• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.239-247
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• 2003

Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, etc. CHAID(Chi-square Automatic Interaction Detector), is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. In this paper we propose and CHAID algorithm by cube-based sampling and explore CHAID algorithm in view of accuracy and speed by the number of variables.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.846-848
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• 2000

In the early development of wind energy, the majority of wind turbines were operated at constant speed. Recently, the number of variable-speed wind turbines adopted pitch control apparatus has increased. This paper deal with a simulation of pitch control of variable-speed wind turbine and the response of pitch angle is traced in a given random wind speed.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.

• Economic and Environmental Geology
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• v.33 no.4
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• pp.295-307
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• 2000

Probabilistic analysis is a powerful method to quantify variability and uncertainty common in engineering geology fields. In rock slope engineering, the uncertainty and variation may be in the form of scatter in orientations and geometries of discontinuities, and also test results. However, in the deterministic analysis, the factor of safety which is used to ensure stability of rock slopes, is based on the fixed representative values for each parameter without a consideration of the scattering in data. For comparison, in the probabilistic analysis, these discontinuity parameters are considered as random variables, and therefore, the reliability and probability theories are utilized to evaluate the possibility of slope failure. Therefore, in the probabilistic analysis, the factor of safety is considered as a random variable and replaced by the probability of failure to measure the level of slope stability. In this study, the stochastic properties of discontinuity parameters are evaluated and the stability of rock slope is analyzed based on the random properties of discontinuity parameters. Then, the results between the deterministic analysis and the probabilistic analysis are compared and the differences between the two analysis methods are explained.

• 한국신재생에너지학회:학술대회논문집
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• 2006.11a
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• pp.290-296
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• 2006

Control algorithms and implementation issues for a wind turbine simulator are presented for realistic emulation of variable wind characteristics using a lab-scale motor and generator set. When the average wind speed nd turbulence level is given, the torque reference of prime mover is decided through various blocks, such as random wind speed generator, blade characteristic curves, and tower effect compensation. The variable nature of wind can be implemented and tested by not only the computer simulation but also the hardware-in-loop-simulator (HILS). Some application examples of HILS include the development and test of turbine control software for more efficient and stable operation. Feasibility of the proposed simulator has verified by computer simulations and experiment.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.49-54
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• 2002

For arbitrary random variables {$X_{n},n{\geq}1$}, the order of growth of the series. $S_{n}\;=\;{\sum}_{j=1}^n\;X_{j}$ is studied in this paper. More specifically, when the series S_{n}$ diverges almost surely, the strong law of large numbers $S_{n}/g_{n}^{-1}$($A_{n}{\psi}(A_{n}))\;{\rightarrow}\;0$ a.s. is constructed by extending the results of Petrov (1973). On the other hand, if the series $S_{n}$ converges almost surely to a random variable S, then the tail series $T_{n}\;=\;S\;-\;S_{n-1}\;=\;{\sum}_{j=n}^{\infty}\;X_{j}$ is a well-defined sequence of random variables and converges to 0 almost surely. For the almost surely convergent series $S_{n}$, a tail series strong law of large numbers $T_{n}/g_{n}^{-1}(B_{n}{\psi}^{\ast}(B_{n}^{-1}))\;{\rightarrow}\;0$ a.s., which generalizes the result of Klesov (1984), is also established by investigating the duality between the limiting behavior of partial sums and that of tail series. In particular, an example is provided showing that the current work can prevail despite the fact that previous tail series strong law of large numbers does not work.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.717-729
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• 2014

Projection pursuit classification tree uses a 1-dimensional projection with the view of the most separating classes in each node. These projection coefficients contain information distinguishing two groups of classes from each other and can be used to calculate the importance measure of classification in each variable. This paper reviews the variable importance measure with increasing interest in line with growing data size. We compared the performances of projection pursuit classification tree with those of classification and regression tree(CART) and random forest. Projection pursuit classification tree are found to produce better performance in most cases, particularly with highly correlated variables. The importance measure of projection pursuit classification tree performs slightly better than the importance measure of random forest.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.123-130
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• 2003

The estimation of key soil properties and subsequent quantitative assessment of the associated uncertainties has always been an important issue in geotechnical engineering. It is well recognized that soil properties vary spatially as a result of depositional and post-depositional processes. The stochastic nature of spatially varying soil properties can be treated as a random field. A practical statistical approach that can be used to systematically model various sources of uncertainty is presented in the context of reliability analysis of slope stability Newly developed expressions for probabilistic characterization of soil properties incorporate sampling and measurement errors, as well as spatial variability and its reduced variance due to spatial averaging. Reliability analyses of the probability of slope failure using the different statistical representations of soil properties show that the incorporation of spatial correlation and conditional simulation leads to significantly lower probability of failure than obtained using simple random variable approach.

• Journal of KSNVE
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• v.8 no.2
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• pp.251-259
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• 1998

The motion of an aircraft landing gear over rough runway at variable speed is nonstationary. In this paper, a method for the computation of nonstationary response variance is presented which uses a state space form for the combination of landing gear and runway excitation. The dynamic characteristics of the landing gear under nonstationary random excitations has also been analyzed using the proposed method. The formulation is for linear systems of arbitrary order and allows any deterministic velocity history. It has been found by a series of simulation that correlation parameter, damping coefficients of landing gear and tire, and velocity profiles play a prominent role on the dynamic characteristics.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.10-16
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• 2015

In this study, we suggest a method to predict probability distribution of a new customer's degree of loyalty using C-CRF that reflects the RFM score and similarity to the neighbors of the customer. An RFM score prediction model is introduced to construct the first feature function of C-CRF. Integrating demographical similarity, purchasing characteristic similarity and purchase history similarity, we make a unified similarity variable to configure the second feature function of C-CRF. Then parameters of each feature function are estimated and we train our C-CRF model by training data set and suggest a probabilistic distribution to estimate a new customer's degree of loyalty. An example is provided to illustrate our model.