• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.96-98
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• 2005

In this paper, an algorithm to compensate the distorted signals due to Current Transformer(CT) saturation is suggested, First, DWT which can be easily realized by filter banks in real-time applications is used to detect a start point and an end point of the saturation. Secondly, For enough Datas those need to use the least-square curve fitting method, the distorted current signal is compensated by the AR(autoregressive) model using the data during the previous healthy section until pick point of Saturation. Thirdly, the least-square curve fitting method is used to restore the distorted section of the secondary current. Finaly, this algorithm had a Hadware test using DSP board(TMS320C32) with Doble test device. DWT has superior detection accuracy and the proposed compensation algorithm which shows very stable features under various levels of remanent flux in the CT core is also satisfactory. And this algorithm is more correct than a previous algorithm which is only using the LSQ fitting method. Also it can be used as a MU involving the compensation function that acquires the second data from CT and PT.

• Structural Engineering and Mechanics
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• v.21 no.5
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• pp.553-570
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• 2005

Dynamic and static analyses of existing structures are very important to obtain reliable information relating to actual structural properties. For this purpose a series of material test, dynamic test and static collapse test of the existing two brick chimneys, in Tokoname, are carried out. From the material tests, Young's modulus and compressive strength of the brick used for these chimneys are estimated to be 3200 MPa and 7.5 MPa, respectively. The results of static collapse test of the existing two brick chimneys are discussed in this paper and composed with the results from FEA (Finite Element analysis). From the results of dynamic tests, the fundamental frequencies of Howa and Iwata brick chimneys are estimated to be about 2.69 Hz and 2.93 Hz, respectively. Their natural modes are identified by ARMAV (Autoregressive Moving Average Vectors) model. On the basis of the static and dynamic experimental tests, a numerical model has been prepared. According to the European code (Eurocode n. 8: "Design of structures for earthquake resistance") non-linear static (Pushover) analysis of the two chimneys is carried out and they seem to be vulnerable to earthquakes with 0.25 to 0.35 g.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.

• Clean Technology
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• v.7 no.2
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• pp.119-126
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• 2001

The prediction of ozone formation was studied using the neural network and the stochastic method. Parameter estimation method and artificial neural network(ANN) method were employed in the stochastic scheme. In the parameter estimation method, extended least squares(ELS) method and recursive maximum likelihood(RML) were used to achieve the real time parameter estimation. Autoregressive moving average model with external input(ARMAX) was used as the ozone formation model for the parameter estimation method. ANN with 3 layers was also tested to predict the ozone formation. To demonstrate the performance of the ozone formation prediction schemes used in this work, the prediction results of ozone formation were compared with the real data. From the comparison it was found that the prediction schemes based on the parameter estimation method and ANN method show an acceptable accuracy with limited prediction horizon.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.287-297
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• 2009

In terms of generalized autoregressive conditional heteroscedastic(GARCH) model, estimation of prediction interval based on likelihood is quite sensitive to distribution of error. Moveover, it is not an easy job to construct prediction interval for conditional variance. Recent studies show that the bootstrap method can be one of the alternatives for solving the problems. In this paper, we introduced the bootstrap approach proposed by Pascual et al. (2006). We employed it to Korean stock price data set.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10787-10790
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• 2015

Background: Ovarian cancer is an important cause of mortality in women. The aim of this study was to evaluate the incidence and mortality rates and trends in the Iranian population and make predictions. Materials and Methods: National incidence from Iranian annual of National Cancer Registration report from 2003 to 2009 and National Death Statistics reported by the Ministry of Health and Medical Education from 1999 to 2004 were included in this study. A time series model (autoregressive) was used to predict the mortality for the years 2007, 2008, 2012 and 2013, with results expressed as annual mortality rates per 100,000. Results: The general mortality rate of ovarian cancer slightly increased during the years under study from 0.01 to 0.75 and reaching plateau according to the prediction model. Mortality was higher for older age. The incidence also increased during the period of the study. Conclusions: Our study indicated remarkable increasing trends in ovarian cancer mortality and incidence. Therefore, attention to high risk groups and setting awareness programs for women are needed to reduce the associated burden in the future.

