• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.302-305
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• 2004

The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology, and land-use were constructed from a spatial database. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, diameter, and density of forest were extracted from the forest database. Lithology was extracted from the geological database and land-use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using landslide-occurrence factors by logistic multiple-regression methods. For validation and cross-validation, the result of the analysis was applied both to the study area, Boun, and another area, Youngin, Korea. The validation and cross-validation results showed satisfactory agreement between the susceptibility map and the existing data with respect to landslide locations. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.525-534
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• 2017

The open set recognition method should be used for the cases that the classes of test data are not known completely in the training phase. So it is required to include two processes of classification and the validation test. This kind of research is very necessary for commercialization of face recognition modules, but few domestic researches results about it have been published. In this paper, we propose an open set face recognition method that includes two sequential validation phases. In the first phase, with dummy classes we perform classification based on sparse representation. Here, when the test data is classified into a dummy class, we conclude that the data is invalid. If the data is classified into one of the regular training classes, for second validation test we extract four features and apply them for the proposed decision function. In experiments, we proposed a simulation method for open set recognition and showed that the proposed validation test outperform SCI of the well-known validation method

• 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.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.995-1004
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• 2007

In this paper, we analyse online word-of-mouth and Korean box-office sales data based on kernel regression method. To do this, we consider the regression model with mixed-data and apply the least square cross-validation method proposed by Li and Racine (2004) to the model. We found the box-office sales can be explained by volume of online word-of-mouth and the characteristics of the movies.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.949-954
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• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.309-316
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• 2010

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among response and input variables. In this paper we propose a weighted SVQR for the longitudinal data. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of SVQR. Experimental results are the presented, which illustrate the performance of the proposed SVQR.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.291-296
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• 2003

This study estimated surface temperature by using split-window technique and NOAA/AVHRR data was used. For surface monitoring, cloud masking procedure was carried out using threshold algorithm. The daily maximum air temperature is estimated by multiple regression method using independent variables such as satellite-derived surface temperature, EDD, and latitude. When the EDD data added, the highest correlation shown. This indicates that EDD data is the necessary element for estimation of the daily maximum air temperature. We derived correlation and experience equation by three approaching method to estimate daily maximum air temperature. 1) non-considering landcover method as season, 2) considering landcover method as season, and 3) just method as landcover. The last approaching method shows the highest correlation. So cross-validation procedure was used in third method for validation of the estimated value. For all landcover type 5, the results using the cross-validation procedure show reasonable agreement with measured values(slope=0.97, intercept=-0.30, R$^2$=0.84, RMSE=4.24$^{\circ}C$). Also, for all landcover type 7, the results using the cross-validation procedure show reasonable agreement with measured values(slope=0.993, Intercept=0.062, R$^2$=0.84, RMSE=4.43$^{\circ}C$).

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1528-1539
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• 2023

Since the leakage of sodium in an SFR (sodium-cooled fast reactor) causes an explosion upon reaction with air and water, sodium leakages represent an important safety issue. In this study, a novel technique for improving the reliability of sodium leakage detection applying DDVR (dynamic data validation and reconciliation) is proposed and verified to resolve this technical issue. DDVR is an approach that aims to improve the accuracy of a target system in a dynamic state by minimizing random errors, such as from the uncertainty of instruments and the surrounding environment, and by eliminating gross errors, such as instrument failure, miscalibration, or aging, using the spatial redundancy of measurements in a physical model and the reliability information of the instruments. DDVR also makes it possible to estimate the state of unmeasured points. To validate this approach for supporting sodium leakage detection, this study applies experimental data from a sodium leakage detection experiment performed by the Korea Atomic Energy Research Institute. The validation results show that the reliability of sodium leakage detection is improved by cooperation between DDVR and hardware measurements. Based on these findings, technology integrating software and hardware approaches is suggested to improve the reliability of sodium leakage detection by presenting the expected true state of the system.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.789-800
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• 2008

Current credit evaluation models based on financial data make use of smoothing estimated default ratios which are transformed from each financial variable. In this work, some problems of the credit evaluation models developed by financial experts are discussed and we propose improved credit evaluation models based on the stepwise variable selection method and Box-Cox transformed data whose distribution is much skewed to the right. After comparing goodness-of-fit tests of these models, the validation of the credit evaluation models using statistical methods such as the stepwise variable selection method and Box-Cox transformation function is explained.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.2
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• pp.114-120
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• 2000

Fossil power plant availability is significantly affected by gradual degradations of equipment as operation of the plant continues. It is quite important to determine whether or not to replace some equipment and when to replace the equipment. Performance calculation and analysis can provide the information. Robustness in the performance calculation can be increased by using verification ＆ validation of measured input data. We suggest new algorithm in which estimation relation for validated measurement can be obtained using correlation between measurements. Input estimation model is obtained using design data and acceptance measurement data of domestic 16 fossil power plant. The model consists of finding mostly correlated state variable in plant state and mapping relation based on the model and current state of power plant.