• Journal of The Korean Association For Science Education
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• v.33 no.4
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• pp.763-777
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• 2013

This is a case study on two schools practising integrated science education (hereafter ISE). The purposes of this study are to investigate the types and features of ISE in the schools actively practising ISE, to identify the contextual factors of the schools, and to give implications for implementing ISE in schools. This study investigated the contextual factors in practicing ISE with a focus on the two schools, a middle school in Gyeonggi-do and a high school in Busan. They were breaking down the boundaries among teaching subjects and providing student-oriented instruction with problems in the real world. The data were collected by observing classes, by interviewing teachers, and by reviewing school documents and students' reports. The research findings are as follows: first, the two schools took part in ISE actively. They teach science to students providing integrated experiences mainly by using interdisciplinary knowledge and/or by solving the problems pertaining to the real world. While the former integrated subjects centering on topics, the latter focused on a project-based learning driven by students. They have differences in regard to the role of teachers and students, the level of integration and the type of integration. Second, the contextual factors that enabled ISE to be implemented there were found. The previous studies revealed six contextual factors in practising ISE: small and stable learning environment, leadership, team activities, in-school planning time, flexible timetable and community links. This study also found similar factors. However, the cases of this study provided ISE on a large scale and in a short period of time, instead of a small and stable learning environment. Teachers viewed the process of ISE not only as a tool to overcome the conservative culture of teachers but also as a pursuit of innovation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1304-1310
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• 2008

The purpose of this study is to propose the system architecture of intelligent ubiquitous house which is able to team the human behavior by itself and to predict the forthcoming situation, and to provide the customized and personalized service based on human behavior. The suggestions for advanced intelligent ubiquitous house are as follows; 1) Service should be combined with dwellers' behavior pattern, location moving pattern and service pattern in order to provide the personalized and customized service. 2) The system should be equipped with 4 components such as Agent, Database, Working Memory, and Log Data. Especially. This proposed system architecture of advanced ubiquitous house, which are equipped with these 4 components, will be the basis of providing customized service to every dwellers by learning dwellers' behavior pattern, accumulating dwellers' information, and recognizing dweller's lift style as time goes by.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.457-466
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• 2005

In this paper, we propose a wavelet neural network(WNN) based predictive control method for path tracking of mobile robots with multi-input and multi-output. In our control method, we use a WNN as a state predictor which combines the capability of artificial neural networks in learning processes and the capability of wavelet decomposition. A WNN predictor is tuned to minimize errors between the WNN outputs and the states of mobile robot using the gradient descent rule. And control signals, linear velocity and angular velocity, are calculated to minimize the predefined cost function using errors between the reference states and the predicted states. Through a computer simulation for the tracking performance according to varied track, we demonstrate the efficiency and the feasibility of our predictive control system.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.117-124
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• 2021

In this paper, we propose CANVAS (Creative ANalytics enVironment And System), an analytics system of the National Research Data Platform (DataON). CANVAS is a personalized analytics cloud service for researchers who need computing resources and tools for research data analysis. CANVAS is designed in consideration of scalability based on micro-services architecture and was built on top of open-source software such as eGovernment Standard framework (Spring framework), Kubernetes, and JupyterLab. The built system provides personalized analytics environments to multiple users, enabling high-speed and large-capacity analysis by utilizing high-performance cloud infrastructure (CPU/GPU). More specifically, modeling and processing data is possible in JupyterLab or GUI workflow environment. Since CANVAS shares data with DataON, the research data registered by users or downloaded data can be directly processed in the CANVAS. As a result, CANVAS enhances the convenience of data analysis for users in DataON and contributes to the sharing and utilization of research data.

• The Research Journal of the Costume Culture
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• v.29 no.1
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• pp.134-151
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• 2021

The aim of this research is to develop a clothing and textiles studio course for preservice home economics teachers applying principles of Project-Based Learning (PBL) and maker education to equip future teachers with the ability to nurture creativity among adolescents. The studio course was developed in the following stages: analysis, design, development, implementation, and evaluation. We concluded that the resulting course met the following objectives extracted from the 2015 revised curriculum of home economics subjects: to promote creative and environmentally-friendly fashion design and styling abilities, gain the ability to use makerspace tools, understand flat pattern making and sewing processes, and develop creative thinking, aesthetic sense, and communication skills. Furthermore, the educational effects of PBL and maker education were confirmed through student comments on the course. Students mentioned the practicality of the material in their actual lives along with their enhanced integration of the subject material, self-directedness, aesthetic sense, ability to learn through trial and error, collaboration and communication, and sharing. Based on results from the implementation and evaluation stages, a clothing and textiles studio course should include the following modules: introduction of terms and tools, submission and sharing of clothing reformation and upcycling techniques, introduction to hand sewing, pouch making, heat-transfer printing, 3D printing, mask making, hat making, vest making, and the final team project on fashion styling. It is important for instructors to provide detailed guidelines on selecting personas for styling, looking for available materials, and selecting materials online.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.83-91
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• 2020

