• 대한기계학회논문집 C: 기술과 교육
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• 제2권2호
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• pp.125-130
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• 2014

기계제도 교과목은 기계공학 교육에서 매우 중요한 위치를 차지하고 있음에도 불구하고, 이론교육과 실습을 병행해야 하고 CAD 소프트웨어 기능 교육도 포함하고 있어서, 내실 있게 운영되기 힘든 교과목이다. 서울과학기술대 기계시스템디자인공학과에서는 기계제도 교육에 PBL 수업 방식을 도입하여 적용하고 있다. 학생들은 이론 수업에서 학습한 기계도면 작성의 규칙, 도면 독해와 작성법 등을 팀별 PBL 과제 수행을 통해 실제로 적용해 봄으로써, 도면 판독과 작성능력을 체득하게 되고 CAD 소프트웨어 활용 기능도 충실히 연습하게 된다. 본 논문에서는 그 동안 시도되었던 다양한 PBL 과제와 교육방법을 소개하고 그 효과를 분석해 보았다.

• 한국초등과학교육학회지:초등과학교육
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• 제38권3호
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• pp.317-330
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• 2019

The purpose of this study is to investigate the influences of science leading schools on primary and middle school students' positive experiences about science (PES) through in-depth interviews with teachers in charge of science leading schools. Science leading schools at the primary and middle school level such as Creative Convergent Science Labs and Student Participatory Science Classes were investigated and 11 teachers were participated in focus group interviews. Teacher in-depth interviews were conducted to explore the factors that led to the effectiveness of science leading schools in improving the student's PES in light of operational characteristics of science leading schools, characteristic factors of science leading schools on students PES, and improvement plans and requirements of science leading schools, as well as implications for general high schools. Science leading schools including Creative Convergent Science Labs and Student Participatory Science Classes applied for the leading school funding to secure supplies, equipments, and lab improvement for authentic science classes. In addition, reconstructed the curriculum more broadly than before, and emphasized and expanded student participatory classes and process-centered assessment at the teacher learning community level. Through student-participatory classes, the science leading schools stimulate students' interest in science, provide students with PES) through various instructions including projects, engage students in interesting science experiences in Creative Convergent Science Labs, and enhance inquiry skills and PES as well as science content knowledge. Based on the results, ways to spread the characteristics of science leading schools to general schools are suggested including expanding budget support, securing the space of science labs and improving spatial composition, providing diverse teaching and learning materials, diversifying assessment subjects and methods, and the necessity of teachers' continuous professional development, etc.

• 대한방사선기술학회지:방사선기술과학
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• 제45권2호
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• pp.151-158
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• 2022

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert's processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.

• 센서학회지
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• 제29권6호
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• pp.433-439
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• 2020

Recently, the need for IoT-based intelligent systems is increasing in various fields. In this study, we implemented the system that automatically measures the sugar content of fruits without damage to fruit's marketability using near-infrared radiation and machine learning. The spectrums were measured several times by passing a broadband near-infrared light through a fruit, and the average value for them was used as the input raw data of the machine-learned DNN(Deep Neural Network). Using this system, he sugar content value of fruits could be predicted within 5 s, and the prediction accuracy was about 93.86%. The proposed non-destructive sugar content measurement system can predict a relatively accurate sugar content value within a short period of time, so it is considered to have sufficient potential for practical use.

• 인터넷정보학회논문지
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• 제18권6호
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• pp.55-63
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• 2017

One of the purposes of Information Technology (IT) is to support human response to natural and social problems such as natural disasters and spread of disease, and to improve the quality of human life. Recent climate change has happened worldwide, natural disasters threaten the quality of life, and human safety is no longer guaranteed. IT must be able to support tasks related to disaster response, and more importantly, it should be used to predict and minimize future damage. In South Korea, the data related to the damage is checked out by each local government and then federal government aggregates it. This data is included in disaster reports that the federal government discloses by disaster case, but it is difficult to obtain raw data of the damage even for research purposes. In order to obtain data, information extraction may be applied to disaster reports. In the field of information extraction, most of the extraction targets are web documents, commercial reports, SNS text, and so on. There is little research on information extraction for government disaster reports. They are mostly text, but the structure of each sentence is very different from that of news articles and commercial reports. The features of the government disaster report should be carefully considered. In this paper, information extraction method for South Korea government reports in the word format is presented. This method is based on patterns and dictionaries and provides some additional ideas for tokenizing the damage representation of the text. The experiment result is F1 score of 80.2 on the test set. This is close to cutting-edge information extraction performance before applying the recent deep learning algorithms.

• 한국과학교육학회지
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• 제24권1호
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• pp.29-47
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• 2004

Two project-centered secondary school programs were studied as part of an effort to elucidate successful components for science reform-based curriculum development. The Teachers for Exciting Science (TES), and Foundational Approaches in Science Teaching (FAST) programs in Korea and U.S., respectively, are project-centered programs because their curricula are centered on the activities initiated and engaged in by the students. Students serve as principal investigators in their projects, and teachers serve as guides. Both programs were analyzed based on criteria such as curriculum design, teaching, lives of students, lives of teachers, evaluation of program, from the Third International Mathematics and Science Study (TIMSS). In the programs, teachers and students directed the development of curricula and their implementation. Students assumed teacher roles as mentors of other students. And emphasis was on development of communication skills through student-delivered talks and written papers, and professional development of teachers as educators and scientists. Participation in TES stimulated secondary school student interest in science, encouraged inquiry thinking, increased achievement in learning science, and promoted better awareness of science related to real life. FAST students practice laboratory and field techniques, experimental design, hypothesis formation, generalization, and practical implications of research as academic and applied disciplinarians. These project-centered programs have been successfully implemented in field, lab, and classroom curricula for secondary science education. Comparison of these programs will provide an opportunity for identifying key elements instrumental in successful implementation of guidelines for science education, as measured through successful outcomes.

