• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.98.5-98
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• 2001

An efficient adaptive nonlinear compensator, based on a wavelet transform domain adaptive Volterra filter along with a modified escalator algorithm, is proposed to speed up the convergence rate of an adaptive LMS algorithm. In particular, it is well known that the e.g., slow convergence speed of an adaptive LMS algorithm depends on the statistical characteristics (e.g., large eigenvalue spread) of the corresponding auto-correlation matrix of the input vector. To solve such a convergence problem, the proposed approach utilizes a modified escalator algorithm and a wavelet transform domain adaptive LMS Volterra filtering technique, which leads to diagonalization of the auto-correlation matrix of the ...

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.644-646
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• 1998

The main drawback of filtered-X LMS(FXLMS) algorithm for the ANC of broadband noises is its low convergence speed when the filtered reference signals are strongly correlated, producing a large eigenvalue spread in correlation matrix. This correlation can be caused either by autocorrelation of the signals of the reference sensors, or by coupling between the error path which introduces intercorrelation in the filtered reference signals. In this paper, we introduce a transform domain FXLMS(TD-FXLMS) algorithm that has a high convergence speed by orthogonal transform's decorrelation properties.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.228-239
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• 2022

Many countries around the world using ICT technologies have various technologies to keep pace with the 4th industrial revolution, and various algorithms and systems have been developed accordingly. Among them, many industries and researchers are investing in unmanned automation systems based on AI. At the time when new technology development and algorithms are developed, decision-making by big data analysis applied to AI systems must be equipped with more sophistication. We apply, Pearson's correlation analysis is applied to six independent variables to find out the job satisfaction that office workers feel according to their job characteristics. First, a correlation coefficient is obtained to find out the degree of correlation for each variable. Second, the presence or absence of correlation for each data is verified through hypothesis testing. Third, after visualization processing using the size of the correlation coefficient, the degree of correlation between data is investigated. Fourth, the degree of correlation between variables will be verified based on the correlation coefficient obtained through the experiment and the results of the hypothesis test

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.595-602
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• 2020

We propose a generative probabilistic model with Dirichlet prior distribution for topic modeling and text similarity analysis. It assigns a topic and calculates text correlation between documents within a corpus. It also provides posterior probabilities that are assigned to each topic of a document based on the prior distribution in the corpus. We then present a Gibbs sampling algorithm for inference about the posterior distribution and compute text correlation among 50 abstracts from the papers published by IEEE. We also conduct a supervised learning to set a benchmark that justifies the performance of the LDA (Latent Dirichlet Allocation). The experiments show that the accuracy for topic assignment to a certain document is 76% for LDA. The results for supervised learning show the accuracy of 61%, the precision of 93% and the f1-score of 96%. A discussion for experimental results indicates a thorough justification based on probabilities, distributions, evaluation metrics and correlation coefficients with respect to topic assignment.

• Journal of Korea Water Resources Association
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• v.33 no.S1
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• pp.37-47
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• 2000

The method of delays is widely used for reconstruction chaotic attractors from experimental observations. Many studies have used a fixed delay time ${\tau}_d$ as the embedding dimension m is increased, but this is not necessarily the best choice for obtaining good convergence of the correlation dimension. Recently, some researchers have suggested that it is better to fix the delay time window ${\tau}_w$ instead. Unfortunately, ${\tau}_w$ cannot be estimated using either the autocorrelation function or the mutual information, and no standard procedure for estimating ${\tau}_w$ has yet emerged. However, a new technique, called the C-C method, can be used to estimate either ${\tau}_d\;or\;{\tau}_w$. Using this method, we show that, for small data sets, fixing ${\tau}_w$, rather than ${\tau}_d$, does indeed lead to a more rapid convergence of the correlation dimension as the embedding dimension m in increased.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.661-680
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• 2016

The vehicle license plate recognition (VLPR) system analyzes and monitors the speed of vehicles, theft of vehicles, the violation of traffic rules, illegal parking, etc., on the motorway. The VLPR consists of three major parts: license plate detection (LPD), license plate character segmentation (LPCS), and license plate character recognition (LPCR). This paper presents an efficient method for the LPCS and LPCR of Korean vehicle license plates (LPs). LP tilt adjustment is a very important process in LPCS. Radon transformation is used to correct the tilt adjustment of LP. The global threshold segmentation method is used for segmented LP characters from two different types of Korean LPs, which are a single row LP (SRLP) and double row LP (DRLP). The cross-correlation matching method is used for LPCR. Our experimental results show that the proposed methods for LPCS and LPCR can be easily implemented, and they achieved 99.35% and 99.85% segmentation and recognition accuracy rates, respectively for Korean LPs.

• Proceedings of the Korea Water Resources Association Conference
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• 2000.05a
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• pp.37-47
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• 2000

The method of delays is widely used fur reconstructing chaotic attractors from experimental observations. Many studies have used a fixed delay time ${\tau}_d$ as the embedding dimension m is increased, but this is not necessarily the best choice for obtaining good convergence of the correlation dimension. Recently, some researchers have suggested that it is better to fix the delay time window ${\tau}_w$ instead. Unfortunately, ${\tau}_w$ cannot be estimated using either the autocorrelation function or the mutual information, and no standard procedure for estimating ${\tau}_w$has yet emerged. However, a new technique, called the C-C method, can be used to estimate either ${\tau}_d{\;}or{\;}{\tau}_w$. Using this method, we show that, for small data sets, fixing ${\tau}_w$, rather than ${\tau}_d$, does indeed lead to a more rapid convergence of the correlation dimension as the embedding dimension m is increased.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1051-1054
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• 1988

In this paper we present initial convergence properties of the Kalman filtering algorithm, we put an arbitrary small positive correlation matrix as an initial condition in the recursive algorithm. This arbitrary small initial condition perturbs the Kalman filtering algorithm and may lead to initial instability. We derive a condition which insures the stable operation of the Kalman filtering algorithm from the stochastic Lyapunov difference equation.

• ETRI Journal
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• v.30 no.6
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• pp.765-773
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• 2008

Spatial correlation is a result of insufficient antenna spacing among multiple antenna elements, while temporal correlation is caused by Doppler spread. This paper compares the effect of spatial and temporal correlation in order to investigate the performance of multiuser scheduling algorithms in multiple-input multiple-output (MIMO) broadcast channels. This comparison includes the effect on the ergodic capacity, on fairness among users, and on the sum-rate capacity of a multiuser scheduling algorithm utilizing statistical channel state information in spatio-temporally correlated MIMO broadcast channels. Numerical results demonstrate that temporal correlation is more meaningful than spatial correlation in view of the multiuser scheduling algorithm in MIMO broadcast channels. Indeed, the multiuser scheduling algorithm can reduce the effect of the Doppler spread if it exploits the information of temporal correlation appropriately. However, the effect of spatial correlation can be minimized if the antenna spacing is sufficient in rich scattering MIMO channels regardless of the multiuser scheduling algorithm used.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.202-211
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• 2019

This study was conducted to identify the correlation between smartphone addiction, sleep quality, and depression in college students. The participants were 304 college students in G area. Data were collected using self-report questionnaires and data were analyzed by frequency, percent, mean, standard deviation, t-test, One way ANOVA and Pearson correlation coefficient. The results showed that there was a significant negative correlation between smartphone addiction and sleep quality, a significant positive correlation between smart addiction and depression, and a significant negative correlation between sleep quality and depression. Therefore, the results of this study can be used as a useful basic data when looking for education and program development strategies to improve and prevent smartphone addiction, sleep quality, and depression of college students. In addition, it is required to apply a systematic educational program considering this.