• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.5
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• pp.1-6
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• 2010

The wavelet transform is a popular tool for studying intermittent and localized phenomena in signals. In this study the wavelet transform of cutting force signals was conducted for the detection of a tool failure in turning process. We used the Daubechies wavelet analyzing function to detect a sudden change in cutting signal level. A preliminary stepped workpiece which had intentionally a hard condition was cut by the inserted cermet tool and a tool dynamometer obtained cutting force signals. From the results of the wavelet transform, the obtained signals were divided into approximation terms and detailed terms. At tool failure, the approximation signals were suddenly increased and the detailed signals were extremely oscillated just before tool failure.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.643-649
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• 1999

In this paper, we proposed that ｉ) noise-tolerant four kinds of AM(Amplitude Modulation)-FM(Frequency Modulation) demodulators are designed, ⅱ) we derived undersampling frequency through the product via energy operator of the monocomponent AM-FM signals separated form two-component AM-FM signals, and ⅲ) these four kinds of AM-FM demodulators detect respectively information signals of the IA(Instantaneous Amplitude) and IF(Instantaneous Frequency) by undersampling frequency to be different each other from the undersampled monocomponet AM-FM signals. Particularly, the proposed algorithm can control undersampling frequency by an integer factor. And these efficient AM-FM demodulators are well worked with the undersampled AM-FM signals.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.125-131
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• 2012

This paper is on a stereo acoustic echo canceller with the blind signal separation. The convergence speed of the stereo acoustic echo canceller is deteriorated due to mixing two residual signals in the update signal of each echo canceller. To solve this problem, we are to use the blind signal separation(BSS) method separating the mixed signals. The blind signal separation method can extracts the source signals by means of the iterative computations with two input signals. We had verified performances of the proposed acoustic echo canceller for stereo through simulations. The results of simulations show that the acoustic echo canceller for stereo using this algorithm operates stably without divergence in the normal state. And, when the speech signals were inputted, this echo canceller achieved about 3dB higher ERLE in the case of using the BSS algorithm than the case of not using the BSS algorithm. But this echo canceller didn't get good performances in the case of inputting the white noises as stereo signals.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1454-1463
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• 2008

Blind source separation by independent component analysis (ICA) has applied in signal processing, telecommunication, and image processing to recover unknown original source signals from mutually independent observation signals. Neural networks are learned to estimate the original signals by unsupervised learning algorithm. Because the outputs of the neural networks which yield original source signals are mutually independent, then mutual information is zero. This is equivalent to minimizing the Kullback-Leibler convergence between probability density function and the corresponding factorial distribution of the output in neural networks. In this paper, we present a learning algorithm using information theory and higher order statistics to solve problem of blind source separation. For computer simulation two deterministic signals and a Gaussian noise are used as original source signals. We also test the proposed algorithm by applying it to several discrete images.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.4
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• pp.440-443
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• 2010

This paper deals with the method of vibration measurement of a gun barrel structure using mechanical filter. When a bullet with high speed is moving within a gun barrel type structure with low bending vibration frequencies, it is difficult to measure the bending vibration signals of the structure. For example, noncontact type sensors such as displacement or velocity sensor are not appropriate for the measurement of vibrational signals because of the movement effect of the equipment frame through the moving structures or effect of the ground vibration. One of contact type sensors such as accelerometer is profitable for measurement of vibrational signals because of its wide measurement ranges. In the case of a gun barrel structure including high vibrational signals like shock waves, however, it is necessary to propose vibration measurement method filtering high frequencies. The purpose of the paper is to propose the proper vibrational measurement technique filtering high frequencies of a gun barrel type structure.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.6
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• pp.627-636
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• 2002

Identification and sizing of surface breaking vertical cracks using angle beam ultrasonic testing in practical situation quite often become very difficult tasks due to the presence of non-relevant signals caused by geometric reflectors. The present work introduces effective and systematic approaches to take care of such a difficulty by use oi angle beam ultrasonic testing models that can predict the expected signals from various targets very accurately. Specifically, the model-based TIFD (Technique for Identification of Flaw signals using Deconvolution) is Proposed for the identification of the crack tip signals from the non-relevant geometric reflection signals. In addition, the model-based Size-Amplitude Curve is introduced for the reliable sizing of surface breaking vertical cracks.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.6
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• pp.520-530
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• 2007

Combination of the parametric and the wavelet analyses of acoustic emission (AE) signals was applied to identify the failure modes in carbon fiber reinforced plastic (CFRP) composite laminates during tensile testing. AE signals detected by surface mounted lead-zirconate-titanate (PZT) and polyvinylidene fluoride (PVDF) sensors were analyzed by parametric analysis based on the time of occurrence which classifies AE signals corresponding to failure modes. The frequency band level-energy analysis can distinguish the dominant frequency band for each failure mode. It was observed that the same type of failure mechanism produced signals with different characteristics depending on the stacking sequences and the type of sensors. This indicates that the proposed method can identify the failure modes of the signals if the stacking sequences and the sensors used are known.

• Proceedings of the KSR Conference
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• 1998.11a
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• pp.329-336
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• 1998

The characteristics of vibration and sound signals which occurs at the driving system of electric train are investigated in this study since the vibration of driving system is one of the main sources of vibration and sound in electric train. The vibration signals are changed its signal patterns during the transmission from the source to passengers due to noise or several unknown factors. To avoid the complexity of actual signals of electric train, signals from experimental apparatus of motor/gear driving system are analyzed to find the appropriate method of analysis and to characterize the signal patterns. The used methods are waterfall diagram, transfer function and modal analysis. The results shows that the vibration signals are usually originated from motor bearing and gear meshing and these signals are transmitted to bottom or bogie. Also, the sound signal is similar to the vibration of bottom or bogie, but it is not so clear to figure out the source of vibration.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.410-421
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• 2019

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

• Journal of IKEEE
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• v.25 no.2
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• pp.252-260
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• 2021

In this paper, a method of using Software Defined Radio (SDR) is proposed for improving the accuracy of identifying two kinds of signals as Wireless Fidelity (Wi-Fi) signal and Bluetooth signal at the same frequency band of 2.4 GHz based on the time-domain signal characteristic. An SDR device was set up for collecting transmitting signals from Wi-Fi access points (Wi-Fi) and mobile phones (Bluetooth). Different characteristics between Wi-Fi and Bluetooth signals were extracted from the measured result. The SDR device is programmed with a Wi-Fi and Bluetooth detection algorithm and a collision detection algorithm to detect and verify the Wi-Fi and Bluetooth signals based on collected IQ data. These methods are necessary for some applications like wireless communication optimization, Wi-Fi fingerprint localization, which helps to avoid interference and collision between two kinds of signals.