• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.2
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• pp.263-269
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• 2013

In this paper we give a proof for finding the values of the cross-correlation function $C_d({\tau})$, when $d=3{\cdot}2^m-2$ where n=2m, m=4k ($k{\geq}2$). And the linear span of the sequences in the proposed sequence family are derived in the some cases.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.4
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• pp.4251-4258
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• 1976

In the construction of earth dam, embankment and highway by filling, a compaction contributes to increasing the density of soil by applying pressure. The effect of compaction depends on various factors such as soil type, moisture content, gradation, consistency, and compaction energy. In this study, the correlations amone maximum dry density, moisture content, dry density, and moisture content are analyzed. Some results obtained in this study are summarizep as follows. 1. The maximum dry density sinoreases with increased of optimum moisture content and the correlations of them can be represented by; ${\gamma}$dmx=a-b(W0) 2. Maximum dry density and liquid limit show negative linear correlation and can be represented by; ${\gamma}$dmx=a-b(LL). 3. Optimum moisture content and liquid limit, plastic limit show positive linear correlation and can be represented by the following equation, W0=a+b(LL) W0=a+b(PL). 4. Liquid limit and plastic limit show positive linear correlation, and can be represented by the following equation, LL=a+b(PL).

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2815-2817
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• 2001

This study describes the emotion classification using two different feature extraction methods for four-channel EEG signals. One of the methods is linear prediction analysis based on AR model. Another method is cross-correlation coefficients on frequencies of ${\theta}$, ${\alpha}$, ${\beta}$ bands. Using the linear predictor coefficients and the cross-correlation coefficients of frequencies, the emotion classification test for four emotions, such as anger, sad, joy, and relaxation is performed with a neural network. Comparing the results of two methods, it seems that the linear predictor coefficients produce the better results than the cross-correlation coefficients of frequencies for-emotion classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.2023-2032
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• 1996

In this paper, we calculate average bit error probabilities of asynchronous hybrid DS/FH-SSMA systems in AWGN chnnel and nonselective Rayleigh fading channel. We analyze and compare the performance of systems in AWGN channel and Rayleigh fading channel by using linear correlation receiver and hard limiting correlation receiver(nonlinear correlation receiver). Binary PSK scheme is considered and random spreading code sequences and random hopping patterns are used. Bit error probabilities of the systems with/without near-far problem under the same bandwidth expansion are calculated. the result shows that the performance of hard limiting correlation receiver is better than that of linear correlation receiver over nonselective Rayleigh fading channel.

• Annals of Clinical Neurophysiology
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• v.8 no.2
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• pp.135-145
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• 2006

Background: Parkinson's disease is movement disorder due to dopaminergic deficiency. It has been noted that cognitive dysfunction also presented on Parkinson's disease patients. But, it is not clear whether such a cognitive dysfunction was a dopaminergic dysfunction or cholinergic dysfunction. Using linear and non-linear analyses, we analysed the effect of cognitive and motor symptom on EEG change. Methods: EEGs were recorded from patients with Parkinson's disease and essential tremor, and normal controls during rest. We calculated the power spectrum, correlation dimension and Lyapunov exponent by using 'Complexity'program. The power spectrum, correlation dimension, and Lyapunov exponent were compared between Parkinson's disease patients and essential tremor patients. Results: Theta power was increased in Parkinson's disease patient group. Correlation dimension was increased in Parkinson's disease patients. Positive correlation was noted between MMSE and correlation dimension, and negative correlation was noted between MMSE and Lyapunov exponent. Lyapunov exponent was decreased in Parkinson's disease patient. Conclusions: We conclude that the state of Parkinson's disease patient is characterized by increased correlation dimension and decreased Lyapunov exponent.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2875-2882
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• 2013

The design of PN(Pseudo Noise) sequences with good cross-correlation properties is important for many research areas in communication systems. Also analyses of cross-correlation frequency between designed sequences have been researched. In this paper we analyze of cross-correlation distribution and properties of non-linear binary sequences family $S^r=\{Tr^m_1\{[Tr^n_m(a{\alpha}^t+{\alpha}^{dt}]^r\}{\mid}a{\in}GF(2^m),0{\leq}t < 2^n-1\}$, where $gcd(r,2^m-1)=1$ with 5-valued cross-correlation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.619-626
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• 2013

