• Journal of Ship and Ocean Technology
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• 제9권1호
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• pp.64-71
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• 2005

In probabilistic design, the challenge is to estimate the uncertainty propagation, since outputs of subsystems at lower levels could constitute inputs of other systems or at higher levels of the multilevel systems. Three uncertainty propagation estimation techniques are compared in this paper in terms of numerical efficiency and accuracy: root sum square (linearization), distribution-based moment approximation, and Taguchi-based integration. When applied to reliability-based design optimization (RBDO) under uncertainty, it is investigated which type of applications each method is best suitable for. Two nonlinear analytical examples and one vehicle crashworthiness for side-impact simulation example are employed to investigate the unique features of the presented techniques for uncertainty propagation. This study aims at helping potential users to identify appropriate techniques for their applications in the multilevel design.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.588-598
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• 2020

An alternative state estimation filtering algorithm is designed for continuous time systems with noises as well as control input. Two kinds of estimation filters, which have different measurement memory structures, are operated selectively in order to use both filters effectively as needed. Firstly, the estimation filter with infinite memory structure is operated for a certain continuous time system. Secondly, the estimation filter with finite memory structure is operated for temporarily uncertain continuous time system. That is, depending on the presence of uncertainty, one of infinite memory structure and finite memory structure filtered estimates is operated selectively to obtain the valid estimate. A couple of test variables and declaration rule are developed to detect uncertainty presence or uncertainty absence, to operate the suitable one from two kinds of filtered estimates, and to obtain ultimately the valid filtered estimate. Through computer simulations for a continuous time aircraft engine system with different measurement memory lengths and temporary model uncertainties, the proposed state estimation filtering algorithm can work well in temporarily uncertain as well as certain continuous time systems. Moreover, the proposed state estimation filtering algorithm shows remarkable superiority to the infinite memory structure filtering when temporary uncertainties occur in succession.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.48.2-48
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• 2002

This paper presents an integrated sliding mode adaptive control scheme for general nonlinear uncertain systems, where structured uncertainty is assumed can be linearly parameterized and unstructured uncertainty is assumed be bounded by unknown constant A certain estimation scheme for this unknown constant is introduced to attenuate the unstructured uncertainty. Presented control scheme is shown to be stable and numerical expressions of bounds of all error signals are given, from which we can acquire some useful information about practical trade-off between tracking performance and parameter estimation property.

• 대한원격탐사학회지
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• 제39권3호
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• pp.355-361
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• 2023

지구 표면에 대한 정보는 기상 및 대기 역학의 이해나 인간을 포함한 동물에 큰 영향을 미치는 극한 열현상에 대응함에 있어서 핵심적인 지구 물리량이다. 지구 영역에 대한 온도를 추정하기 위하여 위성에 탑재된 열적외 센서가 널리 활용되어 왔는데, 정밀한 활용을 위해서는 온도 추정 과정의 불확도에 대한 이해가 선행되어야 한다. 하지만 온도추정 불확도에 영향을 미치는 많은 요소 중에서 한반도 주변의 환경 하에서의 온도추정 알고리즘의 불확도 산정에 대한 연구는 미미하였다. 본 연구에서는 한반도 주변의 대기 및 해양 조건하에서 범용성이 높은 single-channel 알고리즘의 불확도를 추정하는 연구를 수행하였다. 알고리즘의 입력자료로 필요한 재분석자료(reanalysis)의 영향성을 평가하기 위하여 두 가지의 재분석자료, 즉 fifth generation of European Centre for Medium-Range Weather Forecasts reanalysis of the global climate and weather (ERA5)와 Modern-Era Retrospective analysis for Research and Applications-2 (MERRA-2)를 사용하였고, 복사전달모델은 MODerate resolution atmospheric TRANsmission (MODTRAN)을 사용하였다. MODTRAN 모의와 온도 추정 정확도 검증에 사용되는 현장 관측 수온은 한반도 인근 해역에 위치한 해양 기상 부이(buoy)로부터 획득했다. 실험 결과, 알고리즘 불확도는 대기 수증기량에 따라서 선형에 가깝게 증가하는 것을 확인하였고, 가장 건조한 조건에서는 약 0.35K 그리고 평균적으로 0.45K 가량의 불확도가 발생함을 확인하였다. 이러한 결과는 재분석자료의 종류에 상관없이 유사하게 도출되어 알고리즘이 가지는 순수한 불확도라고 추정할 수 있었다.

