• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1153-1164
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• 2024

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.

• Journal of The Korean Association For Science Education
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• v.4 no.1
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• pp.34-43
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• 1984

Since the study of student's preconceptions and their effects on the learning of relevant subjects became an influential research area with high significance, the research area bas mainly been concerned by science educators. However, it was not until the year of 1983 that the area received recognition of various fields other than science education. The recognition was given by the Scientific American when it published a paper reporting a misconceptions in mechanics. Studies concerning misconceptions primarily interested in the following questions: What kinds of theoretical bases do preconceptions or misconceptions have? What are the sources of those conceptions? How are the misconceptions changed into or improved to scientific concepts? What are the efficient teaching methods appropriate for reducing the number of the misconceptions after instruction? Those questions are partly answered by experimental psychology and by philosophy of science, especially epistemology. Therefore, the paper will examine the theoretical background for and the sources of the misconceptions through literature review. Then, a few learning and teaching theories currently carrying great prestige in educational practice will be interpreted in terms of the knowledge of preconceptions or misconceptions.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.265-268
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• 2001

This paper proposes the strategy of noise injection into inputs in the Kohonen learning algorithm (KKA) to improve the local convergence problem of the KLA. Noise strengths are high in the begin of the learning and gradually lowered as the teaming proceeds. This strategy is a kind of stochastic relaxation (SR) method which is broadly used in the general optimization problems. It is convenient to implement and improves the convergence properties of the KLA with moderately increased computing time compared to the KLA. Experimental results for Gauss-Markov sources and real speech demonstrate that the proposed method can consistently provide better codebooks than the KLA.

• Culinary science and hospitality research
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• v.24 no.3
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• pp.144-155
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• 2018

This study was conducted to explore the direction of culinary education based brain-based education with analysis of comprehensive research. Questionnaire was completed by frequency analysis, factor analysis, reliability analysis and regression analysis by using SPSS 21. The purpose of this study was to investigate the educational system for creative development through cooking sources and to develop brain-based learning theory, and thus to generate the characteristics and effects of the practice in culinary educational context. The basic principles of brain- based learning are brain plasticity, emotional brain, and ecological brain. Students need to be able to enrich their understanding of social interaction so that social brain's function will be activated through consistent and high-quality feedback. Likewise, students should be capable of collecting everything what they have learned. Defining main ideas and goal of the lesson, four factors were derived from development of competency, personality, application, and diversity. Regarding to the result of this study, the implications for the development of a brain-base program were suggested.

• Hwankyungkyoyuk
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• v.22 no.3
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• pp.72-82
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• 2009

The purpose of this study was to develope experience-education program for elementary students which use environmental resources and learning sources of Jeju island. This study designed developing framework of environmental education program for using local resources and developed experience-education program on the basis of it. Especially this program consisted of direct experience, indirect experience, and local community learning. This program consisted of five activities -'Jeju's water', 'Mecca of wind power', 'Rushing jellyfish', 'Ramsar wetland', and 'Searching the fossil'. Each activity themes was to use environmental resources of Jeju island. And this program had relationship with science, social studies, and ethics in the curriculum. The result of this study will serve to activate environmental education program for using local resources if we solve program's problem through application process.

• Journal of Information Technology Applications and Management
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• v.16 no.1
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• pp.21-36
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• 2009

As the World Wide Web (WWW) has become a major channel for information delivery, the data accumulated in the Internet increases at an incredible speed, and it derives the advances of information search technologies. It is the search engine that solves the problem of information overloading and helps people to identify relevant information. However, as search engines become a powerful tool for finding information, the opportunities of plagiarizing have increased significantly in e-Learning. In this paper, we developed an online plagiarism detection system for detecting plagiarized documents that incorporates the functions of search engines and acts in exactly the same way of plagiarizing. The plagiarism detection system uses morpheme analysis to improve the performance and sentence-based comparison to investigate document comes from multiple sources. As a result of applying this system in e-Learning, the performance of plagiarism detection was improved.

• SUVANNABHUMI
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• v.9 no.1
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• pp.173-192
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• 2017

The Sasaq community in Lombok, Indonesia has been recognized as a peasant community with its unique and strong social capital. Sources of social capital recognition can be derived from common terms or expressions and institutions practiced in community daily life. However, there is a trend of neglecting and ignoring those values by the community, especially the youth. Through action research, we would like to revitalize social capital of the community in supporting social and economic development in the rural level. In this paper, we introduce a Strategic Leadership and Learning Organization (SLLO) approach to build community participation in solving social and economic problems. Through regular dialogue, communities come with common agreements and collective action that are perceived as emergence property. Several common agreements are intended to solve community problems actually in line with the objectives of government designated development.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.393-399
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• 2021

In this study, experiments were conducted to estimate the PM concentrations by learning the nighttime CCTV images of various PM concentrations environments. In the case of daytime images, there have been many related studies, and the various texture and brightness information of images is well expressed, so the information affecting learning is clear. However, nighttime images contain less information than daytime images, and studies using only nighttime images are rare. Therefore, we conducted an experiment combining nighttime images with non-uniform characteristics due to light sources such as vehicles and streetlights and building roofs, building walls, and streetlights with relatively constant light sources as an ROI (Region of Interest). After that, the correlation was analyzed compared to the daytime experiment to see if deep learning-based PM concentrations estimation was possible with nighttime images. As a result of the experiment, the result of roof ROI learning was the highest, and the combined learning model with the entire image showed more improved results. Overall, R² exceeded 0.9, indicating that PM estimation is possible from nighttime CCTV images, and it was calculated that additional combined learning of weather data did not significantly affect the experimental results.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• Journal of The Korean Association For Science Education
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• v.21 no.1
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• pp.59-75
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• 2001

While previous studies have recognized and have researched the resistance of students' scientific conception to change and the difficulty of the change of a conception's status, few have investigated the idea of conceptual ecology as a context of conceptual change learning, including the role that affective and motivational aspects might play when students are exposed to conceptual change learning, The present study was conducted to describe in detail high school students' views about learning and knowing science by summarizing of students' conceptual ecologies. The study was interpretive, using multiple data sources to achieve a triangulation of data. Three students from a public high school for boys serve as cases representative of students' views about learning and knowing science. Students' enthusiasm to pursue science was closely connected to their views about learning and knowing science. Students' views about learning and knowing science are influenced by their views regarding science and science class including the nature of knowledge, learning, and their epistemological commitments, They influence students' self-efficacy and motivation on learning science.