• Proceedings of the KIEE Conference
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• 1995.07c
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• pp.1359-1361
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• 1995

This paper describes the method of diagnosis for power transformer, using vibration signal analysis. Vibration signal to be made from transformer is the liner combination of load current, applied voltage and internal temperature. This study measured the vibration signal by before and after short circuit test of the transformer. And the signal analysis and comparison was carried out for AR modeling and frequency analysis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.122-132
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• 2005

This paper presents a novel approach based on Prony method to analysis of small signal stability in power system. Prony method is a valuable tool in identifying transfer function and estimating the modal parameter of power system oscillation from measured or computed discrete time signal. This paper define the relative residue of time signal and propose the condition to select low frequency oscillation in each generator. This paper describes the application results of proposed algorithm with respect to KEPCO systems. Simulation results show that the proposed algorithm can be used as another tools of power systems analysis.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.670-679
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• 2003

This paper deals with application of wavelet transform, which is known to be good for time-frequency analysis, in order to detect the underwater transient signals embedded in ambient noise. A new detector of acoustic transient signals is presented. It combines two detection tools: wavelet analysis and higher-order statistics. Using both techniques, the detection of the transient signal is possible in low signal to noise ratio condition. The proposed algorithm uses the wavelet transform of a partition of the signal on frequency domain, and then higher-order statistics tests the Gaussian nature of the segments.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1109-1110
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• 2008

It is required to optimize the system operation efficiency to allocate maintenance task and period using systemic maintenance process. To allocate maintenance task and period must analysis the failure distribution mode at first. In this paper, we introduce the linear regression analysis and estimate the failure distribution for the railroad signal equipment using that.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.84-87
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• 2000

The system has a function of acquirement PCM signal of the preferred channel from the subhighway (SHW), connecting a universal signal transceiver unit and time switch unit, and then it classifies the type of signal such as R2MFC/ DTMF/ CCT/ VOICE, and finally discriminates the digit. This paper describes the spectral analysis of the PCM acquisition system usng the quick Fourier transform(QFT), and discusses the algorithm of signal analysis and discrimination.

• 전기의세계
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• v.13 no.2
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• pp.5-10
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• 1964

Some nonlinearities in feedback control systems, such as relay servo, Coulomb friction, saturating amplifier of backlash in gears cause some undesirable effects. These may be static errors, limit cycles or jump phenomena. So, linearizing method of these nonlinearities using dither signals was first suggested by Loeb. He pointed out that the addition of high frequency signal to either the input or the output of a nonlinear system would make the nonlinear performance approximate the performance of linear systems. He used high-frequency sinusoidal wave as dither signal. But, in this thesis, general method of analysis is suggested for nonlinear control systmes using various dither signals, such as noise signal of Gaussian amplitude distribution, sinusoidal dither signal and sawtooth dither signal. Also, the advantage and disadvantage of these dither signals are compared. Throughout the analysis statistical method is adopted and lastly analog computer is used for the experiment of various nonlinear systems using dither signals.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1210-1216
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• 2014

This paper deals with an application of Teager Energy Operator (TEO) and Energy Separation Algorithm(ESA) to detect and determine various voltage waveform distortions like harmonics, inter-harmonics and frequency variation. Because the TEO and DESA algorithm was initially proposed for speech or communication analysis, its applications are limited to some types of waveform in the power quality analysis area. For example, an undistorted voltage signal is similar with a pure sinusoid. A voltage fluctuation is very similar with an amplitude-modulated signal, from the viewpoint of signal theory. And a continuous frequency variation is similar with a frequency-modulated signal, which is also known as a chirp signal. This paper is written to show that the TEO and DESA algorithm can be used for detecting occurrences of the representative waveform distortions and determining their instantaneous information of amplitude and frequency.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.173-177
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• 2009

With the advent of light-weight daily physiological signal monitoring sensors, intelligent inference and analysis method for physiological signal monitoring application, commercialized products and services are released. However, practical constraints still remain for daily physiological signal monitoring. Most devices provide rough health check function and analyze with randomly sampled measurements. In this work, we propose the probabilistic modeling of physiological signal analysis. This model represent the relationship between previous user measurement (history), other group`s type, model and current observation. From the experiment, we found that the personalized analysis with long term regular data shows reliable result and reduces the analyzing errors. In addition, participants agree that the personalized analysis shows reliable and adaptive information than other standard analysis method.

• Convergence Security Journal
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• v.23 no.3
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• pp.13-20
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• 2023

This study conducted a digital forensic analysis of Signal and Telegram, two secure messengers widely used in the Android environment. As mobile messengers currently play an important role in daily life, data management and security within these apps have become very important issues. Signal and Telegram, among others, are secure messengers that are highly reliable among users, and they safely protect users' personal information based on encryption technology. However, much research is still needed on how to analyze these encrypted data. In order to solve these problems, in this study, an in-depth analysis was conducted on the message encryption of Signal and Telegram and the database structure and encryption method in Android devices. In the case of Signal, we were able to successfully decrypt encrypted messages that are difficult to access from the outside due to complex algorithms and confirm the contents. In addition, the database structure of the two messenger apps was analyzed in detail and the information was organized into a folder structure and file format that could be used at any time. It is expected that more accurate and detailed digital forensic analysis will be possible in the future by applying more advanced technology and methodology based on the analyzed information. It is expected that this research will help increase understanding of secure messengers such as Signal and Telegram, which will open up possibilities for use in various aspects such as personal information protection and crime prevention.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.42-47
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• 2001

Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. In this paper, we would like to introduce the signal processing for ECG analysis and the device made for wireless communication of ECG data. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the polynomial approximation partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with the database, we can detect and classify the heart disease. The ECG detection device consists of amplifier, filters, A/D converter and RF module. After amplification and filtering, the ECG signal is fed through the A/D converter to be digitalized. The digital ECG data is transmitted to the personal computer through the RF transceiver module and serial port.