• 한국기계가공학회지
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• 제12권2호
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• pp.107-113
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• 2013

When the main spindle of high precision machine tools are run many hours, heat is generated in bearing parts of the inside of the spindle. Also, headstock is appeared distortion by inside and outside temperature difference of a machine. This paper studies method to measure behavior of machine tool about these thermal effects. In addition, it estimates measurement uncertainty factors which can be appeared in thermal effects measurement. Finding the factor of thermal affect measurement is important for estimation of measurement uncertainty. This paper measures thermal effects of high precision machine tools and evaluates the important factors of uncertainty.

• 한국전산구조공학회논문집
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• 제23권6호
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• pp.641-649
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• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• Computational Structural Engineering : An International Journal
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• 제1권2호
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• pp.81-87
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• 2001

A framework for reliability analysis of structural components and systems under conditions of statistical and model uncertainty is presented. The Bayesian parameter estimation method is used to derive the posterior distribution of model parameters reflecting epistemic uncertainties. Point, predictive and bound estimates of reliability accounting for parameter uncertainties are derived. The bounds estimates explicitly reflect the effect of epistemic uncertainties on the reliability measure. These developments are enhance-ments of second-moment uncertainty analysis methods developed by A. H-S. Ang and others three decades ago.

• 한국기후변화학회지
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• 제7권4호
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• pp.407-419
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• 2016

The low flow is the necessary and important index to establish national water planning, however there are lots of uncertainty in the low flow estimation. Therefore, the objectives of this study are to assess the climate change uncertainty and the effects of hydrological models on low flow estimation. The 5 RCMs (HadGEM3-RA, RegCM4, MM5, WRF, and RSM), 5 statistical post-processing methods and 2 hydrological models were applied for evaluation. The study area were selected as Chungju dam and Soyang river dam basin, and the 30 days minimum flow is used for the low flow evaluation. The results of the uncertainty analysis showed that the hydrological model was the largest source of uncertainty about 41.5% in the low flow projection. The uncertainty of hydrological model is higher than the other steps (RCM, statistical post-processing). Also, VIC model is more sensitive for climate change compared to SWAT model. Therefore, the hydrological model should be thoroughly reviewed for the climate change impact assessment on low flow.

• 제어로봇시스템학회논문지
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• 제1권1호
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• pp.20-24
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• 1995

In this paper we analyze the performance bounds of the optimal FIR filter in continuous time systems with modeling uncertainty. The performance bounds are presented by the estimation error convariance and they are here expressed by the upper bounds of the difference of the estimation error covariance between the real and nominal values in case of the system with model uncertainties whose upper bounds are imperfrctly known a priori. The performance bounds of the optimal FIR filter are compared with those of the Kalman filter via a numerical example applied to the estimation of the motion of an aircraft carrier at sea, which shows the former has better performances than the latter.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1589-1592
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• 1997

Among the prerequisite abilities (perception of environment, path planning and position estimation) of an autonomous mobile robot, position estimation has been seldom studied by mobile robot researchers. In most cases, conventional positioin estimation has been performed by placing landmarks or giving the entrire environmental information in advance. Unlikely to the conventional ones, the study addresses a new method that the robot itself can select distinctive features in the environment and save them as landmarks without any a priori knowledge, which can maximize the autonomous behavior of the robot. First, an orjentaion probaility model is applied to construct a lcoal map of robot's surrounding. The feature of the object in the map is then extracted and the map is saved as landmark. Also, presented is the position estimation method that utilizes the correspondence between landmarks and current local map. In dong this, the uncertainty of the robot's current positioin is estimated in order to select the corresponding landmark stored in the previous steps. The usefulness of all these approaches are illustrated with the results porduced by a real robot equipped with ultrasonic sensors.