• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.75-85
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• 2020

This study examines machine learning cross-validation to utilized create ROI for classification of land cover. The study area located in Sejong and one KOMPSAT-3A image was used in this analysis: procedure on October 28, 2019. We used four bands(Red, Green, Blue, Near infra-red) for learning cross validation process. In this study, we used K-fold method in cross validation and used SVM kernel type with cross validation result. In addition, we used 4 kernels of SVM(Linear, Polynomial, RBF, Sigmoid) for supervised classification land cover map using extracted ROI. During the cross validation process, 1,813 data extracted from 3,500 data, and the most of the building, road and grass class data were removed about 60% during cross validation process. Based on this, the supervised SVM linear technique showed the highest classification accuracy of 91.77% compared to other kernel methods. The grass' producer accuracy showed 79.43% and identified a large mis-classification in forests. Depending on the results of the study, extraction ROI using cross validation may be effective in forest, water and agriculture areas, but it is deemed necessary to improve the distinction of built-up, grass and bare-soil area.

• KSBB Journal
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• v.17 no.4
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• pp.326-332
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• 2002

The quality of biopharmaceutical proteins is strongly affected by a manufacturing process employed to produce Et, and thus validation of the manufacturing bioprocess is a very important issue. Chromatography is probably the most widely used bioprocess unit operation for protein purification. In this study, a miniaturized automatic chromatography system was designed and constructed for scale-down studies for process chromatography validation. This system, named MiniValChrom, has the following features: automatic and repeated operation, flexible sequences and intervals among the steps, on-line and real-time monitoring and control, method files savings, etc. Using the MiniValChrom, we peformed a case study of an abbreviated experiment to estimate chromatographic resin lifetime. BSA (bovine serum albumin) and Cibacron Blue 3G-A were used as the model protein and the resin, respectively. The resin deterioration was evaluated by determining and monitoring the HETP and NTP values from the chromatograms every 5 cycles. It was observed that the HETP and the NTP values were changed by 9% after 15 cycles. The resin lifetime validation could be completed by repeating this experiment until the HETP value reached a predetermined value. The MiniValChrom's concept and the protocol suggested in this study can serve as a rapid and economical tool for the validation studies of bioprocess chromatography system.

• 시스템엔지니어링워크숍
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• s.1
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• pp.27-31
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• 2003

This study is in a part of requirements validation plan for korean rubber-tired AGT system on test track. The AGT system is consisted subsystems as vehicle, signalling, communication, power distribution and infrastructure for rubber tire running on track. The subsystems will be installed and integrated on test track till next year for test and evaluation. This paper shows overview for test and evaluation in terms of system requirements and its validation classification, test track configuration, measuring system requirements and its configuration. The whole process of system integration and its validation will be controlled by means of KMS including documentation.

• Korean Journal of Pharmacognosy
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• v.51 no.1
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• pp.78-85
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• 2020

In this study, we shortly introduced the HPLC method validation guideline for the analysis of functional food which was released from the Ministry of Food and Drug Safety of Korea in Dec 2018. The HPLC method validation was performed through the aforementioned HPLC method validation guideline in order to quantitate scopoletin content from the hot-water extract powder of Artemisia annua Linné. The HPLC method was validated by evaluating specificity, accuracy, precision, limit of quantitation and linearity. All parameters were in the suitable ranges which are designated in the guideline, which indicated the current HPLC method is reliable to quantitate the scopoletin content from the hot-water extract of A. annua.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.155-163
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• 2001

There are cases that revoke the certification because of disclosure of private key, deprivation of qualification and the expiration of a term of validity based on PKI. So, a user have to confirm the public key whether valid or invalid in the certification. There are many method such as CRL, Delta-CRL, OCSP for the cert-validation of certification. But these method many problems which are overload traffic on network and the CRL server because of processing for cert-validation of certification. In this paper we proposed the realtime cert-validation of certification method which solved problems that are data integrity by different time between transmission and receiving for CRL, and overload traffic on network and the CRL server based on DARC.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.10a
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• pp.158-159
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• 2016

This paper aims to analyze design errors prevented by building information modeling (BIM)-based design validation to identify consideration factors for successfully implementing BIM-based design validation in the architecture, engineering, and construction (AEC) projects. More than 1,300 design errors detected by BIM-based design validation in three BIM-based projects in South Korea are categorized according to its causes, work types, and likelihoods to cause project delay and cost overrun. Each design error is analyzed by conducting face-to-face interviews with practitioners in the three projects.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.11
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• pp.485-491
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• 2006

In this paper, we study the problem of model selection for Support Vector Machine(SVM) predictor for short-term load forecasting. The model selection amounts to tuning SVM parameters, such as the cost coefficient C and kernel parameters and so on, in order to maximize the prediction performance of SVM. We propose that Cross-Validation method can be used as a model selection algorithm for SVM-based load forecasting technique. Through the various experiments on several data sets, we found that the difference between the prediction error of SVM using Cross-Validation and that of ideal SVM is less than 5%. This shows that SVM parameters for load forecasting can be efficiently tuned by using Cross-Validation.

• Journal of the Korean Society of Visualization
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• v.19 no.1
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• pp.68-73
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• 2021

In this study, the detection of rubber o-ring defects was carried out using k-fold cross validation and Support Vector Machine (SVM) algorithm. The data process was carried out in 3 steps. First, we proceeded with a frame alignment to eliminate unnecessary regions in the learning and secondly, we applied gray-scale changes for computational reduction. Finally, data processing was carried out using image augmentation to prevent data overfitting. After processing data, SVM algorithm was used to obtain normal and defect detection accuracy. In addition, we applied the SVM algorithm through the k-fold cross validation method to compare the classification accuracy. As a result, we obtain results that show better performance by applying the k-fold cross validation method.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.71-88
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• 2017

This paper compared self-validation and regulatory focus between consumers who felt happy vs. sad prior to decision and explored the effects of self-validation on regulatory focus and information distortion. The results of empirical analysis are as follows. First, consumers who felt happy beforehand revealed larger self-validation and stronger promotion focus than those who felt sad in advance. Second, compared to sadness, just-felt happiness was found to have partially positive impact on promotion focus by means of self-validation and exercise entirely positive impact on information distortion through mediation of self-validation. This study has made theoretic contributions by identifying the differences in the extent of self-validation and promotion focus between happiness and sadness as ambient emotion felt prior to the impending decision making as well as by investigating the effects of self-validation upon information distortion.

• Korean Chemical Engineering Research
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• v.59 no.4
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• pp.532-541
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• 2021

Recently, research on the application of artificial intelligence in the chemical process has been increasing rapidly. However, overfitting is a significant problem that prevents the model from being generalized well to predict unseen data on test data, as well as observed training data. Cross validation is one of the ways to solve the overfitting problem. In this study, the time-series cross validation method was applied to optimize the number of batch and epoch in the hyperparameters of the prediction model for the 2,3-BDO distillation process, and it compared with K-fold cross validation generally used. As a result, the RMSE of the model with time-series cross validation was lower by 9.06%, and the MAPE was higher by 0.61% than the model with K-fold cross validation. Also, the calculation time was 198.29 sec less than the K-fold cross validation method.