• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.443-451
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• 2015

This paper proposes a signal modelling method that can effectively detect the shock wave(SW) sound and muzzle blast(MB) sound from the gunshot of a small arm rifle. In order to localize a counter sniper in battlefield, an accurate detection of both shock wave sound and muzzle blast sound are the necessary keys in estimating the direction and the distance of the counter sniper. To verify the performance of the proposed algorithm, a real gunshot sound in a domestic military shooting range was recorded and analyzed. From the experimental results, the proposed signal modelling method was found to be superior to the comparative system more than 20% in a shock wave detection and 5% in a muzzle blast detection, respectively.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.665-675
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• 2007

The morphological change of ECG is the important diagnostic parameter to finding the malfunction of a heart. Generally ST segment deviation is concerned with myocardial abnormality. The aim of this study is to detect the change of ST in shape using a polynomial approximation method and the reference ST type. The developed algorithm consists of feature point detection, ST level detection and ST shape classification. The detection of QRS complex is accomplished using it's the morphological characteristics such as the steep slope and high amplitude. The developed algorithm detects the ST level change, and then classifies the ST shape type using the polynomial approximation. The algorithm finds the least squares curve for the data between S wave and T wave in ECG. This curve is used for the classification of the ST shapes. ST type is classified by comparing the slopes of the specified points between the reference ST set and the least square curve. Through the result from the developed algorithm, we can know when the ST level change occurs and what the ST shape type is.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1063-1066
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• 1999

The delay of cardiac depolarization wave detection in the conventional pacemakers or AICD (automatic implantable cardioverter/ defibrillator, or ICD) has been overlooked. However, it is known that the delay may cause hemodynamic problems and may prevent the proper operation of a new automatic feature, automatic capture verification, that is to be appeared in the near-future devices. In order to reduce the effects of the delay, a delay prevention algorithm was developed and tested by applying three human electrograms. The algorithm set the sensing threshold just above the measured noise level to reduce the detection delay. It is found that the low threshold was able to reduce the delay by 20msec(average) In most cases. The implementation results showed reliability and efficacy of the algorithm, and the algorithm could be applicable to the existing hardware and software of the conventional pacemakers and AICD without any significant modifications.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.33-40
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• 2016

Previous works for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods require accurate detection of ECG signal, higher computational cost and larger processing time. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system based IOT that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose R wave detection considering complexity and arrhythmia classification based on binary coding. For this purpose, we detected R wave through SOM and then RR interval from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. R wave detection and PVC, PAC, Normal classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41%, 97.18%, 94.14%, 99.83% in R wave, PVC, PAC, Normal.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.107.1-107.1
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• 2014

We apply a machine learning algorithm, artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. We also evaluate the gravitational-wave data within a few seconds of the selected short gamma-ray bursts' event times using the trained networks and obtain the false alarm probability. We suggest that artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.33 no.1
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• pp.65-69
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• 2020

The aim of this study is to improve the fault decision ability of FRTU (Feeder remote terminal unit) in DAS (Distribution automation system). FRTU uses the FI (Fault indicator) algorithm based on fault current pickup and operation of the protection device. Even if the inrush current flows or the protection device is sensitive to the transient current, FRTU may indicate incorrect fault information. To address these problems, we propose an improved fault recognition algorithm that can be applied to FRTU. We will detect a specific wave that is indicative of a fault, and use this information to identify a fault wave. The specific wave-detection algorithm is based on the duration and periodicity of the voltage, current, and harmonic variations. In addition, we propose fault recognition algorithms using voltage factor variation analysis and DWT (Discrete wavelet transform). All the wave data used in this study were actual data stored in FRTU.

• Smart Structures and Systems
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• v.10 no.1
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• pp.47-65
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• 2012

The presence of crack-like defects in mechanical and structural elements produces failures during their service life that in some cases can be catastrophic. So, the early detection of the fatigue cracks is particularly important because they grow rapidly, with a propagation velocity that increases exponentially, and may lead to long out-of-service periods, heavy damages of machines and severe economic consequences. In this work, a non-destructive method for the detection and identification of elliptical cracks in shafts based on stress wave propagation is proposed. The propagation of a stress wave in a cracked shaft has been numerically analyzed and numerical results have been used to detect and identify the crack through the genetic algorithm optimization method. The results obtained in this work allow the development of an on-line method for damage detection and identification for cracked shaft-like components using an easy and portable dynamic testing device.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.245-254
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• 2013

In oriental medicine, it is possible to classify and treat many diseases using the pulse wave detection system. Other problems may arise. As it is a very subjective way to analyze the pulse wave. One problem of the conventional pulse wave detection system is that the arterial pulse sensor is not located correctly at the radial artery. Threrefore measurement results can differ depending on the measurement position and the measurement procedure. This is mostly due to it's sensitivity to high reproducibility. In order to solve this problem this paper proposes an algorithm to analyze the weak pulse wave symptom and strong pulse wave symptom. It uses the portable pulse wave detection system which includes a Hall Sensor. As a final result, it analyzed the weak pulse wave symptom and strong pulse wave symptom by the SPSS statistics technique. It proves that N time (notch point time) and S Amp (rise waveform size) mean values are significantly different in 95% confidence interval.

• Journal of Biomedical Engineering Research
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• v.35 no.6
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• pp.211-218
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• 2014

The objective of this research is to develop an automatic algorithm based on electrocardiogram (ECG) to estimate slow-wave sleep (SWS). An algorithm is based on 7 indices extracted from heart rate on ECG which simultaneously recorded with standard full night polysomnography from 31 subjects. Those 7 indices were then applied to independent component analysis to extract a feature that discriminates SWS and other sleep stages. Overall Cohen's kappa, accuracy, sensitivity and specificity of the algorithm to detect 30s epochs of SWS were 0.52, 0.87, 0.70 and 0.90, respectively. The automatic SWS detection algorithm could be useful combining with existing REM and wake estimation technique on unattended home-based sleep monitoring.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.505-508
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• 2000

The success of target reconstruction in SAR(Synthetic Aperture Radar) imaging system is greatly dependent on the coherent detection. Primary causes of incoherent detection are uncompensated target or sensor motion, random turbulence in propagation media, wrong path in radar platform, and etc. And these appear as multiplicative phase error to the echoed signal, which consequently, causes fatal degradations such as fading or dislocation of target image. In this paper, we present iterative phase error estimation scheme which uses echoed data in all temporal frequencies. We started with analyzing wave equation for one point target and extend to overall echoed data from the target scene - The two wave equations governing the SAR signal at two temporal frequencies of the radar signal are combined to derive a method to reconstruct the complex phase error function. Eventually, this operation attains phase error correction algorithm from the total received SAR signal. We verify the success of the proposed algorithm by applying it to the simulated spotlight-mode SAR data.