• The Journal of the Acoustical Society of Korea
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• v.4 no.1
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• pp.43-54
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• 1985

이 논문에서는 Frequency weighting을 이용하여 선형예측 부호화기의 명료성을 개선하는 방법 을 연구한다. 잡음이 섞이지 않은 음성에 대해서는 음성을 분석하기전에 frequency weighting을 행한다. 또한 잡음이 섞인 음성인 경우에는 잡음성분을 spectral subtraction 방법에 의해서 제거한 다음에 frequency weighting을 준다. 이 때 frequency weighting을 주기 위해서 귀의 특성과 연관되어 잘 알려 진 C- message weighting 함수, flanagan weighting 함수 및 articulation index를 약간 수정한 weighting 함수를 사용했다. 여러 객관적인 distance measure를 사용하여 frequency weighting 방법의 성능을 측정하고 귀로 들어 본 결과, frequency weighting 방법을 사용하여 선형예측 방법에 의한 합성 음의 명료도를 효율적으로 개선할 수 있었다.

• Journal of the Korean Wood Science and Technology
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• v.27 no.3
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• pp.23-30
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• 1999

have been many studies to analyze the behavior of metal plate connector that most widely used to connect light frame wood trusses. Finite element method{FEM) was one of the methods for those studies. FEM using linear model may well be applicable to predict the initial slope of load-displacement curve for metal plate connection. However, displacement may be overestimated above experimental results with the increase of load. Therefore, linear model cannot be used for the nonlinear behavior part. To predict real behavior more exactly, nonlinear term was included to FEM model in this study. It was found out that EA and AA mode showed the high agreement between predicted results and experimental ones. However, EE and AE mode showed a little difference between predicted results and experimental ones in nonlinear part. This results might be caused by insufficient reflection of the slip effect. Consequently, the effect of slip shall be considered to approve the accuracy of nonlinear analysis for the behavior of metal plate connection.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.139-147
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• 2003

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing such as signal prediction. This paper proposes the hybrid network, composed of locally(LRNN) and globally recurrent neural networks(GRNN), to improve dynamics of multilayered recurrent networks(RNN) and then describes nonlinear adaptive prediction using the proposed network as an adaptive filter. The hybrid network consists of IIR-MLP and Elman RNN as LRNN and GRNN, respectively. The proposed network is evaluated in nonlinear signal prediction and compared with Elman RNN and IIR-MLP networks for the relative comparison of prediction performance. Experimental results show that the hybrid network performs better with respect to convergence speed and accuracy, indicating that the proposed network can be a more effective prediction model than conventional multilayered recurrent networks in nonlinear prediction for nonstationary signals.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.5
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• pp.467-475
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• 2015

In this paper, the behaviour of a cantilevered beam was predicted to examine the difference between linear and non-linear static, dynamic analysis for a structure by using CR nonlinear formulation. Then, external transverse static and dynamic loads were applied at the free tip of the beam. Classical theories were used for the present linear analysis and co-rotational dynamic FEM program was used for the present nonlinear analysis. In the static analysis, effects of the load for the beam deflection were observed in both linear and nonlinear analysis. Then, normalized displacement at the tip of the beam was predicted for different frequency ratio and a significant difference was obtained in the vicinity of the resonant frequency. In addition, effects of frequency and time for the beam deflection were investigated to find the frequency delay.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.217-225
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• 2011

In this paper, a seasonal variable selection method using the spectral analysis accompanied with seasonal linear model is suggested. The suggested method is applied to the prediction of intra-day call arrivals at a large North American commercial bank call center and a signi cant intra-month seasonal variable I detected. This newly detected seasonal factor is included in the seasonal linear model and is compared with the seasonal linear models without this variable to see whether the new variable helps to improve the forecasting performance. The seasonal linear model with the new variable outperformed the models without it in one-day-ahead forecasting.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.6
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• pp.32-40
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• 2012

For predicting eigenfrequency and initial growth rate of combustion instabilities in lean premixed gas turbine combustor, linear thermoacoustic analysis model was developed in the current paper. A model combustor was selected for the model validation, which has well-defined inlet and outlet conditions and a relatively simple geometry, compared to the combustor in the previous works. Analytical linear equations for thermoacoustic waves were derived for a given combustion system. It was found that the prediction results showed a good agreement with the measurements, even though there was underestimation for instability frequencies. This underestimation was more obvious for a longer flame (i.e. wider temperature distribution) than for a shorter flame.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2003.08a
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• pp.240-244
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• 2003

파랑의 변형 가운데 천수, 굴절, 회절, 반사를 예측하는 수학적 모형은 크게 두 가지 유형으로 나눌 수 있는데, 첫 번째로 파형경사인 ha(k：파수. $\alpha$：진폭)를 비선형의 매개변수로 하는 Stokes 파랑식이 있고, 두 번째로 상대파고인 $\alpha$/h를 비선형의 매개변수로 하고 상대수심인 kh를 분산성의 매개변수로 하는 천수방정식(Shallow water equation)이 있다. 파랑의 변형 가운데 천수, 굴절만을 예측하고 회절, 반사를 예측하지 못하는 수학적 모형으로는 에너지 이송방정식이 있다. (중략)

• Journal of Biomedical Engineering Research
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• v.20 no.6
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• pp.591-599
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• 1999

연구개에 결함이 있는 사람의 발음은 부적절한 비음이 섞이게 되어 과비음성 비음이 되어 연구개를 복원해주는 시술을 하게 되는데, 과비음성 비음을 정량적으로 측정할 수있다면 시술 결과를 객관화 할 수 있게 된다. 현재 임상적으로 사용되고 있는 방법들은 관혈적이거나 고가의 장비를 필요로 한다. 본 논문에서는 비음의 특징인 스펙트럼에서 zero 의 존재와 비강에 의한 포만트의 존재 사실, 그리고 선형 예측 모델을 이용하여 마이크로폰과 사운드 카드가 장착된 PC로 구현할 수 있는 새로운 과비음성 비음 추정 알고리즘을 제안하였다. 음성 신호의 스펙트럼에 zero가 존재하는 경우, 낮은 차수(order)의 선형 예측 모델이 그 음성을 발음한 성도 시스템에 정확히 적용되지 않는다는 점을 이용하여, 같은 음성에 대한 높은 차수의 선형 예측 모델과의 차이를 이용해서 과비음성의 정량화를 시도했다. 본 논문에서는 제안된 알고리즘은 기존의 Teager Operator를 이용한 알고리즘에 비해서 Nasonmeter 의 측정결과와 더 높은 통계적 상관관계를 보여주었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.37-38
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• 2022

농산물의 산지 가격이나 도매가격이 등락하면, 즉시 또는 일정한 시차 이후에 소비자가격도 등락한다. 본 논문에서는 선형회귀모델을 통해 쌀 가격을 예측하고 쌀 가격에 영향을 미치는 날씨의 시기를 찾아보고자 한다. 이에 따라 KAMIS, 기상자료개방포털, KOSIS에서 수집한 날씨, 생산량, 그리고 소비자물가 등락률 데이터를 이용하여 쌀 가격 예측을 수행하고, 날씨 데이터와 쌀 가격 데이터의 날짜 간격을 두어 날씨가 쌀 가격에 영향을 미치는 시기를 알아보았다. 모델 평가 결과, 2개월 간격을 두고 예측한 RMSE가 164.135로 가장 큰 영향을 미쳤다. 본 연구를 기반으로 향후 다른 농산물의 가격 예측도 가능할 것이며 농산물에 영향을 미치는 변수의 시기도 예측할 수 있을 것으로 기대한다.