• 한국시뮬레이션학회논문지
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• 제28권2호
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• pp.89-95
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• 2019

본 연구에서는 수중 무기체계의 효과도 예측을 위하여 개발한 교전 수준 시뮬레이터에 대해서 표적탐지 여부의 확률적 접근 방법을 적용한 개념 및 그 예시를 소개하고 있다. 소나 방정식에 의해서 산출되는 SE(Signal excess)을 이용하여 표적의 존재 여부($H_0$ 혹은 $H_1$)에 따른 확률 분포 함수(PDF, Probability density function)을 유도하였다. 이후 Neyman-Pearson 탐지기에 따라 $P_{FA}$(Probability of false alarm)을 만족하는 $P_D$(Probablity of detection)을 구하고 탐지 여부를 확률적으로 판단하도록 설계하였다. 표적의 탐지와 관련된 현실적인 접근방법을 도입함으로써 시뮬레이터의 충실도를 높일 수 있었으며, 실험 결과는 전술 구상의 보조 정보로 활용할 수 있을 것이라 예상한다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2001년도 가을 학술발표회 논문집
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• pp.859-864
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• 2001

An effective live load model for analyzing probable maximum live load effects such as moment and shear in transverse direction was developed. The main procedure of this live load model is composed of four parts, i.e., firstly, determination of the appropriate influence lines in longitudinal direction, secondly, application of the characteristics of vehicles and traffic patterns in longitudinal direction, thirdly, determination of the appropriate influence lines in transverse direction, and fourthly, application of the characteristics of vehicles and traffic patterns in transverse direction. Through this procedure, the probabilistic distributions of maximum probable load effects are deduced in the form of probability density function (PDF) and/or cumulative density function(CDF). This live load model is able to consider local or global deterioration of bridges in the structural analysis.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2001년도 가을 학술발표회 논문집
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• pp.865-870
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• 2001

An effective live load model for analyzing probable maximum live load effects in longitudinal direction such as moment and shear was developed. The main procedure of this live load model is composed of two parts. Firstly, determination of the appropriate influence lines, and secondly, application of the characteristics of vehicles and traffic patterns. Through this procedure, probabilistic distributions of maximum probable load effects are deduced in the form of probability density function (PDF) or cumulative density function (CDF). The proposed live load model is not limited by bridge types(number of spans or girders) and can consider local or global deterioration of bridges in the analysis. Besides, load effects can be determined at any section without restrictions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.427-432
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• 2005

This paper is concerned with a simultaneous design of TSK (Takagi-Sugeno-Kang)-linguistic fuzzy models with uncertain model output and the computationally efficient representation. For this purpose, we use the fundamental idea of linguistic models introduced by Pedrycz and develop their comprehensive design framework. The design process consists of several main phases such as (a) the automatic generation of the linguistic contexts by probabilistic distribution using CDF (conditional density function) and PDF (probability density function) (b) performing context-based fuzzy clustering preserving homogeneity based on the concept of fuzzy granulation (c) augment of bias term to compensate bias error (d) combination of TSK and linguistic context in the consequent part. Finally, we contrast the performance of the enhanced models with other fuzzy models for automobile MPG predication data and coagulant dosing process in a water purification plant.

• Wind and Structures
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• 제2권2호
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• pp.127-131
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• 1999

This paper describes probabilistic characteristics of damping in a tall building based on the results of full-scale measurement. It is found, through statistical analysis of the damping data, that the probability density function(PDF) of damping at the high amplitude plateau can be well represented by Normal distribution (Gaussian distribution). A stochastic damping model is proposed to estimate amplitude-dependent damping for practical application.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.520-524
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• 2009

In compressed color images, colors are usually represented by luminance and chrominance (YCbCr) components. Considering characteristics of human vision system, chrominance (CbCr) components are generally represented more coarsely than luminance component. Aiming at possible recovery of chrominance components, we propose a model-based chrominance estimation algorithm where color images are modeled by a Markov random field (MRF). A simple MRF model is here used whose local conditional probability density function (pdf) for a color vector of a pixel is a Gaussian pdf depending on color vectors of its neighboring pixels. Chrominance components of a pixel are estimated by maximizing the conditional pdf given its luminance component and its neighboring color vectors. Experimental results show that the proposed chrominance estimation algorithm is effective for quality improvement of compressed color images such as JPEG and JPEG2000.

• 한국항공운항학회지
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• 제18권4호
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• pp.44-50
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• 2010

Structural vibration is a significant problem in many multi-part or multi-component assemblies. In aircraft industry, structures are composed of various fasteners, such as bolts, snap, hinge, weld or other fastener or connector (collectively "fasteners"). Due to these, prediction and design involving dynamic characteristics is quite complicated. However, the current state of the art does not provide an analytical tool to effectively predict structure's dynamic characteristics, because consideration of structural uncertainties (i.e. material properties, geometric tolerance, dimensional tolerance, environment and so on) is difficult and very small fasteners in the structure cause a huge amount of analysis time to predict dynamic characteristics using the FEM (finite element method). In this study, to resolve the current state of the art, a new approach is proposed using the FEM and probabilistic analysis. Firstly, equivalent elements are developed using simple element (e.g. bar, beam, mass) to replace fasteners' finite element model. Developed equivalent elements enable to explain static behavior and dynamic behavior of the structure. Secondly, probabilistic analysis is applied to evaluate the PDF (probability density function) of dynamic characteristics due to tolerance, material properties and so on. MCS (Monte-Carlo simulation) is employed for this. Proposed methodology offers efficiency of dynamic analysis and reality of the field as well. Simple plates joined by fasteners are taken as an example to illustrate the proposed method.

• 대한금속재료학회지
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• 제46권9호
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• pp.554-562
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• 2008

The distribution and prediction interval for the misorientation angle of grain boundary at which $Cr_2N$ was precipitated during heating at $900^{\circ}C$ for $10^4$ sec were newly estimated, and followed by the estimation of mathematical and median rank methods. The probability density function of the misorientation angle can be estimated by a statistical analysis. And then the ($1-{\alpha}$)100% prediction interval of misorientation angle obtained by the estimated probability density function. If the estimated probability density function was symmetric then a prediction interval for the misorientation angle could be derived by the estimated probability density function. In the case of non-symmetric probability density function, the prediction interval could be obtained from the cumulative distribution function of the estimated probability density function. In this paper, 95, 99 and 99.73% prediction interval obtained by probability density function method and cumulative distribution function method and compared with the former results by median rank regression or mathematical method.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.657-666
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• 2020

A reliable and cost-effective technique for the development of corrosion damage model is introduced to predict nonlinear time-dependent corrosion wastage of steel structures. A detailed explanation on how to propose a generalised mathematical formulation of the corrosion model is investigated in this paper (Part I), and verification and application of the developed method are covered in the following paper (Part II) by adopting corrosion data of a ship's ballast tank structure. In this study, probabilistic approaches including statistical analysis were applied to select the best fit probability density function (PDF) for the measured corrosion data. The sub-parameters of selected PDF, e.g., the largest extreme value distribution consisting of scale, and shape parameters, can be formulated as a function of time using curve fitting method. The proposed technique to formulate the refined time-dependent corrosion wastage model (TDCWM) will be useful for engineers as it provides an easy and accurate prediction of the 1) starting time of corrosion, 2) remaining life of the structure, and 3) nonlinear corrosion damage amount over time. In addition, the obtained outcome can be utilised for the development of simplified engineering software shown in Appendix B.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.