• 응용통계연구
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• 제34권4호
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• pp.633-658
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• 2021

통계공학은 실험계획법, 품질관리/품질경영, 신뢰성공학으로 구성된다. Python은 무료로 개방되어 있는 패키지로서 머신러닝, 데이터사이언스, 공학 및 그래픽 관련 패키지가 방대하다. 우리는 이러한 Python 패키지를 통계공학을 위한 기본 패키지로 유용하게 사용할 수 있다. 본 논문에서는 통계공학을 위한 Python 패키지 응용을 살펴보고 통계공학 관련 종합 Python projects가 필요함을 제안하였다.

• Genomics & Informatics
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• 제21권1호
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• pp.14.1-14.4
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• 2023

OncoPrint, the plot to visualize an overview of genetic variants in sequencing data, has been widely used in the field of cancer genomics. However, still, there have been no Python libraries capable to generate OncoPrint yet, a big hassle to plot OncoPrints within Python-based genetic variants analysis pipelines. This paper introduces a new Python package PyOncoPrint, which can be easily used to plot OncoPrints in Python. The package is based on the existing widely used scientific plotting library Matplotlib, the resulting plots are easy to be adjusted for various needs.

• 천문학회보
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• 제46권2호
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• pp.53.3-53.3
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• 2021

Adaptive Optics (AO) was first studied in the field of astronomy, and its applications have been extended to the field of laser, microscopy, bio, medical, and free space laser communication. AO modelling and simulation are required throughout the system development process. It is necessary not only for proper design but also for performance verification after the final system is built. In KASI, we are trying to develop the AO Python Package for AO modelling and simulation. It includes modelling classes of atmosphere, telescope, Shack-Hartmann wavefront sensor, deformable mirror, which are the components for an AO system. It also includes the ability to simulate the entire AO system over time. It is being developed in the Super Eye Bridge project to develop a segmented mirror, an adaptive optics, and an emersion grating spectrograph, which are future telescope technologies. And it is planned to be used as a performance analysis system for several telescope projects in Korea.

• 한국농림기상학회지
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• 제25권2호
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• pp.99-107
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• 2023

농업은 기상에 매우 민감한 산업으로, 따라서 농업분야의 기상을 이용한 연구는 더욱 중요해지고 있다. 연구자들은 기상청과 농촌진흥청에서 제공하는 기상정보서비스 웹사이트에 접속해 기상관측자료를 다운로드할 수 있다. 그러나 대량의 기상자료를 받아야 할 때는 여러 번의 조회작업이 필요한 단점이 있다. 본 데이터 논문은 기상청과 농촌진흥청에서 수집한 자료를 원격 저장소 서비스인 깃허브에 업로드하고 소프트웨어 프로그램인 파이썬을 이용해 기상자료에 쉽게 접근할 수 있는 패키지를 제작했다. 이를 통해 추가적인 인증 절차 없이 누구나 자료를 가져갈 수 있는 방식을 채택하여 농업 관계자들의 기상자료에 대한 접근성 및 활용성을 높이는 방법을 제안한다. 자료와 패키지는 분산 버전 관리 시스템인 깃에 업로드하여 수정 및 관리가 용이하게 하였다.

• 천문학회보
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• 제42권2호
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• pp.70.2-70.2
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• 2017

Transit event of exoplanet is a good example of observational studies with youth, because the event is geometrically simple and its analysis is essential to astronomical observation. Therefore, we developed the package of data reduction and aperture photometry in Python for educational purpose. In 27 July, we observed the transit event of TrES-3b with the students of "NYSC Space Science Club" program, and presented the Python package, PyPhotW for data reduction and aperture photometry. PyPhotW consists of simple functions for youth to understand the processes easier. Nonetheless, the photometric results of PyPhotW show a good agreement with those of Source Extractor, ${\Delta}m{\sim}-0.01{\pm}0.03$ and $-0.04{\pm}0.08$ for TrES-3b and TrES-5b time-series observations in 27 - 28 July.

