• Title/Summary/Keyword: Model uncertainty

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Optimal iterative learning control with model uncertainty

  • Le, Dang Khanh;Nam, Taek-Kun
    • Journal of Advanced Marine Engineering and Technology
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    • v.37 no.7
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    • pp.743-751
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    • 2013
  • In this paper, an approach to deal with model uncertainty using norm-optimal iterative learning control (ILC) is mentioned. Model uncertainty generally degrades the convergence and performance of conventional learning algorithms. To deal with model uncertainty, a worst-case norm-optimal ILC is introduced. The problem is then reformulated as a convex minimization problem, which can be solved efficiently to generate the control signal. The paper also investigates the relationship between the proposed approach and conventional norm-optimal ILC; where it is found that the suggested design method is equivalent to conventional norm-optimal ILC with trial-varying parameters. Finally, simulation results of the presented technique are given.

Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

  • Ohn, Ilsang;Seo, Seung Beom;Kim, Seonghyeon;Kim, Young-Oh;Kim, Yongdai
    • Communications for Statistical Applications and Methods
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    • v.27 no.1
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    • pp.109-128
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    • 2020
  • A climate-impact projection usually consists of several stages, and the uncertainty of the projection is known to be quite large. It is necessary to assess how much each stage contributed to the uncertainty. We call an uncertainty quantification method in which relative contribution of each stage can be evaluated as uncertainty decomposition. We propose a new Bayesian model for uncertainty decomposition in climate change impact assessments. The proposed Bayesian model can incorporate uncertainty of natural variability and utilize data in control period. We provide a simple and efficient Gibbs sampling algorithm using the auxiliary variable technique. We compare the proposed method with other existing uncertainty decomposition methods by analyzing streamflow data for Yongdam Dam basin located at Geum River in South Korea.

Application of Monte Carlo simulations to uncertainty assessment of ship powering prediction by the 1978 ITTC method

  • Seo, Jeonghwa;Park, Jongyeol;Go, Seok Cheon;Rhee, Shin Hyung;Yoo, Jaehoon
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.13 no.1
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    • pp.292-305
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    • 2021
  • The present study concerns uncertainty assessment of powering prediction from towing tank model tests, suggested by the International Towing Tank Conference (ITTC). The systematic uncertainty of towing tank tests was estimated by allowance of test setup and measurement accuracy of ITTC. The random uncertainty was varied from 0 to 8% of the measurement. Randomly generated inputs of test conditions and measurement data sets under systematic and random uncertainty are used to statistically analyze resistance and propulsive performance parameters at the full scale. The error propagation through an extrapolation procedure is investigated in terms of the sensitivity and coefficient of determination. By the uncertainty assessment, it is found that the uncertainty of resultant powering prediction was smaller than the test uncertainty.

A New Form of Nondestructive Strength-Estimating Statistical Models Accounting for Uncertainty of Model and Aging Effect of Concrete

  • Hong, Kee-Jeung;Kim, Jee-Sang
    • Journal of the Korean Society for Nondestructive Testing
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    • v.29 no.3
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    • pp.230-234
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    • 2009
  • As concrete ages, the surrounding environment is expected to have growing influences on the concrete. As all the impacts of the environment cannot be considered in the strength-estimating model of a nondestructive concrete test, the increase in concrete age leads to growing uncertainty in the strength-estimating model. Therefore, the variation of the model error increases. It is necessary to include those impacts in the probability model of concrete strength attained from the nondestructive tests so as to build a more accurate reliability model for structural performance evaluation. This paper reviews and categorizes the existing strength-estimating statistical models of nondestructive concrete test, and suggests a new form of the strength-estimating statistical models to properly reflect the model uncertainty due to aging of the concrete. This new form of the statistical models will lay foundation for more accurate structural performance evaluation.

Analysis on the Dependence Structure between Energy Price and Economic Uncertainty Using Copula Model (Copula 모형을 이용한 에너지 가격과 경제적 불확실성 사이의 의존관계 분석)

  • Kim, Bu-Kwon;Choi, Ki-Hong;Yoon, Seong-Min
    • Environmental and Resource Economics Review
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    • v.29 no.2
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    • pp.145-170
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    • 2020
  • This study analyzes the dependence structure between energy (crude oil, natural gas, coal) prices and economic (real and financial) uncertainty. Summary of the results of the dependence structure between energy prices and economic uncertainty analysis is as follows. First, the results of model selection show that the BB7 copula model for the pair of crude oil price and economic uncertainty, the Joe copula model for the pair of natural gas price and economic uncertainty, and the Clayton copula model for the pair of coal price and economic uncertainty were chosen. Second, looking at the dependency structure, it showed that the pair of energy (crude oil, natural gas, coal) prices and real market uncertainty show positive dependence. Whereas, the only pair of financial market uncertainty-crude oil price shows positive dependency. In particular, crude oil price was found to have the greatest dependence on economic uncertainty. Third, looking at the results of tail dependency, the pair of real market uncertainty-crude oil price and pair of real market uncertainty-natural gas price have an asymmetric relationship with the upper tail dependency. It can be seen that the only pair of financial market uncertainty-crude oil represents asymmetric relationships with the upper tail dependencies. In other words, combinations with asymmetric relationships have shown strong dependence when negative extreme events occur. On the other hand, tail dependence between economic uncertainty and coal price be not found.

