• Wind and Structures
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• v.37 no.3
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• pp.211-227
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• 2023

To investigate the non-Gaussian feature of fluctuating wind pressures on rectangular high-rise buildings, wind tunnel tests were conducted on scale models with side ratios ranging from 1/9~9 in an open exposure for various wind directions. The high-order statistical moments, time histories, probability density distributions, and peak factors of pressure fluctuations are analyzed. The mixed normal-Weibull distribution, Gumbel-Weibull distribution, and lognormal-Weibull distribution are adopted to fit the probability density distribution of different non-Gaussian wind pressures. Zones of Gaussian and non-Gaussian are classified for rectangular buildings with various side ratios. The results indicate that on the side wall, the non-Gaussian wind pressures are related to the distance from the leading edge. Apart from the non-Gaussianity in the separated flow regions noted by some literature, wind pressures behind the area where reattachment happens present non-Gaussian nature as well. There is a new probability density distribution type of non-Gaussian wind pressure which has both long positive and negative tail found behind the reattachment regions. The correlation coefficient of wind pressures is proved to reflect the non-Gaussianity and a new method to estimate the mean reattachment length of rectangular high-rise building side wall is proposed by evaluating the correlation coefficient. For rectangular high-rise buildings, the mean reattachment length calculated by the correlation coefficient method along the height changes in a parabolic shape. Distributions of Gaussian and non-Gaussian wind pressures vary with side ratios. It is inappropriate to estimate the extreme loads of wind pressures using a fixed peak factor. The trend of the peak factor with side ratios on different walls is given.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1276-1282
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• 2013

This paper proposes a stochastic modeling of plug-in electric vehicles (PEVs) distribution in power systems, and analyzes the corresponding clustering characteristic. It is essential for power utilities to estimate the PEV charging demand as the penetration level of PEV is expected to increase rapidly in the near future. Although the distribution of PEVs in power systems is the primary factor for estimating the PEV charging demand, the data currently available are statistics related to fuel-driven vehicles and to existing electric demands in power systems. In this paper, we calculate the number of households using electricity at individual ending buses of a power system based on the electric demands. Then, we estimate the number of PEVs per household using the probability density function of PEVs derived from the given statistics about fuel-driven vehicles. Finally, we present the clustering characteristic of the PEV distribution via case studies employing the test systems.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.9-18
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• 2011

In this study the optimal volume for non-point source control retention is estimated considering spatio-temporal variation of land surface. The 3-parameter mixed exponential probability density function is used to represent the statistical properties of rainfall events, and NRCS-CN method is applied as rainfall-runoff transformation. The catchment drainage area is divided into individual $30m{\times}30m$ cells, and runoff curve number is estimated at each cell. Using the derived probability density function theory, the stormwater probability density function at each cell is derived from the rainfall probability density function and NRCS-CN rainfall-runoff transformation. Considering the antecedent soil moisture condition at each cell and the spatial variation of CN value at the whole catchment drainage area, the ensemble stormwater capture curve is established to estimate the optimal volume for an non-point source control retention. The comparison between spatio-temporally varied land surface and constant land surface is presented as a case study for a urban drainage area.

• Structural Engineering and Mechanics
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• v.57 no.1
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• pp.21-43
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• 2016

A conditional probability based approach known as Particle Filter Method (PFM) is a powerful tool for system parameter identification. In this paper, PFM has been applied to identify the vehicle parameters based on response statistics of the bridge. The flexibility of vehicle model has been considered in the formulation of bridge-vehicle interaction dynamics. The random unevenness of bridge has been idealized as non homogeneous random process in space. The simulated response has been contaminated with artificial noise to reflect the field condition. The performance of the identification system has been examined for various measurement location, vehicle velocity, bridge surface roughness factor, noise level and assumption of prior probability density. Identified vehicle parameters are found reasonably accurate and reconstructed interactive force time history with identified parameters closely matches with the simulated results. The study also reveals that crude assumption of prior probability density function does not end up with an incorrect estimate of parameters except requiring longer time for the iterative process to converge.

• 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.

