• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.453-457
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• 2004

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with first-order autoregressive disturbance.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.3
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• pp.166-172
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• 1989

The model reference adaptive control algorithm (MRAC), which is one of the methods for controlling the speed of a permanent magnet synchronous motor (PMSM), has been developed using the autoregressive (ARMAX) method. Applying this algorithm to a microprocessor which is used in driving PMSM with PI controller, it has been proved that the response speed of the reference input follows closely that of the reference model. It has also been proved by experiments that the quick speed response without over-shoot could be obtained for the motor system with variable parameters.

• Journal of Biosystems Engineering
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• v.34 no.2
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• pp.127-132
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• 2009

In the working population, muscle fatigue and musculoskeletal discomfort are common, which, in the case of insufficient recovery may lead to musculoskeletal pain. Workers suffering from musculoskeletal pains need to be rehabilitated for recovery. Isokinetic testing has been used in physical strengthening, rehabilitation and post-operative orthopedic surgery. Frequency analysis of electromyography (EMG) signals using the mean frequency (MNF) has been widely used to characterize muscle fatigue. During isokinetic contractions, EMG signals present strong nonstationarities. Hilbert-Haung transform (HHT) and autoregressive (AR) model have been known more suitable than Fourier or wavelet transform for nonstationary signals. Moreover, several analyses have been performed within each active phase during isokinetic contractions. Thus, the aims of this study were i) to determine which one was better suitable for the analysis of MNF between HHT and AR model during repetitive maximum isokinetic extensions and ii) to investigate whether the analysis could be repeated for sequential fixed epoch lengths. Seven healthy volunteers (five males and two females) performed isokinetic knee extensions at $60^{\circ}/s$ and $240^{\circ}/s$ until 50% of the maximum peak torque was reached. Surface EMG signals were recorded from the rectus femoris of the right thigh. An algorithm detecting the onset and offset of EMG signals was applied to extract each active phase of the muscle. Following the results, slopes from the least-square error linear regression of MNF values showed that muscle fatigue of all subjects occurred. The AR model is better suited than HHT for estimating MNF from nonstationary EMG signals during isokinetic knee extensions. Moreover, the linear regression can be extracted from MNF values calculated by sequential fixed epoch lengths (p> 0.0I).

• Korean Journal of Social Welfare Studies
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• v.42 no.3
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• pp.183-206
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• 2011

This study examined the longitudinal relationship between academic achievement and school satisfaction using a data of the Seoul Panel Study of Children(SPSC). Fully autoregressive cross-lagged analysis and multi-group comparison were performed to measure the longitudinal relationship between two constructs as well as differences between poverty and non-poverty groups. The results showed that both academic achievement and school satisfaction were stable over time in non-poverty group. Academic achievement at the 4th grade significantly affected the school satisfaction at the 6th grade and it subsequently affected on the academic achievement at the 8th grade in non-poverty group. In contrast, academic achievement was not consistent over time in poverty group. Only the school satisfaction at the 6th grade affected the academic achievement at the 8th grade. The findings of this study have various practical implication for school interventions. It is more important to keep supporting the children to maintain the level of academic achievement in non-poverty group. While, in poverty group, it is essential to make school satisfaction and academic motivation increase with school attachment programs.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.429-442
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• 2015

Volatility forecasting in financial markets is an important issue because it is directly related to the profit of return. The volatility is generally modeled as time-varying conditional heteroskedasticity. A generalized autoregressive conditional heteroskedastic (GARCH) model is often used for modeling; however, it is not suitable to reflect structural changes (such as a financial crisis or debt crisis) into the volatility. As a remedy, we introduce the Markov regime switching GARCH (MRS-GARCH) model. For the empirical example, we analyze and forecast the volatility of the daily Korea Composite Stock Price Index (KOSPI) data from January 4, 2000 to October 30, 2014. The result shows that the regime of low volatility persists with a leverage effect. We also observe that the performance of MRS-GARCH is superior to other GARCH models for in-sample fitting; in addition, it is also superior to other models for long-term forecasting in out-of-sample fitting. The MRS-GARCH model can be a good alternative to GARCH-type models because it can reflect financial market structural changes into modeling and volatility forecasting.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1203-1214
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• 2016

Obesity is a risk factor for various diseases as well as itself a disease and associated with socioeconomic factors. The obesity proportion has been increasing in Korea over about 15 years so that investigation of the socioeconomic factors related with obesity is important in terms of preventation of obesity. In particular, the association between obesity and socioeconomic status varies with gender and has spatial dependency. In the paper, we estimate the effects of socioeconomic factors on obesity proportion by gender, considering the spatial correlation. Here, a conditional autoregressive model under the Bayesian framework is used in order to take into account the spatial dependency. For the real applicaiton, we use the obestiy proportion dataset at 25 districts of Seoul in 2010. We compare the proposed spatial model with a non-spatial model in terms of the goodness-of-fit and prediction measures so the spatial model performs well.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1409-1416
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• 2015

The change of temperature influences on the various aspect, especially human health, plant and animal's growth, economics, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly temperature data at the Suwon monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). Among five meteorological variables, radiation, amount of cloud, and wind speed are more influence on the temperature. The radiation influences during spring, summer and fall, whereas wind speed influences for the winter time. Also, among four greenhouse gas variables and five pollution variables, chlorofluorocarbon, methane, and ozone are more influence on the temperature. The monthly ARE model explained about 43-69% for describing the temperature.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.155-167
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• 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
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• v.38 no.6
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• pp.56-64
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• 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 Korea Society of Computer and Information
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• v.19 no.8
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• pp.19-26
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• 2014

Analysis of influential factors on water consumption in households will help predicting the water demand of end-use in household and give an explanation to cause on the change of trend. In this research, the data are gathered by radio telemetry system which is combined electronic flow-meter and wireless communication system in 140 household in Korea. Using this data, we estimate for each residential type to determine liter per capita day. we used real data to predict bathtub and washbowl water-uses and compared the ordinary least square regression model and autoregressive regression error model. The results of this study can be applied in the planning stages of water and waste water facilities.