• 한국수학사학회지
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• 제23권2호
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• pp.1-21
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• 2010

광무 9년(1905년) 12월5일 한국인 최초의 이학사인 유일선이 발행한 우리나라 첫 번째 수학 저널인 <수리학잡지 (數理學雜誌)> 가 사료로 최근 수학계에 그 존재를 알렸다. 이 저널은 매월 5일 발행한 정기간행물로 2년 정도 (광무10년 6월5일 제 7호까지) 발간된 후 폐간되었다. 발행은 유일선에 의해 1907년 설립된 정리사(精理舍) 가 위치한 서울 남대문 근처 상동 (尙洞) 의 '수리학잡지사' 를 통하여 이루어졌다. 그 내용은 근대수학의 기초 내용을 강론형식으로 서술한 '산술신강의' 와 '산술문제 해설' 및 '이과 (理科)' 로 구성되었다. 본 논문은 유일선이 발행한 한국 최초의 수학 저널 <수리학잡지> 의 창간호의 내용을 분석하고 그 내용을 구체적으로 소개하는데 그 목적이 있다.

• 대한방사선기술학회지:방사선기술과학
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• 제46권3호
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• pp.219-229
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• 2023

In this study, basic characteristics such as reproducibility, linearity, and directionality of RPL glass dosimeters were evaluated to improve the reliability of dose evaluation through RPL glass dosimeters, and uncertainty elements such as sensitivity by glass element and magazine slot sensitivity were evaluated. Using a mathematical model to calibrate the measured values of the RPL glass dosimeter, the measurement uncertainty was calculated assuming an example. As a result of the characteristic evaluation, the RPL glass dosimeter showed excellent performance with a standard deviation of ±1% (1 SD) for the reproducibility of the reading process, a coefficient of determination for linearity of 0.99997. And the read-out of the RPL glass dosimeter are affected by the circular rotation direction of the glass dosimeter during irradiation, fading according to the period after irradiation, the number of laser pulses of the reader, and response degradation due to repeated reading, it is judged that measurement uncertainty can be reduced by irradiation and reading in consideration of these factors. In addition, it was confirmed that the dose should be determined by calculating the correction factors for the sensitivity of each element and, the sensitivity of each reading magazine slot. It is believed that the reliability of dosimetry using glass dosimeters can be improved by using a mathematical model for correction of glass dosimeter readings and calculating measurement uncertainty.

• 전자통신동향분석
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• 제36권3호
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• pp.106-118
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• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.