A Predictive Two-Group Multinormal Classification Rule Accounting for Model Uncertainty

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • v.26 no.4
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    • pp.477-491
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    • 1997
  • A new predictive classification rule for assigning future cases into one of two multivariate normal population (with unknown normal mixture model) is considered. The development involves calculation of posterior probability of each possible normal-mixture model via a default Bayesian test criterion, called intrinsic Bayes factor, and suggests predictive distribution for future cases to be classified that accounts for model uncertainty by weighting the effect of each model by its posterior probabiliy. In this paper, our interest is focused on constructing the classification rule that takes care of uncertainty about the types of covariance matrices (homogeneity/heterogeneity) involved in the model. For the constructed rule, a Monte Carlo simulation study demonstrates routine application and notes benefits over traditional predictive calssification rule by Geisser (1982).

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A Study on Robust Identification Based on the Validation Evaluation of Model (모델의 타당성 평가에 기초한 로바스트 동정에 관한 연구)

  • Lee, Dong-Cheol;Chung, Hyung-Hwan;Bae, Jong-Il
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.2690-2692
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    • 2000
  • In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identifcation. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identifcation are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identify the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

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Uncertainty Quantification Index of SWMM Model Parameters (SWMM 모형 매개변수의 불확실성 정량화 지수 산정)

  • Chung, Gunhui;Sim, Kyu Bum;Kim, Eung Seok
    • Journal of Korea Water Resources Association
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    • v.48 no.2
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    • pp.105-114
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    • 2015
  • In the case of rapidly developed urban and industrial complex, the most area becomes impervious, which causes the increasing runoff and high probability of flooding. SWMM model has been widely used to calculate stormwater runoff in urban areas, however, the model is limited to interpreting the actual natural phenomenon. It has the uncertainty in the model structure, so it is difficult to calculate the accurate runoff from the urban basin. In this study, the model parameters were investigated and uncertainty was quantified using Uncertainty Quantification Index (UQI). As a result, pipe roughness coefficient has the largest total uncertainty and largest effect on the total runoff. Therefore, when the stormwater pipe network is designed, pipe roughness coefficient has to be calibrated accurately. The quantified uncertainty should be considered in the runoff calculation. It is recommended to understand the characteristics of each parameter for the prevention and mitigation of urban flood.

Uncertainty reaction force model of ship stern bearing based on random theory and improved transition matrix method

  • Zhang, Sheng dong;Liu, Zheng lin
    • Ocean Systems Engineering
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    • v.6 no.2
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    • pp.191-201
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    • 2016
  • Stern bearing is a key component of marine propulsion plant. Its environment is diverse, working condition changeable, and condition severe, so that stern bearing load is of strong time variability, which directly affects the safety and reliability of the system and the normal navigation of ships. In this paper, three affecting factors of the stern bearing load such as hull deformation, propeller hydrodynamic vertical force and bearing wear are calculated and characterized by random theory. The uncertainty mathematical model of stern bearing load is established to research the relationships between factors and uncertainty load of stern bearing. The validity of calculation mathematical model and results is verified by examples and experiment yet. Therefore, the research on the uncertainty load of stern bearing has important theoretical significance and engineering practical value.

An Economic Evaluation by a Scoring Model in the Nuclear Power Plants under Uncertainty (원전에서 점수산정모형에 의한 경제성 평가)

  • 강영식;함효준
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.22 no.52
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    • pp.311-322
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    • 1999
  • Major problems involved in an electrical utility expansion planning within a time horizon are how to efficiently deal with objectives considering multiple factors and uncertainty. But justification factors in study these days have considered only quantitative factors except qualitative factors. Therefore, the purpose of this paper is to develop a new model for economic evaluation of nuclear power plants through the scoring model with the quantitative and qualitative factors under uncertainty. The quantitative factors use a levelized generation cost method considering time value of money. Especially, the environmental, risk, and safety factors in this paper have been also explained for the rational economic justification of the qualitative factors under uncertainty. This paper not only proposes a new approach method using the scoring model in evaluating economy of the nuclear power plant in the long term, but also provides the more efficient decision making criterion for nuclear power plants under uncertainty.

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