• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.359-364
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• 2004

This paper deals with the comparison of signal processing techniques of ultrasonic data. The goal of signal processing is the ultrasonic speckle suppression and the visibility enhancement of flaw-reflected ultrasonic echo. The performance of conventional SSP(split spectrum processing) method and the wavelet denoising method are compared and discussed for tested ultrasonic data. Tested ultrasonic data obtained from the weld area of centrifugal-casted stainless steel material and safe-ending material with holes and notch of variable depths are presented. In experimental results, the outputs of wavelet-based denoising method show the clear and sharp peaks at the positions of flaw-reflected echos comparing with those of SSP method.

• Journal of Korea Multimedia Society
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• v.20 no.10
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• pp.1637-1645
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• 2017

Target/Nontarget classification can be divided into the study of shape estimation of the target analysing reflected echo signal and of type classification of the target using acoustical features. In active sonar system, the feature vectors are extracted from the signal reflected from the target, and an classification algorithm is applied to determine whether the received signal is a target or not. However, received sonar signals can be distorted in the underwater environments, and the spatio-temporal characteristics of active sonar signals change according to the aspect of the target. In addition, it is very difficult to collect real sea-trial data for research. In this paper, target/non-target classification were performed using real sea-trial data. Feature vectors are extracted using MFCC(Mel-Frequency Cepstral Coefficients), filterbank energy in the Fourier spectrum and wavelet domain. For the performance verification, classification experiments were performed using backpropagation neural network classifiers.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.1
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• pp.27-34
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• 2017

The signal transmitted from radar is not reflected from a single point when the signal reflected by complex target. Resultantly, the amplitude and phase of the received signal can be changed because the target has lots of scatterers. The changes of the amplitude and the phase mean Glint and RCS, respectively. Although the Glint and RCS that caused by the same scatters are uncorrelated, however, they are not independent completely. Therefore, this paper proposes a method for generating the Glint and RCS by using same random number generator. And the time correlations of the Glint and RCS are respectively implemented in frequency domain by using each power spectral density of them.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.51-53
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• 2004

In this paper, we are going to propose the new algorithms to detect, classify, discriminate the transient and the reflected signal from noise and thus discriminate the fault section and locale the fault accurately on underground power cable system. Actually, at this system, it's very difficult to discriminate the transient because of the reflected signal including many noises. Therefore, how to solve the noise interference is a big problem. In this paper, authors present a solution based on multiple scales correlation of the transient using stationary wavelet transform. It's simple, quick and straightforward. For applying all algorithms, we just use the signal captured in single end.

• Smart Structures and Systems
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• v.30 no.5
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• pp.513-520
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• 2022

During the long-time service of a bolt, its preload may suffer slight perturbations or significant reductions. It is a dilemma to monitor preload changes at high resolution and full scale. Approaches for bolt preload monitoring with multi-resolution should be developed. In this paper, a simple and effective multi-resolution bolt preload monitoring approach using ultrasonic guided waves (UGW) is proposed. A linear relationship between the time-of-flight (TOF) variation of multi-reflected waves and preload is derived to theoretically reveal the multi-resolution properties of UGW. The variations of TOF before and after the slight preload perturbations are extracted by using a global evaluation method. Experimental results show that the signal-to-noise ratio (SNR) of the 1^st, 2^nd, and 3^rd-reflected UGWs is larger than 20 dB. The resolution of the 2^nd-reflected UGW is higher than that of the 1^st-reflected UGW and lower than that of the 3^rd-reflected UGW. The ultimate detectable resolutions of bolt preload (DRBP) of the 1^st and 3^th-reflected UGWs are 0.9% and 0.5%, respectively. By using the 1^st and 3^th-reflected guided waves, the bolt looseness with different degrees can be monitored simultaneously.

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.179-190
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• 2020

In this paper, we estimated the Direction of Arrival (DOA) using Conventional BeamForming (CBF), adaptive beamforming and compressive beamforming. Minimum Variance Distortionless Response (MVDR) and Multiple Signal Classification (MUSIC) are used as the adaptive beamforming, and grid-free compressive sensing is applied for the compressive sensing beamforming. Theoretical background and limitations of each technique are introduced, and the performance of each technique is compared through simulation and real experiments. The real experiments are conducted in the presence of reflected signal, transmitting a sound using two speakers and receiving acoustic data through a linear array consisting of eight microphones. Simulation and experimental results show that the adaptive beamforming and the grid-free compressive beamforming have a higher resolution than conventional beamforming when there are uncorrelated signals. On the other hand, the performance of the adaptive beamforming is degraded by the reflected signals whereas the grid-free compressive beamforming still improves the conventional beamforming resolution regardless of reflected signal presence.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.3
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• pp.347-352
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• 2015

Time domain reflectometry (TDR) is widely used for wire failure detection. It transmits a pulse that is reflected at the boundaries of different characteristic impedances. By analyzing the reflected signal, TDR makes it possible to locate the failure. In this study, TDR was used to detect the water leakage at a pipe joint. A wire attached to the pipe surface was soaked by water when a leak occurred, which affected the characteristic impedance of the wet part, resulting in a change in the reflected signal. To infer the leakage from the TDR signal, we first developed a finite difference time domain-based forward model that provided the output of the TDR signal given the configuration of the transmission line. Then, by solving the inverse problem, the locations of the leaks were found.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.26-30
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• 1997

In this paper, the thickness measurement method of adhesive layers of multilayers using power cepstrum signal processing technique has been proposed. The peak values for reflected signal from each layer have been separated by power cepstrum technique. Therefore, thickness of adhesive layers have been measured by the intervals of peak signal. In the experiment, the adhesive layers of 0.5mm-0.75mm thickness using epoxy(2-Ton and Plastic Steel Putty(A)) between the aluminum and the brass were formed. The adhesive layer thickness which is calculated with data of reflected signal by ultrasonic pulse-echo method was within error 1.34% of the measured values.

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.107-113
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• 2011

LSPR(Localized Surface Plasmon Resonance) sensor measures the refractive index change on the sensor surface. The detection of biological reaction with the unknown refractive index needs to be converted into the signal sensitivity for the refractive index change for comparison with other measurements. To find the signal sensitivity, the three steps of signal processing are proposed, which are signal modeling, signal calibration and signal normalization of LSPR sensor. The detected signal of biotin-streptavidin interaction has been converted into unit of [RU](Resonance Unit) using the proposed method. The converted signal directly can be compared with the other sensors including commercialized one.