• 제목/요약/키워드: Neutral Beam

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Neutral Beam Injection용 Arc Power Supply 설계 (A Design of Arc Power Supply for Neutral Beam Injection)

  • 이희준;전범수;류동균;이택기;박선순;원충연
    • 전력전자학회:학술대회논문집
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    • 전력전자학회 2010년도 추계학술대회
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    • pp.222-223
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    • 2010

  • KSTAR NBI 시스템은 플라즈마의 온도를 높여 주기 위해 고 에너지의 중성 입자빔을 만들어서 토카막 플라즈마에 투입시키기 위한 중성 입사 장치이다. NBI 아크 전원 공급장치는 토카막 내부에 플라즈마를 만들어 주는 역할을 하는데 본 논문에서는 3상 다이오드 정류기, LC필터, 2.4kW급 6 병렬 벅 컨버터로 설계하여 시뮬레이션과 실험을 통하여 확인 하였다.

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Deep Learning Study of the 21cm Differential Brightness Temperature During the Epoch of Reionization

  • Kwon, Yungi;Hong, Sungwook E.
    • 천문학회보
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    • 제45권1호
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    • pp.66.2-66.2
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    • 2020

  • We propose a deep learning analysis technique with a convolutional neural network (CNN) to predict the evolutionary track of the Epoch of Reionization (EoR) from the 21-cm differential brightness temperature tomography images. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm maps between z = 6 ~ 13. We then apply two observational effects, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA), into the 21-cm maps. We design our deep learning model with CNN to predict the sliced-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction from our CNN model has great agreement with the true value even after coarsely smoothing with broad beam size and frequency bandwidth and heavily covered by noise with narrow beam size and frequency bandwidth. Our results show that the deep learning analyzing method has the potential to reconstruct the EoR history efficiently from the 21-cm tomography surveys in future.

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