• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.447-450
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• 2004

This study was conducted to characterize groundwater and river-water fluctuations at a riverbank filtration site in Daesan-myeon adjacent to the Nakdong River, using time series analysis. Water levels from six observation wells from January 2003 to October 2003 were measured. The autocorrelation analysis indicates that the wells are divided into three groups: group 1 represents strong linearity and memory, group 2 intermediate linearity and memory, and group 3 weak linearity and memory. The analysis indicates that groundwater levels in different monitoring wells vary in response to river-water levels, groundwater withdrawal and seasonal rainfall. Cross-correlation was also divided into three groups. Group 1 shows the highest cross-correlation function (0.49 - 0.54) for a lag time of 0 hours, group 2 intermediate cross-correlation function (0.34 - 0.45), and group 3 the lowest cross-correlation function (0.23 - 0.25). Different cross-correlation functions among the 3 groups are interpreted as an effect of tile distance from the river to the pumping wells.

• Journal of Information Technology Applications and Management
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• v.22 no.3
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• pp.105-114
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• 2015

This paper presents a fast Time Difference of Arrival (TDOA) estimation for sound source localization. TDOA is the time difference between the arrival times of a signal at two sensors. We propose a partial cross correlation method to increase the speed of TDOA estimation for sound source localization. We do this by predicting which part of the cross correlation function contains the required TDOA value with the help of the signal energies, and then we compute the cross correlation function in that direction only. Experiments show approximately 50% reduction in the cross correlation computation time thereby increasing the speed of TDOA computation. This makes it very relevant for real world surveillance.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.703-710
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• 2010

In this paper, an overload detecting algorithm for an excavator is presented. The proposed overload detecting algorithm is based on the time series analysis especially correlation function. The main purpose of this paper is to prevent damage or crack from the fatigue loaded on an excavator in advance. Generally, the larger data, the longer processing time, and the amount of the data used in this paper are also large, especially every sampling period, 1600 data are gathered and calculated. So this paper focuses on minimizing the number of required sensors by using the correlation function. From the cross correlation function, similar pattern sensors are eliminated and dissimilar pattern sensors are considered, and from the auto correlation function, the overload can be detected. To prove the efficiency of the proposed overload detecting algorithm, this paper shows the computer simulation results.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.891-897
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• 2013

The spectrum and the number of the values of the cross-correlation function between the maximal period binary sequences have been extensively studied because of their importance in communications applications. In this paper, we propose the new family of the sequences using the decimation $d=2^{m-1}(3{\cdot}2^{m}-1)$. And we find the spectrum of the cross-correlation function of the sequences and analyze the number of times each value occurs for $0{\leq}{\tau}{\leq}2^{n}-2$.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.599-601
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• 2015

The short time scale X-ray variability associated with the accretion disk around compact objects is complex and is vaguely understood. The study of the cross correlation function gives an insight into the energy dependent behavior of the variations and hence connected processes. Using high resolution RXTE data, we investigate the dynamical cross correlation function of an observation of a black hole source XTE J1550-564 in the steep power law state. The cross correlation between soft and hard X- ray energy bands revealed both correlated and anti-correlated delays (${\leq}{\pm}15s$) on a correlation time scale of 50 s. It was noticed that the observed delays were similar to the delays between X-ray and optical/IR bands in other black hole and neutron star sources. We discuss the possible mechanisms/processes to explain the observed delays in the dynamical CCF.

• Journal of Biomedical Engineering Research
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• v.12 no.1
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• pp.19-22
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• 1991

Cross correlation method was newly applied for the calculation of latency of evoked otoacoustic emission. The latency was calculated froth the main peak of cross correlation function, which is one of Possible definition of latency. The output was also compared with those of conventional autocorrelation method. The results show that cross correlation method has better Performance than that of conventional method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.248-255
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• 2003

This paper presents a fingerprint recognition algorithm using cross-correlation function. This algorithm consists of minutiae extraction, minutiae alignment and minutiae matching, where we propose a new minutiae alignment method. In our alignment method, the rotation angle between two fingerprints is obtained by using cross-correlation function of the minutia directions, thereafter the displacement is obtained from the rotated fingerprint. This alignment method is capable of finding rotation angle and displacement of two fingerprints without resorting to exhaustive search. Our fingerprint recognition algorithm has been tested on fingerprint images captured with inkless scanner. The experiment results show that 17.299% false rejection ratio(FRR) at 2.086% false acceptance ratio(FAR).

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.271-274
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• 1998

This paper describes a new method for separation of the Volterra kernels which are identified by use of M-sequence. One of the authors has proposed a method for identification of Volterra kernels of nonlinear systems using M-sequence and correlation technique. When M-sequence are applied to a nonlinear systems, the cross-correlation function between the input and the output of the nonlinear systems includes cross-sections of high-order Volterra kernels. However, if various order Volterra kernels exixt on the obtained cross-correlation function, it is difficult to separate the Volterra kernels. In this paper, the authors show that the magnitude of Volterra kernels is maginified by the amplitude of M-sequence according to the order of Volterra kernels. By use of this property, each order Volterra kernels is obtained by solving linear equations. Simulations are carried out for some nonlinear systems. The results show that Volterra kernels can be separated in each order successfully by the proposed method.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.540-545
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• 2012

In this paper, the lagged cross-correlation of two probability density functions constructed by kernel density estimation is proposed, and by maximizing the proposed function, adaptive filtering algorithms for supervised and unsupervised training are also introduced. From the results of simulation for blind equalization applications in multipath channels with impulsive and slowly varying direct current (DC) bias noise, it is observed that Gaussian kernel of the proposed algorithm cuts out the large errors due to impulsive noise, and the output affected by the DC bias noise can be effectively controlled by the lag ${\tau}$ intrinsically embedded in the proposed function.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.627-633
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• 2013

An important problem in the transmission performance and efficiency is to find the values and the number of the cross-correlation function between two different maximal sequences. In this paper, we present the new maximal sequences which are obtained by the new decimations $d=\frac{2^{m-st-1}}{2^s-1}(2^n+2^{st+s+1}-2^{m+st+1}-1)$ from some maximal sequences. We will also find the values and the number of occurrences of each value of the cross-correlation function from the proposed decimations.