• Title/Summary/Keyword: stochastic approach

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Continuous Time Markov Process Model for Nuclide Decay Chain Transport in the Fractured Rock Medium (균열 암반 매질에서의 핵종의 붕괴사슬 이동을 위한 연속시간 마코프 프로세스 모델)

  • Lee, Y.M.;Kang, C.H.;Hahn, P.S.;Park, H.H.;Lee, K.J.
    • Nuclear Engineering and Technology
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    • v.25 no.4
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    • pp.539-547
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    • 1993
  • A stochastic approach using continuous time Markov process is presented to model the one-dimensional nuclide transport in fractured rock media as a further extension for previous works[1-3]. Nuclide transport of decay chain of arbitrary length in the single planar fractured rock media in the vicinity of the radioactive waste repository is modeled using a continuous time Markov process. While most of analytical solutions for nuclide transport of decay chain deal with the limited length of decay chain, do not consider the case of having rock matrix diffusion, and have very complicated solution form, the present model offers rather a simplified solution in the form of expectance and its variance resulted from a stochastic modeling. As another deterministic way, even numerical models of decay chain transport, in most cases, show very complicated procedure to get the solution and large discrepancy for the exact solution as opposed to the stochastic model developed in this study. To demonstrate the use of the present model and to verify the model by comparing with the deterministic model, a specific illustration was made for the transport of a chain of three member in single fractured rock medium with constant groundwater flow rate in the fracture, which ignores the rock matrix diffusion and shows good capability to model the fractured media around the repository.

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Predictive analysis of minimum inflow using synthetic inflow in reservoir management: a case study of Seomjingang Dam (자료 발생 기법을 활용한 저수지 최소유입량 예측 기법 개발 : 섬진강댐을 대상으로)

  • Lee, Chulhee;Lee, Seonmi;Lee, Eunkyung;Ji, Jungwon;Yoon, Jeongin;Yi, Jaeeung
    • Journal of Korea Water Resources Association
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    • v.57 no.5
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    • pp.311-320
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    • 2024
  • Climate change has been intensifying drought frequency and severity. Such prolonged droughts reduce reservoir levels, thereby exacerbating drought impacts. While previous studies have focused on optimizing reservoir operations using historical data to mitigate these impacts, their scope is limited to analyzing past events, highlighting the need for predictive methods for future droughts. This research introduces a novel approach for predicting minimum inflow at the Seomjingang dam which has experienced significant droughts. This study utilized the Stochastic Analysis Modeling and Simulation (SAMS) 2007 to generate inflow sequences for the same period of observed inflow. Then we simulate reservoir operations to assess firm yield and predict minimum inflow through synthetic inflow analysis. Minimum inflow is defined as the inflow where firm yield is less than 95% of the synthetic inflow in many sequences during periods matching observed inflow. The results for each case indicated the firm yield for the minimum inflow is on average 9.44 m3/s, approximately 1.07 m3/s lower than the observed inflow's firm yield of 10.51 m3/s. The minimum inflow estimation can inform reservoir operation standards, facilitate multi-reservoir system reviews, and assess supplementary capabilities. Estimating minimum inflow emerges as an effective strategy for enhancing water supply reliability and mitigating shortages.

Structural Safety Assessment of Independent Spherical LNG Tank(1st Report) - Fatigue Strength Analysis Based on the S-N Approach - (독립구형 LNG 탱크의 구조안전성 평가(제1보) - 피로균열 발생수명 예측 -)

  • In-Sik Nho;Yong-Yun Nam;Ho-Sup Lee
    • Journal of the Society of Naval Architects of Korea
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    • v.30 no.2
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    • pp.132-140
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    • 1993
  • The design of LNG ship needs very high level structural design/analysis technology compared with conventional ship types because it requires perfect security against the extremly dangerous and cryogenic cargo. Hence, present paper describes the general procedure of the structural safety assessment for independent tank type LNG ship, which contains following items. 1) Long term prediction of the wave induced stresses including ship motion analysis, structural analysis of hull and tank and stochastic analysis process of ocean waves. 2) Fatigue strength analysis of a tank structure based on the S-N approach. 3) Structural safety assessment against the fatigue crack propagation based on the LBF(Leak Before Failure) concept. The first report focuced on the item (1) (2) and example calculation was performed on a prototype LNG ship. The remained part will be covered by the second report.

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Damage index based seismic risk generalization for concrete gravity dams considering FFDI

  • Nahar, Tahmina T.;Rahman, Md M.;Kim, Dookie
    • Structural Engineering and Mechanics
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    • v.78 no.1
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    • pp.53-66
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    • 2021
  • The determination of the damage index to reveal the performance level of a structure can constitute the seismic risk generalization approach based on the parametric analysis. This study implemented this concept to one kind of civil engineering structure that is the concrete gravity dam. Different cases of the structure exhibit their individual responses, which constitute different considerations. Therefore, this approach allows the parametric study of concrete as well as soil for evaluating the seismic nature in the generalized case. To ensure that the target algorithm applicable to most of the concrete gravity dams, a very simple procedure has been considered. In order to develop a correlated algorithm (by response surface methodology; RSM) between the ground motion and the structural property, randomized sampling was adopted through a stochastic method called half-fractional central composite design. The responses in the case of fluid-foundation-dam interaction (FFDI) make it more reliable by introducing the foundation as being bounded by infinite elements. To evaluate the seismic generalization of FFDI models, incremental dynamic analysis (IDA) was carried out under the impacts of various earthquake records, which have been selected from the Pacific Earthquake Engineering Research Center data. Here, the displacement-based damage indexed fragility curves have been generated to show the variation in the seismic pattern of the dam. The responses to the sensitivity analysis of the various parameters presented here are the most effective controlling factors for the concrete gravity dam. Finally, to establish the accuracy of the proposed approach, reliable verification was adopted in this study.

