• International journal of advanced smart convergence
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• v.11 no.4
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.241-246
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• 2020

Hadoop is a framework to process large data sets in a distributed way across clusters of nodes. It has been a popular platform to process big data, but in recent years, other platforms became competitive ones depending on the characteristics of the application. Spark is one of distributed platforms to enable real-time data processing and improve overall processing performance over Hadoop by introducing in-memory processing instead of disk I/O. Whereas Hadoop is designed to work on Java and data analysis is processed using Java API, Spark provides a variety of APIs with Scala, Python, Java and R. In this paper, the goal is to find out whether the APIs of different programming languages af ect the performances in Spark. We chose two popular APIs: Python and Scala. Python is easy to learn and is used in AI domain in a wide range. Scala is a programming language with advantages of parallelism. Our experiment shows much faster processing with Scala API than Python API. For the performance issues on AI-based analysis, further study is needed.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.729-732
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• 2020

앞으로 4 차 산업혁명 시대에 많은 인공지능 관련 소프트웨어 및 데이터 기반 소프트웨어가 개발이 필수적이다. 문제는 이런 소프트웨어 관련 품질을 고려하지 않고 있다. 또한 많은 Python 관련 공개 소프트웨어에 대해 품질 보장이 불가능하다. 이를 위해, 코드 가시화 메커니즘, 인공지능 관련 코드 품질을 높이기 위해 AI 관련 Python 코드 복잡도 기반 고품질화 및 코드 가시화 메커니즘을 제안한다. 또한 기존의 복잡도를 측정하는 품질 메트릭스 중 하나인 McCabe's Cyclomatic 복잡도의 개선을 제안한다. 기존의 복잡도 공식에 응집도, 결합도를 가중치로 적용하여 개선된 복잡도를 계산한다. 소프트웨어의 내부 구조 및 관계와 복잡도 정보를 가시화하여 소프트웨어의 품질 향상에 기여한다.

• Genomics & Informatics
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• v.20 no.4
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• pp.49.1-49.7
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• 2022

Many packages for a meta-analysis of genome-wide association studies (GWAS) have been developed to discover genetic variants. Although variations across studies must be considered, there are not many currently-accessible packages that estimate between-study heterogeneity. Thus, we propose a python based application called Beta-Meta which can easily process a meta-analysis by automatically selecting between a fixed effects and a random effects model based on heterogeneity. Beta-Meta implements flexible input data manipulation to allow multiple meta-analyses of different genotype-phenotype associations in a single process. It provides a step-by-step meta-analysis of GWAS for each association in the following order: heterogeneity test, two different calculations of an effect size and a p-value based on heterogeneity, and the Benjamini-Hochberg p-value adjustment. These methods enable users to validate the results of individual studies with greater statistical power and better estimation precision. We elaborate on these and illustrate them with examples from several studies of infertility-related disorders.

• Journal of Advanced Navigation Technology
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• v.24 no.4
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• pp.285-290
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• 2020

This paper presents a time synchronization algorithm based on PLL for application to telemetry systems and implement FPGA logic. The large aircraft of the telemetry system acquires status information through each distributed acquisition devices and analyzes the flight status in real time. For this reason, time synchronization between systems is important to improve precision. This paper presents a PLL based time synchronization algorithm that is less complex than other time synchronization methods and takes less time to process data because there is minimized message transmission for synchronization. The validity of proposed algorithm is proved by simulation of Python. And the VHDL logic was implemented in FPGA to check the time synchronization performance.

• Parasites, Hosts and Diseases
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• v.54 no.4
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• pp.423-429
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• 2016

Cryptosporidium is an important pathogen causing gastrointestinal disease in snakes and is distributed worldwide. The main objectives of this study were to detect and identify Cryptosporidium species in captive snakes from exotic pet shops and snake farms in Thailand. In total, 165 fecal samples were examined from 8 snake species, boa constrictor (Boa constrictor constrictor), corn snake (Elaphe guttata), ball python (Python regius), milk snake (Lampropeltis triangulum), king snake (Lampropeltis getula), rock python (Python sebae), rainbow boa (Epicrates cenchria), and carpet python (Morelia spilota). Cryptosporidium oocysts were examined using the dimethyl sulfoxide (DMSO)-modified acid-fast staining and a molecular method based on nested-PCR, PCR-RFLP analysis, and sequencing amplification of the SSU rRNA gene. DMSO-modified acid-fast staining revealed the presence of Cryptosporidium oocysts in 12 out of 165 (7.3%) samples, whereas PCR produced positive results in 40 (24.2%) samples. Molecular characterization indicated the presence of Cryptosporidium parvum (mouse genotype) as the most common species in 24 samples (60%) from 5 species of snake followed by Cryptosporidium serpentis in 9 samples (22.5%) from 2 species of snake and Cryptosporidium muris in 3 samples (7.5%) from P. regius.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.130-145
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• 2016

