기능 전반에 걸쳐 비효율성을 나타내었다. 기초학습기능검사에서는 VLD 집단은 음운 부호화과제, 셈하기 능력, 단어 재인 과제에서 의미있는 저하를 나타내었다. 또한 지능 검사의 소검사들에 기초학습기능검사의 소검사들을 포함하여 판별 분석을 한 결과, 기초학습기능검사를 포함시키지 않은 경우보다 판별율이 높아졌을 뿐 아니라, VLD집단을 유의미하게 판별해 주는 판별 함수를 도출하였다. 각 소검사들의 속성을 분석하기 위하여 요인 분석을 실시하였으며 이를 통하여 소검사들을 유목화하였으며 마지막으로 현 논문의 제한점 및 기초학습기능검사의 제한점을 논의하였다.

• 전기전자학회논문지
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• 제20권1호
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• pp.94-102
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• 2016

본 논문에서는 기계학습 알고리즘을 이용하여 다중물리(Multi-physics) 시뮬레이션의 반복 횟수를 획기적으로 줄일 수 있는 다중물리해석 예측 방법을 제안한다. 기존의 다중물리해석 시뮬레이션의 경우 소요되는 시간과 노력을 줄이기 위해 시뮬레이션 자체에 대한 방법과 환경 개선에 초점이 맞추어져 있으나 본 논문에서는 다중물리 시뮬레이션 결과를 기계학습 알고리즘으로 학습하여 추가적인 시뮬레이션을 수행하지 않고 학습된 기계학습 알고리즘을 사용하여 수십분에서 수시간에 걸리는 다중 물리 해석과 유사한 결과를 수초 내에 예측할 수 있음을 보였다. 기계학습 알고리즘 간의 성능을 비교하여 다중물리해석에 적합한 기계학습 알고리즘을 확인하였으며 가장 우수한 성능을 보인 가우시안 프로세스 회귀(Gaussian Process Regression)의 경우 100개 이하의 학습 샘플만으로도 우수한 예측 결과를 얻어낼 수 있음을 확인하였다. 제안하는 방식을 통해 시뮬레이션을 하고자 하는 모델의 형상이나 재질이 변경될 경우 기존의 시뮬레이션 결과로 학습된 알고리즘이 있다면 시뮬레이션을 반복 수행하기 전에 알고리즘을 이용하여 결과를 예측할 수 있어 시뮬레이션의 반복 횟수를 줄일 수 있을 것으로 기대한다.

• 한국군사과학기술학회지
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• 제27권4호
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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.

• ETRI Journal
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• 제39권3호
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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.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2005년도 춘계학술대회 논문집
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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'.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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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.

• 공학교육연구
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• 제27권4호
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• pp.3-11
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• 2024

This study classified domestic and international systems by type, presenting their key features and examples, with the aim of outlining future directions for system development and research. AI-based learning assistance systems can be categorized into instructional-learning evaluation types and academic recommendation types, depending on their purpose. Instructional-learning evaluation types measure learners' levels through initial diagnostic assessments, provide customized learning, and offer adaptive feedback visualized based on learners' misconceptions identified through learning data. Academic recommendation types provide personalized academic pathways and a variety of information and functions to assist with overall school life, based on the big data held by schools. Based on these characteristics, future system development should clearly define the development purpose from the planning stage, considering data ethics and stability, and should not only approach from a technological perspective but also sufficiently reflect educational contexts.

• 동의신경정신과학회지
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• 제24권2호
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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.