• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.9
• /
• pp.547-553
• /
• 2003

In this paper, we designed the PC-based analyzer of the power spectral density that could estimate the heart rate variability from time series data of R-R interval. The power spectral density estimated that it applied the autoregressive model to the measured electrocardiogram during a short period. Also, the characteristics of the designed analyzer are that it could process of the signal filtering, the generation and recomposition of time series and the feature extraction at the same time. Especially the analyzer reconstructed which applied the lowpass filter of the time series composed by the linear interpolation so as to enhance the signal-to-noise feature. We could estimate the power spectral density that confirmed a variety of power peak with low frequency range and high frequency rang of autonomic nerve by the heart rate variability.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.1
• /
• pp.155-167
• /
• 2014

This study uses a continuous autoregressive (CAR) model to analyze daily average temperature in six Korean metropolitan cities. Data period is Jan. 1, 1954 to Dec. 31, 2010 covering 57 years. Using a relative long time series reveals that the linear time trend components are all statistically significant in the six cities, which was not shown in previous studies. Particularly the plus sign of its coefficient implies the effect on Korea of the global warming. Unit-root test results are that the temperature time series are stationary without unit-root. It turns out that CAR(3) is suitable for stochastic component of the daily temperature. Since developing suitable continuous stochastic model of the underlying weather related variables is crucial in pricing the weather derivatives, the results in this study will likely prove useful in further future studies on pricing weather derivatives.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.38 no.6
• /
• pp.56-64
• /
• 2001

In this paper, we proposed the simple and selective method for the order of model that reflected the feature of the heart rate variability without the complicated calculation in the power spectral analysis of heart rate variability using autoregressive model. The power spectral analysis of short-term of heart rate variability using autoregressive have been problem to resolution of spectral estimates by the selective model order. As a result that the proposed method for the order comparative tested with the AIC and the fixed order method, the calculation process could become very simple and select the order which correspond with the feature of the time series. We verified it could removed the noisy power components by the fixed order.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.5
• /
• pp.126-134
• /
• 2009

This paper describes the vision-based system to track road signs from within a moving vehicle. The proposed system has the standard architecture with particle filter due to its robust tracking performance in complex environment. In the case of tracking road signs in real environment, it has a great difficulty in predicting time series data by reason of an occlusion due to an obstacle and the rapid change of objects on roads. To overcome this problem and improve the tracking performance, this paper proposes the algorithm using an autoregressive model as an state transition model which has affine parameters as states and using robust statistics for determining occlusion due to obstacles. The experiments of this paper show that the proposed method is efficient for real time tracking of road signs and performs well in road signs under occlusion due to obstacles.

• Journal of Korea Water Resources Association
• /
• v.39 no.12 s.173
• /
• pp.997-1012
• /
• 2006

We have presented a nonparametric stochastic approach for the SOI(Southern Oscillation Index) series that used nonlinear methodology called Nonlinear AutoRegressive(NAR) based on conditional kernel density function and CAFPE(Corrected Asymptotic Final Prediction Error) lag selection. The fitted linear AR model represents heteroscedasticity, and besides, a BDS(Brock - Dechert - Sheinkman) statistics is rejected. Hence, we applied NAR model to the SOI series. We can identify the lags 1, 2 and 4 are appropriate one, and estimated conditional mean function. There is no autocorrelation of residuals in the Portmanteau Test. However, the null hypothesis of normality and no heteroscedasticity is rejected in the Jarque-Bera Test and ARCH-LM Test, respectively. Moreover, the lag selection for conditional standard deviation function with CAFPE provides lags 3, 8 and 9. As the results of conditional standard deviation analysis, all I.I.D assumptions of the residuals are accepted. Particularly, the BDS statistics is accepted at the 95% and 99% significance level. Finally, we split the SOI set into a sample for estimating themodel and a sample for out-of-sample prediction, that is, we conduct the one-step ahead forecasts for the last 97 values (15%). The NAR model shows a MSEP of 0.5464 that is 7% lower than those of the linear model. Hence, the relevance of the NAR model may be proved in these results, and the nonparametric NAR model is encouraging rather than a linear one to reflect the nonlinearity of SOI series.