• The Journal of Asian Finance, Economics and Business
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• v.4 no.1
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• pp.25-38
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• 2017

This study investigates the causal relationship between stock prices and exchange rates for six Middle Eastern countries, namely, Egypt, Iran, Jordan, Kuwait, Oman, and Saudi Arabia before and during (after) the 2007 global financial crisis for the period between January 2004 and September 2015. The sample is divided into two sub-periods, that is, the period from January 1, 2004 to September 30, 2007 and the period from October 1, 2007 to September 30, 2015, to represent the pre-crisis period and the post-crisis period, respectively. Using Vector Autoregressive (VAR) model in a multivariate framework (including two control variables, inflation rates and oil prices) the results suggest that in the case of Jordan, Kuwait and Saudi Arabia, there exists bidirectional causalities after the crisis period but not the before. The opposite status is available for the case of Iran. In the case of Oman, there is bidirectional causality between the variables of interest in both periods. The results also reveal that the relationship between stock prices and exchange rates has become stronger after the 2007 global financial crisis. Overall, the results of this study indicate that fluctuations in foreign exchange markets can significantly affect stock markets in the Middle East.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.100.1-100.1
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• 2011

Everybody wants to see the future, but nobody does for sure. Reliably forecasting the solar activity in the near future looks like an easy task, but in fact still remains one of difficult problems in the solar-terrestrial research. We have sought for good univariate methods that can predict future smoothed sunspot numbers reasonably well based on past smoothed sunspot number data only. Here we consider a specific method we call principal component-and-autoregression (PCAR) method. The variation of sunspot number during a period of finite duration (past) before an epoch (present) is modeled by a linear combination of a small number of dominant principal components, and this model is extended to the period (future) beyond the epoch using the autoregressive model of finite order. From the application of this method, we find that the $24^{th}$ solar maximum is likely to occur near the end of the year 2013 (and there is a possibility that it occurs earlier near the start of 2013), and to have a peak sunspot number of about 86, indicating that the activity of the $24^{th}$ cycle will be weaker than the average. We will discuss how much this estimate is reliable.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.533-542
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• 2020

This study estimates the nature of the relationship of entrepreneurship and business confidence on youth unemployment in the Philippines over the 2001-2017 period. The paper employed a range of cointegrating regression models, namely, autoregressive distributed lag (ARDL) bounds testing approach, Johansen-Juselius (JJ) and Engle-Granger (EG) cointegration models, dynamic OLS, fully modified OLS, and canonical cointegrating regression (CCR) estimation techniques. The Granger causality based on error correction model (ECM) was also performed to determine the causal link of entrepreneurship and business confidence on youth unemployment. The ARDL bounds testing approach, Johansen-Juselius (JJ) and Engle-Granger (EG) cointegration models confirmed the existence of long-run equilibrium relationship of entrepreneurship and business confidence on youth unemployment. The long-run coefficients from JJ and dynamic OLS show significant long-run and positive relationship of entrepreneurship and business confidence on youth unemployment. While results of the long-run coefficients from fully modified OLS and canonical cointegrating regression (CCR) found that only entrepreneurship has significant and positive relationship with youth unemployment in the long-run. The Granger causality based on error correction model (ECM) estimates show evidence of long-run causal relationship of entrepreneurship and business confidence on youth unemployment. In the short-run, increases in entrepreneurship and business confidence causes youth unemployment to decrease.

• Journal of the Korean Society of Safety
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• v.7 no.2
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• pp.71-79
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• 1992

This paper presents a least-square algorithms of lattice structures and their use for adaptive prediction of time series generated from the dynamic system. As the view point of adaptive prediction, a new method of Identification of dynamic characteristics by means of estimating the parameters of linear auto regressive model is proposed. The fast convergence of adaptive lattice algorithms is seen to be due to the orthogonalization and decoupling properties of the lattice. The superiority of the least-square lattice is verified by computer simulation, then predictor coefficients are computed from the linear sequential time data. For the application to the dynamic characteristic analysis of unknown system, the transfer function of ideal system represented in frquency domain and the estimated one obtained by predicted coefficients are compared. Using the proposed method, the damping ratio and the natural frequency of a dynamic structure subjected to random excitations can be estimated. It is expected that this method will be widely applicable to other technical dynamic problem in which estimation of damping ratio and fundamental vibration modes are required.