• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.8
• /
• pp.1752-1758
• /
• 2010

A FM-CW radar is used for the various purposes as a remote sensing device since it has the advantages of the relatively simple implementation and the low probability of signal interception. A FM-CW radar uses the same frequency modulated continuous wave for both transmission and demodulation. Therefore, the received beat frequency represents the range and Doppler information of targets. However, using the conventional FFT method, the degree of accuracy and resolution in the spectrum estimation can be seriously degraded in the detection and tracking of fast moving targets because of the short dwell time. Therefore, in this paper, the model parameter estimation methods called as an autoregressive method is applied to overcome these problems and showed that the improved accuracy and resolution can be obtained for the target range and velocity estimation.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.3
• /
• pp.395-400
• /
• 2013

In this paper, we introduce the implementation of high accurate level sensor system using the pulse wave type magnetostriction sensor. When a current pulse flows along the waveguide, the magnetic field also propagates towards the end of waveguide. When this magnetic field just passes the position of the magnet for level detection, the resultant magnetic field by these two magnetic fields makes a torsional reflected signal. This is used to calculate the time difference between a interrogation pulse wave and this torsional reflected signal. The key elements and characteristics were investigated to implement level sensor system based on this principle. We introduce a method to calculate the speed of ultrasonic reflected signal and how to make a model of sensing coil. In particular, we experiment with the characteristics of the torsional reflected signal according to the changes of the interrogation voltage and displacement. To make high accurate level sensor system, two methods were compared. One is to use the comparator and time counter, the other is STFT(Short Time FFT) which is capable of the time-frequency analysis.

• Journal of Ocean Engineering and Technology
• /
• v.15 no.4
• /
• pp.60-65
• /
• 2001

Thermal spray coating is formed by a process in which melted particles flying with high speed towards substrate, then crash and spread on the substrate surface cooled and solidified in a very short time, Stacking of the particles makes coating. In this study, the exfoliation of $Al_2$O$_3$ and Ni-4.5wt.%Al thermally sprayed coating which were deposited by an atmospheric plasma spray apparatus are investigated using an AE method. A tensile test is conducted on notch specimens in a stress range below the elastic limit of substrate. The wave forms of AE generated from the three coating specimens can be classified by FFT analysis into two types which low frequency(type I waveform is considered to corresponds exfoliation of coating layers and type II waveform corresponds the plastic deformation of notch tip or the resultant fracture of coating. The fracture of the coating layers can estimate by AE event and amplitude, because AE features increase when the deformation generates.

• Journal of Industrial Technology
• /
• v.24 no.A
• /
• pp.67-73
• /
• 2004

The purpose of this study is to analyze the vibration signal of the revolution machine using the STFT(Short Time Fourier Transform). It is common to analyze the frequency of signal through FFT algorithm with the fixed sampling rate. However, in this situation the order spectrum information useful rather than the general frequency information with the fixed sampling rate. In this paper, the resampling technique was used for getting the information of order spectrum. In resampling process, the arithmetic amount and MSE(Mean Square Error) for various kinds of the signal interpolation was compared and presented the propriety of the interpolation method while developing analysis equipment. Order tracking was implemented using signal interpolation method which it has selected. Then the analyzed results were obtained through simulation using the STFT technique.

• Structural Engineering and Mechanics
• /
• v.31 no.2
• /
• pp.211-223
• /
• 2009

Transient response analysis can be conducted either in the time domain, or via the frequency domain. Sometimes a frequency domain method (FDM) has advantages over a time domain method. A practical issue in the FDM is to find out an appropriate extended period, which may be affected by several factors, such as the excitation duration, the system damping, the artificial damping, the period of interest, etc. In this report, the extended period of the FDM based on the Duhamel's integral is investigated. This Duhamel's integral based FDM does not involve the unit impulse response function (UIRF) beyond the period of interest. Due to this fact, the ever-lasting UIRF can be simply set as zero beyond the period of interest to shorten the extended period. As a result, the preferred extended period is the summation of the period of interest and the excitation duration. This conclusion is validated by numerical examples. If the extended period is too short, then the front portion of the period of interest is more prone to errors than the rear portion, but the free vibration segment is free of the wraparound error.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.115-126
• /
• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.12
• /
• pp.2511-2517
• /
• 2009

Recently, a FMCW radar is used for the various purposes in the short range detection and tracking of targets. The main advantages of a FMCWradar are the comparative simplicity of implementation and the low peak power transmission characterizing the very low probability of signal interception. Since it uses the frequency modulated continuous wave for transmission and demodulation, the received beat frequency represents the range and Doppler information of targets. Detection and extraction of useful information from targets are performed in this beat frequency domain. Therefore, the resolution and accuracy in the estimation of a beat spectrum are very important. However, using the conventional FFT estimation method, the high resolution spectrum estimation with a low sidelobe level is not possible if the acquisition time is very short in receiving target echoes. This kind of problems deteriorates the detection performance of adjacent targets having the large magnitude differences in return echoes and also degrades the reliability of the extracted information. Therefore, in this paper, the model parameter estimation methods such as autoregressive and eigenvector spectrum estimation are applied to mitigate these problems. Also, simulation results are compared and analyzed for further improvement.

• The Journal of the Acoustical Society of Korea
• /
• v.37 no.1
• /
• pp.39-45
• /
• 2018

Compressive sensing allows recovering an original signal which has a small dimension of the signal compared to the dimension of the entire signal in a short period of time through a small number of observations. In this paper, we proposed a method for detecting tonal signal which caused by the machinery component of a vessel such as an engine, gearbox, and support elements. The tonal signal can be modeled as the sparse signal in the frequency domain when it compares to whole spectrum range. Thus, the target tonal signal can be estimated by S-OMP (Simultaneous-Orthogonal Matching Pursuit) which is one of the sparse signal recovery algorithms. In simulation section, we showed that S-OMP algorithm estimated more precise frequencies than the conventional FFT (Fast Fourier Transform) thresholding algorithm in low SNR (Signal to Noise Ratio) region.

• The Journal of the Acoustical Society of Korea
• /
• v.7 no.2
• /
• pp.26-38
• /
• 1988

This paper describes three methods for the estimation of the impulse reponse in an indoor acoustic transfer system which rather has long reverberation time by the cross spectrum. The first method, which is the conventional one, will use the white noise as the source signal. Therefore, the very long time window data and numerous number of DFT are necessary for this estimation. The second method has been disigned in order to shorten the length of time window of the first method by using a burst of noise as the source signal. The third method which will be suggested in this paper uses too types of definite signal with short duration time of the source signal. According to the view point of computation capacity, and estimation accuracy of the impulse response, the compared experimental results show that the third method will be better than the others.

• Journal of Power System Engineering
• /
• v.6 no.3
• /
• pp.24-30
• /
• 2002

The deformation and fracture behaviors of both Al2O3 and Ni 4.5wt.%Al plasma thermal spray coating were investigated by an acoustic emission method. Plasma thermal spray coating is formed by a process in which melted particles flying with high speed towards substrate, then crash and spread on the substrate surface cooled and solidified in a very short time, stacking of the particles makes coating. A tensile test is conducted on notch specimens in a stress range below the elastic limit of substrate. A bendind test is done on smooth specimens. The waveforms of AE generated from the both test coating specimens can be classified by FFT analysis into two types which low frequency(type I) and high frequency(type II). The type I waveform is considered to corresponds exfoliation of coating layers and type II waveform corresponds the plastic deformation of notch tip. The fracture of the coating layers can estimate by AE event and amplitude, because AE features increase when the deformation generates.