• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2012.04a
• /
• pp.658-664
• /
• 2012

This paper presents an investigation into driveline clonk noise of midi-bus. Test was performed for operational acceleration and pressure, torsional vibration of driveline and FRF to TPA. Wavelet analysis was used to study spectral-time characteristics to all measurement data and specially for torsional vibration, lash analysis was carried out. To investigate the path of the clonk in detail, time domain TPA was introduced. With time domain TPA, the contribution of each path for the clonk was estimated by both subjectively and objectively. As a result, Transmission mount part was came to light as the main contributor of issued clonk. This was verified by reinforcing the bracket of transmission side. And 1D torsional nvh performance simulation model was built with measured torsional vibration. The simulation tool was used for prediction of some clonk behavior in tip in-out transient condition.

• Journal of Korea Multimedia Society
• /
• v.4 no.6
• /
• pp.554-561
• /
• 2001

Wavelet transformation method which has special qualities of frequency domain and spatial domain is used for watermarking. We have compared the decree of similarity to find a robust watermarking sequence for different attacks, using random number, gaussian sequence, chaos sequence and sobel sequence. The experimental results show that chaos sequence is an effective sequence for robust watermarking.

• Journal of the Optical Society of Korea
• /
• v.17 no.5
• /
• pp.454-460
• /
• 2013

We present qualitative and quantitative component analyses on compound explosives via Terahertz time-domain spectroscopy (THz-TDS) based on a combination of wavelet thresholding and wavelength selection. Despite its importance, the field of signal processing of THz signals of compound plastic explosives is relatively unexplored. In this paper, experiment results from explosives Composition B-3 and Pentolite are newly presented, suggesting a novel signal processing procedure for in situ compound explosives detection. The proposed signal processing method demonstrates effective component analysis even in noisy and humid environments, showing significant decrease in component concentration percentage error of approximately 22.7% for Composition B-3 and 48.8% for Pentolite.

• Earthquakes and Structures
• /
• v.3 no.3_4
• /
• pp.249-270
• /
• 2012

This paper investigates the effectiveness of widely used identification methods to identify the response of seismically isolated structures supported on bearings with bilinear behavior. The paper shows that while both time domain and frequency domain methods predict with high accuracy the modal characteristics of structures isolated by linear isolation system, their performance degrades appreciably when the isolation system exhibits bilinear behavior even when its strength assumes moderate values (say 5% of the weight). The paper also shows that the natural period of isolated structure that results from bilinear isolation systems can be satisfactorily predicted with wavelet analysis.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.14 no.2
• /
• pp.49-59
• /
• 2018

Encryption of digital images has been requested various fields. In the meantime, many algorithms based on a text - based encryption algorithm have been proposed. In this paper, we propose a method of encryption in wavelet transform domain to utilize the characteristics of digital image. In particular, wavelet transform is used to reduce the association between the encrypted image and the original image. Wavelet packet transformations can be decomposed into more subband images than wavelet transform, and various position permutation, numerical transformation, and visual transformation are performed on the coefficients of this subband image. As a result, this paper proposes a method that satisfies the characteristics of high encryption strength than the conventional wavelet transform and reversibility. This method also satisfies the lossless symmetric key encryption and decryption algorithm. The performance of the proposed method is confirmed by visual and quantitative. Experimental results show that the visually encrypted image is seen as a completely different signal from the original image. We also confirmed that the proposed method shows lower values of cross correlation than conventional wavelet transform. And PSNR has a sufficiently high value in terms of decoding performance of the proposed method. In this paper, we also proposed that the degree of correlation of the encrypted image can be controlled by adjusting the number of wavelet transform steps according to the characteristics of the image.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.2
• /
• pp.412-434
• /
• 2023

This study discusses the high-order diffusion method in the wavelet domain. It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information. During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels. The high-frequency wavelet coefficients are then used to construct the edge detection function. Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed. Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained. The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods. For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.12 no.7
• /
• pp.1122-1130
• /
• 2001

In this paper, by applying the spectral domain wavelet concept to Green function, a fast spectral domain calculation of scattered fields is proposed to get the solution for the radiation integral. The spectral domain wavelet transform to represent Green function is implemented equivalently in space via the constant-Q windowing technique. The radiation integral can be calculated efficiently in the spectral domain using the windowed Green function expanded by its eigen functions around the observation region. Finally, the same formulation as that of the conventional fast multipole method (FMM) is obtained through the windowed Green function and the spectral domain calculation of the radiation integral.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.4
• /
• pp.76-85
• /
• 2011

In this paper, we propose a texture feature-based language identification using Gabor feature and wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features. In the proposed method, Gabor and wavelet transforms are first applied to a test image. The wavelet subbands are next denoised by Donoho's soft-thresholding. The magnitude operator is then applied to the Gabor image and the BDIP and BVLC operators to the wavelet subbands. Moments for Gabor magnitude image and each subband of BDIP and BVLC are computed and fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for a document image DB.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1103-1108
• /
• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.3
• /
• pp.75-81
• /
• 2007

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to Fourier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier, and more useful method than the Fast Fourier Transform(FFT).