• 한국전기전자재료학회논문지
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• 제35권6호
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• pp.556-561
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• 2022

밀도범함수이론(density functional theory, DFT)이 등장한 이래로, 이를 재료과학에 적용하여 에너지 재료 및 반도체와 같은 전자재료들의 연구개발에 활발하게 활용되고 있다. 하지만 DFT 계산 프로그램을 실행할 때 필요한 입력 파일 생성 시 여러 가지 소재들에 대해 동일한 계산 조건을 맞춰 주고 파라미터들을 알맞게 설정해 줘야 올바른 계산 결과 비교가 가능한데, 이런 부분들에 대해 진입 장벽이 높다는 어려움이 있다. 이에 본 논문에서는 Python Materials Genomics (pymatgen) 파이썬 패키지를 이용해 분자 및 결정구조를 다루고 널리 사용되는 DFT 계산 프로그램인 Vienna Ab initio Simulation Package (VASP) 및 Gaussian 입력 파일 생성에 대해 소개하고자 한다. 이를 통해 해당 프로그램에 대한 전문적인 지식이 많지 않더라도 보다 일관적인 계산 조건에서 결과들을 손쉽게 수행할 수 있게 되기를 기대한다.

• ETRI Journal
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• 제46권3호
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• pp.473-484
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• 2024

We introduce a machine learning-based web application to help travel agents plan a package tour schedule. K-nearest neighbor (KNN) classification predicts the optimal tourists' dwelling time based on a variety of information to automatically generate a convenient tour schedule. A database collected in collaboration with an established travel agency is fed into the KNN algorithm implemented in the Python language, and the predicted dwelling times are sent to the web application via a RESTful application programming interface provided by the Flask framework. The web application displays a page in which the agents can configure the initial data and predict the optimal dwelling time and automatically update the tour schedule. After conducting a performance evaluation by simulating a scenario on a computer running the Windows operating system, the average response time was 1.762 s, and the prediction consistency was 100% over 100 iterations.

• 한국인공지능학회지
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• 제6권2호
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• pp.11-15
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• 2018

Breast cancer is one of the leading causes of cancer related death among women. So prediction of overall survival status is important into decided in adjuvant treatment. Deep belief network is a kind of artificial intelligence (AI). We intended to construct prediction model by deep belief network using associated clinicopathologic factors. 103881 cases were found in the Korean Breast Cancer Registry. After preprocessing of data, a total of 15733 cases were enrolled in this study. The median follow-up period was 82.4 months. In univariate analysis for overall survival (OS), the patients with advanced AJCC stage showed relatively high HR (HR=1.216 95% CI: 0.011-289.331, p=0.001). Based on results of univariate and multivariate analysis, input variables for learning model included 17 variables associated with overall survival rate. output was presented in one of two states: event or cencored. Individual sensitivity of training set and test set for predicting overall survival status were 89.6% and 91.2% respectively. And specificity of that were 49.4% and 48.9% respectively. So the accuracy of our study for predicting overall survival status was 82.78%. Prediction model based on Deep belief network appears to be effective in predicting overall survival status and, in particular, is expected to be applicable to decide on adjuvant treatment after surgical treatment.

• 천문학회보
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• 제38권2호
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• pp.84.1-84.1
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• 2013

We present a python-based data reduction pipeline for the Immersion GRating INfrared Spectrograph (IGRINS). IGRINS covers the complete H- and K-bands in a single exposure with a spectral resolving power of greater than 40,000. IGRINS is designed to be compatible with telescopes of diameters ranging from 2.7-m (the Harlan J. Smith telescope at McDonald Observatory) to 8-10m. Commissioning and initial operation will be on the 2.7-m telescope from late 2013. The pipeline package is a part of the IGRINS software and designed to be compatible with other package that maneuvers the spectrograph during the observation. This package provides high-quality spectra with minimal human intervention and the processes of order extraction, distortion correction, and wavelength calibration can be automatically carried out using the predefined functions (e.g. echellogram mapping and 2D transform). Since the IGRINS is a prototype of the Giant Magellan Telescope Near-Infrared Spectrometer (GMTNIRS), this pipeline will be extended to the GMTNIRS software.

• 천문학회지
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• 제52권5호
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• pp.207-216
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• 2019

In this paper we introduce a software package, the Very long baseline interferometry Network SIMulator (VNSIM), which provides an integrated platform assisting radio astronomers to design Very Long Baseline Interferometry (VLBI) experiments and evaluate the network performance, with a user-friendly interface. Though VNSIM is primarily motivated by the East Asia VLBI Network, it can also be used for other VLBI networks and generic interferometers. The software package not only integrates the functionality of plotting (u, v) coverage, scheduling the observation, and displaying the dirty and CLEAN images, but also adds new features including sensitivity calculations for a given VLBI network. VNSIM provides flexible interactions on both command line and graphical user interface and offers friendly support for log reports and database management. Multi-processing acceleration is also supported, enabling users to handle large survey data. To facilitate future developments and updates, all simulation functions are encapsulated in separate Python modules, allowing independent invoking and testing. In order to verify the performance of VNSIM, we performed simulations and compared the results with other simulation tools, showing good agreement.