• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.30-43
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• 2022

In this study, elementary students' science communication competence was investigated based on the grammatical features expressed in their language-use in classroom discourse and science writings. The classes were designed to integrate the evidence-based reasoning framework and traditional learning cycle and were conducted on fifth graders in an elementary school. Eight elementary students' discourse data and writings were analyzed using lexico-grammatical resource analysis, which examined the discourse text's content and logical relations. The results revealed that the student language used in analyzing data, interpreting evidence, or constructing explanations did not precisely conform to the grammatical features in science language use. However, they provided examples of grammatical metaphors by nominalizing observed events in the classroom discourses and those of causal relations in their writings. Thus, elementary students can use science language grammatically from science language-use experiences through listening to a teacher's instructional discourses or recognizing the grammatical structures of science texts in workbooks. The opportunities in which elementary students experience the language-use model in science learning need to be offered to understand the appropriate language use in the epistemic context of evidence-based reasoning and learn literacy skills in science.

• (The) Research of the performance art and culture
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• no.36
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• pp.327-363
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• 2018

This paper tracks the present state of the Gugak educational programs run by Gugak-specialized museums including Museum of Gugak not only to set a directionality of Museum of Gugak to step forward for their main purposes, but also to find measures to invigorate its Gugak educational programs. There are 826 museums registered in 2016 nationwide, and ten of them are Gugak-specialized museums including Museum of Gugak. An analysis of the educational programs by Museum of Gugak presents high achievements in concentrativeness, participation and satisfaction levels. However, several issues such as difficulty level adjustment, education period arrangement, contents development, setting of a precise aim of education, and overcoming of regional limitations are to be solved in the future. Considering these special circumstances, the study suggests setting a directionality of Gugak education by following four conditions: Firtly, the Gugak education programs by Museum of Gugak should be user-oriented. Secondly, it is necessary to provide customized learning programs to suit users of various ages and generations. Thirdly, a solid education is required to enhance creativity deviating from uniform, unilateral, fragmentary education focused on materials and relics of museums as the users' experiences and learning levels vary. Fourthly, integrated education with relevant study in common use is required as the specialized environments of the museum could cause users psychological resistance and lessen their willingness to approach. Focusing on these four conditions several invigoration measures for the Gugak education programs are discussed: Firstly, a step-by-step approach, not a radical shift, is required in order to turn existing programs into the user-oriented. Secondly, customized learning programs should be planned in consideration of life cycle of the users. Thirdly, it is necessary to establish virtuous circulation reflecting activity-based contents as well as to provide the users experiences through five senses for solid Gugak education, in which various elements such as experiencing, learning, playing, viewing are reflected manifoldly. Fourthly, integrated education can be implemented when the features of Gugak educational programs are internally structured and the external environment matures.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.48-63
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• 2024

This study aims to develop a class procedure for the application of classrooms that value context and to conduct science classes using this procedure to examine the effects. Among various contexts related to scientific knowledge, the study develops a teaching procedure for designing classes that focus on the contexts of discovery and real life. After verifying the content validity of the context-based design and the program to which it was applied, a class was conducted, and the responses of the children were checked. The final draft of the lesson design completed after revision and supplementation is as follows: context-based design was presented in four stages, namely, presenting, exploring the context, adapting the context, and organizing (share and synthesizing; PEAS). The goal is to enable people to experience the overall flow of scientific knowledge instead of focusing on the acquisition of fragmentary knowledge by covering a wide range of topics from the social and historical contexts in which scientific knowledge was created to its use in real life. To aid in understanding the newly proposed class procedure and verifying its effectiveness, we developed a program by selecting the "My Fun Exploration," 2. Biology and Environment unit of the second semester of the fifth grade. The result indicated that the elementary science program that applied the context-centered design effectively improved the self-directed learning ability of students. In addition, the effect was especially notable in terms of intrinsic motivation. As the students experienced the contexts of discovery and real life related to scientific knowledge, they developed the desire to actively participate in science learning. As this becomes an essential condition for deriving active learning effects, a virtuous cycle in which meaningful learning can occur has been created. Based on the implications, developing programs that apply context-based design to various areas and contents will be possible.

• Journal of Korean Elementary Science Education
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• v.35 no.3
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• pp.336-347
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• 2016

The purpose of this study is to analyze the effects of elementary science instruction applying a Brain-Based Evolutionary (ABC-DEF) approach using Rapid-cycling Brassica rapa (RcBr) on the interests of elementary school students. For this study, two elementary school classes in Seoul and one elementary school class in Gyeonggi-do were selected. Comparison group received instruction using textbook and teacher's guidebook. A class taught using only brain-based evolutionary approach is experimental group A, and a class taught through brain-based evolutionary approach using RcBr is experimental group B. In order to analyze the quantitative differences about the interests of students, three kinds of test were administered to the students: 'Applied Unit-Related Interests', 'Follow-up Interests' and 'Interests in the observation material'. To get more information, qualitative data such as portfolios and interviews were analyzed. The major findings are as follows. First, for the test of applied unit-related interests, a statistically significant difference was found between comparison group and experimental group A, and between comparison group and experimental group B. As the results of interviews, the students have shown that the intensified exploration activities on plant in Brain-Based Evolutionary approach applied to experimental groups A and B had a positive effect. Second, for test of follow-up interests, we classified the students' follow-up interests into three types: extended-developed-deepened (EDD) type, simply expanded-maintained (SEM) type, and stopped or decreased (SD) type. Both experimental group A and experimental group B showed the highest percentage of EDD. Also, observation journal applying the evolutionary process (DEF) showed a positive effect on the students' interest. Comparison group showed the highest percentage of SEM. Third, for test of applied interests in the observation material, a statistically significant difference was found between comparison group and experimental group A, and comparison group and experimental group B. Experimental group B using RcBr showed the highest average score, while experimental group A showed a higher score than comparison group. Based on these findings, educational implications of Brain-Based Evolutionary approach and using RcBr are discussed.

