• Transactions of the KSME C: Technology and Education
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• v.2 no.2
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• pp.125-130
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• 2014

Although the subject of Mechanical Drawing is very important in mechanical engineering education, it is hard to teach the subject effectively, because it needs to carry out lectures and labs in parallel and needs substantially large portion of CAD lab time. In the department of Mechanical Systems and Design Engineering of SNUST, Project Based Learning is adopted to teach the subject of Mechanical Drawing. In this course, students experience to read and to draw drawings through the PBL project after the lectures on mechanical drawings. In this way, they can learn by heart the drawing skills and the operation of CAD software tools. In this paper, various PBL projects and teaching methods carried out in recent years are presented and the effects of the projects are discussed.

• Journal of Korean Elementary Science Education
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• v.38 no.3
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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.

• Journal of radiological science and technology
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• v.45 no.2
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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.

• Journal of Sensor Science and Technology
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• v.29 no.6
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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.

• Journal of Internet Computing and Services
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• v.18 no.6
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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.

• Journal of The Korean Association For Science Education
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• v.24 no.1
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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.

• Journal of Science Education
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• v.38 no.2
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• pp.302-316
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• 2014

The purpose of this study is to devise a Small-Scale Chemistry (SSC) lab program for primary school learners and to examine its effects on science learning achievement. In addition, it will be examined whether the type of learning groups affects the achievement or not. The participants in the current study were 173 6th graders from 6 classes of Y elementary school in Changwon city, Gyeongnam. Three classes(86) were assigned to the experimental group and the other three, the comparative group after checking the pre-homogeneity between the two groups through t-test on the scores of the science mid-term exam. We conducted five experimental sessions on the Acid and Base in the science textbook for the sixth graders. The students of one experimental class worked in pairs and another class worked individually, but the students of the comparative classes were divided into groups of six(one group with pair, another group with individual work in the SSC program, and the other group conducting the traditional experiment with groups of six students). The data were analyzed by t-test and ANOVA. The results showed that experimental learning using individual work in the SSC program compared to traditional experimental learning was effective in improving science learning achievement. also it was indicated that the teachers could reduce their burden of preparing for classes and of school hours when they utilized the SSC laboratory learning program. Teachers could also actively support students' experimental activities in employing the program. Based on the results, we suggest that the development of the SSC laboratory learning program is meaningful in the sense that this program can help elementary schoolers to improve science learning achievements more than the existing traditional experimental methods.

• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.2
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• pp.18-26
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• 2011

Computational chemistry is one of the chemistry fields that deals with the theoretical chemistry problem using computer calculations and can be described as the chemistry lab moved on computer space. In line with recent enhancement of processing capability of computers, utilization of high performance computer cannot be overemphasized in the field of computational chemistry in performing complex calculation of huge molecular structure and simulation. While they have to use commands and consoles for high performance computer to execute complex calculation of huge molecular structure and simulation, most of students in natural science and engineering, who are not experts in computer technically, are likely to be unaware of UNIX. Under the circumstances, web-based educational support system for computational chemistry is needed to enable them to practice computational chemistry, even not knowing UNIX command. In this study, e-Chem, one of such educational support systems, is developed by using Liferay portal platform, which is a Java open source more oriented to standard and outstanding in its content management and collaboration function than other web portals. By using this system, even students who are not familiar with computer, are expected to take part in lab classes and save time learning Unix command and also enhance the learning efficiency by using familiar interface.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.349-356
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• 2020

This paper introduces the implementation of a driver's condition warning system using eye aspect ratio to prevent a car accident. The proposed driver's condition warning system using eye aspect ratio consists of a camera, that is required to detect eyes, the Raspberrypie that processes information on eyes from the camera, buzzer and vibrator, that are required to warn the driver. In order to detect and recognize driver's eyes, the histogram of oriented gradients and face landmark estimation based on deep-learning are used. Initially the system calculates the eye aspect ratio of the driver from 6 coordinates around the eye and then gets each eye aspect ratio values when the eyes are opened and closed. These two different eye aspect ratio values are used to calculate the threshold value that is necessary to determine the eye state. Because the threshold value is adaptively determined according to the driver's eye aspect ratio, the system can use the optimal threshold value to determine the driver's condition. In addition, the system synthesizes an input image from the gray-scaled and LAB model images to operate in low lighting conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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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.