• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2010년도 학술발표회
• /
• pp.1645-1648
• /
• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of recurrent neural networks model (RNNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of RNNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2010년도 학술발표회
• /
• pp.1560-1563
• /
• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of support vector machine neural networks model (SVM-NNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of SVM-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• 한국신뢰성학회지:신뢰성응용연구
• /
• 제14권2호
• /
• pp.129-136
• /
• 2014

Reliability test is focusing to detect the unexpected reliability failure and solve them for the high quality of products. The test data should be used to assess and project the current level of interesting product reliability and so it is very important to have the accurately assessing methodology with test data. There are two type of trend for test data as constant and changing one during testing and this paper shows the difference in the assessing results of these two cases. There is less information how to define the existence of reliability growth rate changing and calculate the parameters of the reliability growth models to make an accurate assessment with such condition, so i established the process and mathematical model to calculate the parameters at such condition to make reliability growth curve with high Goodness of Fit. I validated the new method with the data made from Monte Carlo Simulation and case from Demko (1993). Even the assessed result with the new methodology may be different with the case by case because of very diversity in test condition and testing product quality, but the process and method founded in this research can be applied to any case using Duane and AMSAA model for their test data assessment. I also present the evaluation method to see the effectiveness with new one which is a conventional knowledge and not popular to use, so it is possible to compare the results with the newly presented and conventional method for better business decision.

• 한국전기전자재료학회논문지
• /
• 제31권7호
• /
• pp.450-454
• /
• 2018

Because of the rapidly changing environment and high uncertainties, the semiconductor industry is in need of appropriate forecasting technology. In particular, both the cost and time in the test process are increasing because the process becomes complicated and there are more factors to consider. In this paper, we propose a prediction model that predicts a final "good" or "bad" on the basis of preconditioning test data generated in the semiconductor test process. The proposed prediction model solves the classification and regression problems that are often dealt with in the semiconductor process and constructs a reliable prediction model. We also implemented a prediction model through various machine learning algorithms. We compared the performance of the prediction models constructed through each algorithm. Actual data of the semiconductor test process was used for accurate prediction model construction and effective test verification.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2009년도 학술발표회 초록집
• /
• pp.1207-1210
• /
• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the only historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Structural Engineering and Mechanics
• /
• 제9권6호
• /
• pp.601-613
• /
• 2000

Existing models for the shear strength degradation of reinforced concrete members present varied conceptual approaches to interpreting test data. The relative superiority of one approach over the others is difficult to determine, particularly given the sparseness of ideal test data. Nevertheless, existing models are compared using a suite of test data that were used for the development of one such model, and significant differences emerge. Rather than relying purely on column test data, the body of knowledge concerning degradation of concrete as a material is considered. Confined concrete relations are examined to infer details of the degradation process, and to establish a framework for developing phenomenologically-based models for shear strength degradation in reinforced concrete members. The possibility of linking column shear strength degradation with material degradation phenomena is explored with a simple model. The model is applied to the results of 7 column tests, and it is found that such a link is sustainable. It is expected that models founded on material degradation phenomena will be more reliable and more broadly applicable than the current generation of empirical shear strength degradation models.

• Communications for Statistical Applications and Methods
• /
• 제22권4호
• /
• pp.313-320
• /
• 2015

In this research, we compare the efficiency of two test procedures proposed by Prentice and Gloeckler (1978) and Park and Hong (2009) for grouped data with possible right censored observations. Both test statistics were derived using the likelihood ratio principle, but under different semi-parametric models. We review the two statistics with asymptotic normality and consider obtaining empirical powers through a simulation study. The simulation study considers two types of models the location translation model and the scale model. We discuss some interesting features related to the grouped data and obtain null distribution functions with a re-sampling method. Finally we indicate topics for future research.

• Journal of the Korean Statistical Society
• /
• 제28권4호
• /
• pp.489-501
• /
• 1999

In this paper, we consider the panel data regression model in which the disturbances have nested error component. We derive a Lagrange Multiplier(LM) test which is jointly testing for the presence of random individual effects and nested effects under the normality assumption of the disturbances. This test extends the earlier work of Breusch and Pagan(1980) and Baltagi and Li(1991). Further, it is shown that this LM test has the same asymptotic distribution without normality assumption of the disturbances.

• 말소리와 음성과학
• /
• 제9권2호
• /
• pp.85-94
• /
• 2017

Conventional speaker verification systems using time delay neural network, identity vector and probabilistic linear discriminant analysis (TDNN-Ivector-PLDA) are known to be very effective for verifying long-duration speech utterances. However, when test utterances are of short duration, duration mismatch between enrollment and test utterances significantly degrades the performance of TDNN-Ivector-PLDA systems. To compensate for the I-vector mismatch between long and short utterances, this paper proposes to use probabilistic linear discriminant analysis (PLDA) model adaptation with augmented data. A PLDA model is trained on vast amount of speech data, most of which have long duration. Then, the PLDA model is adapted with the I-vectors obtained from short-utterance data which are augmented by using vocal tract length perturbation (VTLP). In computer experiments using the NIST SRE 2008 database, the proposed method is shown to achieve significantly better performance than the conventional TDNN-Ivector-PLDA systems when there exists duration mismatch between enrollment and test utterances.

• Journal of the Korean Data and Information Science Society
• /
• 제9권2호
• /
• pp.263-273
• /
• 1998

In this paper, we consider some approximate testings for the reliability of the stress-strength model when the stress X and strength Y each depends linearly on some explanatory variables z and w, respectively. We construct a bootstrap procedure for testing for various values of the reliability and compare the power of the bootstrap test with the test based on Mann-Whitney type estimator by Park et.al.(1996) for small and moderate sample size.