• The Korean Journal of Applied Statistics
• /
• v.16 no.1
• /
• pp.141-150
• /
• 2003

This paper presents the multiple testing method of an autoregressive parameter in stationary AR(1) model using the usual Bayes factor. As prior distributions of parameters in each model, uniform prior and noninformative improper priors are assumed. Posterior probabilities through the usual Bayes factors are used for the model selection. Finally, to check whether these theoretical results are correct, simulated data and real data are analyzed.

• Proceedings of the KIEE Conference
• /
• 1989.07a
• /
• pp.158-161
• /
• 1989

This paper describes the AR model of EMG signal during maximum voluntary contraction. By comparing the AR coefficients and the reflection coefficients of the AR model with the median frequency of power spectrum, it if proved that muscle fatigue can be measured by the AR and the reflection coefficients. In the estimation procedure of AR model parameter, the auto-correlation method is superior to the covariance method, and it is determined that the optimal order is six. As the muscle becomes fatigue, the median frequency of power spectrum is declined, and the AR coefficient [$a_1$ ] and the reflection coefficient [$k_1$ ] are also decreased. Therefore the muscle fatigue can be measured by the AR parameter.

• Journal of Biomedical Engineering Research
• /
• v.10 no.1
• /
• pp.11-16
• /
• 1989

This paper describes the AR model of EMG signal during maximum voluntary contraction. By comparing the AR coefficients and the reflection coefficients of the AR model with the median frequency of power spectrum, it is proved that muscle fatigue can be measured by the AR and the reflection coefficients. In the estimation procedure of AR model parameter, the autocorrelation method is superior to the covariance method, and it is determined that the optimal order is six. As the muscle becomes fatigue, the median frequency of power spectrum is declined, and the AR coefficient [$a_1$] and the reflection coefficient [$k_1$] are also decreased. Therefore the muscle fatigue can be measured by the AR parameter.

• Journal of Welding and Joining
• /
• v.17 no.6
• /
• pp.100-107
• /
• 1999

In this paper, we deal with the speckle noise reduction and parameter estimation of ultrasonic NDT(non-destructive test) signals obtained during weld inspection of piping. The overall approach consists of three major steps, namely, speckle noise analysis, proposition of wavelet domain AR(autoregressive) model and flaw detection by proposed model parameter. The data are first processed whereby signals obtained using vertical and angle beam transducer. Correlation properties of speckle noise are then analyzed using multiresolution analysis in wavelet domain. The parameter estimation curve obtained using the proposed model is classified a flaw in weld region where is contaminated by severe speckle noise and also clear flaw signal is obtained through CA-CFAR threshold estimator that is a nonlinear post-processing method for removing the noise from reconstructed ultrasonic signal.

• Journal of the military operations research society of Korea
• /
• v.8 no.1
• /
• pp.71-76
• /
• 1982

A procedure is established for combining a regression model and a time series model to fit to a set of autocorrelated data. This procedure is based on an iterative method to compute regression parameter estimates and time series parameter estimates simultaneously. The time series model which is discussed is basically AR(p) model, since MA(q) model or ARMA(p,q) model can be inverted to AR({$\infty$) model which can be approximated by AR(p) model. The procedure discussed in this articled is applied in general to any combination of regression model and time series model.

• Smart Structures and Systems
• /
• v.11 no.1
• /
• pp.1-17
• /
• 2013

This paper intended to investigate the feasibility of bridge health monitoring using a linear system parameter of a time series model identified from traffic-induced vibrations of bridges through a laboratory moving vehicle experiment on scaled model bridges. This study considered the system parameter of the bridge-vehicle interactive system rather than modal ones because signals obtained under a moving vehicle are not the responses of the bridge itself but those of the interactive system. To overcome the shortcomings of modal parameter-based bridge diagnosis using a time series model, this study considered coefficients of Autoregressive model (AR coefficients) as an early indicator of anomaly of bridges. This study also investigated sensitivity of AR coefficients in detecting anomaly of bridges. Observations demonstrated effectiveness of using AR coefficients as an early indicator for anomaly of bridges.

• The Journal of the Acoustical Society of Korea
• /
• v.14 no.3
• /
• pp.90-96
• /
• 1995

A new method for autoregressive parameter estimation from noisy observation sequence is presented. This method, termed the AP method, is a result of an attempt to make use of the adaptive predictor which is a simple and reliable way of parameter estimation. It is shown theoretically that, for noisy input, the parameter vector computed from the prediction sequence is closer to that of the original sequence than the noisy input sequence is, under the spectral distortion criterion. Simulation results with the Kalman filter as a noise reduction filter and real speech data supported the theory. Roughly speaking, the performance of the parameter set obtained by the AP method is better than noisy one but worse than the EM iteration results. When the simplicity is considered, it could provide a useful alternative to more complicated parameter estimation methods in some applications.

• Journal of the Korean Statistical Society
• /
• v.26 no.3
• /
• pp.417-427
• /
• 1997

In this paper the parameter change problem in the stationary time series is considered. We propose a cumulative sum (CUSUM) of squares-type test statistic for detection of parameter changes in the AR(1) process. The proposed test statistic is based on the CUSIM of the squared observations and is shown to converge to a standard Brownian bridge. Simulations are performed to evaluate the performance of the proposed statistic and a real example is provided to illustrate the procedure.

• Journal of Welding and Joining
• /
• v.33 no.4
• /
• pp.17-23
• /
• 2015

Laser-gas metal arc hybrid welding has been considered as an alternative process of gas metal arc welding for offshore pipe laying. Fiber delivered high power lasers which enable deep penetration welding were recently developed but high power welding characteristics were not fully understood yet. In this study, the influence of shielding gas composition on welding phenomena in high power laser welding was investigated. Bead shapes, melt ejection and dropping were observed after autogenous laser welding with 100% Ar, Ar-20% $CO_2$, Ar-50% $CO_2$, and 100% $CO_2$ shielding gas. Process parameter window was widest with Ar-50% $CO_2$ shielding gas and the penetration was deepest with 100% $CO_2$ shielding gas. The melt dropping was not observed when Ar-50% $CO_2$ or 100% $CO_2$ shielding gas was supplied.

• Journal of Advanced Marine Engineering and Technology
• /
• v.35 no.1
• /
• pp.108-117
• /
• 2011

Old train carriages have been used to create artificial reefs (AR) as part of programs to enhance ocean fisheries and recreational resources. This study conducted hydraulic modeling experiments to estimate the structural stability of a train carriage AR. By applying fixed- and movable-bed conditions and Froude similitude, theoretical and hydraulic experiments revealed major design forces(e.g., water waves and currents). The results of this study showed that some dimensionless design parameters (e.g., surf similarity parameters, water particle velocity, scouring, and deposition) also affect the stability of an AR under various wave and current field conditions. In the fixed-bed condition, movement of the AR occurred when dimensionless water particle velocity based on the surf similarity parameter was larger than about 0.32. In the moveable-bed condition, the settlement depth (field values) of the AR ranged from 6 to 30 cm. The results indicated that characteristics of the sediment/bed condition and the direction of external forces acting on an AR should be considered when selecting AR sites.