Intervention analysis for spread of COVID-19 in South Korea using SIR model (SIR 모형을 이용한 한국의 코로나19 확산에 대한 개입 효과 분석)

  • Cho, Sumin;Kim, Jaejik
    • The Korean Journal of Applied Statistics
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    • v.34 no.3
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    • pp.477-489
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    • 2021
  • COVID-19 has spread seriously around the world in 2020 and it is still significantly affecting our whole daily life. Currently, the whole world is still undergoing the pandemic and South Korea is no exception to it. During the pandemic, South Korea had several events that prevented or accelerated its spread. To establish the prevention policies for infectious diseases, it is very important to evaluate the intervention effect of such events. The susceptible-infected-removed (SIR) model is often used to describe the dynamic behavior of the spread of infectious diseases through ordinary differential equations. However, the SIR model is a deterministic model without considering the uncertainty of observed data. To consider the uncertainty in the SIR model, the Bayesian approach can be employed, and this approach allows us to evaluate the intervention effects by time-varying functions of the infection rate in the SIR model. In this study, we describe the time trend of the spread of COVID-19 in South Korea and investigate the intervention effects for the events using the stochastic SIR model based on the Bayesian approach.

A NESTING APPROACH IN DISCRETE EVENT SIMULATION FOR INTEGRATING CONSTRUCTION OPERATION AND SCHEDULE MODELS

  • Chang-Yong Yi;Chan-Sik Park;Doo-Jin Lee;Dong-Eun Lee
    • International conference on construction engineering and project management
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    • 2009.05a
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    • pp.400-408
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    • 2009
  • Simulation applications for analyzing the productivity of construction operations at operation level and project schedules at project level are crucial methods in project management. The application at two different levels should be very tightly linked to each other in practice. However, appropriate integration at the levels is not achieved in that existing systems do not support to integrate operation models into a schedule model. This paper presents a new approach named to Discrete Event Simulation-Nesting modeling approach, which supports not only productivity analysis at operation level but also schedule management at a project level. The system developed by the authors allows creating operation models at the operation level, maintaining them in operation model library, executing sensitivity analysis to find the behaviors of the operation models when different combination of resources are used as existing DES systems do. On top of the conventional functions, the new system facilitates to find the optimum solution of resource combinations which satisfy the user's interest by computing the hourly productivity and the hourly cost of the operation. By drag-and-dropping an operation model kept in the operation model library, the operation models are integrated into an activity of the schedule model. When a complete schedule model is established by nesting operation models into the schedule model, stochastic simulation based scheduling is executed. A case study is presented to demonstrate the new simulation system and verify the validity of the system.

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Spatial Estimation of soil roughness and moisture from Sentinel-1 backscatter over Yanco sites: Artificial Neural Network, and Fractal

  • Lee, Ju Hyoung
    • Proceedings of the Korea Water Resources Association Conference
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    • 2020.06a
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    • pp.125-125
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    • 2020
  • European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

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TGC-based Fish Growth Estimation Model using Gaussian Process Regression Approach (가우시안 프로세스 회귀를 통한 열 성장 계수 기반의 어류 성장 예측 모델)

  • Juhyoung Sung;Sungyoon Cho;Da-Eun Jung;Jongwon Kim;Jeonghwan Park;Kiwon Kwon;Young Myoung Ko
    • Journal of Internet Computing and Services
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    • v.24 no.1
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    • pp.61-69
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    • 2023
  • Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

An Improved Reliability-Based Design Optimization using Moving Least Squares Approximation (이동최소자승근사법을 이용한 개선된 신뢰도 기반 최적설계)

  • Kang, Soo-Chang;Koh, Hyun-Moo
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.29 no.1A
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    • pp.45-52
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    • 2009
  • In conventional structural design, deterministic optimization which satisfies codified constraints is performed to ensure safety and maximize economical efficiency. However, uncertainties are inevitable due to the stochastic nature of structural materials and applied loads. Thus, deterministic optimization without considering these uncertainties could lead to unreliable design. Recently, there has been much research in reliability-based design optimization (RBDO) taking into consideration both the reliability and optimization. RBDO involves the evaluation of probabilistic constraint that can be estimated using the RIA (Reliability Index Approach) and the PMA(Performance Measure Approach). It is generally known that PMA is more stable and efficient than RIA. Despite the significant advancement in PMA, RBDO still requires large computation time for large-scale applications. In this paper, A new reliability-based design optimization (RBDO) method is presented to achieve the more stable and efficient algorithm. The idea of the new method is to integrate a response surface method (RSM) with PMA. For the approximation of a limit state equation, the moving least squares (MLS) method is used. Through a mathematical example and ten-bar truss problem, the proposed method shows better convergence and efficiency than other approaches.

Durability Prediction for Concrete Structures Exposed to Chloride Attack Using a Bayesian Approach (베이지안 기법을 이용한 염해 콘크리트구조물의 내구성 예측)

  • Jung, Hyun-Jun;Zi, Goang-Seup;Kong, Jung-Sik;Kang, Jin-Gu
    • Journal of the Korea Concrete Institute
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    • v.20 no.1
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    • pp.77-88
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    • 2008
  • This paper provides a new approach for predicting the corrosion resistivity of reinforced concrete structures exposed to chloride attack. In this method, the prediction can be updated successively by a Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model. To simplify the procedure of the model, the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored.