• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.583-592
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• 2007

Given the moments of a symmetric random variable, its density and distribution functions can be accurately approximated by making use of the algorithm proposed in this paper. This algorithm is specially designed for approximating symmetric distributions and comprises of four phases. This approach is essentially based on the transformation of variable technique and moment-based density approximants expressed in terms of the product of an appropriate initial approximant and a polynomial adjustment. Probabilistic quantities such as percentage points and percentiles can also be accurately determined from approximation of the corresponding distribution functions. This algorithm is not only conceptually simple but also easy to implement. As illustrated by the first two numerical examples, the density functions so obtained are in good agreement with the exact values. Moreover, the proposed approximation algorithm can provide the more accurate quantities than direct approximation as shown in the last example.

• Smart Structures and Systems
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• v.15 no.3
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.983-990
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• 2004

Multiple-count problem is occurred when rectangle objects span across several buckets. The CD histogram is a technique which selves this problem by keeping four sub-histograms corresponding to the four points of rectangle. Although It provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors nay be occurred when it is applied to real applications. In this paper, we propose selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models : \circled1 probabilistic model which considers the query window area ratio, \circled2 probabilistic model which considers intersection area between a given grid and objects. Our method has the capability of eliminating an impact of the restriction on query window which the existing cumulative density histogram has. We experimented with real datasets to evaluate the proposed methods. Experimental results show that the proposed technique is superior to the existing selectivity estimation techniques. Furthermore, selectivity estimation technique based on probabilistic model considering the intersection area is very accurate(less than 5% errors) at 20% query window. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.5
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• pp.623-628
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• 2007

The accurate peak response estimation of a seismically excited structure with frictional damping system(FDS) is very difficult since the structure with FDS shows nonlinear behavior dependent on the structural period, loading characteristics, and relative magnitude between the frictional force and the excitation load. Previous studies have estimated that by replacing a nonlinear system with an equivalent linear one or by employing the response spectrum obtained based on nonlinear time history and statistical analysis. In the case that an earthquake load is defined with probabilistic characteristics, the corresponding response of the structure with FDS has probabilistic distribution. In this study, nonlinear time history analyses were performed for the structure with FDS subjected to artificial earthquake loads generated using Kanai-Tajimi filter. An equation for the probability density function (PDF) of the displacement response is proposed by adapting the PDF of the normal distribution. Finally, coefficients of the proposed PDF are obtained by regression analysis of the statistical distribution of the time history responses. Finally the correlation between PDFs and statistical response distribution is presented.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.5
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• pp.383-388
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• 2019

It is essential to reduce the Infra-red signature for increasing ship's survivability in ship design stage. However the ship's IR signature is quite sensitive to the maritime and atmosphere. Therefore, it is very important to select the marine meteorological data to be applied to the signature analysis. In this study, we selected the three meteorological sample sets from the population of the Korea Meteorological Administration's marine environment data in 2017. These samples were selected through the two-dimensional stratified sampling method, taking into account the geopolitical threats of the Korean peninsula and the effective area of the buoy. These sample sets were applied to three naval ships classified by their tonnage, and then the IR signature analysis was performed to derive the Contrast Radiant Intensity (CRI) values. Based on the CRI values, the validity of each sample set was determined by comparing Cumulative Distribution Function (CDF), and Probability Density Function (PDF). Also, we checked the degree of scattering in each sample set and determined the efficiency of analysis time and cost according to marine meteorological sample sets to confirm the possibility of a probabilistic method. Through this process, we selected the standard for optimization of marine meteorological sample for ship IR signature analysis. Based on this optimization sample, by applying probabilistic method to the management of IR signature for naval ships, the robust design is possible.

