확률변수의 잔차를 이용한 Mg-Al-Zn 합금의 시편두께 조건에 따른 확률론적 피로균열전파모델 연구

최선순(Seon Soon CHOI) 대한기계학회 2012 大韓機械學會論文集A Vol.36 No.4

본 논문의 주 목적은 확률변수의 잔차를 이용하여 제안된 확률론적 피로균열전파모델들을 평가하고 Mg-Al-Zn 합금의 확률론적 피로거동을 묘사하기에 적합한 모델을 제시하는 것이다. 제안된 모델은 ‘확률론적 Paris-Erdogan 모델’, ‘확률론적 Walker 모델’, ‘확률론적 Forman 모델’과 ‘확률론적 수정 Forman 모델’이다. 이 모델들은 실험적 피로균열전파모델인 Paris-Erdogan 모델, Walker 모델, Forman 모델과 수정 Forman 모델에 확률변수를 도입하여 준비하였다. Mg-Al-Zn 합금의 피로균열전파거동을 묘사하기에 적합한 모델은 일반적으로 ‘확률론적 Paris-Erdogan 모델’과 ‘확률론적 Walker 모델’임을 밝혔으며, 시편두께 9.45㎜ 에서는 ‘확률론적 Forman 모델’이 적합하였다. The primary aim of this paper was to evaluate several probabilistic fatigue crack propagation models using the residual of a random variable, and to present the model fit for probabilistic fatigue behavior in Mg-Al-Zn alloys. The proposed probabilistic models are the probabilistic Paris-Erdogan model, probabilistic Walker model, probabilistic Forman model, and probabilistic modified Forman models. These models were prepared by applying a random variable to the empirical fatigue crack propagation models with these names. The best models for describing fatigue crack propagation behavior in Mg-Al-Zn alloys were generally the probabilistic Paris-Erdogan and probabilistic Walker models. The probabilistic Forman model was a good model only for a specimen with a thickness of 9.45 ㎜.

Mg-Al-Zn 합금의 최대하중 조건에 따른 확률론적 피로균열전파모델 연구(Ⅰ) : 확률변수의 잔차를 이용

최선순(Seon Soon CHOI) 한국마린엔지니어링학회 2010 한국마린엔지니어링학회 학술대회 논문집 Vol.2010 No.10

The aim of this study is to estimate the probabilistic fatigue crack propagation models using the residual of a random variable and to present the model fit for a probabilistic fatigue behavior in Mg-Al-Zn alloy. The probabilistic models proposed are probabilistic Paris-Erdogan model, probabilistic Walker model, probabilistic Forman model, and probabilistic modified Forman model. These models are prepared by applying a random variable to empirical fatigue crack propagation models such as Paris-Erdogan model, Walker model, Forman model, and modified Forman model.

Mg-Al-Zn 합금의 최대하중 조건에 따른 확률론적 피로균열전파모델 연구 (Ⅱ) : 확률변수의 백분위수률 이용

최선순(Seon Soon CHOI) 한국마린엔지니어링학회 2010 한국마린엔지니어링학회 학술대회 논문집 Vol.2010 No.10

The purpose of this study is to estimate the probabilistic fatigue crack propagation models using the percentile of a random variable and to present the model fit for a probabilistic fatigue behavior in Mg-Al-Zn alloy. The probabilistic models proposed are probabilistic Paris-Erdogan model, probabilistic Walker model, probabilistic Forman model, and probabilistic modified Forman model. These models are prepared by applying a random variable to empirical fatigue crack propagation models such as Paris-Erdogan model, Walker model, Forman model, and modified Forman model.

Mg-Al-Zn 합금의 시편두께 조건에 따른 확률론적 피로균열전파모델 연구(Ⅰ)

최선순(Seon Soon CHOI) 대한기계학회 2011 대한기계학회 춘추학술대회 Vol.2011 No.4

본 논문의 주 목적은 확률변수의 잔차를 이용하여 제안된 확률론적 피로균열전파모델들을 평가하고 Mg-Al-Zn 합금의 확률론적 피로거동을 묘사하기에 적합한 모델을 제시하는 것이다. 제안된 모델은 확률론적 Paris-Erdogan 모델, 확률론적 Walket 모델, 확률론적 Forman 모델과 확률론적 수정 Forman 모델이다. 이 모델들은 실험적 피로균열전파모델인 Paris-Erdogan 모델, Walker 모델 Forman 모델과 수정 Forman 모델에 확률변수를 도입하였다. In this paper the primary aim is to estimate the probabilistic fatigue crack propagation models using the residual of a random variable and to present the model fit for a probabilistic fatigue behavior in Mg-Al-Zn alloy. The probabilistic models proposed are probabilistic Paris-Erdogan model, probabilistic Walker model, probabilistic Forman model, and probabilistic modified Forman model. These models are prepared by applying a random variable to empirical fatigue crack propagation models such as Paris-Erdogan model, Walker model, Forman model, and modified Forman model.

