• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• 제16권4호
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• pp.491-505
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• 2018

As an alternative to deep geological disposal technology, which is considered as a reference concept, the domestic applicability of deep borehole disposal technology for high level radioactive waste, including spent fuel, has been preliminarily evaluated. Usually, the environment of deep borehole disposal, at a depth of 3 to 5 km, has more stable geological and geo-hydrological conditions. For this purpose, the characteristics of rock distribution in the domestic area were analyzed and drilling and investigation technologies for deep boreholes with large diameter were evaluated. Based on the results of these analyses, design criteria and requirements for the deep borehole disposal system were reviewed, and preliminary reference concept for a deep borehole disposal system, including disposal container and sealing system meeting the criteria and requirements, was developed. Subsequently, various performance assessments, including thermal stability analysis of the system and simulation of the disposal process, were performed in a 3D graphic disposal environment. With these analysis results, the preliminary evaluation of the domestic applicability of the deep borehole disposal system was performed from various points of view. In summary, due to disposal depth and simplicity, the deep borehole disposal system should bring many safety and economic benefits. However, to reduce uncertainty and to obtain the assent of the regulatory authority, an in-situ demonstration of this technology should be carried out. The current results can be used as input to establish a national high-level radioactive waste management policy. In addition, they may be provided as basic information necessary for stakeholders interested in deep borehole disposal technology.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• 제12권2호
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• pp.121-133
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• 2014

If the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3~5 km and more stable rock formation, it has several advantages. For example, (1)significant fluid flow through basement rock is prevented, in part, by low permeability, poorly connected transport pathways, and (2)overburden self-sealing. (3)Deep fluids also resist vertical movement because they are density stratified and reducing conditions will sharply limit solubility of most dose-critical radionuclides at the depth. Finally, (4) high ionic strengths of deep fluids will prevent colloidal transport. Therefore, as an alternative disposal concept to the deep geological disposal concept(DGD), very deep borehole disposal(DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, for the preliminary applicability analyses of the DBD system for the spent fuels or high level wastes, the DBD concepts which have been developed by some countries according to the rapid advance in the development of drilling technology were reviewed. To do this, the general concept of DBD system was checked and the study cases of foreign countries were described and analyzed. These results will be used as an input for the analyses of applicability for DBD in Korea.

• Electrical & Electronic Materials
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• 제7권6호
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• pp.504-510
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• 1994

In this work the crystal structure, optical absorption and photoluminescence of Cd$_{4}$GeSe$_{6}$ single srystals grown by the vertical bridgman method are investigated. From the observed results of the PICTS, we proposed on energy band model which contains deep levels between the conduction band and the valence band. The energy band model permit us to explain the mechanism of the radiative recombination for the Cd$_{4}$GeSe$_{6}$ single crystals.als.

• Proceedings of the Korean Geotechical Society Conference
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• 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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• pp.584-590
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• 2009

One of the critical design problems involved in deep tunnelling in brittle rock, is the creation of surface spalling damage and breakouts. If weak fault zone is developed in deep tunnel, squeezing problem is added to the problems. According to the results of ground investigation in the study area, hard granitic rockmass and distinguished high angle fault zone are distributed on the tunnel level over 400m depth. To analyse the probability of brittle failure and squeezing, ground characterization with special lab. and field test were carried out. By the results, probability of brittle failures like spalling and rock burst is very low. But squeezing may be probable, if weak fault zone observed surface and drill core is extended to designed tunnel level.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.285-294
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• 2023

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.

• Transactions on Electrical and Electronic Materials
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• 제5권5호
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• pp.194-198
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• 2004

The influences of the deep-level concentration of p-type Si substrates on the optical properties of nano-porous silicon (PS) are investigated by deep level transient spectroscopy (DLTS) and photoluminescence (PL). Utilizing a Si substrate with Fe contaminations significantly enhanced the PL intensity of PS. All the PS samples formed on Fe-contaminated silicon substrates had stronger PL yield than that of reference PS without any intentional Fe contamination but the emission peak is not significantly changed. For the PS 1000 sample with Fe contamination of 1,000 ppb, the maximum PL intensity showed about ten times stronger PL than that of the reference PS sample. From PL and DLTS results, the PL efficiency strongly depends on the Fe-related trap concentration in Si substrates.

