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

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

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.1
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• pp.29-43
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• 2012

We design a DEA-AR model using multiple regression analysis with new methods which limit weights. When there are multiple input and single output variables, our model can be used, and the weights of input variables use the regression coefficient and coefficient of determination. To verify the effectiveness of the new model, we evaluate the efficiency of the Regional Corporations in Korea. Accordance with statistical analysis, it proved that there is no difference between the efficiency value of the DEA-AR using AHP and our DEA-AR model. Our model can be applied to a lot of research by substituting DEA-AR model relying on AHP in the future.

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

• International Journal of Safety
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• v.3 no.1
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• pp.1-5
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• 2004

This study discusses frequency analysis based on autoregressive (AR) time series model, and the characterization of surface quality in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized composite material, namely a glass reinforced polyester (GFRP) was used as workpiece. Analysis method employs a force sensor and the signals from the sensor are processed using AR time series model. The experimental correlations between the fiber pull-out and AR model coefficients are then established.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.8
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• pp.622-628
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• 2001

We developed 1- and 2-dimensional fluid model for the analysis of a capacitively coupled Ar RF(Radio Frequency) glow discharge. This discharge is in pure Ar gas at the pressure 100[mTorr], frequency 13.56[MHz] and voltage amplitude 120[V}. This model is based on the equations of continuity and electron energy conservation coupled with Poison equation. 2-dimensional model is simulated on the condition of GEC(Gaseous Electronic Conference cell). The geometry of the discharge chamber and the electrodes used in the model is cylindrically simmetric; tow cylinders for the electrodes are surrounded by the grounded chamber. It is shown that 1-dimensional model is very useful on the understanding of RF glow discharge property and of the movement of charged particles. 2-dimensional model predicts off-axis maximum structure as in the experiments and has the results in qualitatively and quantitatively good agreement with the experiments. Effects of dc self-bias voltage, guard ring and reactor geometry is discussed.

• Geotechnical Engineering
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• v.9 no.3
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• pp.67-76
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• 1993

Korea being composed of a number of mountains has been damaged and destroyed in lives and properties by the occurrence of many landslides during the wet seasons. Therefore, it is necessary to study the forecast system and risk analysis for the occurrence of landslides : the rise of groundwater levels due to rainfall is the main cause of landslides. In this paper, the autoregressive models are used to predict the grondwater levls using cases of both time invariant and time -varing autoregressive coefficients. In the former case, AR(1), AR(2), and AR(3) models are selected and their single-valued parameters are estimated to fit them to the observed groundwater level series. In the latter case, modified AR(1) and typical AR(2) models are used as process model and a discrete Kalman Filtering technique is utilized to estimate the parameters which are themselves a function of time. The results show that the real time forecast system using the time-varying autoregressive coefficinets as well as time -invariant AR model is good to predict the groundwater level in hillside slopes and we might get better result if we use the time-hourly rainfall intensity as well as the observed groundwater level.

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

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.81-88
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• 2010

The analysis and synthesis of various types of hydrologic variables such as precipitation, surface runoff, and discharge are usually required in planning and management of water resources. These hydrologic variables are mostly represented using stochastic models. One of which is the autoregressive model, that gives promising results in time series modeling. This study is an application of this model, which aimed to determine the AR model that best represents the historical monthly streamflow of the two gauging stations, namely Andong Dam and Imha Dam, both located in the upper Nakdong River Basin. AR(3) model was found to be the best model for both gauging stations. Parameters of the determined order of AR model ($\phi_1$, $\phi_2$ and $\phi_3$) were also estimated. Using several diagnostic tests, the efficiency of the determined AR(3) model was tested. These tests indicated the accuracy of the determined AR(3) model.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.27-37
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• 2010

This article concerns the forecasting in binomial AR(p) models which is proposed by Wei$\ss$ (2009b) for time series of binomial counts. Our method extends to binomial AR(p) models a recent result by Jung and Tremayne (2006) for integer-valued autoregressive model of second order, INAR(2), with simple Poisson innovations. Forecasts are produced by conditional median which gives 'coherent' forecasts, and we estimate the forecast distributions of future values of binomial AR(p) models by means of a Monte Carlo method allowing for parameter uncertainty. Model parameters are estimated by the method of moments and estimated standard errors are calculated by means of block of block bootstrap. The method is fitted to log data set used in Wei$\ss$ (2009b).