• Journal of KSNVE
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• v.7 no.2
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• pp.301-310
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• 1997

This paper presents active noise control method to form the near-field quiet zone for passengers in an automobile. The actuator model including interior acoustic plant, speaker and amplifier is experimentally identified in forms of auto-regressive and moving average by means of least mean square algorithm, The digital controller is composed of the regulator and Kalman filter to be designed based on LQG (linear quadratic gaussian). If the actuator model is prefiltered with digital filter to be properly designed for concentrating control performance index on the frequency band of primary noise source, LQG design approach can be effectively applied for the design of headset controller. Experimental results demonstrate that near-field quiet zone showing about 10dB noise reduction at microphone position can be formed using the headset located at passenger seat.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1437-1440
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• 2007

Many hydroclimatic time series are marked by interannual and longer quasi-period features that are associated with narrow band oscillatory climate modes. A time series modeling approach that directly considers such structures is developed and presented. The essence of the approach is to first develop a wavelet decomposition of the time series that retains only the statistically significant wavelet components, and to then model each such component and the residual time series as univariate autoregressive processes. The efficacy of this approach is demonstrated through the simulation of observed and paleo reconstructions of climate indices related to ENSO and AMO, tree ring and rainfall time series. Long ensemble simulations that preserve the spectral attributes of the time series in each ensemble member can be generated. The usual low order statistics are preserved by the proposed model, and its long memory performance is superior to the direction application of an autoregressive model.

• Science of Emotion and Sensibility
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• v.1 no.1
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• pp.93-103
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• 1998

In this paper, we present change of human sensiblity when the 26 healthy female subjects were exposed with visual picture stimulus. We used Intermational Affective Picture System as the visual stimulus. The methods are AutoRegressive(AR) spectrum which is a linear method and Return Map which is a nonlinear mithod. SR spectrum may variability(HRV). The LF/HF of HRV and the variation of Return Map were analyzed from ECG signal of the female subjects. Return Map of RR intervals were analyzed by computiong the variation. When the subjets were stimulated by the pleasant pictures, LF/HF and variation were decreased compared with unpleasant stimulus, We may obtain good parameters for the measurement of the change of human sensibility for the visual picture stimulus.

• Computational Structural Engineering
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• v.2 no.4
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• pp.111-116
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• 1989

Methods for the estimation of the coefficient matrices in the equation of motion for a linear multi-degree-of-freedom structure are studied. For this purpose, the equation of motion is transformed into an auto-regressive and moving average with auxiliary input(ARMAX) model. The ARMAX parameters are evaluated using several methods of parameter estimation : such as the least squares, the instrumental variable, the maximum likelihood and the limited information maximum likelihood methods. Then the parameters of the equation of motion are recovered therefrom. Numerical example is given for a 3-story building model subjected to an earthquake exitation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1240-1245
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• 2015

The purpose of this paper is to investigate the autonomic nervous system activity in various ambient temperatures. To evaluate autonomic function, we use the frequency domain analysis of heart rate variability such as FFT(fast fourier transformation), AR(Auto-Regressive) model and Lomb-Scargle peridogram. HRV(heart rate variability) is calculated by using ECG recorded from 3 different temperature room which temperature is controlled in 18℃(low), 25℃(mid) and 38℃(high), respectively. Totally 22 subjects were participated in the experiment. In the results, the most significant autonomic changes caused by temperature load were found in the HF(high frequency) component of FFT and AR model. And the HF power is decreased by increasing temperature. Significance level was increased by increasing the difference of temperatures.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2983-2985
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• 2000

이 논문의 목적은 시계열 예측 엔진의 개발과 그 엔진을 Application S/W로 구현하는 것이다 시계열 예측 엔진은 과거의 데이터를 분석하여 예측을 위한 식의 차수와 형태를 결정하고 이를 바탕으로 파라미터를 결정한 후 미래의 간을 예측하는 3가지 단계를 거친다. 석기에 쓰이는 기법들은 여러 가지가 있는데 본 논문에서는 ARMA(Auto Regressive Moving Average)를 기본으로 분석하였다 Application S/W는. 개발된 예측 엔진에서 분석될 과거 데이터를 입력받아 예측 엔진 구동에 사용되고 그 결과를 그래프로 나타내는 일련의 과정을 거친다. Application S/W 개발의 많은 Programming Language가 존재하지만 본 논문에서는 Visual C누 +을 사용하였다. 또한 이 논문에선, 특정 교차로를 통과하는 교통량 변화에 대한 데이터를 이용하여 예측을 수행하고. 그 결과를 Application S/W에 적용시켰다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.04a
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• pp.119-126
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• 1996

In this paper, a method of substructural identification is presented for the estimation of localized structural parameters. for this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for the substructure to process the measurement data impaired by noises. The sequential prediction error method is used fer the estimation of unknown localized parameters. Using the substructural method, the number of unknown parameters can be reduced and the convergence and accuracy of estimation can be improved. For some substructures, the effect of the input excitation is expressed in terms of the responses at the inferences with the main structure, and substructural identification may be carried out without measuring the actual input excitation to the whole structure. Example analysis is carried out for idealized structural models of a multistory building and a truss bridge. The results indicate that the present method is effective and efficient for local damage estimation of complex structures.

• International Journal of Aerospace System Engineering
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• v.2 no.2
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• pp.44-49
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• 2015

A gust response analyses method based on Reduced Order Models (ROMs) was developed in the paper. Firstly, taken random signal as the input signal and adopt Single Input-Multi-Output (SIMO) training fashion, a ROM based on Auto-Regressive and Moving Average model (ARMA) was established and validated with the comparison of CFD/CSD and experiment. Then, by introducing control surface deflection and control laws, flutter active suppress was studied. Lastly, through filtering and transferring function, the gust temporal signal is obtained based on Dryden gust model, and gust response and suppress were simulated.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.567-569
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.151-153
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• 2006

we present a method of sequential speech enhancement, where we infer clean speech signal using a Rao-Blackwellized particle filter (RBPF), given a noise-contaminated observed signal. In contrast to Kalman filtering-based methods, we consider a non-Gaussian speech generative model that is based on the generalized auto-regressive (GAR) model. Model parameters are learned by a sequential Newton-Raphson expectation maximization (SNEM), incorporating the RBPF. Empirical comparison to Kalman filter, confirms the high performance of the proposed method.