• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.69-87
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• 2014

A new output only modal analysis method is developed in this paper. This method uses continuous wavelet transform to modify a popular blind source separation algorithm, second order blind identification (SOBI). The wavelet modified SOBI (WMSOBI) method replaces original time domain signal with selected time-frequency domain wavelet coefficients, which overcomes the shortcomings of SOBI. Both numerical and experimental studies on bridge models are carried out when there are limited number of sensors. Identified modal properties from WMSOBI are analyzed and compared with fast Fourier transform (FFT), SOBI and eigensystem realization algorithm (ERA). The comparison shows WMSOBI can identify as many results as FFT and ERA. Further case study of structural health monitoring (SHM) on an arch bridge verifies the capability to detect damages by combining WMSOBI with incomplete flexibility difference method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.9-15
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• 2017

In this study we performed the experiment to detect the ERP using SVM with wavelet features. The EEG signal that is generated visual stimulated ERP database in SCCN applied for the experiment. The feature vectors for experiment are categorized frequency and continuous wavelet- based vectors. In experimental results, the detection rate of SVM with wavelet feature vectors improved above 10% comparing with frequency- based feature vector. Based on the experimental results we analyzed the relation between the activity degree of the ERP and the band split characteristics of the ERP by wavelet transform.

• Computers and Concrete
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• v.20 no.5
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• Ocean and Polar Research
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• v.29 no.4
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• pp.303-310
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• 2007

2-D wavelet analysis is applied to bathymetric data from the KR1 area of Korea Deepsea Mining Area. The wavelet analysis is one of the quantitative methods to analyze the topography. The wavelet allows us to create filters to select for topography in a continuous variety of shapes, sizes, and orientation. The 2-D Linear B-spline filter, 100 BS and 100 NF, is convolved with bathymetric data to identify the location of abyssal hills and abyssal troughs in bathymetry. In addition, the 2-D derivative of Cubic B-spline filter, 60 BS and 60 NF, is applied to bathymetric data to find the slope of abyssal hill in bathymetry. These filters were rotated $5^{\circ}$ counterclockwise from NS to match the dominant orientation of seafloor lineament. Both filters result in good match with abyssal hills, troughs, and slopes. This method can apply to fault, fold, and other lineament structures description with variable size. The result of application shows that wavelet analysis of bathymetric data could be used with fundamental data of geophysical analysis.

• Structural Engineering and Mechanics
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• v.74 no.2
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• pp.201-214
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• 2020

This study aims at evaluating composite moment frame structures (CFS) using wavelet analysis of the displacement behavior of these structures. Five seismic damage mitigation systems' models of 9-story CFS are examined namely, basic (Model 1), reinforced (Model 2), buckling restrained braced (BRB) (Model 3), lead rubber bearing (LRB) (Model 4), and composite (Model 5) moment frames. A novel integration between continuous and discrete wavelet transforms is designed to estimate the wavelet power energy and variance of measurements' behaviors. The behaviors of the designed models are evaluated under influence of four seismic loads to study the dynamic performance of CFS in the frequency domain. The results show the behaviors of models 3 and 5 are lower than other models in terms of displacement and frequency performances. Model 3 has been shown lower performances in terms of energy and variance wavelets along the monitoring time; therefore, Model 3 demonstrates superior performance and low probability of failure under seismic loads. Furthermore, the wavelet variance analysis is shown a powerful tool that can be used to assess the CFS under seismic hazards.

• International Journal of Automotive Technology
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• v.7 no.3
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• pp.289-294
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• 2006

Better fundamental understanding of the interactions between the in-cylinder flows and combustion process is an important requirement for further improvement in the fuel economy and emissions of internal combustion(IC) engines. Flow near a spark plug at the time of ignition plays an important role for early flame kernel development(EFKD). Velocity data measurements in this study were made with a two-component laser Doppler velocimetry(LDV) near a spark plug in a single cylinder optical spark ignition(SI) engine with a heart-shaped combustion chamber. LDV velocity data were collected on an individual cycle basis under wide-open motored conditions with an engine speed of 1,000rpm. This study examines and compares the flow fields as interpreted through ensemble, cyclic and discrete wavelet transformation(DWT) analysis. The energy distributions in the non-stationary engine flows are also investigated over crank angle phase and frequency through continuous wavelet transformation(CWT) for a position near a spark plug. Wavelet analysis is appropriate for analyzing the flow fields in engines because it gives information about the transient events in a time and frequency plane. The results of CWT analysis are provided and compared with the mean flows of DWT first decomposition level for all cycles at a position. Low frequency high energy found with CWT corresponds well with the peak locations of the mean velocity. The high frequency flows caused by the intake jet gradually decay as the piston approaches the bottom dead center(BDC).

• Journal of Biomedical Engineering Research
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• v.24 no.6 s.81
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• pp.487-494
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• 2003

The sleep stages provides the most basic evidence for diagnosing a variety of sleep diseases. for staging sleep by analysis of EEG(electroencephalogram), it is especially important to detect the characteristic waveforms from EEG. In this paper, sleep EEG signals were analyzed using Fourier transform and continuous wavelet transform as well as discrete wavelet transform. Proposeed system methods. Fourier and wavelet for detecting of important characteristic waves(hump, sleep spindles. K-complex, hill wave, ripple wave) in sleep EEG. Sleep EEG data were analysed using Daubechies wavelet transform method and FFT method. As a result of simulation, we suggest that our neural network system attain high performance in classification of characteristic waves.

• Journal of Korea Multimedia Society
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• v.3 no.5
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• pp.461-469
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• 2000

A wavelet analysis in a field of analytics is to create a reconstruction theorem of Plancherel form. And a series of wavelet is to create a discrete is to create a discrete reconstruction theorem for a frame theory and a multiresolution analysis theory. As a generation of reconstruction theorem, a wavelet correspond to it is generated. That is to be like a basic wavelet which is satisfied an admissibility condition in CWT and a Daubechies wavelet using MRA in wavelet series and a Meyer wavelet using a frame theory. In this paper, we discover a discrete reconstruction theorem which is superior to a conventional discrete reconstruction theorem by extending admissibility condition used in CWT and develop a New $L^1$-wavelet group. A new $L^1$-wavelet is applied to a signal reconstruction and a signal analysis in time-frequency region.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.229-236
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• 2005

This paper is contribute to Inverse continuous wavelets transform(ICWT) which permits to determine real 'time-scale' plan. The application of ICWT is not yet represented because of the numerical difficulty. If the signal can be reconstructed stably by ICWT, the multi scale filter bank system which composed by analysis and synthesis process can be designed. In this work, we represent the ICWT which leads to nearly perfect reconstruction of signal and the multi-scale filter bank system.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.215-218
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• 2000

This paper describes a new algorithm for segmenting continuous handwritten signatures sampled by a digitizer. Signatures are segmented by three procedures. The first step is to calculate the pen tip speed. Then the Gabor wavelet is carried out on the acquired data from the first step. Finally, the local minima of the filtered output are selected as segmentation points of the signature. The proposed method is experimented with numerous signatures with various length and complexity.