• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1290-1294
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• 2004

In this study, new stochastic point rainfall models which can consider the correlation structure between rainfall intensity and duration are developed. In order to consider the negative and positive correlation simultaneously, the Gumbels type-II bivariate distribution is applied, and for the cluster structure of rainfall events, the Neyman-Scott cluster point process is selected. In the theoretical point of view, it is shown that the models considering the dependent structure between rainfall intensity and duration have slightly heavier tail autocorrelation functions than the corresponding independent mode]s. Results from generating long time rainfall events show that the dependent models better reproduce historical rainfall time series than the corresponding independent models in the sense of autocorrelation structures, zero rainfall probabilities and extreme rainfall events.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.897-915
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• 2012

This paper examines the relationship between the stock and futures markets in terms of lead-lag relationship, correlation and the hedge ratio using wavelet analysis. The basic finding is that the relationship between the two markets significantly depends on the time-scale. First, there is a feedback relationship between the stock and futures markets in the long-run scale; however, weaker evidence is observed in shorter-run scales. Second, wavelet correlation between the two markets increases for a longer time scale. Third, the hedge ratio and the effectiveness of hedging strategies increase as the investment horizon gets longer. The results in this paper indicate that the stock and futures series are perfectly correlated in the long run and are tied together over long horizons.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2C
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• pp.238-246
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• 2004

In this paper, an estimator that take advantages of time and frequency correlation within an OFDM symbol is investigated. OFDM systems using the proposed estimator can be very effective in detecting signals under non-sampled space and time-varying channels. Also, under same complexity, the proposed estimator outperforms the previously proposed estimator. Since even if there are no assumption about channel correlation, the linear interpolation method instead of optimal interpolation using correct channel correlation is proposed in case the receiver does not know the channel correlation function in time domain. Therefore the proposed channel estimator help improving the performance of OFDM systems under non-sampled spaced and fast time-varying channels.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.1
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• pp.140-152
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• 1997

A simultaneous measurement of the wall skin friction and near-wall streamwise velocity fluctuations is performed using hot film and hot wire anemometers to investigate the relation between them. Near-wall turbulence statistics measured with a hot-wire probe are in good agreement with previous results. Turbulence properties of the wall skin friction fluctuations measured with a hot film also show fairly good agreements with those measured by others except that rms level is lower in the present study. Long-time averaged space- time correlations show that the wall skin friction is highly correlated with a turbulence structure which is tilted from the wall in the streamwise direction. Tilting angles are obtained from the phase shifts between the wall skin-friction and streamwise velocity fluctuations. The convection velocity of the near-wall streamwise velocity obtained from the space-time correlation is in good agreement with that from the direct numerical simulation database.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.194-198
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• 2005

Although the rainstorm causes local damage on large scale, it is difficult to predict the movement of the rainstorm exactly. In order to reduce the rainstorm damage of the rainstorm, it is necessary to analyze the path of the rainstorm using various statistical methods. In addition, efficient time interval of rainfall observation for the analysis of the rainstorm movement can be derived by applying various statistical methods to rainfall data. In this study, the rainstorm tracking using statistical method is performed for various types of rainfall data. For the tracking of the rainstorm, the methods of temporal distribution, inclined Plane equations, and cross correlation were applied for various types of data including electromagnetic rainfall gauge data and AWS data. The speed and direction of each method were compared with those of real rainfall movement. In addition, the effective time interval of rainfall observation for the analysis of the rainstorm movement was also investigated for the selected time intervals 10, 20, 30, 40, 50, and 60 minutes. As a result, the absolute relative errors of the method of inclined plane equations are smaller than those of other methods in case of electromagnetic rainfall gauges data. The absolute relative errors of the method of cross correlation are smaller than those of other methods in case of AWS data. The absolute relative errors of 30 minutes or less than 30 minutes are smaller than those of other time intervals.

• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.989-995
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• 2009

Short-time Fourier transforms (STFT) are useful for analyzing signals with harmonics that vary with time. If the variation of the harmonics with time is smooth, such as in kinematic vibrations in vehicles, then it is possible to improve the STFT using order spectra and correlation analysis. In this paper, it is shown how correlation analysis can be performed when the speed signal is noisy or unknown and then it is shown how order spectra become simple to compute after this analysis. The results are illustrated by an analysis of axle and car body vibrations in the prototype high-speed train, HSR-350x.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.2
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• pp.175-183
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• 2014

In this paper, we present the design and implementation of real-time sound localization based on $0.13{\mu}m$ CMOS process. Time delay of arrival (TDOA) estimation was used to obtain the direction of the sound signal. The sound localization chip consists of four modules: data buffering, short-term energy calculation, cross correlation, and azimuth calculation. Our chip achieved real-time processing speed with full range ($360^{\circ}$) using three microphones. Additionally, we developed a dedicated sound localization circuit (DSLC) system for measuring the accuracy of the sound localization chip. The DSLC system revealed that our chip gave reasonably accurate results in an experiment that was carried out in a noisy and reverberant environment. In addition, the performance of our chip was compared with those of other chip designs.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.17-23
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• 2009

In many cases, clustering algorithms have been used for extracting and discovering useful information from non-linear data. They have made a great effect on performances of the systems dealing with non-linear data. Thus, this paper presents a new approach called hierarchically structured clustering algorithm, and it is applied to the prediction system for non-linear time series data. The proposed hierarchically structured clustering algorithm (called HCKA: Hierarchical Cross-correlation and K-means clustering Algorithms) in which the cross-correlation and k-means clustering algorithm are combined can accept the correlationship of non-linear time series as well as statistical characteristics. First, the optimal differences of data are generated, which can suitably reveal the characteristics of non-linear time series. Second, the generated differences are classified into the upper clusters for their predictors by the cross-correlation clustering algorithm, and then each classified differences are classified again into the lower fuzzy sets by the k-means clustering algorithm. As a result, the proposed method can give an efficient classification and improve the performance. Finally, we demonstrates the effectiveness of the proposed HCKA via typical time series examples.

• Child Health Nursing Research
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• v.15 no.3
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• pp.261-270
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• 2009

Purpose: The purpose of this study was to compare the result of one-way ANOVA with that of cross-correlation time series analysis in order to evaluate physiologic responses of premature infants to human voices. Methods: Four premature infants born prior to 32 weeks gestational age were included in the study. The Gould 4000TA Recording System recorded the preterm infant's heart and respiratory rate while they were listening to a pre-recorded voice recording. Each infant listened to both male and female voices (1 min each) at each testing session. Results: The results of both one-wayANOVA and cross-correlation time series analysis using heart and respiratory rate data were not consistent in some of premature infants. A cross-correlation time series analysis revealed that the responses of premature infant to vocal stimulation occurred at a varying number of seconds after the stimulus was presented and lasted for over 20-30 sec. Conclusion: The results indicate that a time series analysis can provide more detailed information on the rapidly changing physiologic status of premature infant to the auditory stimulus. In addition, the results provide an insight into an auditory responsitivity of premature infants to a naturally occurring sound, the human voice, in the neonatal intensive care unit.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.39-47
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• 2024

Time series analysis is widely employed by many organizations to solve business problems, as it extracts various information and insights from chronologically ordered data. Among its applications, measuring time series similarity is a step to identify time series with similar patterns, which is very important in time series analysis applications such as time series search and clustering. In this study, we propose an efficient method for measuring time series similarity that focuses on anomalies rather than the entire series. In this regard, we validate the proposed method by measuring and analyzing the rank correlation between the similarity measure for the set of subsets extracted by anomaly detection and the similarity measure for the whole time series. Experimental results, especially with stock time series data and an anomaly proportion of 10%, demonstrate a Spearman's rank correlation coefficient of up to 0.9. In conclusion, the proposed method can significantly reduce computation cost of measuring time series similarity, while providing reliable time series search and clustering results.