The design of PN(Pseudo Noise) sequences with good cross-correlation properties is important for many research areas in communication systems. In this paper we propose new family of binary sequences $S^r=\{Tr_1^m\{[Tr_m^n(a{\alpha}^t+{\alpha}^{dt})]^r\}{\mid}a{\in}GF(2^n),\;0{\leq}t<2^n-1\}$ composed of Gold-like sequences and find the value of cross-correlation function when $d=2^{n-1}(3{\cdot}2^m-1)$, where n=2k, gcd(r, $2^m-1$)=1. Also we analyze the linear span of $S^r$ for some special r. Proposed sequences are extension of Gold-like sequences and GMW-sequences.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.1043-1052
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• 2009

To derive easily the coefficient of permeability from several other soil properties, the estimation model of coefficient of permeability was proposed using linear regression analysis. The coefficient of permeability is one of the major factors to evaluate the soil characteristics. The study area is located in Kangwon-do Pyeongchang-gun Jinbu-Myeon. Soil samples of 45 spots were taken from the study area and various soil tests were carried out in laboratory. After selecting the soil factor influenced by the coefficient of permeability through the correlation analysis, the estimation model of coefficient of permeability was developed using the linear regression analysis between the selected soil factor and the coefficient of permeability from permeability test. Also, the estimation model of coefficient of permeability was compared with the results from permeability test and empirical equation, and the suitability of proposed model was proved. As the result of correlation analysis between various soil factors and the coefficient of permeability using SPSS(statistical package for the social sciences), the largest influence factor of coefficient of permeability were the effective grain size, porosity and dry unit weight. The coefficient of permeability calculated from the proposed model was similar to that resulted from permeability test. Therefore, the proposed model can be used in case of estimating the coefficient of permeability at the same soil condition like study area.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.23-27
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• 2010

This paper performs emotion classification test to find out the best parameter of electroencyphalogram(EEG) signal. Linear predictor coefficients, band cross-correlation coefficients of fast Fourier transform(FFT) and autoregressive model spectra are used as the parameters of 10-channel EEG signal. A multi-layer neural network is used as the pattern classifier. Four emotions for relaxation, joy, sadness, irritation are induced by four university students of an acting circle. Electrode positions are Fp1, Fp2, F3, F4, T3, T4, P3, P4, O1, O2. As a result, the Linear predictor coefficients showed the best performance.

• Journal of Astronomy and Space Sciences
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• v.29 no.3
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• pp.259-267
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• 2012

Polar cap potential has long been considered as an indicator for the amount of energy flowing in the magnetosphere-ionosphere system. Thus, the estimation of polar cap potential is important to understand the physical process of the magnetosphere. To estimate the polar cap potential in the Northern Hemisphere, merging electric field by Kan & Lee (1979) is adopted. Relationships between the PC index and calculated merging electric field ($E^*$) are examined during full-time and storm-time periods separately. For this purpose Dst, AL, and PC indices and solar wind data are utilized during the period from 1996-2003. From this linear relationship, polar cap potential (${\Phi}^*$) is estimated using the formula by Doyle & Burke (1983). The values are represented as $58.1{\pm}26.9$ kV for the full-time period and $123.7{\pm}84.1$ kV for a storm-time period separately. Considering that the average value of polar cap potential of Doyle & Burke (1983) is about 47 kV during moderately quiet intervals with the S3-2 measurements, these results are similar to such. The monthly averaged variation of Dst, AL, and PC indices are then compared. The Dst and AL indices show distinct characteristics with peaks during equinoctial season whereas the average PC index according to the month shows higher values in autumn than in spring. The monthly variations of the linear correlation coefficients between solar wind parameters and geomagnetic indices are also examined. The PC-AL linear correlation coefficient is highest, being 0.82 with peaks during the equinoctial season. As with the AL index, the PC index may also prove useful for predicting the intensity of an auroral substorm. Generally, the linear correlation coefficients are shown low in summer due to conductance differences and other factors. To assess the role of the PC index during the recovery phase of a storm, the relation between the cumulative PC index and the duration is examined. Although the correlation coefficient lowers with the storm size, it is clear that the average correlation coefficient is high. There is a tendency that duration of the recovery phase is longer as the PC index increases.