• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2004년도 춘계종합학술대회
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• pp.199-202
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• 2004

강의 중심적인 공학교육은 공학기피라는 사회현상과 더불어 관련 기술 및 이론의 습득에 어려움을 초래하고 있다. 따라서 학습자의 흥미를 유도할 수 있고, 보다 효율적인 교육방법의 도입이 필요하다. 교수방법의 보조 수단으로서 자바 기술을 이용한 CAI형 웹 기반 전자계 시뮬레이터를 개발하였다. 시뮬레이션 모듈은 자바 애플릿으로 개발하였으며, 학습자가 직접 문제에 대한 물리적 파라메터를 설정할 수 있도록 GUI 환경을 제공하며, 계산결과는 컴퓨터 애니메이션을 통하여 학습자의 흥미와 이해를 도울 수 있도록 배려하였다. 또한 시뮬레이션 모듈의 통합과 효율적 관리 및 학습자원의 재활용성을 고려하여 e-Learning 기술 표준인 SCORM의 적용 가능성도 검토하였다.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.201-208
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• 2021

Recently, artificial intelligence(AI) has been used throughout society, and social interest in it is increasing. Accordingly, the necessity of AI education is becoming a big topic in the education field. As a response to this trend, the Korean education authorities have also announced plans for AI education, and various studies have been performed in academic field to revitalize AI education in the future. However, the curriculum research on what differentiates AI education from existing SW education and what and how to train AI is still in its infancy. In this paper, Therefore, we focused on the experiences of elementary school students in solving problems in their own lives, and developed a teaching-learning model based on design-based research so that students can design a problem-solving process and experience the process of feedback. We applied the developed teaching-learning model to the problem-solving process and confirmed that it increased students' understanding and satisfaction with AI education.

• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.129-133
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• 2023

Image compression is a fundamental technique in the field of digital image processing, which will help to decrease the storage space and to transmit the files efficiently. Recently many deep learning techniques have been proposed to promise results on image compression field. Since many image compression techniques have artifact problems, this paper has compared two deep learning approaches to verify their performance experimentally to solve the problems. One of the approaches is a deep autoencoder technique, and another is a deep convolutional neural network (CNN). For those results in the performance of peak signal-to-noise and root mean square error, this paper shows that deep autoencoder method has more advantages than deep CNN approach.

• 수산해양교육연구
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• 제28권4호
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• pp.1057-1067
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• 2016

National Competency Standards are the major administrative project to build a competence-based society. The manpower for the global society should be recognized by performance rather than educational records. Therefore, all colleges should first nurture NCS-type manpower based on field needs. This study comparatively analyzed the differences between the lecture style of four-year colleges and the outcomes of problem-solving and project-based learning method to prove why it is necessary to introduce the NCS program. Especially, It will review the constraints and measures of NCS introduction to overcome in a four-year university. Through this, it can be used as a means to help improve the field conformity of a four-year college curriculum by presenting the development and utilization of curriculum-based NCS in a four-year university. As a result, it was found that the overall satisfaction with the problem-solving and project-based learning method was above average. Many students were dissatisfied with the traditional teaching methods and the new project-based learning method was relatively effective in college education. Students' participation also improved. Based on the evaluation of learning performance, the new method was found more satisfactory than the old teaching method in terms of comprehension of professional knowledge in various fields, nurturing of logical thinking skills, acquisition of analytical skills, comprehensive thinking skills, creative problem recognition, and open-minded thinking skills.

• 한국전자통신학회논문지
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• 제18권1호
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• pp.85-90
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• 2023

보건의료분야 데이터 분석 방법론이 기존의 통계 중심의 연구방법에서 머신러닝을 이용한 예측 연구로 전환되고 있다. 본 연구에서는 다양한 머신러닝 도구들을 살펴보고, 보건의료분야에서 많이 사용하고 있는 통계 도구인 R을 빅데이터 머신러닝에 적용하기 위해 R과 스파크를 연계한 프로그래밍 모델들을 비교한다. 그리고, R을 스파크 환경에서 수행하는 SparkR을 이용한 선형회귀모델 학습의 성능을 스파크의 기본 언어인 스칼라를 이용한 모델과 비교한다. 실험 결과 SparkR을 이용할 때의 학습 수행 시간이 스칼라와 비교하여 10~20% 정도 증가하였다. 결과로 제시된 성능 저하를 감안한다면 기존의 통계분석 도구인 R을 그대로 활용 가능하다는 측면에서 SparkR의 분산 처리의 유용성을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.576-590
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• 2023

Machine learning is widely used in various academic fields, and recently it has been actively applied in the medical research. In the medical field, machine learning is used in a variety of ways, such as speeding up diagnosis, discovering new biomarkers, or discovering latent traits of a disease. In the respiratory field, a relative regional air volume change (RRAVC) map based on quantitative inspiratory and expiratory computed tomography (CT) imaging can be used as a useful functional imaging biomarker for characterizing regional ventilation. In this study, we seek to predict RRAVC using various regular machine learning models such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP). We experimentally show that MLP performs best, followed by XGBoost. We also propose several relative coordinate systems to minimize intersubjective variability. We confirm a significant experimental performance improvement when we apply a subject's relative proportion coordinates over conventional absolute coordinates.

• 한국임상수의학회지
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• 제40권4호
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• pp.243-259
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• 2023

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice. However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research. Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field. Thus, a scoping review was conducted as a situation analysis. We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Embase databases were used in the initial search. The identified items were screened based on predefined inclusion and exclusion criteria. Information regarding model development, quality of validation, and model performance was extracted from the included studies. The current review found 55 studies that passed the criteria. In terms of target animals, the number of studies on industrial animals was similar to that on companion animals. Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications). Qualitative limitations were also identified, especially regarding validation methodologies. Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended. Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

• 농촌계획
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• 제28권4호
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• pp.105-117
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• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• 정보교육학회논문지
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• 제21권2호
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• pp.231-245
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• 2017

본 연구를 통해 자석 및 자기장과 관련된 웹기반 과학학습 시뮬레이션들의 현황을 살펴보고, 시뮬레이션의 내용과 전략 및 디자인 측면에서 적절성을 평가하였다. 연구를 위해 과학학습 시뮬레이션 평가 기준을 고안하였으며, 초등교사 8명이 참여하여 자석 및 자기장 관련 시뮬레이션 14종을 평가 기준에 맞추어 평가하고 각 시뮬레이션의 특징을 기술하였다. 평가 결과를 바탕으로 시뮬레이션들을 상 그룹과 하 그룹으로 분류하였고, 상 그룹의 시뮬레이션에서 강점과, 하 그룹의 시뮬레이션에서 보완할 점들을 교수학습 내용, 교수학습 전략, 화면구성, 기술의 측면에 따라 분석하고 도출하였다. 연구 결과를 근거로 교수학습에 효과적인 자석 및 자기장 주제의 웹기반 시뮬레이션 개선을 위한 방안을 논의하였다.