• Journal of KIISE
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• 제44권6호
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• pp.607-612
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• 2017

This paper presents an attempt to apply Deep Learning technology to solve the problem of forecasting floods in urban areas. We employ Recurrent Neural Networks (RNNs), which are suitable for analyzing time series data, to learn observed data of river water and to predict the water level. To test the model, we use water observation data of a station in the Trinity river, Texas, the U.S., with data from 2013 to 2015 for training and data in 2016 for testing. Input of the neural networks is a 16-record-length sequence of 15-minute-interval time-series data, and output is the predicted value of the water level at the next 30 minutes and 60 minutes. In the experiment, we compare three Deep Learning models including standard RNN, RNN trained with Back Propagation Through Time (RNN-BPTT), and Long Short-Term Memory (LSTM). The prediction quality of LSTM can obtain Nash Efficiency exceeding 0.98, while the standard RNN and RNN-BPTT also provide very high accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제66권7호
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• pp.1149-1154
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• 2017

In this paper, we propose a sensorless multi-function controller applicable for deep well water pumps using current detection method. The proposed system overcomes various drawbacks of existing sensored system and additional features like Over current protection function due to overload, Under current protection function for idling at low water level and Relay function for starting single phase motors and acts as a level indicator to detect water lever in real time by the current detection method. A prototype of the multi-function controller system is designed and all of its functions are tested in the laboratory. The application of the proposed controller ensures reduction in the power consumption and maintenance cost in the facilities like water and septic tanks, drainage and waste water system, oil and chemical tanks where deep well pumps are used.

• Journal of The Korean Association For Science Education
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• 제17권4호
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• pp.415-423
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• 1997

The purposes of this study were to investigate the intercorrelations among various motivational patterns and learning strategies and to examine the differences in motivation and strategy usage in terms of students' science achievement level, gender, and grade. A questionnaire on achievement goal, self-efficacy, self-concept of ability, expectancy, value, causal attributions, and learning strategies was administered to 360 junior high/high school students (178 males, 182 females). Students who adopted performance-oriented goal tended not to be task oriented. Task-oriented students had high levels of self-efficacy, high self-concept of ability, and expectancies for future performance in science. They also valued science and attributed thier failures to the lack of effort. However, performance-oriented students evaluated their ability negatively, did not value science, and attributed thier failures to uncontrollable causes. With respect to learning strategy, task-oriented students tended to use deep-level strategy, whereas performance-oriented students tended to use surface-level strategy and not to use deep-level strategy. High-achieving students, boys, and junior high school students were more task-oriented, evaluated their ability more positively, and valued science more than low-achieving students, girls, and high school students, respectively. High-achieving students and boys also used deep-level strategy more than each of their counterparts. However, no significant difference in learning strategy was found between junior high school students and high school students. Educational implications of these findings are discussed.

• Proceedings of the Korean Society of Computer Information Conference
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• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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• pp.41-44
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• 2021

In this paper, we propose a monitoring system that can monitor gas leakage concentrations in real time and forecast the amount of gas leaked after one minute. When gas leaks happen, they typically lead to accidents such as poisoning, explosion, and fire, so a monitoring system is needed to reduce such occurrences. Previous research has mainly been focused on analyzing explosion characteristics based on gas types, or on warning systems that sound an alarm when a gas leak occurs in industrial areas. However, there are no studies on creating systems that utilize specific gas explosion characteristic analysis or empirical urban gas data. This research establishes a deep learning model that predicts the gas explosion risk level over time, based on the gas data collected in real time. In order to determine the relative risk level of a gas leak, the gas risk level was divided into five levels based on the lower explosion limit. The monitoring platform displays the current risk level, the predicted risk level, and the amount of gas leaked. It is expected that the development of this system will become a starting point for a monitoring system that can be deployed in urban areas.