• Structural Engineering and Mechanics
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• v.67 no.5
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• pp.439-455
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• 2018

Within a probabilistic framework, this paper addresses the determination of the static structural response of beams and frames with partially restrained (semi-rigid) connections. The flexibility of the nodal connections is incorporated via an idealized linear-elastic behavior of the beam constraints through the use of rotational springs, which are here considered uncertain for taking into account the largely scattered results observed in experimental findings. The analysis is conducted via the Probabilistic Transformation Method, by modelling the spring stiffness terms (or equivalently, the fixity factors of the beam) as uniformly distributed random variables. The limit values of the Eurocode 3 fixity factors for steel semi-rigid connections are assumed. The exact probability density function of a few indicators of the structural response is derived and discussed in order to identify to what extent the uncertainty of the beam constraints affects the resulting beam response. Some design considerations arise which point out the paramount importance of probability-based approaches whenever a comprehensive experimental background regarding the stiffness of the beam connection is lacking, for example in steel frames with semi-rigid connections or in precast reinforced concrete framed structures. Indeed, it is demonstrated that resorting to deterministic approaches may lead to misleading (and in some cases non-conservative) outcomes from a design viewpoint.

• Journal of Environmental Health Sciences
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• v.43 no.3
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• pp.223-232
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• 2017

Objectives: In response to increased interest in the safety of children's products, a risk management system is being prepared through exposure assessment of hazardous chemicals. To estimate exposure levels, risk assessors are using deterministic and probabilistic approaches to statistical methodology and a commercialized Monte Carlo simulation based on tools (MCTool) to efficiently support calculation of the probability density functions. This study was conducted to analyze and discuss the usage patterns and problems associated with the results of these two approaches and MCTools used in the case of probabilistic approaches by reviewing research reports related to exposure assessment for children's products. Methods: We collected six research reports on exposure and risk assessment of children's products and summarized the deterministic results and corresponding underlying distributions for exposure dose and concentration results estimated through deterministic and probabilistic approaches. We focused on mechanisms and differences in the MCTools used for decision making with probabilistic distributions to validate the simulation adequacy in detail. Results: The estimation results of exposure dose and concentration from the deterministic approaches were 0.19-3.98 times higher than the results from the probabilistic approach. For the probabilistic approach, the use of lognormal, Student's T, and Weibull distributions had the highest frequency as underlying distributions of the input parameters. However, we could not examine the reasons for the selection of each distribution because of the absence of test-statistics. In addition, there were some cases estimating the discrete probability distribution model as the underlying distribution for continuous variables, such as weight. To find the cause of abnormal simulations, we applied two MCTools used for all reports and described the improper usage routes of MCTools. Conclusions: For transparent and realistic exposure assessment, it is necessary to 1) establish standardized guidelines for the proper use of the two statistical approaches, including notes by MCTool and 2) consider the development of a new software tool with proper configurations and features specialized for risk assessment. Such guidelines and software will make exposure assessment more user-friendly, consistent, and rapid in the future.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.857-864
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• 2006

Due to the difficulties in numerical generation of random fields that satisfy not only the probabilistic distribution but the spectral characteristics as well. it is relatively hard to find an exact response variability of a structural response with a specific random field which has its features in the spatial and spectral domains. In this study. focusing on the fact that the random field assumes a constant over the domain under consideration when the correlation distance tends to infinity, a semi-theoretical solution of response variability is proposed for in-plane and plate bending structures. In this procedure, the probability density function is used directly resulting in a semi-exact solution for the random field in the state of random variable. It is particularly noteworthy that the proposed methodology provides response variability for virtually any type of probability density functions.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.2
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• pp.124-131
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• 2012

This paper provides prediction of ultimate longitudinal strengths of hull girder of a VLCC considering probabilistic damage extents due to collision and grounding accidents based on IMO Guideline(2003). The probability density functions of damage extents are expressed as a function of nondimensional damage variables. The accumulated probability levels of 10%, 30%, 50%, and 70% are taken into account for the damage extent estimation. The ultimate strengths have been calculated using in-house software UMADS (Ultimate Moment Analysis of Damaged Ships) which is based on the progressive collapse method. Damage indices are provided for all heeling angles due to any possible flooding of compartments from $0^{\circ}$ to $180^{\circ}$ which represent from sagging to hogging conditions, respectively. The analysis results reveal that minimum damage indices show different values according to heeling angles and damage levels.