• Water Engineering Research
• /
• v.2 no.4
• /
• pp.209-218
• /
• 2001

Generation of evaporation data generally assists in planning, operation, and management of reservoirs and other water works. Annual and monthly evaporation series were generated for King Fahad Dam Lake in Bishah, Saudi Arabia. Data was gathered for period of 22 years. Tests of homogeneity and normality were conducted and results showed that data was homogeneous and normally distributed. For generating annual series, an Autoregressive first order model AR(1) was used and for monthly evaporation series method of fragments was used. Fifty replicates for annual series, and fifty replicates for each month series, each with 22 values length, were generated. Performance of the models was evaluated by comparing the statistical parameters of the generated series with those of the historical data. Annual and monthly models were found to be satisfactory in preserving the statistical parameters of the historical series. About 89% of the tested values of the considered parameters were within the assigned confidence limits

• Proceedings of the IEEK Conference
• /
• 2000.09a
• /
• pp.831-834
• /
• 2000

본 논문은 주파수 전이신호와 시간 전이 신호에 대해서 고조파 잡음 여기 방법과 시간 분리 여기 방법을 적용한 2.4kbps 음성부호화 방법을 제안한다. 혼합 여기 부호화 방법은 주기 신호와 비 주기 신호를 효과적으로 표현하기 위해 하모닉 잡음 모델을 사용한다. 혼합신호에 대한 잡음 성분은 캡스트럴 분석 방법을 사용함으로써 추출되고, AR(Autoregressive Model) 모델에 의해 표현된다. 시간 전이구간 신호에서의 모호한 음성을 효과적으로 제거하기 위한 또 다른 방법이 제안된다. 제안된 시간 분리 방법은 시간 에너지 변화정도를 관찰함으로써 전이 시점을 감지하고 다른 시간 길이를 가지는 두 블록으로 분리하여 분석한다. 시간 분리 방법은 분석을 위한 비대칭 윈도우와 합성에서의 위상 합성 방법을 포함한다. 제안된 방법을 사용한 2.4kbps 음성부호화 방법은 주관적 음질 평가에서 전이구간에서의 지각적 음질의 향상을 보여주었으며, 원본 음성 스펙트럼과의 고조파 비 매칭에 의한 윙윙거리는 기계적인 잡음을 감소시킨다.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.799-804
• /
• 1991

This paper is concerned with the artificial control of prosthetic devices using the electromyographic(EMG) activities of biceps and triceps in human subject during isometric contraction adjustments at the elbow. And it was analysised about recognition of EMG signals and dynamic characteristics at arm movements of human. For this study the error signal of autoregressive(AR) model were used to discriminate arm movement patterns of human. Interaction of dynamic characteristics (Position, Velocity, Acceleration) and EMG of biceps and triceps at arm movements of human was measured.

• The Journal of the Acoustical Society of Korea
• /
• v.19 no.7
• /
• pp.53-58
• /
• 2000

본 논문은 주파수 전이신호와 시간 전이 신호에 대해서 고조파 잡음 여기 방법과 시간 분리 여기 방법을 적용한 2.4 kbps 음성부호화 방법을 제안한다. 혼합 여기 부호화 방법은 주기 신호와 비 주기 신호를 효과적으로 표현하기 위해 하모닉 잡음 모델을 사용한다. 혼합신호에 대한 잡음 성분은 캡스트럴 분석 방법을 사용함으로써 추출되고, AR (Autoregressive Model) 모델에 의해 표현된다. 시간 전이구간 신호에서의 모호한 음성을 효과적으로 제거하기 위한 또 다른 방법이 제안된다. 제안된 시간 분리 방법은 시간 에너지 변화정도를 관찰함으로써 전이 시점을 감지하고 다른 시간 길이를 가지는 두 블록으로 분리하여 분석한다. 시간 분리 방법은 분석을 위한 비대칭 윈도우와 합성에서의 위상 합성 방법을 포함한다. 제안된 방법을 사용한 2.4 kbps 음성부호화 방법은 주관적 음질 평가에서 전이구간에서의 지각적 음질의 향상을 보여주었으며, 원본 음성 스펙트럼과의 고조파 비 매칭에 의한 윙윙거리는 기계적인 잡음을 감소시킨다.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.3E
• /
• pp.13-18
• /
• 1996

A real-time identifier for the nonstationary time-varying signals and systems was implemented using a low cost DSP (digital signal processing) chip. The identifier is comprised of I/O units, a central processing unit, a control unit and its supporting software. In order t estimate the system accurately and to reduce quantization error during arithmetic operation, the firmware was programmed with 64-bit extended precision arithmetic. The performance of the identifier was verified by comparing with the simulation results. The implemented real-time identifier has negligible quantization errors and its real-time processing capability crresponds to 0.6kHz for the nonstationary AR (autoregressive) model with n=4 and m=1.