The purpose of this study is to develop creativity-based creative convergence subjects for nursing students. For the purpose of this study, the procedures are conducted that the needs analysis, setting educational goals, segmentation of educational goals, selection of educational contents and organization by F. Bobbitt's curriculum development model and the creative convergence subject was developed through the verification process of the validity of experts. Through a theoretical review, the contents of education in creative convergence subjects consisted of converging with other areas, focusing on creativity. It was presented as a liberal arts subject with two credits, and as an educational method, an online class utilizing blended learning and offline classes centered on activities by teams were presented. In addition, the curriculum was divided into understanding, application, synthesis, and deepening so that students could understand the concept of creative convergence thinking and apply it through thinking techniques and strategies, and finally improve their creative convergence thinking abilities through team projects.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.576-581
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• 2021

Compared to a low concentration, a high concentration clearly entails limitations in terms of predictive performance owing to differences in its frequency and environment of occurrence. To resolve this problem, in this study, an artificial intelligence neural network algorithm was used to classify low and high concentrations; furthermore, two prediction models trained using the characteristics of the classified concentration types were used for prediction. To this end, we constructed training datasets using weather and air pollutant data collected over a decade in the Cheonan region. We designed a DNN-based classification model to classify low and high concentrations; further, we designed low- and high-concentration prediction models to reflect characteristics by concentration type based on the low and high concentrations classified through the classification model. According to the results of the performance assessment of the prediction model by concentration type, the low- and high-concentration prediction accuracies were 90.38% and 96.37%, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.440-446
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• 2016

Military secrets or confidential data of any organization are extremely important assets. They must be discluded from outside. To do this, methods for detecting anomalous attacks and intrusions inside the network have been proposed. However, most anomaly-detection methods only cover aspects of intrusion from outside and do not deal with internal leakage of data, inflicting greater damage than intrusions and attacks from outside. In addition, applying conventional anomaly-detection methods to data exfiltration creates many problems, because the methods do not consider a number of variables or the internal network environment. In this paper, we describe issues considered in data exfiltration detection for anomaly detection (DEDfAD) to improve the accuracy of the methods, classify the methods as profile-based detection or machine learning-based detection, and analyze their advantages and disadvantages. We also suggest future research challenges through comparative analysis of the issues with classification of the detection methods.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.916-923
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• 2023

Purpose: Looking at the status of fatal accidents in the construction industry in the 2022 Industrial Accident Status Supplementary Statistics, 27.8% of all fatal accidents in the construction industry are caused by construction equipment. In order to overcome the limitations of tours and inspections caused by the enlargement of sites and high-rise buildings, we plan to build a model that can extract construction equipment using computer vision technology and analyze the model's accuracy and field applicability. Method: In this study, deep learning is used to learn image data from excavators, dump trucks, and mobile cranes among construction equipment, and then the learning results are evaluated and analyzed and applied to construction sites. Result: At site 'A', objects of excavators and dump trucks were extracted, and the average extraction accuracy was 81.42% for excavators and 78.23% for dump trucks. The mobile crane at site 'B' showed an average accuracy of 78.14%. Conclusion: It is believed that the efficiency of on-site safety management can be increased and the risk factors for disaster occurrence can be minimized. In addition, based on this study, it can be used as basic data on the introduction of smart construction technology at construction sites.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1061-1067
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• 2023

In this letter, we simulated an atmospheric correction for Sentinel-2 images, of which spectral bands are similar to Compact Advanced Satellite 500-4 (CAS500-4). Using the second simulation of the satellite signal in the solar spectrum - vector (6SV)2.1 radiation transfer model and random forest (RF), a type of machine learning, we developed an RF-based atmospheric correction model to simulate 6SV2.1. As a result, the similarity between the reflectance calculated by 6SV2.1 and the reflectance predicted by the RF model was very high.