확률론적 모델 체킹 기법을 이용한 복수 무인기 시스템 모델링 및 검증

정성식,박현진,안재명 한국항공우주학회 2013 한국항공우주학회 학술발표회 논문집 Vol.2013 No.11

확률론적 모델 체킹 기법은 확률 시스템의 모델링 및 분석을 위한 형식 검증 기술이다. 본 논문에서는 복수 무인기 시스템의 수송, 정찰 미션 모델링 및 검증에 확률론적 모델 체킹 기법을 적용하였다. 확률 모델 체킹 툴인 PRISM을 이용하여 복수무인기 시스템의 모델을 고안하였고, 시스템의 확률적 분석을 수행하였다. Probabilistic model checking is a formal verification technique for the modelling and analysis of stochastic systems. In this paper, we apply probabilistic model checking methods to the modeling and verification of a supply transport and surveillance mission using a multi-UAV system. PRISM, a system modeling and probabilistic model checking tool, was used to devise a model for the multi-UAV system and analyze the model"s random or stochastic behavior.

확률적 머신러닝 모델기반의 리튬이온배터리 파라미터 추정 알고리즘

김민호(Minho Kim),송민석(Minseok Song),임정택(Jeongtaek Lim),함경선(Kyung Sun Ham),이도헌(DOHEON LEE),김태형(Taehyoung Kim) 한국에너지학회 2024 에너지공학 Vol.33 No.1

In this study, a new lithium-ion battery performance degradation model and a stochastic machine learning model-based lithium-ion battery parameter estimation method were proposed and verified through actual battery degradation cycle experiment data. The proposed parameter estimation method based on a stochastic machine learning model requires less battery model operation time compared to other methods, enabling efficient parameter estimation. The lithium-ion battery performance degradation model is an equivalent circuit-based model, but it reflects various electrochemical phenomena, including side reactions on the surface of the anode active material, including the formation of a solid electrolyte interphase (SEI) layer, the loss of positive electrode active material due to mechanical stress-induced fatigue failure is included, and the corresponding decrease in the amount of cyclable lithium. In the proposed method of estimating the parameters of a lithium-ion battery model, a probabilistic machine learning model that can estimate battery model parameters from sensible data such as voltage and current is developed and used to generate virtual experiment data. We proposed a technique for learning and finding optimal battery model parameters based on the learned model. The developed performance degradation model and parameter estimation method were verified based on actual experimental data. Since it is impossible to observe the inside of the battery, correct answers to the battery parameters cannot be obtained, so the model and parameter estimation algorithm are indirectly verified through errors of voltage and temperature. As a result of the verification, the errors in voltage and temperature were found to be 0.676% and 0.207%, respectively.

원전 사고 시 RASCAL과 HYSPLIT 전산코드를 이용한 대기확산 모델별 방사성물질의 지표면 침적농도 분석

이진오,권재,류강우,장정환,김광표 (사)한국방사선산업학회 2019 방사선산업학회지 Vol.13 No.2

Introduction of the Lagrangian atmospheric dispersion model was considered insteadof the existing Gaussian atmospheric dispersion models for the level 3 probabilistic safety assessmentto reduce uncertainty in the atmospheric dispersion. The objective of the present study was toanalyze atmospheric dispersion behavior, surface deposition, and atmospheric dispersion models. The study was performed using RASCAL computer code, which employs Gaussian plume modeland Gaussian puff model and HYSPLIT computer code, which employs Lagrangian model. Forthe analysis, long-term station blackout accident was assumed. Released radionuclides includedparticle type 137Cs and gaseous type 131I, which are dispersed widely and extensively for nuclearaccident case. Radionuclide emission rate by time resulting from State-of-the-Art Reactor ConsequenceAnalyses project was used as a source term. The atmospheric dispersion behavior and surfacedeposition was analyzed for two case dates, which include a date with average wind speed ofmore than 3 m·s-1 and a date with wind speed of less than 3 m·s-1. For both 137Cs and 131I, atmosphericdispersion directions were roughly similar among three models. However, deposition concentrationof 131I compared with 137Cs was higher by about 100 folds for the Gaussian models andby about 1,000 folds for the Lagrangian model. For 137Cs, deposition concentrations were higherfor Gaussian plume model, Gaussian puff model, and Lagrangian model, in that sequence. For131I, deposition concentrations were generally higher for the Lagrangian model. The difference inthe deposition concentrations can be attributed to differences in the atmospheric concentration byatmospheric dispersion model and dry deposition velocity of released radionuclides. Therefore, it isrequired to study the physico-chemical characteristics of the released radionuclides for more realisticprediction of surface deposition concentration in applying the Lagrangian model instead of theexisting Gaussian plume models in the level 3 PSA. This study results is expected to contribute asan important base data for more realistic prediction of atmospheric

