• Journal of Advanced Navigation Technology
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• 제23권6호
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• pp.495-500
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• 2019

Foreign object debris (FOD) is a generic term for various metals and non-metal foreign object and materials with potential hazards to aircraft operations. Since the method of manual FOD detection and collection in the aircraft moving area is very low in efficiency and economic efficiency, it is essential to develop to FOD automatic detection system suitable for domestic environment. This paper is the result of the performance comparison test results of the two systems for the combined operation of each optimal detection time and 95% accuracy above 100 m for complex operation using the fixed FOD automatic detection system and the mobile FOD system using EO/IR camera and radar at Taean Airfield Hanseo University. It is expected that FOD can be performed unattended through continuous R & D.

• Journal of the Korean Society for Nondestructive Testing
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• 제32권3호
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• pp.241-262
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• 2012

Some applications require structural health monitoring in inaccessible components. This paper presents a technique useful for Structural Health Monitoring of double wall structures, such as double wall steam pipes and double wall pressure vessels separated from an ultrasonic transducer by three layers. Detection has been demonstrated at distances in excess of one meter for a fixed transducer. The case presented here is for one of the layers, the middle layer, being a fluid. For certain transducer configurations the wave propagating in the fluid is a wave with low velocity and attenuation. The paper presents a model based on wave theory and finite element simulation; the experimental set-up and observations, and comparison between theory and experiment. The results provide a description of the technique, understanding of the phenomenon and its possible applications in Structural Health Monitoring.

• Asian-Australasian Journal of Animal Sciences
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• 제11권5호
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• pp.480-485
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• 1998

Application of trans rectal ultrasonography to buffaloes (n=6) to follow the growth of large antral follicles individually, on each day of one interovulatory interval revealed that follicular turnover during oestrous cycle occured in waves. There was a predominance of a two-wave pattern (5/6 animals) compared to that of a three-wave pattern (1/6 animals). For two-wave pattern, the first wave emerged at Day $0.20{\pm}0.19$ (Day 0 = day of ovulation) and was marked by development of a dominant anovulatory follicle which grew in size from $5.40{\pm}0.24mm$ at the day of detection to a maximum diameter of $12.40{\pm}0.81mm$ on Day $8.60{\pm}1.57$, with a growth rate of $0.88{\pm}0.17mm/day$ and then regressed, with a mean persistence of $19.40{\pm}1.54$ days. The second wave emerged at Day $9.20{\pm}1.06$ and was marked by development of a dominant ovulatory follicle which grew in size from $4.20{\pm}0.37mm$ at the day of detection to a maximum diameter of $13.80{\pm}0.37mm$ on Day $21.00{\pm}1.38$, with a growth rate of $0.66{\pm}0.12mm/day$ and then ovulated on Day $21.60{\pm}1.25$, with a mean persistence of $11.80{\pm}1.39$ days. The maximum diameters attained and the growth rates of dominant anovulatory and dominant ovulatory follicles, and the mean number of follicles ${\geq}3mm$ diameter detected at the time of emergence of first and second waves ($11.80{\pm}1.74$ and $9.00{\pm}2.81$, respectively) were not significantly different. In the animal which showed a three-wave pattern, the first, second and third waves emerged on Days 1, 10 and 19, respectively. All animals, except one had at least one subordinate follicle in the first or second or both waves. The subordinate follicles increased in diameter over a few days and then regressed. The results indicate that in buffaloes, the follicular turnover during oestrous cycle occurs predominantly in a two-wave pattern.

• Journal of the Korea Institute of Military Science and Technology
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• 제25권3호
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• pp.231-238
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• 2022

Mine detection has been mainly studied with images of the forward-looking sonar. Forward-looking sonar assumes the propagation path of the sound wave as a straight path, creating the surrounding images. This might lead to errors in the detection by ignoring the refraction of the sound wave. In this study, we propose a mine localization method that can robustly identify the location of mines in an underwater environment by considering the refraction of sound waves. We propose a method of estimating the elevation angle of arrival of the target echo signal in a single receiver, and estimate the mine location by applying the estimated elevation angle of arrival to ray tracing. As a result of simulation, the method proposed in this paper was more effective in estimating the mine localization than the existing method that assumed the propagation path as a straight line.

