• Journal of Communications and Networks
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• v.16 no.1
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• pp.92-97
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• 2014

A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.

• Journal of Korea Multimedia Society
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• v.19 no.10
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• pp.1775-1781
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• 2016

Generally, in the underwater target recognition, feature vectors are extracted from the target signal utilizing spatial information according to target shape/material characteristics. In addition, various signal processing techniques have been studied to extract feature vectors which are less sensitive to the location of the receiver. In this paper, we synthesized active echo signals using 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to echo signals to extract signal features. For the performance verification, classification experiments were performed using backpropagation and probabilistic neural network classifiers based on single aspect and multi-aspect method. As a result, we obtained a better recognition result using proposed feature extraction and multi-aspect based method.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1107-1110
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• 1999

In speech signal processing, the accurate decision of the voiced/unvoiced sound is important for robust word recognition and analysis and a high coding efficiency. In this paper, we propose the mehod of the voiced/unvoiced decision using the LSP parameter which represents the spectrum characteristics of the speech signal. The voiced sound has many more LSP parameters in low frequency region. To the contrary, the unvoiced sound has many more LSP parameters in high frequency region. That is, the LSP parameter distribution of the voiced sound is different to that of the unvoiced sound. Also, the voiced sound has the minimun value of sequantial intervals of the LSP parameters in low frequency region. The unvoiced sound has it in high frequency region. we decide the voiced/unvoiced sound by using this charateristics. We used the proposed method to some continuous speech and then achieved good performance.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.9
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• pp.828-833
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• 2010

Steam leakage is one of the major issues for the structural fracture of pipes of nuclear power plants. Therefore a method to inspect a large area of piping systems quickly and accurately is needed. In this paper, we proposed the method for the detecting steam leakage by using image signal processing. Our basic idea come from heat shimmer which shine with a soft light that looks as if it shakes slightly. To test the performance of this technique, experiments have been performed for simple heat source and steam generator. Results show that the proposed technique is quite powerful in the steam leak detection.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.7
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• pp.83-91
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• 1993

A ghost cancelling method using the multiplicationless adaptive multipath channel identification is proposed. The IIR filter and the LMS algorithm are used for ghost cancelling. The coefficients of IIR filter are obtained by multipath channel identification. The LMS algorithm which is simple relatively is used as the adaptive algorithm. An MPS is selected as the reference signal and it is used as the input of the adaptive algorithm for the multipath channel identification. If an MPS is not exist, the horizontal syne, and color burst signal can be used as the reference signal. Improving of accuracy of the ghost cancelling in the presence of the phase variation in the multipath channel, a complex processing are also performed.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.55-58
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• 1997

In this paper, we study a signal processing method which extracts each MUAP(motor unit action potential) from EMG(Electromyogram) interference pattern or clinical diagnostic purposes. First of all, differential digital filtering is selected or eliminating the spike components of the MUAP's from the background noise. And, the algorithm identifies the spikes over the certanin threshold by template matching in frequency domain. After missing or false firing actor is cut off at the IPI(inter pulse interval) histogram, we averages the MUAP waveforms from the raw signal using the identified spikes as triggers, and Finally, measures their amplitudes, durations, and numbers of phases. Specially, We introduce algorithm performed by template matching in the frequency domain. A typical 3-s signal recorded from the biceps brachii muscle using a conventional needle electrode during a isometric contraction is used. Finally, the method decomposed five simultaneous active MUAP's from original EMG signal.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.9
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• pp.702-708
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• 2002

The spectrum of impulse response signal from an impulse hammer testing is widely used to obtain frequency response function(FRF). However the FRFs obtained from impact hammer testing have not only leakage errors but also finite record length errors when the record length for the signal processing is not sufficiently long. The errors cannot be removed with the conventional signal analyzer which treats the signals as if they are always steady and periodic. Since the response signals generated by the impact hammer are transient and have damping, they are undoubtedly non-periodic. It is inevitable that the signals be acquired for limited recording time, which causes the errors. This paper makes clear the relation between the errors of FRF and the length of recording time. A new method is suggested to reduce the errors of FRF in this paper. Several numerical examples for 1-dof model are carried out to show the property of the errors and the validity of the proposed method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.8
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• pp.767-773
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• 2009

This research have introduced wavelet signal processing technic for improving navigation signals. INS signals can be distorted with conventional pre-filtering method such as low-pass filtering by unwanted smoothing on real signals. But in this paper, wavelet thresholding method is implemented to INS signal to denoise for INS-GPS integrated system. This method reduces signal noise but not distorts the rapid varing signal. And this paper applied thresholding to INS-GPS integrated navigation system and improved navigation performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.2001-2013
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• 1994

In this paper, the method of processing a blurred noisy image has been researched. The conventional method of processing signal has faluts which are slow convergence speed and long time-consuming process at the singular point and or in the ill condition. There is the process, the Gauss Seidel's method to remove these faults, but it takes too much time because it processed singnal repeatedly. For overcoming the faults, this paper shows a image restoration method which takes shorter than the Gauss-Seidel's by comparing the Gauss Seidel's with proposed alogorithm and accelerating convergence speed at the singular point and/or in the ill condition. In this paper, the conventional process method(Gauss-Seidel) and proposed optimal algorithm were used to get a standard image($256{\times}56{\times}bits$). and then the results are simulated and compared each other in order to examine the variance of MSE(Mean Square Error) by the acceleration parameter in the proposed image restoration. The result of the signal process and the process time was measured at all change of acceleration parameter in order to verify the effectveness of the proposed algorithm.