• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.195-201
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• 2020

The purpose of this study is to examine an influence of university students' openness to diversity on learning agility, career decision level and the mediating effect of learning agility. The survey questionnaire was distributed to 215 university students who are majoring in airline cabin service management and SPSS 23.0, AMOS 23.0 program was used for statistical analysis of the data. The results of this study are as follows: First, openness to diversity has a positive effect on learning agility. Second, learning agility has a positive effect on career decision level. Third, learning agility has a full mediating effect between openness to diversity and career decision level. These results show that the more students enhance their openness to diversity and learning agility, the more students enhance their career decision level.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.61-67
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• 2021

The purpose of this study is to demonstrate how EFL learners can overcome the limitations of traditional classes and practice communication through the learning activity model. As a research method, it was conducted from March to June 2019 to develop and derive strategies and guidelines through model development, validation, and application. After two validity tests, the model was applied to the experimental group, resulting in an increase of self-direction, engagement, problem-solving, and participation. Moreover the post results showed significant results in all fields, the usefulness of this model was confirmed. However, continuous follow-up research is needed, including the development of software that can easily apply AI related to English learning to classes, and the presentation of convergence activities with more systematic maker education in learning activities.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.629-632
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• 2020

현대사회는 4차 산업혁명 시대가 도래하면서 초연결, 초지능, 초융합 사회로 변화되고 있다. 최근 교육부는 많은 변화가 요구되고 있는 교육분야, 교육정책 방안으로 SW(소프트웨어)교육에 AI(인공지능) 교육까지 추가되야 한다고 제안하고 2024년까지 첨단 기술을 활용한 지능형 과학실을 구축한다고 밝혔다. 이에 본 논문에서는 정부의 교육정책 방안이 원활하게 진행될 수 있고 융합 교육 분야에서 활용될 수 있는 "미래 지능형 과학실 활용을 위한 화학원소기호 이미지 기계학습 AI·SW교육 프로그램"을 제안하고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.215-219
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• 2021

In this paper, we propose a deep reinforcement learning algorithm-based bi-directional electricity negotiation scheme that adjusts and propose the price they want to exchange for negotiation over smart building and utility grid. By employing a deep Q network algorithm, which is a kind of deep reinforcement learning algorithm, the proposed scheme adjusts the price proposal of smart building and utility grid. From the simulation results, it can be verified that consensus on electricity price negotiation requires average of 43.78 negotiation process. The negotiation process under simulation settings and scenario can also be confirmed through the simulation results.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.97-97
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• 2023

최근 재해분야에서 인공신경망(ANN), 기계학습(ML), 딥러닝(DL) 등 AI 기술이 활용성이 점차 증가하고 있으며, 센싱정보와 연계한 시설물 안전관리, 원격탐사와 연계한 재해감시(녹조, 산사태, 산불 등), 수문시계열(수위, 유량 등) 예측, 레이더·위성강수 자료의 보정과 예측, 상하수도 관망누수예측 등 다양한 분야에서 AI 기술이 적용되고 그 활용성이 검증된 바 있다. 본 연구에서는 ML, DL, 물리기반신경망(Pysics-informed Neural Networks, PINNs)을 이용한 다양한 재해분석 사례를 소개하고, 그 활용성과 한계에 대해서 논의하고자 한다. 주요사례로는 (1) SAR영상과 기계학습을 이용한 재해피해지역(울진 산불) 감지, (2) 국가 디지털 정보를 이용한 산사태 위험지역 판별(인제 산사태) (3) 기계학습 및 딥러닝 기법을 이용한 위성강수 자료의 보정·예측 및 유출해석, (4) 수리해석을 위한 수치해석분야에서의 PINNs의 적용성(1차원 Saint-Venant 식 해석) 평가 연구결과를 공유한다. 특히, 자료의 입·출력 자료만으로 학습된 인공신경망 모형 대신 지배방정식(물리방정식)을 만족하도록 강제한 PINNs의 경우, 인공신경망 모형보다 우수한 모의능력을 보여주었으며, 향후 복잡한 수리모델링 등 수치해석분야에서 그 활용가능성이 매우 높을 것으로 판단된다.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.425-426
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• 2019

