• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.999-1005
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• 2011

This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.127-133
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• 2001

In this article we establish multivariate cumulative sum (CUSUM) control charts based on residual vector with correlated observations. We first find the residual vector and its expectation and variance-covariance matrix and then evaluate the average run length (ARL) of the control charts.

• Management Science and Financial Engineering
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• v.18 no.1
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• pp.49-53
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• 2012

A novel method of transient stability analysis is presented in this paper. The proposed method extracts data points near the basin-of-attraction boundary and then builds a support vector machine (SVM) model learned from the generated data. The constructed SVM classifier has been shown to reduce dramatically the conservativeness of the estimated basin of attraction.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.363-369
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• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1539-1547
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• 2014

In this paper we apply the autoregressive process to the nonlinear quantile regression in order to infer nonlinear quantile regression models for the autocorrelated data. We propose a kernel method for the autoregressive data which estimates the nonlinear quantile regression function by kernel machines. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of quantile regression function in the presence of autocorrelation between data.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.1 s.3
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• pp.99-106
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• 1994

Raster data mode and Vector data are the two major model in geographic information systems. These two data models are difficult to be intergrated because of their differences in structures and properties. Almost all of the current GIS systems process in one data model by converting one data type to another type. So. the loss and change of information caused by data conversiion degrades the accuracy of data. In this paper, we propose a new data model which can process two data models without conversion. We use quadtree for raster data and topological vector model for vector data. The output is formed as raster data model of quadtree. We can get more accurate overay output, and this intergrated model is more suitable for data like forest, landuses, soils that consist of classes which have small distribution changes.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.789-792
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• 2004

In various fields as web mining, bioinformatics, statistical data analysis, and so forth, very diversely missing values are found. These values make training data to be sparse. Largely, the missing values are replaced by predicted values using mean and mode. We can used the advanced missing value imputation methods as conditional mean, tree method, and Markov Chain Monte Carlo algorithm. But general imputation models have the property that their predictive accuracy is decreased according to increase the ratio of missing in training data. Moreover the number of available imputations is limited by increasing missing ratio. To settle this problem, we proposed statistical learning theory to preprocess for missing values. Our statistical learning theory is the support vector regression by Vapnik. The proposed method can be applied to sparsely training data. We verified the performance of our model using the data sets from UCI machine learning repository.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3475-3496
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• 2018

Most of current watermarking algorithms for GIS vector data embed copyright information by means of modifying the coordinate values, which will do harm to its quality and accuracy. To preserve the fidelity of vector line data and protect its copyright at the same time, a lossless watermarking algorithm is proposed based on storage feature in this paper. Firstly, the superiority of embedding watermark based on storage feature is demonstrated theoretically and technically. Then, the basic concepts and operations on storage feature have been defined including length and angle of the polyline feature. In the process of embedding watermark, the watermark information is embedded into directions of polyline feature by the quantitative mechanism, while the positions of embedding watermark are determined by the feature length. Hence, the watermark can be extracted by the same geometric features without original data or watermark. Finally, experiments have been conducted to show that coordinate values remain unchanged after embedding watermark. Moreover, experimental results are presented to illustrate the effectiveness of the method.

• Journal of Broadcast Engineering
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• v.1 no.1
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• pp.29-34
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• 1996

In data transmission system, some data are more sensitive to channel errors. Unequal error protection method increases transmission reliability by protecting channel error sensitive data more than other data. However, this method cannot be directly applied to vector quantized signals which are designed by LBG algorithm that assumes no channel distortion in the design, process. Therefore, in this paper, to apply unequal error protection to vector quantized signals, we propose a method which systematically assigns binary indexes to code vectors. We applied the proposed method to the transmission of vector quantized first-order Gauss-Marcov signals assuming that the percentage of the important data is 50%

• Korean Journal of Geomatics
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• v.5 no.1
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• pp.27-34
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• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.