• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.1-24
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• 1996

In selecting a final tree, Breiman, Friedman, Olshen, and Stone(1984) compare the prediction risks of a pair of tree, where one contains the other, using the standard error of the prediction risk of the larger one. This paper proposes an approach to selection of a final tree by using the standard error of the difference of the prediction risks between a pair of trees rather than the standard error of the larger one. This approach is compared with CART's for simulated data from a simple regression model. Asymptotic results of the approaches are also derived and compared to each other. Both the asymptotic and the simulation results indicate that final trees by CART tend to be smaller than desired.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.5
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• pp.42-47
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• 2012

Construction equipment is heavily loaded during normal operation. In recent years, there is a trend that lower gear noise levels are demanded for drivers to avoid annoyance and fatigue during operation. For articulated hauler's final drive, meshing transmission error(T.E.) is the excitation that leads the tonal noise known as gear whine, and radiated gear whine is also the dominant source of noise in the whole gearbox. This paper presents a method for the analysis of the tooth profile modification, and the prediction of transmission error under the loaded torques for the spur gear pair of the articulated hauler's final drive. And the transmission error, transmission error harmonics and contact stress are also calculated and compared before and after tooth modification under one torque. The simulation result shows that the transmission error and contact stress under the loads can be minimized by the appropriate tooth profile modification.

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• Journal of Industrial Technology
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• v.28 no.A
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• pp.11-17
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• 2008

Applicability of hyperbolic settlement prediction method to consolidation settlement in the dredged and reclaimed ground was assessed by analyzing results of centrifuge tests modelling self-weight consolidation of soft marine clay. From literature review about self-weight consolidation of soft marine clays located in southern coast in Korea, constitutive relationships of void ratio - effective stress - permeability and typical self-weight consolidation curves with time were obtained by analyzing centrifuge model experiments. For the condition of surcharge loading, exact solution of consolidation settlement curve obtained by using Terzaghi's consolidation theory was compared with results predicted by the hyperbolic method. It was found to have its own inherent error to predict final consolidation settlement. From results of analyzing thc self-weight consolidation with time by using this method, it predicted relatively well in error range of 0.04~18% for the case of showing the linearity in the relationship between T vs T/S in the stage of consolidation degree of 60~90 %. However, it overestimated the final settlement with large errors if those relation curves were nonlinear.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2562-2570
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• 2014

One of the important hints for inferring the function of unknown proteins is the knowledge about protein subcellular localization. Recently, there are considerable researches on the prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular localization. In this paper, label power-set classification is improved for the accurate prediction of multiple subcellular localization. The predicted multi-labels from the label power-set classifier are combined with their prediction probability to give the final result. To find the accurate probability estimates of multi-classes, this paper employs pair-wise comparison and error-correcting output codes frameworks. Prediction experiments on protein subcellular localization show significant performance improvement.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.4
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• pp.43-52
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• 1986

With increasing of computer use, a least squares method is now widely used in the regression analysis of various data. Unreliable results of regression coefficients due to the floating point of computer and problems of ordinary least squares method are described in detail. To improve these problems, a least squares method using orthogonal function is developed. Also, Comparison and analysis are performed through an example of numerical test, and re-orthogonalization method is used to increase the accuracy. As an example of application, the optimum order of AR process for the time series of monthly flow at the Pyungchang station is determined using Akaike's FPE(Final Prediction Error) which decides optimum degree of AR process. The result shows the AR(2) process is optimum to the series at the station.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.3
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• pp.49-58
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• 2020

Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.

• Transactions of Materials Processing
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• v.23 no.7
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• pp.446-452
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• 2014

In the current study finite element forming analysis is performed to understand the final geometric accuracy limitations for the stamping of an automotive S-rail from four different steel sheets having tensile strengths of 340MPa, 440MPa, 590MPa and 780MPa. Comparisons between the analysis and the experiments for both springback and formability as measured by the amount of edge draw-in and the thickness distribution were conducted. The springback modes were classified according to a scheme proposed in the current investigation and the error was calculated using the normalized root mean square error method. While the analysis results show fairly good agreement with the experimental data for deformation and formability, the simulation accuracy is lower for predicting wall curl, camber and section twist as the UTS of steel sheet increases.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.6
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• pp.681-689
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• 2009

Disk grinding was often applied to deburring process in order to enhance the final product quality. Inherent chamfering capability of the flexible disk grinding process in the early stage was analyzed with respect to various process parameters including workpiece length, wheel speed, depth of cut and feed. Initial chamfered edge defined as arc zone was characterized with local radius of curvature. Averaged radius and arc zone ratio was well evaluated using neural network system. Additional neural network analysis adding workpiece length showed enhance performance in predicting arc zone ratio and curvature radius with reduced error rate. A process condition design parameter was estimated using remaining input and output parameters with the prediction error rate lower than 2.0% depending on the relevant input parameter combination and neural network structure composition.