• 과학교육연구지
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• 제38권2호
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• pp.302-316
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• 2014

본 연구는 6학년 1학기 산과 염기 단원 중 5차시를 Small-Scale Chemistry를 적용한 실험 수업 프로그램으로 개발하였다. 개발한 프로그램 적용을 경남 창원시에 소재한 Y초등학교 6학년 3개반은 SSC를 활용한 과학수업(n=86)을, 3개 반은 전통적인 실험 수업(n=87)을 진행한 후 학생들의 과학 학업성취도와 과학 학업성취도에 미치는 영향을 알아보았다. 개발한 수업 프로그램을 학생들에게 적용하기에 앞서, 중간학력평가 과학 학업성취도 점수에 대한 t검증을 통해 실험집단과 비교집단 간의 동질성을 확인하였고 실험집단은 2인 1조 또는 개별로, 비교집단은 6명 1모둠으로 구성하여 5차시에 걸쳐 수업을 진행하였다. 그 결과 t-검증을 통한 과학 학업성취도에서 유의확률 0.034로 유의수준 0.05에서 실험집단과 비교집단 사이에 유의미한 차이가 있었다. 추가적으로 전통적인 실험을 한 1개 반과 2명 1조 SSC 적용 실험 수업을 한 1개 반, 개별 SSC 적용 실험 수업을 한 1개 반의 과학 학업성취도를 살펴보았다. 일원배치 분산분석을 통해 살펴 본 결과 F 통계값 3.759, 유의확률 0.027로 유의수준 0.05에서 유의미한 차이가 있었으며, 전통적인 실험반의 평균은 67.58, 2인 1조 SSC 적용 실험반은 75.86, 개별 SSC 적용 실험반은 80.89로 개별 SSC 적용 실험반에서 과학 학업성취도가 가장 높았다. 또한 SSC를 적용한 실험 수업 프로그램을 준비할 때 교사는 수업 준비 및 수업 시간에 대한 부담이 줄었으며, 수업시간 동안 학생활동을 적극적으로 도와 줄 수 있었을 뿐 아니라 학생들의 실험활동도 적극적으로 이루어졌다. 이러한 결과를 통해 SSC 적용 실험 수업 프로그램 개발은 의미가 있으며 기존의 전통적인 실험 방법보다 학생들의 과학 학업성취도를 향상시킬 수 있음을 제시하였다.

• 한국실천공학교육학회논문지
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• 제3권2호
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• pp.18-26
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• 2011

계산화학이란 컴퓨터를 이용한 계산을 통하여 이론 화학의 문제를 다루는 화학의 한 분야로 화학실험실을 컴퓨터로 옮겨 놓은 것이라고 말할 수 있다. 컴퓨터 처리 능력이 향상됨에 따라 거대 분자 구조의 복잡한 계산과 시뮬레이션을 수행하여야 하는 계산화학분야에서의 고성능 컴퓨터 활용은 매우 중요하다. 분자 구조 계산과 시뮬레이션 등의 작업을 위하여 고성능 컴퓨터를 이용하려면 Unix 명령어와 콘솔을 활용하여야 하는데 화학과목을 배우는 이공계 학생들 대부분은 컴퓨터 비전공자로서 Unix에 관하여 모르는 경우가 대부분이다. 그래서 Unix 명령어를 모르더라도 계산화학 실습이 가능한 웹 기반 계산화학 실습 교육 지원 시스템이 필요하다. 본 논문에서 개발한 웹 기반 계산화학 실습 교육 지원 시스템(e-Chem)은 다른 웹 포털 플랫폼보다 표준 지향적이고 콘텐츠 관리 및 협업 기능이 뛰어난 자바 오픈소스인 Liferay 포털 플랫폼을 활용하여 개발하였다. 본 시스템을 활용하면 컴퓨터 비전공자들도 쉽게 계산화학 실습 수업에 참여할 수 있고, Unix 명령어 등을 배우는 시간을 절약할 수 있으며, 친숙한 웹 인터페이스로 학습 효과도 높아질 것으로 기대된다.

• 한국전자통신학회논문지
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• 제15권2호
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• pp.349-356
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• 2020

본 논문은 교통사고 방지를 위한 운전자의 눈 영상비를 이용한 상태 경고시스템의 설계에 대해 소개하고 있다. 제안하는 운전자 상태 경고 시스템은 눈 인식을 위한 카메라, 카메라를 통해 들어오는 정보를 처리하는 라즈베리파이, 그리고 그 정보를 통해 운전자에게 경고를 줄 때 필요한 부저와 진동기로 구성되어 있다. 운전자의 눈을 인식하기 위해서 기울기 방향성 히스토그램 기술과 딥러닝 기반의 얼굴 표지점 추정 기법을 사용하였다. 동작을 시작하면, 시스템은 눈 주변의 6개의 좌표를 통해 눈 영상비를 계산한다. 그리고 눈을 뜬 상태와 감은 상태의 눈 영상비를 각각 계산한 후 이 두 값으로부터 눈의 상태를 판단하는데 사용하는 문턱 값을 설정한다. 문턱 값이 운전자의 눈 크기에 적응하면서 설정되기 때문에 시스템은 최적의 문턱 값을 사용하여 운전자의 상태를 판단할 수 있다. 또한 낮은 조도에서도 눈을 인식할 수 있도록 회색조 변환 이미지와 LAB모델 이미지를 합성하여 사용하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3567-3582
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• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.