• Journal of Navigation and Port Research
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• v.36 no.7
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• pp.553-559
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• 2012

It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Erdos as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Erdos and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.835-844
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• 2010

Tools for statistical analysis of extreme values include the classical annual maximum method, the modern threshold method and variants improving the second one. While the annual maximum method is to t th generalized extreme value distribution to the annual maxima of a time series, the threshold method is to the generalized Pareto distribution to the excesses over a high threshold from the series. In this paper we deal with the Poisson-GPD method, a variant of the threshold method with a further assumption that the total number of exceedances follows the Poisson distribution, and apply it to the daily percentage increases and decreases computed from the spot prices of West Texas Intermediate, which were collected from January 4th, 1988 until December 31st, 2009. According to this analysis, the distribution of daily percentage increases as well as decreases turns out to have a heavy tail, unlike the normal distribution, which coincides well with the general phenomenon appearing in the analysis of lots of nowaday nancial data.

• Journal of Nutrition and Health
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• v.44 no.4
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• pp.338-343
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• 2011

This study assessed the current EAR, RDA, and AMDR for protein, which were set in 2005 and revised in 2010 as the DRIs for Koreans. A classical approach to establish the EAR for protein has been the nitrogen balance method. This method has practical limitations and problems in statistical analysis by giving over estimations of nitrogen balance. Thus, the present EAR for protein might be lower than the true requirement. Recent reevaluations of nitrogen balance studies by bilinear regression analysis and the IAAO method have indicated that the EAR of 0.66 g/kg bw/d should be increased by 39% to give 0.92 g/kg bw/d. The AMDR for protein in the Korean DRIs was set at 7-10%, which covers almost the entire population's protein intake. Since the 5th percentile of Korean protein intake is close to 10% of energy and due to the beneficial effects of protein beyond the maintenance of nitrogen equilibrium, the lower range of 7% needs to be increased up to 10%. For practical meal arrangement, 15% of energy as protein, which is close to the average protein intake of Koreans, seems to be proper, although the value is almost two times the EAR.

• Structural Engineering and Mechanics
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• v.72 no.2
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• pp.203-216
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• 2019

Reliability analysis of composite structures considering random variation of involved parameters is quite important as composite materials revealed large statistical variations in their mechanical properties. The reliability analysis of such structures by the first order reliability method (FORM) and Monte Carlo Simulation (MCS) based approach involves repetitive evaluations of performance function. The response surface method (RSM) based metamodeling technique has emerged as an effective solution to such problems. In the application of metamodeling for uncertainty quantification and reliability analysis of composite structures; the finite element model is usually formulated by either classical laminate theory or first order shear deformation theory. But such theories show significant error in calculating the structural responses of composite structures. The present study attempted to apply the RSM based MCS for reliability analysis of composite shell structures where the surrogate model is constructed using higher order shear deformation theory (HSDT) of composite structures considering the uncertainties in the material properties, load, ply thickness and radius of curvature of the shell structure. The sensitivity of responses of the shell is also obtained by RSM and finite element method based direct approach to elucidate the advantages of RSM for response sensitivity analysis. The reliability results obtained by the proposed RSM based MCS and FORM are compared with the accurate reliability analysis results obtained by the direct MCS by considering two numerical examples.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.3-9
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• 2010

Drainage density, defined as the degree to which a landscape is dissected by streams, is a fundamental property of natural terrain that reflect the comprehensive morphologic response of watershed. In this study the spatial variability of drainage density is analyzed by statistical approach to it and its plotting method is proposed. Overland flow length is confirmed to be a highly variable spatial factor from the result of statistical analysis. Distribution map of drainage density based on spatial autocorrelation length in this study would be a superior tool to the classical definition of drainage density.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.4
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• pp.151-163
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• 1989

A statistical approach for the segmentation of speed signals is described in this paper. The main idea of this algorithm is the use of three AR models. Two fixed models are identified at the stationary parts of the signal before and after the spectral change. Changes are detected when the distance between these two models is high. Another model is located between two fixed models and is used to estimate spectral change time. This segmentation algorithm has been tested with connected words and compared to classical methods. The results showed that it can provide more accurate locations of boundaries of segments and can reduce the amount of oversegmentation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.197-202
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• 2006

The low-altitude earth observation satellite is generally equipped with high performance camera as a main payload which is vulnerable to vibration environment. During the launch process of a satellite, the combustion and jet noise of launch vehicle produce severe acoustic environment and the acoustic loads induced may damage the critical equipments of the satellite including the camera. Therefore to predict and simulate the effect of the acoustic environment which the satellite has to sustain at the lift-off event is very important process to support the load-resistive design and test-qualification of components. Statistical Energy Analysis(SEA) has been widely used to estimate the vibro-acoustic responses of the structures and gives statistical but reliable results in the higher frequency region with less modeling efforts and calculation time than the standard FEA. In this study, SEA technique has been applied to a 3-Dimensional model of a low-altitude earth observation satellite to predict the acceleration responses on the structural components induced by the high level acoustic field in the launch vehicle fairing. In addition, the expected response on each critical component panel was calculated by the classical method in consideration of the mass loading and imposed sound pressure level, and then compared with SEA results.

• Journal of Environmental Science International
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• v.20 no.12
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• pp.1541-1551
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• 2011

This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.119-123
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• 2009

Recently, a MEMS gyroscope has been broadly fabricated and used due to development of a micromachining. However, there is a difference between the modeling design and the actual product and this difference can lead to the performance variation of a MEMS gyroscope. A classical design method does not exactly estimate the performance of a MEMS gyroscope. Therefore a design process considering the design variable uncertainty has to be employed to design MEMS gyroscope model. In this paper, the equation of motion of a MEMS gyroscope model is obtained to analyze the performance of a MEMS gyroscope and the effects of the design variables on the MEMS gyroscope performance are investigated. Finally the performance of MEMS gyroscope is estimated through a statistical analysis based on sample statistics.