http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
역-레일리와 레일리 분포 특성을 이용한 유한고장 NHPP모형에 근거한 소프트웨어 신뢰성장 모형에 관한 비교연구
신현철,김희철 (사)디지털산업정보학회 2014 디지털산업정보학회논문지 Vol.10 No.3
The inverse Rayleigh model distribution and Rayleigh distribution model were widelyused in the field of reliability station. In this paper applied using the finite failure NHPPmodels in order to growth model. In other words, a large change in the course of thesoftware is modified, and the occurrence of defects is almost inevitable reality. Finite failure NHPP software reliability models can have, in the literature, exhibit eitherconstant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. Inthis paper, proposes the inverse Rayleigh and Rayleigh software reliability growth model,which made out efficiency application for software reliability. Algorithm to estimate theparameters used to maximum likelihood estimator and bisection method, model selectionbased on mean square error (MSE) and coefficient of determination(R²), for the sake ofefficient model, were employed. In order to insurance for the reliability of data, Laplacetrend test was employed. In many aspects, Rayleigh distribution model is more efficient than the reverse-Rayleighdistribution model was proved. From this paper, software developers have to consider thegrowth model by prior knowledge of the software to identify failure modes which can helped.
신현철,하구선,김성준,조영제,전영중,이재흥 한국산업미생물학회 1992 한국미생물·생명공학회지 Vol.20 No.6
Corynebacterium glutamicum CH 1516(L-leucine 생산주)를 사용하여 7ℓ 발효조에서 배양온도 및 pH, 산소전달속도 등을 최적화한 결과 각각 30℃ , 7.0, 0.21 kmole O_2/㎥·hr이었다. 산소전달속도가 0.19 kmole O_2/㎥·hr보다 낮은 조건에서는 상당량의 lactic acid가 축적되었고 0.23 kmole O_2/㎥·hr보다 높은조건에서는 glutamic acid가 생성되고 PCV가 크게 증가하였다. 한편 1200ℓ 실험공장에의 L-leucine 생산을 위해 7ℓ 발효조에서 최적화된 배양조건을 적용하여 산소전달속도를 지표로 공정확대를 실시한 결과 7ℓ 발효조와 거의 대등한 결과를 얻을 수 있었으며 산화환원전위 -150∼-170mV에서 L-leucine이 왕성하게 생산되었다. The effects of pH, temperature and oxygen transfer rate(OTR) on L-leucine fermentation were investigated employing Corynebacterium glutamicum CH 1516 in 7ℓ fermentor. The optimum pH, temperature and OTR were determined to 7.0, 30℃ and 0.21 kmole O_2/㎥·hr, respectively. For the values of OTR lower than 0.19 kmole O_2/㎥·hr a significant amount of lactic acid was accumulated, while the packed cell volume(PCV) was rapidly increased at higher OTR values above 0.23 kmole O_2/㎥·hr and glutamic acid was produced to some extent. Scale-up studies for L-leucine fermentation which was carried out in 1200ℓ pilot scale fermentor reaffirmed the results of 7ℓ fermentation. The optimum redox potential value for L-leucine production was found to be -150 to -170 mV.
곰페르츠형 형상모수에 근거한 소프트웨어 신뢰성모형에 대한 비교연구
신현철,김희철 (사)디지털산업정보학회 2014 디지털산업정보학회논문지 Vol.10 No.2
Finite failure NHPP software reliability models presented in the literature exhibit eitherconstant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. Inthis paper, proposes the Gompertz distribution reliability model, which made out efficiencyapplication for software reliability. Algorithm to estimate the parameters used to maximumlikelihood estimator and bisection method, model selection based on mean square error(MSE) and coefficient of determination(R²), for the sake of efficient model, was employed. Analysis of failure using real data set for the sake of proposing fixed shape parameter of theGompertz distribution was employed. This analysis of failure data compared with theGompertz distribution model of shape parameter. In order to insurance for the reliability ofdata, Laplace trend test was employed. In this study, the proposed Gompertz model is more efficient in terms of reliability in thisarea. Thus, Gompertz model can also be used as an alternative model. From this paper,software developers have to consider the growth model by prior knowledge of the softwareto identify failure modes which can was helped
신현철 건강보험심사평가원 심사평가연구소 2021 연구보고서 Vol.2021 No.0
Healthcare data forecasting is used in a variety of fields, including policy evaluation, health insurance financial estimation, and the detection of unusual claims symptoms. Furthermore, the evidence generated by the predictive model is critical in the development of health policies and decision-making processes. As a result, the prediction process must be scientific and systematic, and accuracy measures should be a key selection criterion for forecasting methods. By examining the data prediction methods used in the health care field as well as the most recent forecasting techniques, we attempted to suggest a forecasting method suitable for the characteristics of healthcare data in this study. To that end, a literature review, investigations on the most recent forecasting methodologies, and a comparative analysis of forecasting performance based on data type were performed. And the results of the analysis were synthesized to suggest a forecasting method appropriate for the type of healthcare data. The subject of forecasting method review included regression models, time series models, and machine learning. The data types were separated into continuous variables and count variables, and data sets of 12, 24, and 30 sizes were created. Health insurance claim data was used to compare forecasting performance, and SAS and Python were used as analysis tools. According to the findings, the machine learning forecasting method performed best for both continuous and count data types, while the ARIMA, time series analysis method performed reasonably well for continuous variables. Forecasting performance improved as the number of data points increased. In this study, we recommend a sample size of at least 30 subjects. This research is anticipated to help in the selection of an appropriate forecasting method for performing complex prediction tasks.