• The Transactions of the Korean Institute of Electrical Engineers D
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• 제53권10호
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• pp.733-740
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• 2004

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) is proposed. The whole system consists of two-lead electrocardiogram acquisition (lead Ⅰ and Ⅱ), baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problem of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we proposed a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 subjects, and we could obtain satisfactory respiration signals that shows high correlation (r>0.9) with the signal acquired from the chest-belt respiration sensor.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제38B권9호
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• pp.728-735
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• 2013

In this paper, we present a method of transmitting ECG signals in real-time mobile environment to be possible to implement the ubiquitous healthcare system. Because of the excessive amount of data transmission of ECG signals, it is necessary to propose a limitation to the real-time transmission. We propose a real-time electrocardiographic monitoring system based on the proposal of unusual waveform detection algorithm which detects the R-wave distortions from the arrhythmia ECG signals having unusual waveform of about 10% on average. It is very effective in terms of time and cost for medical staffs to monitor and analyze ECG signals for a long period of time. Monitoring unusual waveform by gradually adjusting the threshold values of potential and kurtosis makes the amount of data transmitted decrease and significance level of waveform to be enhanced. The unusual waveform detection algorithm is implemented with ubiquitous environment inter-working device client. It is applicable to ubiquitous healthcare system capable of real-time monitoring the ECG signal. While ensuring the mobility, it allows for real-time continuous monitoring of ECG signals.

• Journal of Yeungnam Medical Science
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• 제2권1호
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• pp.77-80
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• 1985

The R-R interval analyzer was developed to measure the autonomic nervous system function using microcomputer. The system based on 8 bit microcomputer including bandpass filter, R-wave detector and clock generator in order to obtain the mean value, standard deviation, total time, CV value, maximum value and minimum value in the specific view point of R-R interval variation. The pattern of R-R interval change after resting, voluntary standing and deep breathing can be analysed in normal subjects and diabetics with autonomic nervous dysfunction. The amplitude of the R-R interval variation showed sensitive pattern for normal subjects at resting, standing and deep breathing. On the contrary, the periodicities of amplitude for abnormal subjects with autonomic nervous dysfunction showed dull pattern. It was suggested that R-R interval analyzer is a good detection method for dysfunction of autonomic nervous system.

• Proceedings of the KOSOMBE Conference
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.181-182
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• 1998

In previous study for correlation between P-wave Signal Averaged Electrocardiography (SAECG) and Paroxysmal Atrial Fibrillation (PAF) subjects, we showed that the duration of P-wave in subjects is longer than in controls. In this respect, the P-wave SAECG is a new method proving to be an accurate and independent noninvasive marker for the risk of PAF. To prove this suggestion, accurate detection and alignment of P-wave are indispensible. In previous study, we measured P-wave duration by manual. So it was not accurate and consistent. To measure the P-wave duration accurately and automatically, we have developed an automatic algorithm for P-wave duration measurement. We showed that the duration of P- wave in the subjects is longer than in controls with this algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제19권1호
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• pp.192-200
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• 2015

Several algorithms have been developed to classify arrhythmia which rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.

• Proceedings of the KOSOMBE Conference
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.205-206
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• 1998

In this paper, we present the detection algorithm of interictal epileptic waveform using LVQ network and wavelet transform. First wavelet coefficients is used to represent the characteristics of a single channel EEG wave, and make a number of neural network input node smaller. Then, three-layer neural network employing LVQ network is trained and tested using parameters obtained from the first stage. This study showed that preprocessed EEG data can be successfully used to train ANNs to detect epileptogenic discharges with a high success.