• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1447-1448
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• 2011

The CSPE cables are used for three years in nuclear power plant. The accelerated thermal aging of chloro sulfonate polyethylene(CSPE) jacket of test cables were carried out for the period equal to 10, 20 and 30 years in air at 90 and $100^{\circ}C$, respectively. The electrical volume resistivity, density, XPS, FE-SEM, EDS and XRF of the accelerated thermal aging of CSPE were measured. The validation of condition monitering method of accelerated thermal aging CSPE was estimated by them. The best validation of condition monitoring method of accelerated aging CSPE is electrical volume resistivity because change thermal of the specimen showed distinction.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.4
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• pp.341-348
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• 2007

One of the controversial research issues on nanofluids is the temperature dependence of the thermal conductivity of nanofluids, that is, whether it will increase or decrease according to the temperature rise. To evaluate precisely the thermal conductivity behavior of nanofluids, a systematic way of validation experiments for the measuring instrument has been highly recommended. In this paper, procedure of the validation test for transient hot-wire method using the temperature dependence of the base fluids was explained comprehensively and the comparison of the temperature dependence of water-$Al_2O_3$ nanofluids is made between the present work and that of Das et al.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.99-105
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• 2011

We propose a semi-supervised learning algorithm based on a form of regularization that incorporates similarity and dissimilarity penalty terms. Our approach uses a graph-based encoding of similarity and dissimilarity. We also present a model-selection method which employs cross-validation techniques to choose hyperparameters which affect the performance of the proposed method. Simulations using two types of dat sets demonstrate that the proposed method is promising.

• 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 Welding and Joining
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• v.22 no.1
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• pp.50-57
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• 2004

This paper provides FE and experimental validation of the defect assessment method for strength mismatched welded structures, resulting from the Brite Euram SINTAP (Structural Integrity Assessment Procedures for European Industry) project. This shows that the proposed method is conservative, and that the degree of conservatism is similar to that embedded in the methods for homogeneous structures. It provides confidence in the use of the proposed SINTAP method for assessing defective weld strength mismatched structures.

• 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 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.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.13-18
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• 2022

In this paper, we proposed VW-FedAVG(Validation based Weighted FedAVG) which updates the global model by weighting according to performance verification from the models of each device participating in the training. The first method is designed to validate each local client model through validation dataset before updating the global model with a server side validation structure. The second is a client-side validation structure, which is designed in such a way that the validation data set is evenly distributed to each client and the global model is after validation. MNIST, CIFAR-10 is used, and the IID, Non-IID distribution for image classification obtained higher accuracy than previous studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.161-166
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• 2018

In this paper, we propose a method for the random selection of pooling operations for the regularization and reduction of cross validation in convolutional neural networks. The pooling operation in convolutional neural networks is used to reduce the size of the feature map and for its shift invariant properties. In the existing pooling method, one pooling operation is applied in each pooling layer. Because this method fixes the convolution network, the network suffers from overfitting, which means that it excessively fits the models to the training samples. In addition, to find the best combination of pooling operations to maximize the performance, cross validation must be performed. To solve these problems, we introduce the probability concept into the pooling layers. The proposed method does not select one pooling operation in each pooling layer. Instead, we randomly select one pooling operation among multiple pooling operations in each pooling region during training, and for testing purposes, we use probabilistic weighting to produce the expected output. The proposed method can be seen as a technique in which many networks are approximately averaged using a different pooling operation in each pooling region. Therefore, this method avoids the overfitting problem, as well as reducing the amount of cross validation. The experimental results show that the proposed method can achieve better generalization performance and reduce the need for cross validation.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.920-923
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• 2002

In this paper, we propose Class Diagram With Constraints (CDWC) as an object oriented modeling technique which makes validation possible in software development. CDWC is a simple and basic model for the object oriented analysis, and has a reasonable strictness for software developers. CDWC consists of class diagrams and constraints (invariant and pre/post conditions), using UML and a subset of OCL.. We introduce a method of validation of CDWC using the verification technique of algebraic formal specification language CafeOBJ.