• Environmental Analysis Health and Toxicology
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• v.20 no.4 s.51
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• pp.297-302
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• 2005

The purpose of this study was to provide the standard method for the analysis of organophosphorous pesticides such as chlorpyrifos, diazinon, malathion and parathion in blood. We performed method validation for these pesticides in blood according to EURACHEM (A focus For Analytical Chemistry in Europe) guide. For the analysis of the pesticides, we used solid-phase extraction ,column (Waters Oasis $HLB^{(R)}$. After the extraction, the supernatants were evaporated to dryness under the nitrogen stream. They were analyzed by gas chromatography/mass spectrometry (GC/MS) after reconstituting with ethanol. Terbufos was used as an internal standard. To validate this method, we performed verification procedures with the following parameters: selectivity, linearity of calibration, accuracy, precision, limit of detection and quantification. Validation data according to Eurachem guide were adequate for our purpose for the analysis of chlorpyrifos, diazinon, malathion and parathion in blood.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.593-605
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• 2006

We have developed a catenary-pantograph dynamic simulation program by using the finite element method and verified the accuracy according to the EN 50319. During the validation process, we have reviewed which the time integration methods is proper for this application. among the Newmark method, Wilson theta method and alpha method. We conclude that the alpha method is the best in terms of computation time and accuracy.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.17-21
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• 2017

In this paper, the service execution accuracy was compared by ontology based rule inference method and machine learning method, and the amount of data at the point when the service execution accuracy of the machine learning method becomes equal to the service execution accuracy of the rule inference was found. The rule inference, which measures service execution accuracy and service execution accuracy using accumulated data and pattern matching on service results. And then machine learning method measures service execution accuracy using cross validation data. After creating a confusion matrix and measuring the accuracy of each service execution, the inference algorithm can be selected from the results.

• Proceedings of the Korean Society of Applied Pharmacology
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• 2002.07a
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• pp.158-169
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• 2002

본 발표에서는 약물 분석 중 특히 생체 매질을 이용하여 임상약리학적 연구나 생체 내 이용률(bioavailability) 연구, 생물학적 동등성(bioequivalence) 연구를 하는 경우의 분석법 검증(bioanalytical method validation)에 대하여 상세히 설명하고자 한다.

• Bulletin of the Korean Chemical Society
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• v.27 no.11
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• pp.1923-1926
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• 2006

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.291-293
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• 2004

The aim of this study was to cross-validate a spatial probabilistic model of landslide likelihood ratios at Boun, Janghung and Yongin, in Korea, using a Geographic Information System (GIS). Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and field surveys. Maps of the topography, soil type, forest cover, lineaments and land cover were constructed from the spatial data sets. The 14 factors that influence landslide occurrence were extracted from the database and the likelihood ratio of each factor was computed. 'Landslide susceptibility maps were drawn for these three areas using likelihood ratios derived not only from the data for that area but also using the likelihood ratios calculated from each of the other two areas (nine maps in all) as a cross-check of the validity of the method For validation and cross-validation, the results of the analyses were compared, in each study area, with actual landslide locations. The validation and cross-validation of the results showed satisfactory agreement between the susceptibility map and the existing landslide locations.

• Industrial Engineering and Management Systems
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• v.8 no.2
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• pp.101-108
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• 2009

Biclustering is a method to extract subsets of objects and features from a dataset which are characterized in some way. In contrast to traditional clustering algorithms which group objects similar in a whole feature set, biclustering methods find groups of objects which have similar values or patterns in some features. Both in clustering and biclustering, validating how much the result is informative or reliable is a very important task. Whereas validation methods of cluster solutions have been studied actively, there are only few measures to validate bicluster solutions. Furthermore, the existing validation methods of bicluster solutions have some critical problems to be used in general cases. In this paper, we review several well-known validation measures for cluster and bicluster solutions and discuss their limitations. Then, we propose several improved validation indices as modified versions of existing ones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.707-718
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• 2008

The methodology of classifying traffics is changing from payload based or port based to machine learning based in order to overcome the dynamic changes of application's characteristics. However, current state of traffic classification using machine learning (ML) algorithms is ongoing under the offline environment. Specifically, most of the current works provide results of traffic classification using cross validation as a test method. Also, they show classification results based on traffic flows. However, these traffic classification results are not useful for practical environments of the network traffic monitoring. This paper compares the classification results using cross validation with those of using split validation as the test method. Also, this paper compares the classification results based on flow to those based on bytes. We classify network traffics by using various feature sets and machine learning algorithms such as J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, and NaiveBayes. In this paper, we find the best feature sets and the best ML algorithm for classifying traffics using the split validation.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.549-557
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• 2016

In this paper we propose multiclassification method for large data sets by ensembling least squares support vector machines (LS-SVM) with principal components instead of raw input vector. We use the revised one-vs-all method for multiclassification, which is one of voting scheme based on combining several binary classifications. The revised one-vs-all method is performed by using the hat matrix of LS-SVM ensemble, which is obtained by ensembling LS-SVMs trained using each random sample from the whole large training data. The leave-one-out cross validation (CV) function is used for the optimal values of hyper-parameters which affect the performance of multiclass LS-SVM ensemble. We present the generalized cross validation function to reduce computational burden of leave-one-out CV functions. Experimental results from real data sets are then obtained to illustrate the performance of the proposed multiclass LS-SVM ensemble.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.4
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• pp.675-688
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• 2014

In this paper, we present an efficient way to determine a suitable value of the regularization parameter using the global generalized cross validation and analyze the experimental results from preconditioned global conjugate gradient linear least squares(Gl-CGLS) method in solving image deblurring problems. Preconditioned Gl-CGLS solves general linear systems with multiple right-hand sides. It has been shown in [10] that this method can be effectively applied to image deblurring problems. The regularization parameter, chosen from the global generalized cross validation, with preconditioned Gl-CGLS method can give better reconstructions of the true image than other parameters considered in this study.