• Korean Journal of Radiology
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• 제23권12호
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• pp.1119-1125
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• 2022

• Nuclear Engineering and Technology
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• 제56권9호
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• pp.3981-3981
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• 2024

• 한국산학기술학회논문지
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• 제16권7호
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• pp.4459-4466
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• 2015

본 연구는 간호대학생의 대학생활 적응에 영향을 미치는 요인을 파악하기 위한 서술적 조사연구이다. 연구대상자는 간호대학에 재학 중인 학생 333명으로, 정서지능, 완벽주의적 자기제시, 임상실습 스트레스 정도를 설문지로 측정하였다. 자료수집기간은 2014년 10월 20일부터 12월 8일까지이었다. 수집된 자료는 t-test, one way ANOVA, Pearson correlation coefficient, multiple linear regression analysis로 분석하였다. 연구결과, 대학생활 적응에 영향을 미치는 요인은 정서지능, 종교, 완벽주의적 자기제시였으며, 모델의 설명력은 27.2%였다. 본 연구의 결과를 토대로 대학생활 적응을 위해서는 정서지능을 향상시킬 수 있는 교육전략을 개발하여 적용하는 것이 필요하다고 제언한다.

• 수산해양교육연구
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• 제25권1호
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• pp.113-127
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• 2013

This study investigated the direction of program development in emotional education through storytelling without separating intellectual education in current schooling. As the basic research on program development for elementary school students' emotional intelligence, this study examined how storytelling affects program development in emotional education, and the direction of program contents and structures using storytelling. Firstly, current storytelling and existing programs used for emotional education were analyzed and the direction of program development in emotional education was investigated. Secondly, the structures and procedures of programs for emotional education through storytelling were examined. Finally, stressed contents and items by each factor consisting of emotional intelligence related to the curriculum, and more discussions to consider were inquired. For systematic emotional education, there must be efforts made on developing contents of each factor comprising emotional intelligence; and teaching must be done through the interrelatedness among home, society and school. Furthermore, the construction of social system across the country is required. In conclusion, this study suggest the emotional education needs new curricula based on narrative, because it is dependent on personal emotions and situations.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.95-98
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• 2021

최근 비정상적인 네트워크 활동 감지 및 네트워크 서비스 프로비저닝과 같은 다양한 분야에서 응용되는 네트워크 트래픽 예측 기술이 네트워크 통신 문제에 의한 트래픽의 결측 및 네트워크 유저의 불규칙한 활동에 의한 비선형 특성 때문에 발생하는 성능 저하를 극복하기 위해 딥러닝 신경망에 대한 연구가 활성화되고 있다. 이 딥러닝 신경망 중 시계열 딥러닝 신경망은 단기 네트워크 트래픽 볼륨을 예측할 때 낮은 오류율을 보인다. 하지만, 시계열 딥러닝 신경망은 기울기 소멸 및 폭발과 같은 비선형성, 다중 계절성 및 장기적 의존성 문제와 같은 한계를 보여준다. 이 논문에서는 계절성 임베딩을 고려한 주의 신경망 기반 트래픽 예측 기법을 제안한다. 제안하는 기법은 STL 분해 기법을 통해 분해된 트래픽 트랜드, 계절성, 잔차를 이용하여 일별 및 주별 계절성을 임베딩하고 이를 주의 신경망을 기반으로 향후 트래픽을 예측한다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2023년도 제68차 하계학술대회논문집 31권2호
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• pp.7-10
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• 2023

최근 기후 변화가 심각해짐에 따라 수소 에너지에 대한 관심이 집중되고 있으며 이를 안전하게 운송/보관할 수 있는 용기에 대한 연구도 활발히 진행되고 있다. 특히 고압 가스를 저장하는 TypeIII 용기의 노후화 및 안전과 관련되어 결함을 인지하는 연구가 활발하다. 그러나 이 용기의 외각층을 이루는 CFRP 소재는 탄소 섬유와 에폭시가 복잡한 구조로 구성되어 결함별 탐지가 매우 어렵다. 본 논문에서는 음향방출시험과 딥러닝을 활용하여 CFRP 결함 데이터셋을 구축하고 이를 분류할 수 있는 모델을 제안한다. 특히 CFRP 시편을 직접 제작하여 AE 센서를 부착하고 파괴하여 파형 데이터를 수집하였다. 이후 표현 학습을 통해 데이터의 특징을 압축/추출하고 유사도를 비교해 결함별 데이터를 판별하는 알고리즘을 개발하였다. 구축된 데이터셋의 실루엣 계수는 0.86으로 높은 군집도를 보였다. 마지막으로 구축된 데이터셋을 실시간으로 분류할 수 있는 1D-CNN 딥러닝 모델을 개발하였으며 99.33%의 높은 분류 정확도를 보였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.510-512
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• 2023

In order to enhance a model's capability for detecting facial expressions, this research suggests a pipeline that makes use of the GradCAM component. The patching module and the pseudo-labeling module make up the pipeline. The patching component takes the original face image and divides it into four equal parts. These parts are then each input into a 2Dconvolutional layer to produce a feature vector. Each picture segment is assigned a weight token using GradCAM in the pseudo-labeling module, and this token is then merged with the feature vector using principal component analysis. A convolutional neural network based on transfer learning technique is then utilized to extract the deep features. This technique applied on a public dataset MMI and achieved a validation accuracy of 96.06% which is showing the effectiveness of our method.

• Asia pacific journal of information systems
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• 제33권3호
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• pp.624-651
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• 2023

The study makes several contributions to not only the adoption literature by examining the influence of Emotional Intelligence (EI) and Big-Five traits on adoption of Augmented Reality (AR) but also given its utility in both industry and research, it contributes to the interesting inter-disciplinary domain of psychology, information systems, and human behaviour. A quantitative based approach using a sample of 275 respondents was undertaken. It is found that emotional intelligence influence both perceived ease-of-use and perceived usefulness. They in turn influence intention to use. Another important observation is that personality traits (openness and agreeableness) have a significant moderating effect on the relation between attitude and intention to use AR. This research will help academicians and executives working on the adoption of AR in various sectors ranging from retail industry to the education sector. The originality of this study is that it explores the impact of EI on the acceptance of AR and helps in extending the literature in interdisciplinary research.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2006년도 춘계학술대회
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• pp.294-300
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• 2006

This case study shows constructing the knowledge-based system using a rule-based approach for detecting transactions regarding money laundering in the Korea Financial Intelligence Unit (KoFIU). To better manage the explosive increment of low risk suspicious transactions reporting from financial institutions and to conjugate data converged into the KoFIU from various organizations, the adoption of a knowledge-based system is definitely required. We designed and constructed the knowledge-based system for anti-money laundering by committing experts of each specific financial industry co-worked with a knowledge engineer. The outcome of the knowledge base implementation shows that the knowledge-based system is filtering STRs in the primary analysis step efficiently and so has made great contribution to improve efficiency and effectiveness of the analysis process. It can be said that establishing the foundation of the knowledge base under the entire framework of the knowledge-based system for consideration of knowledge creation and management is indeed valuable.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.