• Journal of the Korea Society for Simulation
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• v.1 no.1
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• pp.77-86
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• 1992

In this paper, we study validation test methods of DEVSA(Descrete Event system Specification) models using SPN(Stochastic Petri Net) models. We discuss conventional validation test methods, by which DEVS models can be transformed to SPN models, by reviewing the features of DEVS model. Based on the model transformation method, we define a new homogeneous function for validation test and suggest a new validation test method of DEVS models using the property of SPN models and the new homogeneous function.

• 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.

• Toxicological Research
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• v.32 no.1
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• pp.9-14
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• 2016

The National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) provides validation support for US Federal agencies and the US Tox21 interagency consortium, an interagency collaboration that is using high throughput screening (HTS) and other advanced approaches to better understand and predict chemical hazards to humans and the environment. The use of HTS data from assays relevant to the estrogen receptor signaling data pathway is used as an example of how HTS data can be combined with computational modeling to meet the needs of US agencies. As brief summary of US efforts in the areas of biologics testing, acute toxicity, and skin sensitization will also be provided.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.765-775
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• 2012

The cross-validation is a popular method to select bandwidth in all types of kernel estimation. The maximum likelihood cross-validation, the least squares cross-validation and biased cross-validation have been proposed for bandwidth selection in kernel density estimation. In the case that the probability density function has a discontinuity point, Huh (2012) proposed a method of bandwidth selection using the maximum likelihood cross-validation. In this paper, two forms of cross-validation with the one-sided kernel function are proposed for bandwidth selection to estimate the location and jump size of the discontinuity point of density. These methods are motivated by the least squares cross-validation and the biased cross-validation. By simulated examples, the finite sample performances of two proposed methods with the one of Huh (2012) are compared.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.139-152
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• 2012

The result of the analysis of a population pharmacokinetic model can directly influence the decision of the dose level applied to the targeted patients. Therefore the validation procedure of the final model is very important in this area. This paper reviews the validation methods of population pharmacokinetic models from a statistical viewpoint. In addition, the whole procedure of the analysis of population pharmacokinetics, from the base model to the final model (that includes various validation procedures for the final model) is tested with real clinical data.

• Journal of Korean Society for Quality Management
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• v.41 no.3
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• pp.423-432
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• 2013

Purpose: A Study on the application of quality management system specification to production validation and audit in military airworthiness certification. Methods: Aircraft quality management system specification for quality assurance and production validation and audit requirements were examined to verify. Also, the system for domestic and foreign production certification were investigated. Results: Production validation and audit criteria for military aircraft by applying methods suggested Aircraft Certifications Systems Evaluation Program(ACSEP). ACSEP evaluation of the items, some items were complementary and not applied. Conclusion: As a way to ensure the safety of aircraft, confirm the correction of Production validation & audit criteria and rulemaking is necessary and how to manage for Critical Safety Item(CSI) is a need to improve.

• Journal of Digestive Cancer Research
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• v.11 no.3
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• pp.157-164
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• 2023

Prediction is a significant topic in clinical research. The development and validation of a prediction model has been increasingly published in clinical research. In this review, we investigated analytical methods and validation schemes for a clinical prediction model used in digestive cancer research. Deep learning and logistic regression, with split-sample validation as an internal or external validation, were the most commonly used methods. Furthermore, we briefly introduced and summarized the advantages and disadvantages of each method. Finally, we discussed several points to consider when conducting prediction model studies.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.277-285
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• 1998

The smoothing parameter $\lambda$ in smoothing spline regression is usually selected by minimizing cross-validation (CV) or generalized cross-validation (GCV). But, simple CV or GCV is poor candidate for estimating prediction error. We defined MGCV (Multifold Generalized Cross-validation) as a criterion for selecting smoothing parameter in smoothing spline regression. This is a version of cross-validation using $leave-\kappa-out$ method. Some numerical results comparing MGCV and GCV are done.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1403-1413
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• 2021

In this paper, we investigate automated data validation techniques for artificial intelligence training, and also disclose open-source frameworks, such as Google's TensorFlow Data Validation (TFDV), that support automated data validation in the AI model development process. We also introduce an experimental study using public data sets to demonstrate the effectiveness of the open-source data validation framework. In particular, we presents experimental results of the data validation functions for schema testing and discuss the limitations of the current open-source frameworks for semantic data. Last, we introduce the latest studies for the semantic data validation using machine learning techniques.

• Journal of Pharmaceutical Investigation
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• v.21 no.3
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• pp.179-188
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• 1991

An HPLC assay method of a drug to be applied to the pharmacokinetic studies of the drug should be completely validated. The validation process for an HPLC assay method in a biological sample was discussed using the data obtained from the development of HPLC method for the simultaneous quantitation of verapamil and norverapamil in human serum. The validation criteria included were specificity, linearity, accuracy, precision, sensitivity, recovery, drug stability, and ruggedness of an assay method.