• 한국초전도학회:학술대회논문집
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• 한국초전도학회 2002년도 High Temperature Superconductivity Vol.XII
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• pp.39-39
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• 2002

• 한국진공학회:학술대회논문집
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• 한국진공학회 1999년도 제16회 학술발표회 논문개요집
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• pp.135-135
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• 1999

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1999년도 추계학술발표회요약집
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• pp.366-366
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• 1999

• 한국자기학회:학술대회 개요집
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• 한국자기학회 2003년도 하계학술연구발표회 및 한.일 공동심포지엄
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• pp.274-275
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• 2003

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Journal of the Optical Society of Korea
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• 제20권4호
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• pp.500-509
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• 2016

Integrating spheres play a central role in the radiometric calibration of remote sensors. With the development of the wide field of view (FOV) remote sensors, aperture diameters of remote sensors are becoming larger and larger. To satisfy the radiometric calibration requirements of full FOV and full aperture, an 8000mm diameter large aperture integrating sphere uniform source with a variable exit port was designed and manufactured. This integrating sphere will be used for pre-launch test and radiometric calibration of remote satellites. In this paper, optical theories were used to design the output spectral radiance. The LightTools software based on ray-tracing simulation method was used to determine the best combination and distribution of inner light sources. A spectral experiment was made to verify the spectral radiance design. To reduce the influence of longtime power-on, a new characteristic measurement method was developed to obtain the radiation characteristic of the integrating sphere, which could greatly improve the measuring efficiency. This method could also be applied to measure other large aperture uniform sources. The obtained results indicate that the spatial uniformity is 98.35%, and the angular uniformity at center position is 98.78%.

• 과학교육연구지
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• 제38권3호
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• pp.703-717
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• 2014

물리에서 멘탈 시뮬레이션은 개념을 이해하거나 생성하기 위한 중요한 매커니즘이다. 만약 학생들이 멘탈 시뮬레이션에 어려움을 겪는다면 물리 개념의 이해 또한 어려워진다. 3차원 시뮬레이션 콘텐츠는 학생들에게 공간적 조작의 가이드를 제공함으로써 물리 개념 이해를 도울 수 있다. 이 연구에서는 빛의 직진 개념의 이해를 돕기 위한 3차원 시뮬레이션 콘텐츠를 개발하여 대학생 20명에게 적용하였다. 적용 결과, Hake gain이 0.93으로 수업에 대해 매우 높은 수준의 이해도를 보였다. 또한 학생들은 새로운 맥락에 대해서도 멘탈 시뮬레이션을 통해 현상을 잘 예측하였다. 이를 통해 3차원 시뮬레이션 콘텐츠를 통해 학생들의 개념 이해가 잘 이루어졌음을 알 수 있다.

• 대한기계학회논문집 C: 기술과 교육
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• 제1권2호
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• pp.205-210
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• 2013

본 연구에서는 스키 시뮬레이터의 사용자 감응형 제어를 위한 물리모델과 동작인식 시스템의 개발되었으며, 스키 선수의 자세 변화에 따른 스키 슬로프 상에서의 거동과의 부합성 확보를 위하여 실제 현장 실험데이터의 회귀분석을 통해 동작인식 시스템에 사용될 파라미터 및 관계식을 도출하였다. 개발된 물리모델 기반 스키 동작 인식 시뮬레이터는 실시간으로 Kinect 장치를 사용하여 사용자의 관절별 질량을 분석하여, 정확한 체중심을 추정하고, 시뮬레이터 하드웨어에서 적용할 수 있도록 힘, 속도, 가속도에 대한 피드백을 전달하도록 구성되었다. 본 연구결과는 스키시뮬레이터의 인식모듈로 사용되었으며, 물리모델 기반 가상 스포츠 시뮬레이터 제작에 응용 할 수 있는 자료로 활용될 것이다.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2011년도 제40회 동계학술대회 초록집
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• pp.428-429
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• 2011

Recently, we performed ab initio total energy calculation and tight-binding molecular dynamics (TBMD) simulation to study structures and the reconstruction of native defects in graphene. In the previous study, we predicted by TBMD simulation that a double vacancy in graphene is reconstructed into a 555-777 composed of triple pentagons and triple heptagons [1]. The structural change from pentagon-octagon-pentagon (5-8-5) to 555-777 has been confirmed by recent experiments [2,3] and the detail of the reconstruction process is carefully studied by ab initio calculation. Pentagon-heptagon (5-7) pairs are also found to play an important role in the reconstruction of vacancy in graphene and single wall carbon nanotube [4]. In the TBMD simulation of graphene nanoribbon (GNR), we found the evaporation of carbon atoms from both the zigzag and armchair edges is preceded by the formation of heptagon rings, which serve as a gateway for carbon atoms to escape. In the simulation for a GNR armchair-zigzag-armchair junction, carbon atoms are evaporated row-by-row from the outermost row of the zigzag edge [5], which is in excellent agreement with recent experiments [2, 6]. We also present the recent results on the formation and development of dislocation in graphene. It is found that the coalescence of 5-7 pairs with vacancy defects develops dislocation in graphene and induces the separation of two 5-7 pairs. Our TBMD simulations also show that adatoms are ejected and evaporated from graphene surface due to large strain around 5-7 pairs. It is observed that an adatom wanders on the graphene surface and helps non-hexagonal rings change into stable hexagonal rings before its evaporation.

• 한국의학물리학회지:의학물리
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• 제33권4호
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• pp.129-135
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• 2022

Purpose: A full-energy-peak (FEP) efficiency correction is required through a Monte Carlo simulation for accurate radioactivity measurement, considering the geometrical characteristics of the detector and the sample. However, a relative deviation (RD) occurs between the measurement and calculation efficiencies when modeling using the data provided by the manufacturers due to the randomly generated dead layer. This study aims to optimize the structure of the detector by determining the dead layer thickness based on Monte Carlo simulation. Methods: The high-purity germanium (HPGe) detector used in this study was a coaxial p-type GC2518 model, and a certified reference material (CRM) was used to measure the FEP efficiency. Using the MC N-Particle Transport Code (MCNP) code, the FEP efficiency was calculated by increasing the thickness of the outer and inner dead layer in proportion to the thickness of the electrode. Results: As the thickness of the outer and inner dead layer increased by 0.1 mm and 0.1 ㎛, the efficiency difference decreased by 2.43% on average up to 1.0 mm and 1.0 ㎛ and increased by 1.86% thereafter. Therefore, the structure of the detector was optimized by determining 1.0 mm and 1.0 ㎛ as thickness of the dead layer. Conclusions: The effect of the dead layer on the FEP efficiency was evaluated, and an excellent agreement between the measured and calculated efficiencies was confirmed with RDs of less than 4%. It suggests that the optimized HPGe detector can be used to measure the accurate radioactivity using in dismantling and disposing medical linear accelerators.