• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.957-963
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• 2007

In this paper, Nonlinear Autoregressive (NAR) method based on Least Square-Support Vector Regression (LS-SVR) is introduced and tested for nonlinear sustained vowel modeling. In the database of total 43 sustained vowel of Benign Vocal Fold Lesions having aperiodic waveform, this nonlinear synthesizer near perfectly reproduced chaotic sustained vowels, and also conserved the naturalness of sound such as jitter, compared to Linear Predictive Coding does not keep these naturalness. However, the results of some phonation are quite different from the original sounds. These results are assumed that single-band model can not afford to control and decompose the high frequency components. Therefore multi-band model with wavelet filterbank is adopted for substituting single band model. As a results, multi-band model results in improved stability. Finally, nonlinear sustained vowel modeling using NAR based on LS-SVR can successfully reconstruct synthesized sounds nearly similar to original voiced sounds.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1117-1120
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• 2004

경제적인 공정분석과 최적화를 위해서는 컴퓨터를 이용한 플라즈마 예측모델이 요구되고 있다. 본 연구에서는 일반화된 회귀 신경망 (GRNN)을 이용하여 플라즈마 증착공정 모델을 개발한다. GRNN의 예측성능은 패턴층 뉴런의 가우시안 함수를 구성하는 학습인자, 즉 spread에 의존한다. 종래의 모델에서는 모든 가우시안 함수의 spread가 동일한 값에서 최적화되었으며, 이로 인해 모델의 예측성능을 향상시키는 데에는 한계가 있었다. 본 연구에서는 유전자 알고리즘 (GA)를 이용하여 다변수 spread를 최적화하는 기법을 개발하였으며, 그 성능을 PECVD 공정에 의해 증착된 SiN 박막의 증착률에 적용하여 평가하였다. $2^{6-1}$ 부분인자 실험계획법에 의해 수집된 데이터를 이용하여 신경망을 학습하였고, 모델적합성 점검을 위해 별도의 12번의 실험을 수행하였다. 가우시안 함수의 spread는 0.2에서 2.0까지 0.2간격으로 증가시켰으며, 최적화한 GA-GRNN모델의 예측성능은 6.6 ${\AA}/min$이었다. 이는 종래의 방식으로 최적화한 모델의 예측성능 (13.5 ${\AA}/min$)과 비교하여 50.7% 향상된 예측성능이며, 이러한 향상은 제안한 GA-GRNN 모델이 플라즈마 공정 모델의 예측성능을 증진하는데 매우 효과적임을 보여준다.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.147-158
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• 2009

Recently, the data model in stream data environment has massive, continuous, and infinity properties. However the stream data processing like query process or data analysis is conducted using a limited capacity of disk or memory. In these environment, the traditional frequent pattern discovery on transaction database can be performed because it is difficult to manage the information continuously whether a continuous stream data is the frequent item or not. In this paper, we propose the method which we are able to predict the frequent items using the regression model on continuous stream data environment. We can use as a prediction model on indefinite items by constructing the regression model on stream data. We will show that the proposed method is able to be efficiently used on stream data environment through a variety of experiments.

• Journal of Korean Tunnelling and Underground Space Association
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• v.13 no.6
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• pp.531-555
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• 2011

This study aims to derive the statistical models for the estimation of the required specifications of a rock TBM as well as for its cutterhead design suitable for a given rock mass condition. From a series of multi-variate regression analysis of 871 TBM driving data and 51 linear rock cutting test results, the optimum models were newly proposed to consider a variety of rock properties and mechanical cutting conditions. When the derived models were applied to two domestic shield tunnels, their predictions of cutter penetration depth, cutter acting forces and cutter spacing were very close to real TBM driving data, showing their high applicability.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• Korean journal of applied entomology
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• v.26 no.1 s.70
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• pp.31-36
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• 1987

A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1805-1806
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• 2007

Silicon nitride (SiN) 박막을 플라즈마 응용화학기상법을 이용하여 증착하였다. SiN박막의 전하밀도는 일반화된 회귀 신경망 (GRNN)을 이용하여 모델링하였다. PECVD 공정은 Box Wilson 실험계획표를 이용하여 수행하였다. GRNN 모델의 예측수행은 유전자 알고리즘 (GA)을 이용하여 최적화하였다. 최적화한 GA-GRNN 모델은 종래의 GRNN 모델과 비교하여, 약55%정도의 예측성능의 향상을 보였다.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.137-148
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• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.6
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• pp.131-142
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• 2007

The road user cost in indirect costs as well as direct costs such as the inspection/ diagnosis cost and the repair/reinforcement cost should be considered as one of the important items in the life-cycle cost-effective design and maintenance of the bridges. To estimate the road user cost, this paper formulates the road user cost as a sum of the user delay cost and the vehicle operating cost considering the detour effect. A numerical traffic simulation and a regression analysis are performed to develop a regression model due to a time delay. The proposed regression model is applied to the generation of the maintenance strategy based on the life-cycle cost and performance, and its effectiveness and applicability is investigated. The road user cost has a great influence on establishing the maintenance strategy, and the proposed regression model could be successfully utilized to estimate the road user cost of a bridge.

• Korean Journal of Construction Engineering and Management
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• v.7 no.5
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• pp.85-93
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• 2006

This study is started from a situation of korean construction which has been undergoing diversity. And risk of construction project has been increased recently. The purpose of this study is to propose the model which is able to estimate the proper manpower by eliciting the variable which is offered in the pre-design and construction phase. The existing method of estimate has a problem with calculating exact costs. For this model, it was analyzed the existing manpower estimating model and used historical data of 38 apartment houses, constructed from 2000 to now. Based on these, the regression model of the construction manpower was built. And then the regression model was verified. The result of verification was relatively adequate in the statistics exept for some cases. This regression model will help make it possible for constructor to estimate the deduction of retirement more accurate than existing method.