• Smart Structures and Systems
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• v.22 no.3
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• pp.291-302
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• 2018

This paper presents a flexibility based method for damage identification from static measurements in beam-type structures. The response of the beam at the Damaged State is decomposed into the response at the Reference State plus the response at an Incremental State, which represents the effect of damage. The damage is localized by detecting slope discontinuities in the deflection of the structure at the Incremental State. A denoising filtering technique is applied to reduce the effect of experimental noise. The extent of the damage is estimated through comparing the experimental flexural stiffness of the damaged cross-sections with the corresponding values provided by analytical models of cracked beams. The paper illustrates the method by showing a numerical example with two cracks and an experimental case study of a simply supported steel beam with one artificially introduced notch type crack at three damage levels. A Digital Image Correlation system was used to accurately measure the deflections of the beam at a dense measurement grid under a set of point loads. The results indicate that the method can successfully detect and quantify a small damage from the experimental data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.877-879
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• 2013

Imaging device such as digital TV is being popular in a modern society based on communication technology. However, because of internal and external cause of system in the process of transmission, storage and acquisition, image is degraded by noise. Therefore, the importance of denoising technology is being increased, and a research for that is being actively made. In this paper, a weighted filter algorithm that considers different pixels of masks and estimated noise variance was proposed. in order to remove AWGN. And, PSNR(peak signal to noise ratio) was used to represent the excellence of proposed algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.26-31
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• 2012

The highest frequency band of wavelet decomposition band is divided into 4 equal subbands and by the minimum power of the subbands and by the monotonic transform, the level adapted threshold is obtained. The adapted threshold is applied to the soft threshold technique to denoise high and middle frequency band noise of image signals. And the results of PSNRs are compared with the results obtained by the VisuShrink technique and by the technique using the monotonic transform and the weight value. The results showed the validity of this technique.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.141-147
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• 2014

The wavelet shrinkage is a technique that reduces the wavelet coefficients to minimize the MSE(Mean Square Error) between the signal and the noisy signal by making use of the threshold determined by the variance of the wavelet coefficients. In this paper, by using the monotonic transform and the power of wavelet subbands, new thresholds applicable to the high and the low frequency wavelet bands are proposed, and the thresholds are applied to the ST(soft-threshold) technique to denoise on image signals with additive Gaussian noise. And the results of PSNRs are compared with the results obtained by the VisuShrink technique and those of [15]. The results shows the validity of this technique.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.93-99
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• 2007

Robust nonlinear image denoising algorithms for the class of ${\alpha}$-stable distribution are introduced. The proposed amplitude-limited sample average filter(ALSAF) proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. The error norm for this estimator is equivalent to Huber#s minimax norm. It is optimal in the respect of maximizing the efficacy under the above noise environment. It is mired with the myriad filter to propose an amplitude-limited myriad filter(ALMF). The behavior and performance of the ALSAF and ALMF in ${\alpha}$-stable noise environment are illustrated and analyzed through simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2798-2804
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• 2010

The audio and image signal are corrupted by various noises in signal processing, many studies are being accomplished to restore those signals. In this paper, the algorithm is proposed to remove additive Gaussian noise and impulse noise at one dimension signal like an speech signal. The algorithm is composed to remove Gaussian noise after removing impulse noise. And the method using wavelet coefficient accumulation is used to remove the Gaussian noise, and the median filter based on element deviation is applied to remove the impulse noise. Also we compare existing methods using SNR(signal-to-noise ratio) as the standard of judgement of improvemental effect.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.693-698
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• 2001

Noisy data are often fitted using a smoothing parameter, controlling the importance of two objectives that are opposite to a certain extent. One of these two is smoothness and the other is closeness to the input data. The optimal value of this parameter minimizes the error of the result. This optimum cannot be found exactly, simply because the exact data are unknown. This paper propose the threshold value for noise reduction based on wavelet-thresholding. In the proposed method PSNR results show that the threshold value performs excellently in comparison with conventional methods without knowing the noise variance and volume of signal.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.57-63
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• 2008

In this paper, we present an efficient noise reduction method using bivariate Gaussian density function in the wavelet domain. In our method, the probability model for the interstate dependency in the wavelet domain is modeled by bivariate Gaussian function, and then, the noise reduction is performed by Bayesian estimation. The statistical parameter for Bayesian estimation can be approximately obtained by the $H{\ddot{o}}lder$ inequality. The simulation results show that our method outperforms the previous methods using bivariate probability models.

• Journal of Biomedical Engineering Research
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• v.28 no.6
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• pp.817-824
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• 2007

Tractography using Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a method to determine the architecture of axonal fibers in the central nervous system by computing the direction of the principal eigenvector in the white matter of the brain. However, the fiber tracking methods suffer from the noise included in the diffusion tensor images that affects the determination of the principal eigenvector. As the fiber tracking progresses, the accumulated error creates a large deviation between the calculated fiber and the real fiber. This problem of the DT-MRI tractography is known mathematically as the ill-posed problem which means that tractography is very sensitive to perturbations by noise. To reduce the noise in DT-MRI measurements, a tensor-valued median filter which is reported to be denoising and structure-preserving in fiber tracking, is applied in the tractography. In this paper, we proposed the modified gradient descent method which converges fast and accurately to the optimal tensor-valued median filter by changing the step size. In addition, the performance of the modified gradient descent method is compared with others. We used the synthetic image which consists of 45 degree principal eigenvectors and the corticospinal tract. For the synthetic image, the proposed method achieved 4.66%, 16.66% and 15.08% less error than the conventional gradient descent method for error measures AE, AAE, AFA respectively. For the corticospinal tract, at iteration number ten the proposed method achieved 3.78%, 25.71 % and 11.54% less error than the conventional gradient descent method for error measures AE, AAE, AFA respectively.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.42-48
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• 2007

In x-ray imaging system, two kinds of noises are involved. First, the charge generated from the radiation interaction with the detector during exposure. Second, the signal is then added by readout electronics noise. But, x-ray images are not modeled by Gaussian noise but as the realization of a Poisson process. In this paper, we apply a new approach to remove Poisson noise from medical X-ray image in the wavelet domain, the applied methods shows more excellent results in cone-beam CT.