• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.1
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• pp.13-18
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• 2010

In this study, heart rate through ECG R-R intervals using the methods about how to automatically extract studied. Heart rate as measured by the naked eye, using the 2-order differential equations to extract heart rate, using self-correlation function to extract the heart rate was compared contemplate. To verify its efficacy and validity in practical applications, these method has been applied to MIT/BIH database. Based on this, making a ECG meter automatic heart rate measurements, and our ECG meter was compared with the existing ICU.

• 한국신재생에너지학회:학술대회논문집
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• 2007.06a
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• pp.489-492
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• 2007

The P-wave velocity at the formation which contains gas hydrate varies very wide upon gas hydrate existence. These features on seismic shot gather can not be simulated normally by numerical modeling of homogeneous medium so that we need that of random inhomogeneous medium instead. We, in this study generated random inhomogeneous medium using gaussian ACF, exponential ACF and von Karman ACF and that we supposed the random inhomogeneous medium be gas hydrate formation to execute numeric modeling. The modeling result shows the typical effect by scattering caused by random hydrate formation as is observed from seismic shot gather where hydrate exist.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.96-99
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• 1994

Despite the simplicity of processing, a conventional autocorrelation function(ACF) method for the precise determination of fetal heart rate (FHR) has many problems. In case of weak or noise corrupted Doppler ultrasound signal. the ACF method is very sensitive to the threshold level and data window length. It is very troublesome to extract FHR when there is a data loss. To overcome these problems, the high resolution pitch detection algorithm was adopted to estimate the FHR. This method is more accurate, robust and reliable than the ACF method. With a lot of calculation, however, it is impossible to process real time FHR estimation. This paper is presented a new FHR estimation algorithm for real time processing.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.205-213
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• 2004

In this paper, we proposed a wavelet phase-only filter modulation synthetic discriminant function joint transform correlator(WPOfSDF-JTC) for scale invariant pattern recognition, and an improved algorithm to reduce the filter synthesis time. Computer simulation showed that the proposed filter has better SNR than CWMF if input image has random noise and the improved synthesis algorithm can reduce the iteration time. We used frequency selective JTC to solve the problem of the optical alignment and eliminate the autocorrelation and crosscorrelation between each input image.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.99-102
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• 1990

Cross correlation method was newly applied for the calculation of latency of evoked otoacoustic emission. The latency was calculated from 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.2
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• pp.373-380
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• 1994

In this paper, we proposed a method of extracting optimum features of speech signal to adjust signal level. For extracting features of speech signal we used FRLS(Fast Recursive Least Square) algorithm, we adjusted each frames of equal to constant level, and extracted optimum features of speech signal by using equalized autocorrelation function proposed in this paper.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.1-12
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• 1996

We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.3
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• pp.479-486
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• 2012

Runoff characteristics of pollutant loads of the lower Han River was studied before full implementation of Total Pollution Load Management System(TPLMS). Magnitude of macroscopic(annual) fluctuation was in the order of Namhan River > Han River > Bukhan River, gross weight TP > gross weight TN > gross weight BOD, gross weight deviation > concentration deviation. Flux variation was higher than that of concentration. Microscopic(weekly) fluctuation showed similar pattern to macroscopic scale. TP showed the highest deviation resulting in the lowest reliability. 60% of annual flux passed during summer 3months resulting in 43-46% pass of gross weight at the lower Han River. Strong correlation was found between flux and gross weight especially in gross weight TN. Gross weight pollution increased as high as 400% while passing Seoul area due to the concentration. The deviation from moving average increased during summer season in the gross weight TP and BOD. Seasonal tendency was confirmed especially in gross weight TN and TP using autocorrelation function.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.5
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• pp.788-797
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• 1995

In this paper, a variable time-scale modification using SOLA(Synchronized OverLap and Add) is proposed, which takes into consideration the different time-scaled characteristics of voiced and unvoiced speech, Generally, voiced speech is subject to higher variations in length during time-scale modification than unvoiced speech, but the conventional method performs time-scale modification at a uniform rate for all speech. For this purpose, voiced and unvoiced speech duration at various talking speeds were statistically analyzed. The sentences were then spoken at rates of 0.7, 1.3, 1.5 and 1.8 times normal speed. A clipping autocorrelation function was applied to each analysis frame to determine voiced and unvoiced speech to obtain respective variation rates. The results were used to perform variable time-scale modification to produce sentences at rates of 0.7, 1.3, 1.5, 1.8 times normal speed. To evaluate performance, a MOS test was conducted to compare the proposed voiced/unvoiced variable time-scale modification and the uniform SOLA method. Results indicate that the proposed method produces sentence quality superior to that of the conventional method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.12
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• pp.1679-1689
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• 1997

In this paper, a precise description is given to the basic theory as well as the detailed algorithms for the numerical treatment of the method of POD (proper orthogonal decomposition). This method is then applied to analysing the numerical solutions of one-dimensional shallow-water equations to show how the method is affected by various parameters such as the sampling time, sampling numbers, and the spatial resolution for the autocorrelation function. A few curious features associated with this flow model found through the analysis are further explained and discussed.