Soil erosion processes are affected by weather factors, such as rainfall, temperature, wind, and humidity. Among these factors, rainfall directly influences soil erosion by breaking away soil particles. The kinetic energy of rainfall and water flow caused by rain entrains and transports soil particles downstream. Therefore, in order to estimate soil erosion, it is important to accurately determine the rainfall erosivity factor(R) in RUSLE(Revised Universal Soil Loss Equation). The objective of this study is to evaluate the average annual R using 14 years(2002~2015) of 1 minute rainfall data from 55 KMA(Korea Meteorological Administration) weather stations. The R results from 1 min rainfall were compared with previous R studies using 1 h rainfall data. The determination coefficients($R^2$) between R calculated using 1 min rainfall data and annual rainfall were 0.70-0.98. The estimation of 30 min rainfall intensity from 1 min rainfall data showed better $R^2$ results than results from 1 h rainfall data. For estimation of physical spatial rain erosivity(R), distribution of annual rainfall was estimated by IDW(Inverse Distance Weights) interpolation, taking elevation into consideration. Because of the computation burden, the R calculation process was programmed using the python GUI(Graphical User Interface) tool.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.318-328
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• 2022

The purpose of this study is to analyze the impact of ownership concentration and R&D investment on earnings growth of listed companies in China. For this purpose, this study utilized 14,196 samples from 2,366 Chinese listed companies using the WIND database and conducted empirical analysis by Python. The results of the analysis are as follows. First, the data shows that ownership concentration has a positive (+) impact on revenue growth of Chinese listed firms. Second, ownership concentration has a postive(+) impact on R&D investment of Chinese listed firms. Third, the survey shows that R&D investment has a positive (+) impact on revenue growth of Chinese listed firms. Fourth, the impact of R&D investment on earnings growth of Chinese listed firms has time lag effect. Fifth, R&D investment has a partial mediating effect in ownership concentration and earnings growth of Chinese listed firms. Based on these analytical results, this study proposes measures to promote firms' earnings increase by optimizing ownership concentration and increasing R&D investment in Chinese listed firms.

• Journal of Radiation Protection and Research
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• v.48 no.2
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• pp.90-99
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• 2023

Background: Quantitative risk assessments should be accompanied by uncertainty analyses of the risk models employed in the calculations. In this study, we aim to develop a computational code named SUMRAY for use in cancer risk projections from radiation exposure taking into account uncertainties. We also aim to make SUMRAY publicly available as a resource for further improvement of risk projection. Materials and Methods: SUMRAY has two versions of code written in R and Python. The risk models used in SUMRAY for all-solid-cancer mortality and incidence were those published in the Life Span Study of a cohort of the atomic bomb survivors in Hiroshima and Nagasaki. The confidence intervals associated with the evaluated risks were derived by propagating the statistical uncertainties in the risk model parameter estimates by the Monte Carlo method. Results and Discussion: SUMRAY was used to calculate the lifetime or time-integrated attributable risks of cancer under an exposure scenario (baseline rates, dose[s], age[s] at exposure, age at the end of follow-up, sex) specified by the user. The results were compared with those calculated using another well-known web-based tool, Radiation Risk Assessment Tool (RadRAT; National Institutes of Health), and showed a reasonable agreement within the estimated confidential interval. Compared with RadRAT, SUMRAY can be used for a wide range of applications, as it allows the risk projection with arbitrarily specified risk models and/or population reference data. Conclusion: The reliabilities of SUMRAY with the present risk-model parameters and their variance-covariance matrices were verified by comparing them with those of the other codes. The SUMRAY code is distributed to the public as an open-source code under the Massachusetts Institute of Technology license.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.113-113
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• 2018

본 연구는 다목적 입자군집최적화(Particle Swarm Optimization, PSO) 알고리즘을 SWAT(Soil and Water Assessment Tool) 모형에 적용하여 자동보정 알고리즘의 적용 가능성을 평가하고자 한다. PSO 알고리즘은 Python을 활용해 다목적 함수를 고려할 수 있도록 새롭게 개발되었다. SWAT 모형의 유출 해석은 안성천의 공도 수위 관측소 상류유역($366.5km^2$)을 대상으로 하였으며, 공도 지점의 2000년부터 2017년까지의 일 유량 자료를 이용하여 검보정하였다. 모형을 위한 기상자료는 공도유역 주변 3개 기상관측소(수원, 천안, 이천)의 일별 강수량, 최고 및 최저기온, 평균 풍속, 상대습도 및 일사량을 구축하였다. SWAT 모형의 유출 해석은 결정계수(Coefficient of determination, $R^2$), RMSE(Root mean square error), Nash-Sutcliffe 모형효율계수(NSE) 및 IOA(index of agreement) 등을 활용하여, 기존 연구 결과와 PSO 알고리즘을 활용한 결과를 비교 분석하고자 한다. 본 연구에서 개발한 다목적 PSO 알고리즘을 활용한 SWAT모형의 유출 해석은 보다 높은 정확도를 얻을 수 있을 것으로 예상되며, Python으로 개발되어 SWAT모형 이외에도 널리 적용될 수 있을 것으로 판단된다.