• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.9
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• pp.1244-1249
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• 1995

Wavelet transform technique is applied to two important electromagnetic problems:1) to analyze the frequency-domain radar echo from finite-size targets and 2) to the integral solution of two- dimensional electromagnetic scattering problems. Since the frequency- domain radar echo consists of both small-scale natural resonances and large-scale scattering center information, the multiresolution property of the wavelet transform is well suited for analyzing such ulti-scale signals. Wavelet analysis examples of backscattered data from an open- ended waveguide cavity are presented. The different scattering mechanisms are clearly resolved in the wavelet-domain representation. In the wavelet transform domain, the moment method impedance matrix becomes sparse and sparse matrix algorithms can be utilized to solve the resulting matrix equationl. Using the fast wavelet transform in conjunction with the conjugate gradient method, we present the time performance for the solution of a dihedral corner reflector. The total computational time is found to be reduced.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.194-202
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• 1999

A method for pattern recognition based on wavelet transform is proposed in this paper. The boundary of the object to be recognized includes shape information for object of machine parts. The contour is first represented using a one-dimensional signal and normalized about translation, rotation and scale, then is used to build the wavelet transform representation of the object. Wavelets allow us to decompose a function into multi-resolution hierarchy of localized frequency bands. The recognition of 2-dimensional object based on the wavelet is described to analyze the shape of analysis technique; the discrete wavelet transform(DWT). The feature vectors obtained using wavelet analysis is classified using a multi-layer neural network. The results show that, compared with the use of fourier descriptors, recognition using wavelet is more stable and efficient representation. And particularly the performance for objects corrupted with noise is better than that of other method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.4
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• pp.381-387
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• 2009

The end-milling force behaviour is very complex and it is related to a de-noising phenomenon, so it is very difficult to detect and diagnose this static cutting force phenomenon. This paper presents a new method of filtering of end-milling force in end-milling operation using filter bank technique, based on the wavelet transform. In this paper by comparing the history of end-milling force using wavelet filtering the fundamental end-milling property of the wavelet transform is well reviewed and analyzed. This result of wavelet transform using filter bank shows the possible static prediction of end-milling force with severe dynamic properties such as chatter in end-milling operation.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.138-140
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• 2006

A new diagnosis model was constructed by combining atomic force microscopy (AFM), wavelet, and neural network. Plasma faults were characterized by filtering AFM-measured etch surface roughness with wavelet. The presented technique was evaluated with the data collected during the etching of silicon oxynitride thin film. A total of 17 etch experiments were conducted. Applying wavelet to AFM, surface roughness was detailed into vertical, horizon%at, and diagonal components. For each component, neural network recognition models were constructed and evaluated. Comparisons revealed that the vertical component-based model yielded about 30% improvement in the recognition accuracy over others. The presented technique was evaluated with the data collected during the etching of silicon oxynitride thin film. A total of 17 etch experiments were conducted

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.184-190
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• 2008

The fault location algorithm based on stationary wavelet transform was developed to locate the fault point more accurately. The stationary wavelet transform(SWT) was introduced instead of conventional discrete wavelet transform(DWT) because SWT has redundancy properties which is more useful in noise signal processing. In previous paper, noise cancellation technique based on the correlation of wavelet coefficients at multi-scales was introduced, and the efficiency was also proved in full. In this paper, fault section discrimination and fault location algorithm using noise cancellation technique were tested by ATP simulation on real power cable systems. From these results, the fault can be located even in very difficult and complicated situations such as different inception angle and fault resistance.

• Journal of Ocean Engineering and Technology
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• v.20 no.2 s.69
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• pp.8-15
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• 2006

Guided-waves-based mortar-filled steel pipe inspection is carried out through using EMAT (Electro magnetic acoustic transducer) and wavelet transform. Possibly existing anomalies such as separation (or void) and inclusion are made in the fabricated mortar-fled steel pipes: these anomalies are infected. Since guided waves have the long range inspection capability, EMAT has its own advantages over the conventional PZT (Piezoelectric zirconate titanate), and wavelet transform gives the multi-resolution on time-frequency domain results, the suggested technique gives an alternative way for inspecting mortar-filled steel pipes, which are popularly used for supporting marine structures such as piers, wharfs, moles, and dolphins. Through this study, it is show that the suggested technique is promising for detecting the amounts of separations and inclusions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.16-22
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• 2007

This study proposes a new wavelet watermark technique based on the Linear Bit Expansion. To ensure the security of the watermark, enlarged watermark by applying linear bit expansion is inserted in a given intensity to a low frequency subband of the image which is wavelet transformed after the Arnold Transformation. When detecting the presence of watermart F norm function is applied unlike the existing methods. The experiment results verify that the proposed watermarking technique has outstanding quality in regards to fidelity and robustness.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.39-47
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• 2023

This study compares various transformation techniques for multifocus image fusion. Multi-focus image fusion is a procedure of merging multiple images captured at unalike focus distances to produce a single composite image with improved sharpness and clarity. In this research, the purpose is to compare different popular frequency domain approaches for multi-focus image fusion, such as Discrete Wavelet Transforms (DWT), Stationary Wavelet Transforms (SWT), DCT-based Laplacian Pyramid (DCT-LP), Discrete Cosine Harmonic Wavelet Transform (DC-HWT), and Dual-Tree Complex Wavelet Transform (DT-CWT). The objective is to increase the understanding of these transformation techniques and how they can be utilized in conjunction with one another. The analysis will evaluate the 10 most crucial parameters and highlight the unique features of each method. The results will help determine which transformation technique is the best for multi-focus image fusion applications. Based on the visual and statistical analysis, it is suggested that the DCT-LP is the most appropriate technique, but the results also provide valuable insights into choosing the right approach.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.257-261
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• 2005

The helicopter system is non-linear and complex. Futhermore, because of absence of accurate mathematical model, it is difficult accurately to control its attitude. therefore, we propose a WAVENET control technique to control efficiently its elevation angle and azimuth one. Wavelet neural network(WAVENET) can construct systematically initial neural network as applying wavelet theory to feedforward network. It is proved through computer simulation that WAVENET has more excellent approximation capability than existing neural network. The simulation results using MATLAB are introduced.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.437-440
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• 2003

As we know, roof is composed of heterogeneous rock. When roof fractures, a large amount of energy would be released in the form of seismic wave. How to identify the abnormal signal of seismic wave is a much difficult problem, there are many methods used usually, such as Fourier Transformation, filter technique etc., but abnormal signal can't be recognized accurately. In this paper, multi-resolution wavelet technique is used to identify the first and second variation point, based on the Lipschitz $\alpha$. A living example analysis shows, multi-resolution wavelet technique can identify the abnormal signal of seismic wave effectively in different scale, and the omen of roof fall can be grasped in order to forecast the roof fall accurately. It provides a new idea for the predication of catastrophe on rock mechanics and engineering.