전통적인 마이닝 기법은 다양한 디지털 매체와 센서 등에서 생산되는 빅데이터를 처리하기 어려울 뿐 아니라 신규 데이터 누적시 전체 데이터를 재분석 해야하는 비효율성과 대용량의 문서를 학습함에 있어 메모리부족 문제, 학습 소요시간 문제 등이 있다. 이러한 문제를 해결하기 위하여 본 논문에서는 자질축소 기법에 의존하지 않고 대량의 문서를 자유롭게 학습하고 부분적인 자질 추가 변경 시에 변경요소만을 추가 반영할 수 있는 범용적이고 일반적인 분류기의 구조설계 방법을 설계 및 구현하였다. 점진적 학습 모듈은 일반적인 학습 방법이 데이터의 추가 및 변동시마다 모든 데이터를 재학습하는 데 반해, 기존의 학습 결과에 증분된 데이터만 재처리 없이 추가적으로 학습한다. 재학습을 위해 사용자는 작업 수행 중 자원 관리를 통해 기존에 처리된 데이터를 자유롭게 가져와서 새로운 데이터와 병합이 가능하다. 이러한 점직적 학습 효율성은 빅데이터 기반 데이터 처리에 주요한 특성인 데이터 생산 속도를 극복하기 위한 좋은 대안이 될 수 있음을 확인하였다.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.75-81
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• 2021

In the new era of the 4th industrial revolution, new jobs using the latest technologies such as big data, cloud, IoT, and AI are increasing. In the field of various industries, talents who can converge IT and industrial fields are needed, but such convergence-type talents are insufficient. This study analyzed the effects of IT convergence startup education on the learning effect and educational performance of trainees preparing for re-employment. A survey was conducted with 160 trainees preparing for reemployment. Frequency analysis, reliability analysis, correlation analysis, and multiple regression analysis were performed using the analysis tool SPSS 22.0 program. As a result of the study, first, in the IT convergence startup education of trainees who are preparing for re-employment, it was found that the sub-factors such as education content, instructor, and member satisfaction had a positive effect on the learning effect. Second, in the IT convergence start-up education for employment trainees, it was found that the sub-factors such as education contents, instructors, and the satisfaction of learning members had a significant effect on educational performance. It is expected that this study will serve as a basic data for preparing a start-up support system to revitalize start-ups in the IT convergence field.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.346-349
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• 2021

표 기계독해에서는 도메인에 따라 언어모형에 필요한 지식이나 표의 구조적인 형태가 변화하면서 텍스트 데이터에 비해서 더 큰 성능 하락을 보인다. 본 논문에서는 표 기계독해에서 이러한 도메인의 변화에 강건한 사전학습 표 언어모형 구축을 위한 의미있는 표 데이터 선별을 통한 사전학습 데이터 구축 방법과 적대적인 학습 방법을 제안한다. 추출한 표 데이터에서 구조적인 정보가 없이 웹 문서의 장식을 위해 사용되는 표 데이터 검출을 위해 Heuristic을 통한 규칙을 정의하여 HEAD 데이터를 식별하고 표 데이터를 선별하는 방법을 적용했으며, 구조적인 정보를 가지는 일반적인 표 데이터와 엔티티에 대한 지식 정보를 가지는 인포박스 데이터간의 적대적 학습 방법을 적용했다. 기존의 정제되지 않는 데이터로 학습했을 때와 비교하여 데이터를 정제하였을 때, KorQuAD 표 데이터에서 f1 3.45, EM 4.14가 증가하였으며, Spec 표 질의응답 데이터에서 정제하지 않았을 때와 비교하여 f1 19.38, EM 4.22가 증가한 성능을 보였다.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.375-383
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• 2020

The study was to examine the mediating effect of self-efficacy in learning in the relationship between the learning satisfaction and motivation to transfer of nursing students who received the psychiatric nursing convergence simulation practicum using standardized patients. Participants were 144 third grade nursing students. Data were analyzed descriptive statistics, t-test, one-way ANOVA, Pearson's correlation coefficient analysis, and multiple regression following the Baron and Kenny's method and Sobel test for mediation. There were significant correlations between learning satisfaction and self-efficacy in learning(r=.686, p<.001), learning satisfaction and motivation to transfer(r=.633, p<.001) and self-efficacy in learning and motivation to transfer(r=.804, p<.001). Self-efficacy in learning showed partial mediating effects in the relationship between learning satisfaction and motivation to transfer(Z=7.63, p<.001). To increase the motivation to transfer, strategies to enhance the self-efficacy of nursing students are required.

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.331-341
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• 2018

The purpose of this study was to develop a learning motivation program and to test its effect to provide basic data to be used for freshmen who are going to enter nursing college and various educational strategies and policies for successful university life. In order to develop the program, the contents of the program were structured so as to improve the learning ability, self-directed ability, and social competence through the current research and literature review. As a result, motivation (F=3.45 p=.033), core competence (F=7.35 p=.001), time management (F=9.80 p<.001) and career attitude maturity (F=19.83 p<.001) were significantly increased before the program. This suggests that the composition of the learning motivation program includes various learning strategies unlike nursing and majors.