• Journal of Technology Innovation
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• v.29 no.4
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• pp.1-30
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• 2021

Due to the recent development of system semiconductors, technical innovation for the electric devices of the automobile industry is rapidly progressing. In particular, the electric device of automobiles is accelerating technology development competition among automobile parts makers, and the development cycle is also changing rapidly. Due to these changes, the importance of strategic planning for R&D is further strengthened. Due to the paradigm shift in the automobile industry, the Product-Technical Roadmap (P/TRM), one of the R&D strategies, analyzes technology forecasting, technology level evaluation, and technology acquisition method (Make/Collaborate/Buy) at the planning stage. The product-technical roadmap is a tool that identifies customer needs of products and technologies, selects technologies and sets development directions. However, most companies are developing the product-technical roadmap through a qualitative method that mainly relies on the technical papers, patent analysis, and expert Delphi method. In this study, empirical research was conducted through simulations that can supplement and strengthen the product-technical roadmap centered on the automobile industry by fusing Gartner's hype cycle, cumulative moving average-based data preprocessing, and deep learning (LSTM) time series analysis techniques. The empirical study presented in this paper can be used not only in the automobile industry but also in other manufacturing fields in general. In addition, from the corporate point of view, it is considered that it will become a foundation for moving forward as a leading company by providing products to the market in a timely manner through a more accurate product-technical roadmap, breaking away from the roadmap preparation method that has relied on qualitative methods.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1141-1180
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• 2022

The application of satellite imageries has increased in the field of hydrology and water resources in recent years. However, challenges have been encountered on obtaining accurate evapotranspiration and soil moisture. Therefore, present researches have emphasized the necessity to obtain estimations of satellite-based evapotranspiration and soil moisture with related development researches. In this study, we presented the research status in Korea by investigating the current trends and methodologies for evapotranspiration and soil moisture. As a result of examining the detailed methodologies, we have ascertained that, in general, evapotranspiration is estimated using Energy balance models, such as Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapotranspiration with Internalized Calibration (METRIC). In addition, Penman-Monteith and Priestley-Taylor equations are also used to estimate evapotranspiration. In the case of soil moisture, in general, active (AMSR-E, AMSR2, MIRAS, and SMAP) and passive (ASCAT and SAR)sensors are used for estimation. In terms of statistics, deep learning, as well as linear regression equations and artificial neural networks, are used for estimating these parameters. There were a number of research cases in which various indices were calculated using satellite-based data and applied to the characterization of drought. In some cases, hydrological cycle factors of evapotranspiration and soil moisture were calculated based on the Land Surface Model (LSM). Through this process, by comparing, reviewing, and presenting major detailed methodologies, we intend to use these references in related research, and lay the foundation for the advancement of researches on the calculation of satellite-based hydrological cycle data in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.5
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• pp.535-546
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• 2020

Land cover maps are derived from satellite and aerial images by the Ministry of Environment for the entire Korea since 1998. Even with their wide application in many sectors, their usage in research community is limited. The main reason for this is the map compilation cycle varies too much over the different regions. The situation requires us a new and quicker methodology for generating land cover maps. This study was conducted to automatically generate land cover map using aerial ortho-images and Landsat 8 satellite images. The input aerial and Landsat 8 image data were trained by Residual U-Net, one of the deep learning-based segmentation techniques. Study was carried out by dividing three groups. First and second group include part of level-II (medium) categories and third uses group level-III (large) classification category defined in land cover map. In the first group, the results using all 7 classes showed 86.6 % of classification accuracy The other two groups, which include level-II class, showed 71 % of classification accuracy. Based on the results of the study, the deep learning-based research for generating automatic level-III classification was presented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.25-39
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• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.367-380
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• 2022

In this study, the periodic and simplified update and production way of the ECVAM (Environmental Conservation Value Assessment Map) was presented through the classification of environmental values using KOMPSAT-3A satellite imagery and land cover map. ECVAM is a map that evaluates the environmental value of the country in five stages based on 62 legal evaluation items and 8 environmental and ecological evaluation items, and is provided on two scales: 1:25000 and 1:5000. However, the 1:5000 scale environmental assessment map is being produced and serviced with a slow renewal cycle of one year due to various constraints such as the absence of reference materials and different production years. Therefore, in this study, one of the deep learning techniques, KOMPSAT-3A satellite image, SI (Spectral Indices), and land cover map were used to conduct this study to confirm the possibility of establishing an environmental assessment map. As a result, the accuracy was calculated to be 87.25% and 85.88%, respectively. Through the results of the study, it was possible to confirm the possibility of constructing an environmental assessment map using satellite imagery, optical index, and land cover classification.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.521-528
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• 2000

One of the hidden problems in a domain theory is that an agent does not understand the meaning of its action. Graphplan uses mutex to improve efficiency, but it does not understand negation and suffers from a redundancy problem. Introducing a negative function not in IPP partially helps to solve this kind of problem. However, using a negative function comes with its own price in terms of time and space. Observing that a human utilizes opposite concept to negate a fact based on MDL principle, we hypothesize that using a positive atom rather than a negative function to represent a negative fact is a more efficient technique for building an intelligent agent. We show empirical results supporting our hypothesis in IPP domains. To autonomously learn the human-like concept, we generate a cycle composed of opposite operators from a domain theory and extract opposite literals through experimenting with the operators.