최대하중조건에 따른 Mg-Al-Zn 합금의 확률변수 잔차를 이용한 확률론적 피로균열전파모델 평가

최선순(Seon Soon Choi) 대한기계학회 2015 大韓機械學會論文集A Vol.39 No.1

본 논문의 주 목적은 최대하중조건을 변화시키면서 확률변수의 잔차를 이용하여 확률론적 피로균열전파모델들을 평가하고, Mg-Al-Zn 합금의 피로균열성장거동의 변동성을 묘사하기에 적합한 확률론적 모델을 제시하는 것이다. 평가에 사용된 모델은 피로균열성장의 변동성을 나타내기 위하여 실험적 피로균열전파모델인 Paris-Erdogan 모델, Walker 모델, Forman 모델과 수정 Forman 모델에 확률변수를 도입한 모델이다. 최대하중조건에 따른 Mg-Al-Zn 합금의 피로균열전파거동의 확률적 변동성을 묘사하기에 적합한 모델은 ‘확률론적 Paris-Erdogan 모델’과 ‘확률론적 Walker 모델’임을 밝혔으며, 최대하중조건이 피로균열성장의 확률적 변동성에 미치는 영향 또한 고찰하였다. The primary aim of this paper is to evaluate the probabilistic fatigue crack propagation models using the residual of a random variable and to present the probabilistic model fit for the probabilistic fatigue crack growth behavior in Mg-Al-Zn alloys under maximum load conditions. The models used in this study were prepared by applying a random variable to empirical fatigue crack propagation models such as the Paris-Erdogan model, Walker model, Forman model, and modified Forman model. It was verified that the good models for describing the stochastic variation of the fatigue crack propagation behavior in Mg-Al-Zn alloys under maximum load conditions were the ‘probabilistic Paris-Erdogan model’ and ‘probabilistic Walker model’. The influence of the maximum load conditions on the stochastic variation of fatigue crack growth is also considered.

확률모델을 이용한 산림전용지역의 스크리닝방법 개발

이정수 ( Jung Soo Lee ) 한국지리정보학회 2008 한국지리정보학회지 Vol.11 No.2

본 연구에서는 행정정보, GIS, RS정보, 확률모델을 이용하여 교토의정서에서 정의하는 산림전용지역의 추출가능성에 대하여 검토하였다. 1989년의 정사사진과 2001년의 IKONOS화상을 이용한 산림전용지역의 특성을 보면, 1989년부터 2001년까지의 산림전용지역은 약 40ha로 나타났다. 산림전용지역의 종류를 살펴보면, 도로(임도) 개설 및 주택지 개발을 위한 산림전용이 대부분을 차지하였고, 택지전용지의 80%는 기존의 도로로부터 100m이내에 분포하였으며, 신설된 도로 또한 20% 이상이 기존의 도로로부터 100m이내에 분포하였다. 산림전용지역의 추출모델 구축을 위하여 지형인자와 위성영상인자를 이용하였으며, 확률 개념을 도입한 산림전용지 발생 확률 지도를 작성하였다. 구축한 산지전용지 발생 모델의 유효성을 검증하기 위하여, 대상지역을 시스템적으로 구분하여, 추출 정도를 비교·검토하였다. 베이즈 모델과 Regression모델을 비교한 결과, 베이즈모델이 Regression모델보다 높은 추출확률을 나타냈다. 모델의 적합성을 평가하기위해서 대상지역을 2지역으로 구분하여 한쪽의 정보만을 가지고 발생확률지도를 작성하고, 나머지 지역에 대하여 발생확률을 검토한 결과에서도 베이즈모델이 높은 추출확률을 나타냈다. This paper discusses the prediction of deforestation areas using probability models from forest census database, Geographic information system (GIS) database and the land cover database. The land cover data was analyzed using remotely-sensed (RS) data of the Landsat TM data from 1989 to 2001. Over the analysis period of 12 years, the deforestation area was about 40ha. Most of the deforestation areas were attributable to road construction and residential development activities. About 80% of the deforestation areas for residential development were found within 100m of the road network. More than 20% of the deforestation areas for forest road construction were within 100m of the road network. Geographic factors and vegetation change detection (VCD) factors were used in probability models to construct deforestation occurrence map. We examined the size effect of area partition as training area and validation area for the probability models. The Bayes model provided a better deforestation prediction rate than that of the regression model.

Probabilistic Prediction Model for the Chloride Diffusion Coefficient of Concrete under Tensile and Compressive Stresses

Ruiqi Guo,Zengwei Guo,Yueyi Shi 대한토목학회 2022 KSCE Journal of Civil Engineering Vol.26 No.2

To accurately describe the uncertainty of chloride diffusion coefficient of concrete under tensile and compressive stresses, an iterative algorithm is proposed to reduce the influence of the uncertainty of prior variance on the posterior results. Subsequently, a probabilistic prediction model is developed to take into account the stress effect on chloride diffusion coefficient by using a Bayesian algorithm and Markov Chain Monte Carlo (MCMC) method. The existing deterministic model is adopted as prior model for this probabilistic model, and 180 sets of chloride diffusion coefficient data under different stresses obtained from 23 published journal papers are used as posterior information for this probabilistic model. The accuracy of the proposed model is validated by comparing with the experimental samples and other existing models. Analysis results show that the proposed probabilistic prediction model provided a reasonable confidence interval of the correction coefficient of stress effect on chloride diffusion coefficient. The provided iterative algorithm reduce the length of confidence interval with the exact prediction precision.