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심층 학습 기법을 이용한 탄성파 자료 잡음 제거 적용사례 분석
조준현,하완수 한국지구물리.물리탐사학회 2020 지구물리와 물리탐사 Vol.23 No.2
Recent rapid advances in computer hardware performance have led to relatively low computational costs, increasing the number of applications of machine-learning techniques to geophysical problems. In particular, deep-learning techniques are gaining in popularity as the number of cases successfully solving complex and nonlinear problems has gradually increased. In this paper, applications of seismic data denoising methods using deep-learning techniques are introduced and investigated. Depending on the type of attenuated noise, these studies are grouped into denoising applications of coherent noise, random noise, and the combination of these two types of noise. Then, we investigate the deep-learning techniques used to remove the corresponding noise. Unlike conventional methods used to attenuate seismic noise, deep neural networks, a typical deep-learning technique, learn the characteristics of the noise independently and then automatically optimize the parameters. Therefore, such methods are less sensitive to generalized problems than conventional methods and can reduce labor costs. Several studies have also demonstrated that deep-learning techniques perform well in terms of computational cost and denoising performance. Based on the results of the applications covered in this paper, the pros and cons of the deep-learning techniques used to remove seismic noise are analyzed and discussed. 최근 컴퓨터 하드웨어 성능의 급속한 발전으로 인해 계산 비용이 상대적으로 낮아지면서 기계 학습 기법을 지구물리학적 문제에 적용하는 사례가 점차 증가하고 있다. 특히 심층 학습 기법이 복잡하고 비선형적인 문제를 성공적으로해결하는 사례가 많아지면서 큰 인기를 얻고 있다. 이 논문에서는 심층 학습 기법을 이용한 탄성파 자료 잡음 제거 적용사례를 조사하고 소개하였다. 감쇠하고자 하는 잡음 유형에 따라 일관성 잡음 적용사례, 무작위 잡음 적용사례, 일관성잡음 및 무작위 잡음 적용사례로 분류하였고 해당 잡음 제거에 사용된 심층 학습 기법에 대해 조사하였다. 대표적인 심층 학습 기법인 심층 신경망은 탄성파 잡음 제거에 사용된 기존 기법과 달리 잡음의 특징을 스스로 학습하며 매개변수를 자동으로 최적화한다. 따라서 기존 기법에 비해 일반화 문제에 덜 민감하며 인적 비용을 절감할 수 있다. 또한 여러연구 사례를 통해 계산 비용이나 잡음 제거 성능 측면에서도 심층 학습 기법이 뛰어난 성과를 달성하는 것을 보여주었다. 연구 결과들을 토대로 탄성파 잡음 제거에 사용된 심층 학습 기법의 장단점에 대해 분석하고 논의하였다.
조준현,남웅식,김선주,이두현,민경훈,이태호,이상규 사단법인 한국질량분석학회 2016 Mass spectrometry letters Vol.7 No.3
High-resolution quadrupole-Orbitrap mass spectrometry (HRMS), with high-resolution (> 10,000 at full-width at half-maximum) and accurate mass (< 5 ppm deviation) capabilities, plays an important role in the structural elucidation of drug metabolites in the pharmaceutical industry. ML106, a derivative of imidazobenzimidazole, decreased melanin content and tyrosinase activity in a dose-dependent manner. Here, we investigated the phase 1 metabolic pathway of ML106 using HRMS in human liver microsomes (HLMs) and recombinant cDNA-expressed cytochrome P450 (CYP). After the incubation of ML106 with pooled HLMs and recombinant cDNA-expressed CYP in the presence of NADPH, five phase 1 metabolites, including three mono-hydroxylated metabolites (M1-3) and two di-hydroxylated metabolites (M4 and M5), were investigated. The metabolite structures were postulated by the elucidation of protonated mass spectra using HRMS. The CYP isoforms related to the hydroxylation of ML106 were studied after incubation with recombinant cDNA-expressed CYP. Here, we identified the phase 1 metabolic pathway of ML106 induced by CYP in HLMs.