• Journal of Korean Society for Quality Management
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• v.18 no.2
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• pp.127-133
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• 1990

In this article, several problems inherent in histogram estimate of unknown probability density function are discussed. Those include so called sharp comers and bin edge effect. A resolution for these problems occurred with histogram is discussed. The resulting estimate is called kernel density estimate which is most widely used by data analysts. One of the most recent and reliable data-based choices of scale factor (bandwidth) of the estimate, which has been known to be most crucial, is also discussed.

• Computers and Concrete
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• v.8 no.2
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• pp.163-176
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• 2011

By virtue of chord-length density function from the field of statistical physics, this paper introduced a quantitative approach to estimate the distribution of cement paste thickness between aggregates in concrete. Dynamics mixing method based on molecular dynamics was employed to generate one model structure, then image analysis algorithm was used to obtain the distribution of thickness of cement paste in model structure for the purpose of verification. By comparison of probability density curves and cumulative probability curves of the cement paste thickness among neighboring aggregates, it is found that the theoretical results are consistent with the simulation. Furthermore, for the model mortar and concrete mixtures with practical volume fraction of Fuller-type aggregate, this analytical formula was employed to predict the influence of aggregate volume fraction and aggregate fineness. And evolution of its mean values were also investigated with the variation of volume fraction of aggregate as well as the fineness of aggregates in model mortars and concretes.

• Journal of Forest and Environmental Science
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• v.33 no.4
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• pp.305-314
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• 2017

This study aimed to predict changes in forest area using a probability density function, in order to promote effective forest management in the area north of the civilian control line (known as the Minbuk area) in Korea. Time series analysis (2010 and 2016) of forest area using land cover maps and accessibility expressed by distance covariates (distance from buildings, roads, and civilian control line) was applied to a probability density function. In order to estimate the probability density function, mean and variance were calculated using three methods: area weight (AW), area rate weight (ARW), and sample area change rate weight (SRW). Forest area increases in regions with lower accessibility (i.e., greater distance) from buildings and roads, but no relationship with accessibility from the civilian control line was found. Estimation of forest area change using different distance covariates shows that SRW using distance from buildings provides the most accurate estimation, with around 0.98-fold difference from actual forest area change, and performs well in a Chi-Square test. Furthermore, estimation of forest area until 2028 using SRW and distance from buildings most closely replicates patterns of actual forest area changes, suggesting that estimation of future change could be possible using this method. The method allows investigation of the current status of land cover in the Minbuk area, as well as predictions of future changes in forest area that could be utilized in forest management planning and policymaking in the northern area.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1758-1764
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• 2012

A large number of wire rope has been used in various inderstiries as Cranes and Elevators from expanding the scale of the industrial market. But now, the management of wire rope is used as manually operated by rope replacement from over time or after the accident.It is caused to major accidents as well as economic losses and personal injury. Therefore its time to need periodic fault diagnosis of wire rope or supply of real-time monitoring system. Currently, there are several methods has been reported for fault diagnosis method of the wire rope, to find out the feature point from extracting method is becoming more common compared to time wave and model-based system. This method has implemented a deterministic modeling like the observer and neural network through considering the state of the system as a deterministic signal. However, the out-put of real system has probability characteristics, and if it is used as a current method on this system, the performance will be decreased at the real time. And if the random noise is occurred from unstable measure/experiment environment in wire rope system, diagnostic criterion becomes unclear and accuracy of diagnosis becomes blurred. Thus, more sophisticated techniques are required rather than deterministic fault diagnosis algorithm. In this paper, we developed the fault diagnosis of the wire rope using probability density estimation techniques algorithm. At first, The steady-state wire rope fault signal detection is defined as the probability model through probability distribution estimate. Wire rope defects signal is detected by a hall sensor in real-time, it is estimated by proposed probability estimation algorithm. we judge whether wire rope has defection or not using the error value from comparing two probability distribution.

• International Journal of Reliability and Applications
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• v.14 no.2
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• pp.107-114
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• 2013

Modelling of failures is an important element of reliability modelling. Empirical modelling approach suitable for complex item is explored in this paper. First step of the empirical modelling approach is to plot hazard function, density function, Weibull probability plot as well as cumulative intensity function to see which model fits best for the given data. Next step of the empirical modelling approach is select appropriate model for the data and fit the parametric model accordingly and estimate the parameters.