• 농업과학연구
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• 제46권2호
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.468-472
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• 1989

In this paper a Korean authoring system is designed to write a CAI courseware in Hangul/English by an author who is a nonprogrammer. It saves nock time in authoring a courseware and maintains high level transplantity among CAI systems. By interfacing ah expert graphic utility, image information can be processed more easily and efficiently. Programming control of the flow of CAI courseware can be ramification and individual learning possible, fitting various demands of learners and learning and learning ability.

• Journal of Korean Neurosurgical Society
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• 제62권4호
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• 공학교육연구
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• 제24권3호
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• pp.21-31
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• 2021

Flipped learning, which involves listening to lectures at home and performing dynamic group-based problem-solving activities in the classroom, is recently evaluated as a learner-centered teaching method, and interest and applications in engineering education are increasing. Therefore, this study aims to provide practical guidelines for successful application through empirical research analysis on the use of flipped learning in domestic engineering education. Through the selection criteria and keyword search, a systematic review of 36 articles was conducted. As a result of the analysis, flipped learning research in engineering education has increased sharply since 2016, focusing on academic journals and reporting its application cases and effects. Most of the research supported that flipped learning was effective not only for learners' learning activities(e.g., academic achievement, satisfaction, engagement, learning-flow, interaction), but also for individualized learning and securing sufficient practice time. It was often used in major classes with 15 to less than 50 students, especially in computer-related major courses. Most of them consisted of watching lecture videos, active learning activities, and lectures by instructors, and showed differences in management strategies for each class type. Based on the analysis results, suggestions for effective flipped learning management in future engineering education were presented.

• ETRI Journal
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• 제41권5호
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.370-373
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• 2022

최근 Tensorflow나 Pytorch, Keras 같은 여러가지의 딥러닝 프레임워크 모델들이 나왔다. 또한 이미지 인식에 Tensorflow, Pytorch, Keras 같은 프레임 워크를 이용하여 CNN(Convolutional Neural Network)을 적용시켜 이미지 분류에서의 최적화 모델을 주로 이용한다. 본 논문에서는 딥러닝 이미지 인식분야에서 가장 많이 사용하고 있는 파이토치와 텐서플로우의 프레임 워크를 CNN모델에 학습을 시킨 결과를 토대로 두 프레임 워크를 비교 분석하여 이미지 분석할 때 최적화 된 프레임워크를 도출하였다.

• 한국통신학회논문지
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• 제33권8B호
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• pp.707-718
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• 2008

Traffic classification의 방법은 동적으로 변하는 application의 변화에 대처하기 위하여 페이로드나 port를 기반으로 하는 것에서 ML 알고리즘을 기반으로 하는 것으로 변하여 가고 있다. 그러나 현재의 ML 알고리즘을 이용한 traffic classification 연구는 offline 환경에 맞추어 진행되고 있다. 특히, 현재의 기존 연구들은 testing 방법으로 cross validation을 이용하여 traffic classification을 수행하고 있으며, traffic flow를 기반으로 classification 결과를 제시하고 있다. 본 논문에서는 testing방법으로 cross validation과 split validation을 이용했을 때, traffic classification의 정확도 결과를 비교한다. 또한 바이트를 기반으로 한 classification의 결과와 flow를 기반으로 한 classification의 결과를 비교해 본다. 본 논문에서는 J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, NaiveBayes와 같은 ML 알고리즘과 다양한 feature set을 이용하여 트래픽을 분류한다. 그리고 split validation을 이용한 traffic classification에 적합한 최적의 ML 알고리즘과 feature set을 제시한다.

• 디지털콘텐츠학회 논문지
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• 제19권9호
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• pp.1811-1817
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• 2018

증강현실기술에 기반하여, 구강방사선촬영 실습에 활용할 수 있는 새로운 임상시뮬레이터를 개발하였고, 이에 대한 효과를 검증하였다. 이 시뮬레이터를 사용한 실험집단과 기존의 텍스트 형식 교재를 사용한 통제집단으로 구분하여, 국내 치위생학과 학생 217명에 대해 설문조사 및 통계분석을 시행하였다. 증강현실형 시뮬레이터를 사용한 실험집단에서 자아효능감, 학습흥미도, 학습몰입도, 실습만족도가 높게 나타났다. 이를 통해, 증강현실형 치과방사선촬영 시뮬레이터가 구강방사선촬영 실습에 효과적인 학습매체로 활용할 수 있으며, 학습자의 임상실무역량 강화에 도움을 줄 수 있을 것으로 기대한다.

• 한국정보통신학회논문지
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• 제22권9호
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• pp.1208-1213
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• 2018

시뮬레이션 교육 시 구조화된 디브리핑 유형이 간호대학생의 학습몰입, 비판적사고성향과 임상수행능력에 미치는 효과를 검증하고자 시도된 비동등성 대조군 사후 시차설계의 유사실험 연구이다. 연구대상자는 P 대학교 간호학과 4학년 학생으로, 실험군 22명, 비교군 24명, 대조군 20명으로 총 66명이었다. 실험군에게는 LCJR 질문을 이용한 구조화된 비디오 디브리핑, 비교군은 구조화된 구두 디브리핑, 대조군은 구조화된 그룹 토론 디브리핑을 실시하였다. 연구결과 학습몰입과 비판적사고성향 및 임상수행능력은 세 군간 유의한 차이가 없었으나 사전-사후 차이 검정시 모두 향상되었다. 또한 임상수행능력의 세부영역 중 계획과 중재는 실험군이 다른 두 군에 비해 유의하게 향상되었다. 이를 통해 LCJR의 임상판단 루브릭을 활용한 디브리핑은 시뮬레이션교육에 효과적이며 특히 비디오를 활용한 구조화된 디브리핑 유형은 임상수행능력을 높이는데 영향을 끼치는 것으로 나타났다.

• 대한지구과학교육학회지
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• 제3권1호
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• pp.47-54
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• 2010

The purpose of this study is finding examine the Thinking Maps and how to use Thinking Maps effectively in Science Education. The result of this study were as follows: First, There are 8 type Maps, Circle Map, Tree Maps, Bubble Map, Double Bubble Map, Flow Map, Multi Flow Map, Brace Map, Bridge Map. Each Maps are useful in the following activities ; Circle Map-Express their thoughts. Tree Map-Activities as like determine the structure, classification, information organization. Bubble Maps-Construction. Double Bubble Map-Comparison of similarities and differences. Flow Map-Set goals, determine the result of changes in time or place. Multi Flow Map-Analysis cause and effect, expectation and reasoning. Brace Map-Analysis whole and part. Bridge Map-Activities need analogies. Second, each element of inquiry has 1~2 appropriate type of Thinking Maps. So student can choose the desired map. Third, the result of analysing of Science Curriculum Subjects, depending on the subject variety maps can be used. Therefore the Thinking Maps can be used for a variety on activities and subject. And student can be selected according to their learning style. So Thinking Maps are effective to improve student's Self-Directed Learning.