• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.615-627
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• 2009

In micro marketing promotion, it is important to know the behavior of customers. In this study we are interested in the forecasting of repurchase of customers from customers' behavior. By analyzing the cosmetic transaction data we derive some variables which play an important role in the knowledge of the customers' behavior and in the modeling of repurchase. As modeling tools we use the decision tree, logistic regression and neural network model. Finally we decide to use the decision tree as a final model since it yields the smallest RASE (root average squared error) and the greatest correct classification rate.

• The Journal of Engineering Geology
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• v.13 no.3
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• pp.369-378
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• 2003

The statistical modeling was introduced to satisfy various experimental conditions on the sorption of heavy metals (Pb, Cu, Cd, and Zn) by clay minerals, i.e. kaolinite, illite and chlorite. The Box-Behnken model designed statistically was applied to determine a relative impact among three variables such as pH, HCO3(or K) concentration and initial concentration of heavy metals. The SAS program was used to obtain the statistical solution by surface response analysis. The results of a statistical sorption modelling indicated that pH is a strong impact of the variables influencing the sorption of heavy metals. A relative effect between an initial concentration of heavy metals and bicarbonate(or K) concentration is dependent on solution condition. The sorption edge of heavy metals as function of pH shows sigmoidal curve, and a great increase in the range of pH 6～8. The sorption sequence among heavy metals is Cu>Pb>>Zn>Cd. The solution chemistry exerts greater influence on the sorption of heavy metals rather than the crystal chemistry of clay minerals. The potassium exerts some effect into a sorption competition with heavy metals. The research suggests that the statistical modeling is an effective method to demonstrate sorption results in three dimension and to reduce the effort of batch sorption experiment.

• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.10
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• pp.1139-1145
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• 2014

Creation of a human skeleton model and characterization of the variation in the bone shape are fundamentally important in many applications of biomechanics. In this paper, we present a parametric shape modeling method for femurs that is based on extracting the main parameter of variations of the femur shape from a 3D model database by using statistical shape analysis. For this shape analysis, principal component analysis (PCA) is used. Application of the PCA to 3D data requires bringing all the models in correspondence to each other. For this reason, anatomical landmarks are used for guiding the deformation of the template model to fit the 3D model data. After subsequent application of PCA to a set of femur models, we calculate the correlation between the dominant components of shape variability for a target population and the anatomical parameters of the femur shape. Finally, we provide tools for visualizing and creating the femur shape using the main parameter of femur shape variation.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.12 no.2
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• pp.40-49
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• 2016

Flow accelerated corrosion (FAC) of the carbon steel piping has been a significant problem in nuclear power plants. FAC occurs under certain hydrodynamic, environmental, and material conditions, and extensive research into the factors of FAC has been conducted. The basic process of FAC is now relatively well understood; however, a full mechanistic model has not yet been established. Recently, the Korea Atomic Energy Research Institute (KAERI) has built a large experiment loop system for FAC. To produce significant experimental results using this system, the factors affecting on FAC should be analyzed quantitatively, and a model needs to be developed. In this work, a statistical modeling methodology to develop an empirical model is described in detail, and a preliminary model is suggested. Firstly, FAC data were collected from the research literature in Japan and the results of domestic experiments. The flow rate, water temperature, pH at room temperature, and the Cr content are selected as major factors, and nonlinear regression is used to find the best fit of the available data. An iterative procedure between suggesting and evaluating a model is used until an optimum model is obtained. The developed model gives the FAC rate comparable to the measured FAC rate. The developed model is going to be refined using additional laboratory data in the future.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.101-104
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• 2000

In this paper, we analyze the propagation modeling methods to evaluate the IMT-2000 satellite RTT (Radio Transmission Technology). To generate Rayleigh random numbers having Jake's Doppler power density spectrum, the Rice's sum of sinusoids methods are used and their statistical characteristics are compared with each other.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.156-159
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• 2008

Proper orthogonal decomposition is a statistical pattern analysis technique for finding the dominant components, called the proper orthogonal modes, in ensembles of spatially distributed data. We present recent ideas based on proper orthogonal decomposition (POD) and detailed experiments that yield new perspectives into the microscale structures. The linearized modeling technique based on POD is very useful to show the principal characteristics of the complex dynamic responses.

• Journal of the Korean Society of Combustion
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• v.2 no.1
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• pp.39-51
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• 1997

A review of probability density function(PDF) methodology and direct numerical simulation for the purpose of modeling turbulent combustion are presented in this study where particular attention is focused on the modeling problem of turbulent molecular mixing term appearing in PDF transport equation. Existing mixing models results were compared to those evaluated by direct numerical simulation in a turbulent premixed medium with finite rate chemistry in which the initial scalar field is composed of pockets of partially burnt gases to simulate autoignition. Two traditional mixing models, the least mean square estimations(LMSE) and Curl#s model are examined to see their prediction capability as well as their modeling approach. Test calculations report that the stochastically based Curl#s approach, though qualitatively demonstrates some unphysical behaviors, predicts scalar evolutions which are found to be in good agreement with statistical data of direct numerical simulation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07a
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• pp.252-255
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• 2003

The pulsed laser deposition process modeling is investigated using neural networks based on radial basis function networks and multi-layer perceptron. Two input factors are examined with respect to the PL intensity. In order to minimize the joint confidence region of fabrication process with varying the conditions, D-optimal experimental design technique is performed and photoluminescence intensity is characterized by neural networks. The statistical results were then used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can be optimized process conditions for pulsed laser deposition process.