• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.857-865
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• 2013

Extreme rainfall causes heavy losses in human life and properties. Hence many works have been done to predict extreme rainfall by using extreme value distributions. In this study, we use a generalized extreme value distribution to derive the posterior predictive density with hierarchical Bayesian approach based on the data of Seoul area from 1973 to 2010. It becomes clear that the probability of the extreme rainfall is increasing for last 20 years in Seoul area and the model proposed works relatively well for both point prediction and predictive interval approach.

• Geomechanics and Engineering
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• v.13 no.3
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• pp.371-384
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• 2017

Physical and mechanical properties of rocks are of interest in many fields, including materials science, petrophysics, geophysics and geotechnical engineering. Uniaxial compressive strength UCS is one of the key mechanical properties, while density and porosity are important physical parameters for the characterization of rocks. The economic interest of carbonate rocks is very important in chemical or biological procedures and in the field of construction. Carbonate rocks exploitation depends on their quality and their physical, chemical and geotechnical characteristics. A fast, economic and reliable technique would be an evolutionary advance in the exploration of carbonate rocks. This paper discusses the ability of ultrasonic wave velocity to evaluate some mechanical and physical parameters within carbonate rocks (collected from different regions within Tunisia). The ultrasonic technique was used to establish empirical correlations allowing the estimation of UCS values, the density and the porosity of carbonate rocks. The results illustrated the behavior of ultrasonic pulse velocity as a function of the applied stress. The main output of the work is the confirmation that ultrasonic velocity can be effectively used as a simple and economical non-destructive method for a preliminary prediction of mechanical behavior and physical properties of rocks.

• 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 Institute of Industrial Engineers
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• v.21 no.3
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• pp.371-385
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• 1995

The yield of semiconductor chips is dependent not only on the average defect density but also on the distribution of defects over a wafer. The distribution of defects leads to consider a cluster index. This paper briefly reviews the existing yield prediction models ad proposes a new cluster index, which utilizes the information about the defect location on a wafer in terms of the coefficient of variation. An extensive simulation is performed under a variety of defect distributions and a yield prediction model is derived through the regression analysis to relate the yield with the proposed cluster index and the average number of defects per chip. The performance of the proposed simulation-based yield prediction model is compared with that of the well-known negative binomial model.

• Journal of Forest and Environmental Science
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• v.28 no.2
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• pp.68-74
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• 2012

A total of 45 Pinus sylvestnis var. mongolica trees from 9 plots in northeast China were destructively sampled to develop aboveground prediction equations for inventory application. Sampling plots covered a range of stand ages (12-47-years-old) and densities (450-3,840/ha). The distribution of aboveground biomass of whole-trees and tree component (stems, branches and leaves) of individual trees were studied and 4 equations were developed as functions of diameter at breast height (DBH), total height (HT). All the equations have good estimation effect with high prediction precision over 90%. Forest biomass was estimated based on the individual biomass prediction equations. It was found forest biomass of all organs increased with the increasing of stand age and density. And the period of 45-50 years was the suitable harvest time for Pinus sylvesris plantation.

• Journal of KSNVE
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• v.6 no.2
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• pp.253-259
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• 1996

Neighboring buildings which are sufficiently close to both sides of an urban street reflect sound back to the road and sound energy is increased by thses reflectors. Therefore, this study is forcussed on the prediction modeling for road traffic noise under reflective conditions. A part of a block in urban road is regared as a box. The sound energy density in the box is employed to establish prediction formulas in terms of independent variables. The variables. The validity of the proposed prediction method has been experimentally confirmed by applying it to actually measured road traffic noise data. On the whole, the agreement between measured and predicted noise levels appeared to be satisfactory.

• Wind and Structures
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• v.29 no.2
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• pp.129-137
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• 2019

The maximum along-wind displacement of a considerable amount of building under simulated wind loads is computed with the aim to produce a simple prediction model using multiple regression analysis with variables transformation. The Shinozuka and Newmark methods are used to simulate the turbulent wind and to calculate the dynamic response, respectively. In order to evaluate the prediction performance of the regression model with longer degree of determination, two complex structural models were analyzed dynamically. In addition, the prediction model proposed is used to estimate and compare the maximum response of two test buildings studied with wind loads by other authors. Finally, it was proved that the prediction model is reliable to estimate the maximum displacements of structures subjected to the wind loads.

• 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.

• Journal of Powder Materials
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• v.30 no.3
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• pp.203-209
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• 2023

Aluminum alloy-based additive manufacturing (AM) has emerged as a popular manufacturing process for the fabrication of complex parts in the automotive and aerospace industries. The addition of an inoculant to aluminum alloy powder has been demonstrated to effectively reduce cracking by promoting the formation of equiaxed grains. However, the optimization of the AM process parameters remains challenging owing to their variability. In this study, the response surface methodology (RSM) was used to predict the crack density of AM-processed Al alloy samples. RSM was performed by setting the process parameters and equiaxed grain ratio, which influence crack propagation, as independent variables and designating crack density as a response variable. The RSM-based quadratic polynomial models for crack-density prediction were found to be highly accurate. The relationship among the process parameters, crack density, and equiaxed grain fraction was also investigated using RSM. The findings of this study highlight the efficacy of RSM as a reliable approach for optimizing the properties of AM-processed parts with limited experimental data. These results can contribute to the development of robust AM processing strategies for the fabrication of high-quality Al alloy components for various applications.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.1-11
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• 2023

Due to climate change and aging of reservoirs, damage to embankment slopes is increasing. However, the safety diagnosis of the reservoir slope is mainly conducted by visual observation, and the time and economic cost are formidable to apply soil mechanical tests and slope stability analysis. Accordingly, this study presented a predicting method for the compaction and strength characteristics of the reservoir embankment soil using a portable static cone penetration test. The predicted items consisted of dry density, cohesion, and internal friction angle, which are the main factors of slope stability analysis. Portable static cone penetration tests were performed at 19 reservoir sites, and prediction equations were constructed from the correlation between penetration resistance data and test results of soil samples. The predicted dry density and strength parameters showed a correlation with test results between R² 0.40 and 0.93, and it was found to replace the test results well when used as input data for slope stability analysis (R² 0.8134 or more, RMSE 0.0320 or less). In addition, the prediction equations for the minimum safety factor of the slope were presented using the penetration resistance and gradient. As a result of comparing the predicted safety factor with the analysis results, R² 0.5125, RMSE 0.0382 in coarse-grained soil, R² 0.4182 and RMSE 0.0628 in fine-grained soil. The results of this study can be used as a way to improve the existing slope safety diagnosis method, and are expected to be used to predict the characteristics of various soils and inspect slopes.