• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.729-734
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• 2008

Vibroarthrographic(VAG) signals, emitted by human knee joints, are non-stationary and multi-component in nature and time-frequency distributions(TFD) provide powerful means to analyze such signals. The objective of this paper is to classify VAG signals, generated during joint movement, into two groups(normal and patient group) using the characteristic parameters extracted by time-frequency transform, and to evaluate the classification accuracy. Noise within TFD was reduced by singular value decomposition and back-propagation neural network(BPNN) was used for classifying VAG signals. The characteristic parameters consist of the energy parameter, energy spread parameter, frequency parameter, frequency spread parameter by Wigner-Ville distribution and the amplitude of frequency distribution, the mean and the median frequency by fast Fourier transform. Totally 1408 segments(normal 1031, patient 377) were used for training and evaluating BPNN. As a result, the average value of the classification accuracy was 92.3(standard deviation ${\pm}0.9$)%. The proposed method was independent of clinical information, and showed good potential for non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis and chondromalacia patella.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.16-25
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• 1998

This paper attempts to examine the characteristics of underwater acoustic signals distorted in shallow water environments. Time signals are simulated using an acoustic model that employs the Fourier synthesis scheme. An acoustic experiment was conducted in the shallow sea near Pohang, Korea, where water depth is about 60m. The environment in the simulation is set up so that it approximates the experimental condition, which can be regarded as range-independent. The signal is LFM(linar frequency modulated) type centered on one of the four frequencies 200, 400, 600 and 800Hz, each being swept up or down with the bandwidth of 100Hz. To analyze the signal characteristics, the study introduces a spectrum estimation scheme, pseudo Wigner-Ville distribution (PWVD). The simulated and measured signals suffer great interference by the interaction of neighboring rays. Although there are constructive or destructive interference, the signals keep LFM characteristics well. This is thought that only a few dominant rays of small loss contribute to the receive signals in a shallow water environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2333-2338
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• 2011

In this paper, we develope a wavelet transform based time-frequency domain reflectometry (WTFDR) for the fault localization of underground power cable. The conventional TFDR (CTFDR) is more accurate than other reflectometries to localize the cable fault. However, the CTFDR has some weak points such as long computation time and hard implementation because of the nonlinearity of the Wigner-Ville distribution used in the CTFDR. To solve the problem, we use the complex wavelet transform (CWT) because the CWT has the linearity and the reference signal in the TFDR has a complex form. To confirm the effectiveness and accuracy of the proposed method, the actual experiments are carried out for various fault types of the underground power cable.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.619-622
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• 2001

In this paper, an efficient approach to classification of transient and harmonic disturbances in power systems is proposed. First, the Stop-and-Go CA CFAR Detector is utilized to detect a disturbance from the power signals which are mixed with other disturbances and noise. Then, (i) Wigner Distribution, SVD(Singular Value Decomposition) and Fisher´s Criterion (ii) DWT and Fisher´s Criterion, are applied to extract an efficient feature vector. For the classification procedure, a combined neural network classifier is proposed to classify each corresponding disturbance class. Finally, the 10 class data simulated by Matlab power system blockset are used to demonstrate the performance of the proposed classification system.

• Journal of Ocean Engineering and Technology
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• v.15 no.3
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• pp.87-92
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• 2001

In this studies, joint time-frequency analysis techniques were applied to analyze ultrasonic signals in the degraded austenitic 316 stainless steels, to study the evolution of damage in these materials. It was demonstrated that the nonstationary characteristics of ultrasonic signals could be analyzed effectively by these methods. The WVD was more effective for analyzing the attenuation and frequency characteristics of the degraded materials through ultrasonic. It is indicated that the joint time-frequency analysis, WVD method, should also be useful in evaluating various damages and defects in structural members.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.2 s.173
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• pp.518-525
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• 2000

This paper presents a new wavelet-based structural diagnostic technique. A continuous Gabor wavelet transform is shown to a very effective method in detecting damage in a beam. The beam is excited by a broad-band excitation force. For satisfactory results, the selection of an optimal wavelet is very important though the wavelet transform outperforms existing techniques such as the Wigner-Ville distribution. A specific example is given in a solid circular cylinder with a small defect.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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• pp.135-141
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• 1999

Recently, advance signal analysis which is called "Time-Frequency Analysis" has been developed. Wavelet and Wigner Distribution are used to the method. Wavelet transform(WT) is applied to time-frequency analysis of waveforms obtained by an ultrasonic pulse-echo technique. The Gabor function is adopted as the analyzing wavelet. Wavelet analysis method is an attractive technique for evolution of material characterization evoluation. In this paper, the feasibility of suppression of ultrasonic background noise signal using WT has been presented. These results suggest that ultrasonic background noise ginal can be suppressed and enhanced even for SNR of 20.8 dB. This property of the WT is extremely useful for the detecting flaw echos embedded in background noise.und noise.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.531-536
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• 1994

Wrapping machines in cigarette factories are equipped with indexing drive units with roller gear cam. At present there are no simple, visual, diagnostic techniques for predicting failure in these nits at an early stage. This paper proposes that failure could be predicted by using either a modified version of kurtosis, or the Wigner distribution method. The nonlinear vibration model proposed in this paper takes into consideration the play between the m and the cam follower, and precisely simulates the actual vibration. Statistics on the variance in play, obtained from the data on time history, call then be used to evaluate the effects of tile mage oil the cam and cam follower.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.48 no.2
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• pp.221-232
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• 2015

Broadband echoes measured in live chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, and fat greenling Hexagrammos otakii with different morphologies and internal characteristics were analyzed in time and frequency domains to understand the species-specific echo feature characteristics for classifying fish species. The mean echo image for each time-frequency representation dataset obtained as a function of orientation angle was extracted to mitigate the effect of fish orientation on acoustic scattering. The joint time-frequency content of the broadband echo signals was obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The SPWVDs were analyzed for each echo signature of the three fish species. The results show that the time-frequency analysis provided species-specific echo structure patterns and metrics of the broadband acoustic signals to facilitate fish species classification.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.50 no.2
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• pp.195-206
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• 2017

Joint time-frequency images of the broadband echo signals of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution in controlled environments. Affine transformation and principal component analysis were used to obtain eigenimages that provided species-specific acoustic features for each of the six fish species. The echo images of an unknown fish species, acquired in real time and in a fully automated fashion, were identified by finding the smallest Euclidean or Mahalanobis distance between each combination of weight matrices of the test image of the fish species to be identified and of the eigenimage classes of each of six fish species in the training set. The experimental results showed that the Mahalanobis classifier performed better than the Euclidean classifier in identifying both single- and mixed-species groups of all species assessed.