• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1991.10a
• /
• pp.303-304
• /
• 1991

• Journal of the Korean Statistical Society
• /
• v.19 no.2
• /
• pp.139-144
• /
• 1990

For the first-order bilinear time series model $X_t = aX_{t-1} + e_i + be_{t-1}X_{t-1}$ where ${e_i}$ is a sequence of independent normal random variables with mean 0 and variance $\sigma^2$, the asymptotic distribution of sample autocarrelation function is obtained and shown to follow a normal distribution. The variance of the asymptotic distribution is of a complicated form and hence a bootstrap estimate of the variance is proposed for large sample inference. This result can be used to distinguish between different bilinear models.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.8 no.3
• /
• pp.105-112
• /
• 1983

This paper discribes technique of the recursive restoration for the images degraded by linear space invariant blur and additive white Gaussian noise. The image is characterized statistically by tis mean and correlation function. An exponential autocorrelation function has been used to model neighborhood model. The vector model was used because of analytical simplicitly and capability to implement brightness correlation function. Base on the vector model, a two-dimensional discrete stochastic a 12 point neighborhood model for represeting images was developme and used the technique of moving window processing to restore blurred and noisy images without dimensionality increesing, It has been shown a 12 point neighborhood model was found to be more adequate than a 8 point pixel model to obtain optimum pixel estimated. If the image is highly correlated, it is necessary to use a large number of points in the neighborhood in order to have improvements in restoring image. It is believed that these result could be applied to a wide range of image processing problem. Because image processing thchniques normally required a 2-D linear filtering.

• The Korean Journal of Applied Statistics
• /
• v.23 no.1
• /
• pp.49-61
• /
• 2010

The time series dependencies of Financial volatility are frequently measured by the autocorrelation function of power-transformed absolute returns. It is known as the Taylor property that the autocorrelations of the absolute returns are larger than those of the squared returns. Hass (2009) developed a simple method for detecting the Taylor property in absolute-value-GAROH(1,1) (AVGAROH(1,1)) model. In this article, we fitted AVGAROH(1,1) model for various Korean financial time series and observed the Taylor property.

• Water for future
• /
• v.22 no.2
• /
• pp.201-211
• /
• 1989

Using the storm data which was augmented by the stochastic correlation with it's neighbors, the multiquadric equation of random surface of total storm depth is constructed. And to separate the local components from it's regionals and find the regional characteristics, a double Fourier analysis was applied to the total depths of storm data. The local components, storm residuals of each storm was assumed to be homogeneous random field and investigated with it's autocorrelation function. For the practical application, isotropic was assumed and that was identified with emprical data. Coefficients of normalized autocorrelation for all storms showed similar apperance. Using this emprical result, an example of the radial spectral distribution function which represints the spatial characteristics of rainfall over Han River Basin during 1975-1983 is presented.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.3
• /
• pp.521-526
• /
• 2020

This paper presents an improved HOLP neural network that adds 25 average values to a typical HOLP neural network using 25 feature vector values as input values by applying high-order local autocorrelation function, which is excellent for extracting immutable feature values of iris images. Compared with deep learning structures with different types, we compared the recognition rate of iris recognition using Back-Propagation neural network, which shows excellent performance in voice and image field, and synthetic product neural network that integrates feature extractor and classifier.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2008.11a
• /
• pp.241-242
• /
• 2008

The Velocity Autocorrelation Function (VAF) of the sodium ions is calculated for a range of temperature from 250K to 1000K and converted into the linear ac-conductivity and ac-susceptibility response via Fourier transformation. A peak is found in the conductivity around $6\times10^{12}$ Hz that has some of the character of a Poley absorption. Here it is shown to be due to an harmonically coupled site vibrations of the sodium atoms, which extend only over a limited range. At frequencies below the peak the conductivity tends towards a constant i.e. dc value corresponding to a constant flow of ions through the simulation cell. At high temperatures the conductivity due to this ion transport process behaves like a metal with an insulator to metal transition occurring around a specific temperature.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.7C
• /
• pp.459-466
• /
• 2011

This paper addresses the problem in the acquisition of binary offset carrier (BOC) signals employed in global navigation satellite systems, which is caused by the multiple side-peaks of the BOC autocorrelation function. We first observe that the side-peaks arise due to the fact that the BOC autocorrelation is made up of the sum of the sub-correlations shaped irregularly, and then, propose a novel acquisition scheme based on a recombination of the sub-correlations with cyclostationarity. The proposed scheme is demonstrated to remove the side-peaks completely for any type of BOC signal and to provide a performance improvement over the conventional schemes in terms of the mean acquisition time.

• Journal of Biosystems Engineering
• /
• v.38 no.1
• /
• pp.9-17
• /
• 2013

Purpose: The goal of this study was to develop a methodology for the demand forecast of tractor, riding type rice transplanter and combine harvester using an ARIMA (autoregressive integrated moving average) model, one of time series analysis methods, and to forecast their demands from 2012 to 2021 in South Korea. Methods: To forecast the demands of three kinds of machines, ARIMA models were constructed by following three stages; identification, estimation and diagnose. Time series used were supply and stock of each machine and the analysis tool was SAS 9.2 for Windows XP. Results: Six final models, supply based ones and stock based ones for each machine, were constructed from 32 tentative models identified by examining the ACF (autocorrelation function) plots and the PACF (partial autocorrelation function) plots. All demand series forecasted by the final models showed increasing trends and fluctuations with two-year period. Conclusions: Some forecast results of this study are not applicable immediately due to periodic fluctuation and large variation. However, it can be advanced by incorporating treatment of outliers or combining with another forecast methods.

• Proceedings of the KOSOMBE Conference
• /
• v.1996 no.05
• /
• pp.320-324
• /
• 1996

Digital fetal monitoring system based on the personal computer combined with the digital signal processing board was implemented. The DSP board acquires and digitally processes ultrasound fetal Doppler signal for digital rectification, FIR filtering, autocorrelation function calculation, its peak detection and MEDIAN filtering. The personal computer interfaced with the DSP board is in charge of graphic display, hardcopy, data transmission and on-line analysis of fetal heart rate change including and variability. I used a recursive technique for autocorrelation function computation method and MEDIAN filter which can greatly reduce the amount of calculation and accuracy. I also implemented analysis algorithm of fetal heart rate change based on normal fetal sample data in order to exact diagnosis.