• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1222-1227
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• 1990

The problem of identification of continuous systems is considered when both the discrete input and output measurements are contaminated by white noises. Using a predesigned digital low-pass filter, a discrete-time estimation model is constructed easily without direct approximations of system signal derivatives from sampled data. If the pass-band of the filter is designed so that it includes the main frequencies of both the system input and output signals in some range, the noise effects are sufficiently reduced, accurate estimates can be obtained by least squares(LS) algorithm in the presence of low measurement noises. Two classes of filters(infinite impulse response(IIR) filter and finite impulse response(FIR) filter) are employed. The former requires less computational burden and memory than the latter while the latter is suitable for the bias compensated least squares(BCLS) method, which compensates the bias of the LS estimate by the estimates of the input-output noise variances and thus yields unbiased estimates in the presence of high noises.

• ETRI Journal
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• v.9 no.3
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• pp.3-12
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• 1987

In this paper, a non-classical autocorrelation detector adopting a newly defined test statistic is introduced to solve the typical problem of detecting a narrowband signal transmitted over an additive white Gaussian noise (AWGN) channel. Error probability analyses are performed for a noncoherent frequency-shift-keying (FSK) system employing the proposed test-statistic. Through the histogram approach, the probability density functions of the test-statistics are plotted to explain the analysis model. All numerical results obtained indicate the limited improvement in error performance under the lower signal-to-noise ratio (SNR) and the use of higher number of samples per bit will finally provide the almost same confident potential of improvement in error rate as the system using matched filters (MFs) gives.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.851-854
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• 2000

Digital halftoning is a technique to make an equivalent binary image from scanned photo or graphic images. Low pass filtering characteristic of human visual system can be applied to get the effect of spatial averaging of local area consisted of black and white pixels for gray image. The overlapping of black dot decreases brightness and black dot is very sensitive to human visual system in the bright region. In this paper, for gray-level expression, only bright gray region in the color image is considered for blue noise mask (BNM) approach. To solve this problem, BNM with CMY dot is used for the bright region instead of black dot. Dot-on-dot model with single mask causes the problem making much black dot overlap, color distortion. Therefore approach with three masks for C, M and Y each is proposed to decrease pixel overlap and color distortion.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.239-242
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• 2000

In the present study, the influence of car body structures to the noise and vibration characteristics has been sought. The numerical modal analysis for the body-in-white is employed to predict the vibratory response of structure, and then followed by the experimental modal testing to confirm the validity of the model. Using the results of numerical simulations with the designated modal parameters, the optimal structural configuration has been deduced. Special interests have been paid to the sensitivity of sound quality to the structural integrity. Since the structural integrity has a close relationship to the structure-born noise, the substantially low frequency range, which is far below the frequency range almost barely sensible by human auditory organ but still quite influential to overall impression, is especially examined. The subjective assessment agrees with the objective evaluation by means of traditional sound measures as well as psychoacoustic metrics.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.7
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• pp.74-81
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• 1995

In this paper, a composite controller cosisted of bandpass feedback controller and LQG/LTR controller is applied to a quarter-car model moving on a randomly profiled road. The LQG/LTR controller is used to achieve a design transfer toop. A bandpass feedback controller is adopted to eliminate the response due to the disturbance, which generally can not be measured, confined within an interested frequence range. The random road profile considered as colored noise is shaped from white noise by use of shaping filter. The performance of the composite control system is compared with that of an LQG/LTR control system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.524-529
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• 1993

Component Cost Analysis considers any given system driven by a white noise process as an interconnection of different components, and assigns a metric called "component cost" to each component. These component costs measure the contribution of each component to a predefined quadratic cost function. One possible use of component costs is for model reduction by deleting those components that have the smallest component cost. The theory of Component Cost Analysis is extended to include finite-bandwidth colored noises. The results also apply when actuators have dynamics of their own. When the dynamics of this input are added to the plant, which is to be reduced by CCA, the algorithm for model reduction process will be called Weighted Component Cost Analysis (WCCA). Closed-form analytical expressions of component costs for continuous time case, are also derived for a mechanical system described by its modal data. This is very useful to compute the modal costs of very high order systems beyond Lyapunov solvable dimension. A numerical example for NASA's MINIMAST system is presented.presented.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.382-382
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• 2020

