• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.179-186
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• 2009

This paper presents an approach to classify normal and Ventricular Tachycardia/Ventricular Fibrillation(VT/VF) from the Creighton University Ventricular Tachyarrhythmia DataBase(CUDB) using the neural network with weighted fuzzy membership functions(NEWFM). In the first step, wavelet transform is used for producing input values which are used in the next step. In the second step, two numbers of input features are extracted by phase space reconstruction method and peak extraction method using coefficients produced by wavelet transform in the previous step. NEWFM classifies normal and VT/VF beats using two numbers of input features, and then the accuracy rate is 90.13%.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.3
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• pp.96-102
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• 2005

Acoustic Emission(AE) technique has been applied to not only material characterization evaluation but also on-line monitoring of the structural integrity. The AE source location technique is very important to identify the source, such as crack, leak detection. Since the AE waveforms obtained from sensors are very difficult to distinguish the defect signals, therefore, it is necessary to consider the signal analysis of the transient wave-form. In this study, we have divided the region of interest into a set finite elements, and calculated the arrival time differences between sensors by using the velocities at every degree from 0 to 90. A new technique for the source location of acoustic emission in fiberboard plates has been studied by introducing Wavelet Transform(WT) do-noising technique. WT is a powerful tool for processing transient signals with temporally varying spectra. If the WT de-noising was employed, we could successfully filter out the errors of source location in fiberboard plates by arrival time difference method. The accuracy of source location appeared to be significantly improved.

• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.49-62
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• 1994

Look direction bias in a single look SAR image can often be misinterpreted in the geological application of radar data. This paper investigates digital processing techniques for SAR image data integration and compensation of the SAR data look direction bias. The two important approaches for reducing look direction bias and integration of multiple SAR data sets are (1) principal component analysis (PCA), and (2) wavelet transform(WT) integration techniques. These two methods were investigated and tested with the ERS-1 (VV-polarization) and CCRS*s airborne (HH-polarization) C-SAR image data sets recorded over the Sudbury test site, Canada. The PCA technique has been very effective for integration of more than two layers of digital image data. When there only two sets of SAR data are available, the PCA thchnique requires at least one more set of auxiliary data for proper rendition of the fine surface features. The WT processing approach of SAR data integration utilizes the property which decomposes images into approximated image ( low frequencies) characterizing the spatially large and relatively distinct structures, and detailed image (high frequencies) in which the information on detailed fine structures are preserved. The test results with the ERS-1and CCRS*s C-SAR data indicate that the new WT approach is more efficient and robust in enhancibng the fine details of the multiple SAR images than the PCA approach.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.04a
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• pp.13-16
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• 2005

The mechanical properties of composite materials may degrade severely in the presence of damage. Especially, the high-velocity impact such as bird strike, a hailstorm, and a small piece of tire or stone during high taxing, can cause sever damage to the structures and sub-system in spite of a very small mass. However, it is not easy to detect the damage in composite plates using a single technique or any conventional methods. In this paper, the PYDF(polyvinylidene fluoride) film sensors and strain gages were used for monitoring impact damage initiation and propagation in composite laminates. The WT(wavelet transform) and STFT(short time Fourier transform) are used to decompose the sensor signals. A ultrasonic C-scan and a digital microscope are also used to examine the extent of the damage in each case. This research demonstrate how various sensing techniques, PVDF sensor in particular, can be used to characterize high-velocity impact damage in advanced composites.

• The Journal of Engineering Geology
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• v.27 no.4
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• pp.513-518
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• 2017

Groundroll is a coherent noise showing dispersive behavior in land seismic-reflection records and its rejection has been a stubborn problem in data processing because they severely degrade the continuities and resolution of reflection signals. Conventional processing schemes of attenuating noises are the kind of frequency filtering (i.e., bandpass and f-k) that uses the Fourier transform (FT) along the entire trace in the time domain. To suppress them in this study, 1D wavelet filtering (WT) that can control time-varying frequency properties is tested and performed in the land-based synthetic and field seismic data. The results are compared to the ones from conventional filtering techniques in terms of continuities and resolution of reflection events. This filtering technique enhanced the reflection events by effectively eliminating the dispersive groundroll and random noises with control of time-scale function on wavelet domain.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.287-293
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• 2015

This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.

• The KSFM Journal of Fluid Machinery
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• v.15 no.6
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• pp.31-38
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• 2012

Rotor of rotating machinery is the highly damaged part. Fault of 7 different types was confirmed as the main causes of rotor damage from the pump failure history data in domestic and U.S. nuclear. For each fault types, simulation testing was performed and fault signals were collected form the sensors. To calculate the statistical parameters of time-domain & frequency-domain, measured signals were analyzed by using the discrete wavelet transform, fast fourier transform, statistical analysis. Total 84 parameters were obtained. And Effectiveness factor were used to evaluate the discrimination capacity of each parameter. From the effectiveness factor, RAW-P4/RAW-P7/WT2-NNL/WT2-EE/WT1-P1 showed high ranking. Finally, these parameters were selected as the feature parameters of intelligent fault diagnostics for rotor.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.358-361
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• 1997

This paper describes a multichannel epileptic seizure detection algorithm based on wavelet transform(WT), artificial neural network(ANN) and expert system. First, through the WT, a small number of wavelet coefficients is used to represent the single channel epileptic spike. Next, 3-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained above. Finally, 16 channel expert system which is based on clinical experience is introduced as a artifact rejection and reliable detection. The suggested algorithm was implemented on personal computer(PC). Two main events i.e., epileptiform and normal activities, were selected from 32 person's EEGs(normal: 20, seizure disorder: 12) in consensus among experts. The result was that WT reduced data input size and ANN detected 97 of the 100 EEGs containing definite spike - sensitivity of 97%. Expert rule system was capable of rejecting a wide variety of artifacts commonly found in EEG recordings. It also reduced false positive detections of ANN.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.665-672
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• 2005

We present a novel pilot-aided channel estimation method for OFDM (Orthogonal Frequency Division Muitiplexing) system using WT(Wavelet transform) and interpolation. Due to excellent AWGN (Additive White Gaussian Noise) cancellation capability of n, pilot channels are estimated quite exactly and then, Dey are used in 2-degree polynomial interpolating the other remaining data symbol channels. The simulation results for Short WATM (Wireless Asynchronous Transfer Mode) channel show that the degradation in BER (Bit Error Ratio) performance of OFDM system iか this estimator is negligible compared to the case of perfect knowledge of CSI (Channel State Information).

• Proceedings of the IEEK Conference
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• summer
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• pp.249-253
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• 2004

Wavelet transform (WT) has attracted most engineers and scientists because of its excellent properties. The coherence of practical approach and a theoretical basis not only solves currently important problems, but also gives the potential of formulating and solving completely new problems. It has been show that multi-resolution analysis of Wavelet transforms is good solution in speech analysis and threshold of wavelet coefficients has near optimal noise reduction property for many classes of signals. This paper proposed applications of wavelet in speech processing: pitch detection, voice-unvoice (V -UV) decision, denoising with the detailed algorithms and results.