• 방사성폐기물학회지
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• 제16권4호
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• pp.491-505
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• 2018

현재 기준개념으로 개발하여 상용화 단계에 있는 심층 동굴 처분기술에 대한 대안으로서 지질학적 조건이 더 안정적인 지하 3~5 km의 심도에 사용후핵연료를 포함한 고준위폐기물을 처분하는 심부시추공 처분기술의 국내 적용 가능성을 예비 평가 하였다. 이를 위하여 심부시추공 처분개념의 기술적 적용성 분석에 필요한 국내 기반암 분포특성 및 심부시추공 처분부지 적합성 평가 기술 분석과 대구경 심부시추기술을 평가하였다. 이들 분석결과를 바탕으로 심부시추공 처분시스템 설계 기준 및 요건에 적합한 심부시추공 처분용기 및 밀봉시스템 개념을 설정하여 예비 기준 심부시추공 처분 개념을 도출하였다. 그리고 도출된 예비 기준 처분시스템에 대하여 열적 안정성 및 그래픽 처분환경에서의 처분공정 모사 등 다양한 성능평가를 수행하고 이들을 종합하여 심부시추공 처분시스템의 국내 적용성에 대하여 다양한 관점에서의 예비평가를 수행하였다. 결론적으로, 심부시추공 처분시스템은 처분심도와 단순한 방법으로 인하여 안전성 및 경제적 타당성 측면에서 많은 장점이 있지만, 불확실성을 줄이고 인허가를 획득하기 위해서는 이 기술에 대한 현장실증이 필수적이다. 본 연구결과는 사용후핵연료 관리 국가정책 수립을 위한 공학적 근거자료로 활용이 가능하며, 심부시추공 처분기술에 관심을 갖는 방사성폐기물 관리 이해당사자들에게 필요한 정보자료로 제공될 수 있다.

• 방사성폐기물학회지
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• 제12권2호
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• pp.121-133
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• 2014

사용후핵연료를 포함하는 고준위 방사성폐기물을 지질학적 조건이 안정적인 지하 3~5 km의 심도에 처분할 수 있다면 다음과 같은 많은 장점이 있는 것으로 평가되고 있다. 즉, (1)암반 수리전도도가 매우 낮아 지하수가 생태계까지 도달하는데 속도가 현저히 감소되며, (2)상부층 두께로 인하여 생태계와의 이격거리 확보에 유리하고, (3)지하수가 환원상태이므로 핵종의 용해도가 매우 낮을 뿐만 아니라 (4)오랜 연령의 지하수에서는 핵종이 흡착된 콜로이드 생성과 이동이 극히 제한된다는 점이다. 이와 관련하여 심부시추공 처분(Deep Borehole Disposal) 연구는 심층 처분(Deep Geological Disposal) 시스템에 대한 이상적인 처분 대안기술로서 꾸준하게 진행되어 왔다. 본 논문에서는 최근 심부 시추기술이 비약적으로 발전됨에 따라 의미있게 연구가 진행되고 있는 심부시추공 처분시스템을 국내 적용하기 위한 초기 단계로서 해외의 심부시추공 처분시스템 기술개발 사례를 분석하였다. 이를 통하여 심부시추공 처분에 대한 일반적인 개념과 심부시추공 처분시스템 개념을 도출한 연구사례를 국가별로 정리하였다. 이들 분석결과는 향후 심부시추공 처분기술의 국내 적용을 위한 입력자료로서 유용하게 활용될 수 있을 것이다.

• E2M - 전기 전자와 첨단 소재
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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.

• 한국지반공학회:학술대회논문집
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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.

• 정보과학회 논문지
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• 제44권6호
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• pp.607-612
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• 2017

도시에서 홍수 피해를 방지하기 위한 침수를 예측하기 위해 본 논문에서는 딥러닝(Deep Learning) 기법을 적용한다. 딥러닝 기법 중 시계열 데이터 분석에 적합한 Recurrent Neural Networks (RNNs)을 활용하여 강의 수위 관측 데이터를 학습하고 침수 가능성을 예측하였다. 예측 정확도 검증을 위해 사용한 데이터는 미국의 트리니티강의 데이터로, 학습을 위해 2013 년부터 2015 년까지 데이터를 사용하였고 평가 데이터로는 2016 년 데이터를 사용하였다. 입력은 16개의 레코드로 구성된 15분단위의 시계열 데이터를 사용하였고, 출력으로는 30분과 60분 후의 강의 수위 예측 정보이다. 실험에 사용한 딥러닝 모델들은 표준 RNN, RNN-BPTT(Back Propagation Through Time), LSTM(Long Short-Term Memory)을 사용했는데, 그 중 LSTM의 NE(Nash Efficiency)가 0.98을 넘는 정확도로 기존 연구에 비해 매우 높은 성능 향상을 보였고, 표준 RNN과 RNN-BPTT에 비해서도 좋은 성능을 보였다.

• 전기학회논문지
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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.

• 한국과학교육학회지
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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.

• 한국컴퓨터정보학회:학술대회논문집
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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.