• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.342-348
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• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.29-37
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• 2002

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 diagnosis of grinding conditions in grinding process. We used the Daubechies wavelet analyzing function to detect a sudden change in cutting signal level. STD11 workpiece was 85 times of machined pieces cut by the WA wheel 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 dressing time, the approximation signals were slowly increased and 45 machined times noticed dressing time.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.31-37
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• 1996

This Paper is concerned with effective signal identification method for tool breakage and micro chipping using discrete wavelet transform of feed motor current in milling operations. The wavelet transform uses an analyzing waveletfunction which is localized in both frequency and time domain to detect subtle time localized changes in input signals. The changing pattern of wavelet coefficient is continuously compared to detect tool breakage and micro chipping over one spindle revolution. The results indicate that the wavelet transform can identify tool failure with much greater sensi- tivity than the time domain monitoring and frequency domain monitoring such as FFT. Experimental results are presented to support the proposed scheme.

• Journal of IKEEE
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• v.15 no.3
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• pp.255-260
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• 2011

In this paper, we propose a parallelization technique in lane detection by using Hough transform. Hough transform has a weakness that it has a lot computation quantity, because it has to compute ${\rho}$ value in all candidate ${\Theta}$ to be detected in an image. We propose an architecture of parallel processing for this transform in a multi-core environment. The parallel processing has application to Hough transform as well as noise reduction and edge detection. This proposed architecture has 5.17 times improvement in performance compare to the existing algorithm.

• Journal of the Chungcheong Mathematical Society
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• v.22 no.3
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• pp.481-495
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• 2009

Huffman, Park and Skoug introduced various results for the $L_{p}$ analytic Fourier-Feynman transform and the convolution for functionals on classical Wiener space which belong to some Banach algebra $\mathcal{S}$ introduced by Cameron and Storvick. Also Chang, Kim and Yoo extended the above results to an abstract Wiener space for functionals in the Fresnel class $\mathcal{F}(B)$ which corresponds to $\mathcal{S}$. Moreover they introduced the $L_{p}$ analytic Fourier-Feynman transform for functionals on a product abstract Wiener space and then established the above results for functionals in the generalized Fresnel class $\mathcal{F}_{A1,A2}$ containing $\mathcal{F}(B)$. In this paper, we investigate more generalized relationships, between the Fourier-Feynman transform and the convolution product for functionals in $\mathcal{F}_{A1,A2}$, than the above results.

• 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 Korean Society on Water Environment
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• v.22 no.5
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• pp.831-837
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• 2006

The present study applies wavelet transform for the extraction of various periodicities which are included in TOC and pH time series of water quality and rainfall data. The primary objective of the present study is to detect the relationships between the respective data through the correlation analysis using the approximation components which are decomposed by wavelet transform. The results reveal the approximation components of TOC and pH in the 5th level of wavelet transform can explain more than 99% of the whole energy for the raw data respectively and there are considerably high correlation between the approximation components of the respective data used for the study even through no significant correlation between the raw data has been detected.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.571-579
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• 1999

In this paper, a Hadamard transform imaging spectrometer(HTIS) is proposed by using a grill spectrometer. And we reconfigure the system by using the grill sectrometer which uses a left cyclic S-matrix instead of the conventional right cyclic one. Then, we model the Hadamard transform imaging spectrometer and apply the mask characteristics compensation method, i.e. $ {T}^{-1}$ method, to complete fast algorithm. Also, through computer simulations the superiority of the proposed system in this paper to the conventional Hadamard transform spectrometer(HTS) is proved and the performance of the two systems are compared by introducing average mean square error(AMSE) as the algebraic criterion.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.1020-1026
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• 2016

Motion estimation (ME) algorithms supporting quarter-pixel accuracy have been recently introduced to retain detailed motion information for high quality of video in the state-of-the-art video compression standard of H.264/AVC. Conventional sub-pixel ME algorithms in the spatial domain are faced with a common problem of computational complexity because of embedded interpolation schemes. This paper proposes a low-complexity sub-pixel motion estimation algorithm in the transform domain utilizing shifting matrix. Simulations are performed to compare the performances of spatial-domain ME algorithms and transform-domain ME algorithms in terms of peak signal-to-noise ratio (PSNR) and the number of bits per frame. Simulation results confirm that the transform-domain approach not only improves the video quality and the compression efficiency, but also remarkably alleviates the computational complexity, compared to the spatial-domain approach.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.1
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• pp.159-166
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• 1987

A new family of sinusoidal orthogoal trnasform is introduced. For a derivation, a parametric sinusoidal matrix whose transform might be implemented by a suitable FFT algorithm is modeled basically on the analogy of well-known sinusoidal transform such as DCT,SCT, etc., and its orthogonality condition is calculated. The parameters satisfying orthogonality condition are determined, in a sense, by particular solution after trial and error. However more than then transform matrices not yet known are obtained. It is also shown that these transforms can be computed by a DFT. of an image.