과거 많은 연구에서 다수의 모형의 결과를 이용한 앙상블 방법론은 인공지능 모형 (artificial neural network)의 예측 능력에 향상을 갖고 온다 논하였다. 본 연구에서는 미계측유역의 저수량(low flow)의 예측을 위하여 Jittering을 기반으로 한 인공지능 모형을 제시하고자 한다. 기본적인 방법론은 설명변수들에게 백색 잡음(white noise)를 삽입하여 훈련되는 자료를 증가시키는 것이다. Jittering을 기반으로 한 인공지능 모형에 대한 효과를 검증하기 위하여 본 연구에서는 Multi-output neural network model을 기반으로 모형을 구축하였다. 다음으로 Jittering을 기반으로 한 앙상블 모형을 variable importance measuring algorithm과 결합시켜서 유역특성치와 예측되는 저수량의 특성치들의 관계를 추론하였다. 본 연구에서 사용되는 방법론들의 효용성을 평가하기 위해서 미동북부에 위치하고 있는 총 207개의 유역을 사용하였다. 결과적으로 본 연구에서 제시한 Jittering을 기반으로 한 인공지능 앙상블 모형은 단일예측모형 (single modeling approach)을 정확도 측면에서 우수한 것으로 확인되었다. 또한, 적은 숫자의 앙상블 모형에서도 그 정확성이 단일예측모형보다 우수한 것을 확인하였다. 마지막으로 본 연구에서는 유역특성치들의 효과가 살펴보고자 하는 저수량의 특성치들에 따라서 일관적으로 영향을 미치거나 그 중요도가 변화하는 것을 확인하였다.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2
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• pp.76-85
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• 1994

A speech enhancement algorithm which attenuates nonstationary colored noise is presented In this paper. The algorithm consists of a stationary Kalman filter and the simple speech/nonspeech detector. While the conventional enhancement systems are focused on a stationary and/or white background noise, this study Is focused on the mort realistic nonstationary and nonwhite noise. An AR model-based vector Kalman filter is used as a noise suppression system and a short-time energy threshold logic is used as a speech/nonspeech classifier. For Kalman filtering. noise coefficients are estimated in the nonspeech frame, and speech coefficients are estimated by applying the EM iteration algorithm. Simulation results using the car noise are presented based on the signal-to-noise ratio and informal listening tests. According to the experimental results, background noises in the nonspeech frames are eliminated almost completely, while some distortions are noticed in the speech frames. The distortion becomes severer as the SNR is reduced to 0dB and -5dB. Intelligibility, however, is not degraded significantly.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.405-415
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• 2000

Time series of wind-induced pressure on a structure are modeled using autoregressive moving average (ARMA) model. In an AR process, the current value of the time series is expressed in terms of a finite, linear combination of the previous values and a white noise. In a MA process, the value of the time series is linearly dependent on a finite number of the previous white noises. The ARMA process is a combination of the AR and MA processes. In this paper, the ARMA models with several different combinations of the AR and MA orders are fitted to the wind-induced pressure time series, and the procedure to select the most appropriate ARMA model to represent the data is described. The maximum likelihood method is used to estimate the model parameters, and the AICC model selection criterion is employed in the optimization of the model order, which is assumed to be a measure of the temporal complexity of the pressure time series. The goodness of fit of the model is examined using the LBP test. It is shown that AR processes adequately fit wind pressure time series.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.342-345
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• 2005

This paper describes dynamics analysis of a small training boat and a new type of ship's autopilot not only to keep her course but also to reduce her roll motion. Firstly, statistical analysis through multi-variate auto regressive model is carried out using the real data collected from the sea trial on an actual small training boat Sazanami after the navigational system of the boat was upgraded. It is shown that the roll motion is strongly influenced by the rudder motion and it is suggested that there is a possibility of reducing the roll motion by controlling the rudder order properly. Based on this observation, a new type of ship's autopilot that takes the roll motion into account is designed using the muti-variate modern control theory. Lastly, digital simulations by white noise are carried out in order to evaluate the proposed system and a typical result is demonstrated. As results of simulations, the proposed autopilot had good performance compared with the original data.