• Proceedings of the KSME Conference
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• 2002.08a
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• pp.377-380
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• 2002

This paper presents the results of a study investigating methods of interpretation of wave directionality based on wavelet transforms. Two-dimensional discrete wavelet was used for the analysis. The proposed scheme utilizes a single frame of ocean waves to detect their directionality. This fact is striking considering the fact that traditional methods require long time histories of ocean wave elevation measured at various locations. The developed schemes were applied to the data generated from numerical simulations and video images to test the efficiency of the proposed scheme in detecting the directionality of ocean waves.

• Structural Engineering and Mechanics
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• v.38 no.1
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• pp.81-97
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• 2011

In this research, the wavelet transform is used to analyze time response of a cracked beam carrying moving mass for damage detection. In this respect, a new damage detection method based on the combined use of continuous and discrete wavelet transforms is proposed. It is shown that this method is more capable in making damage signature evident than the traditional two approaches based on direct investigation of the wavelet coefficients of structural response. By the proposed method, it is concluded that strain data outperforms displacement data at the same point in revealing damage signature. In addition, influence of moving mass-induced terms such as gravitational, Coriolis, centrifuge forces, and pure inertia force along the deflection direction to damage detection is investigated on a sample case. From this analysis it is concluded that centrifuge force has the most influence on making both displacement and strain data damage-sensitive. The Coriolis effect is the second to improve the damage-sensitivity of data. However, its impact is considerably less than the former. The rest, on the other hand, are observed to be insufficient alone.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 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 Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1469-1474
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• 2004

A wavelet-based image watermarking scheme is proposed, based on the insertion of pseudo-random codes in the frequency domain. The original image is required for the watermark detection. The 'strength' of the signature to be embedded depends on the image to be marked, thus fitting signal capacity. The experimental results prove that the method is more tamper resistant and less perceptible for any type of images than other well-known private methods in the frequency domains.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.45-52
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• 2021

Although many studies have been conducted to identify unknown attacks in cyber security intrusion detection systems, studies based on outliers are attracting attention. Accordingly, we identify outliers by defining categories for unknown attacks. The unknown attacks were investigated in two categories: first, there are factors that generate variant attacks, and second, studies that classify them into new types. We have conducted outlier studies that can identify similar data, such as variants, in the category of studies that generate variant attacks. The big problem of identifying anomalies in the intrusion detection system is that normal and aggressive behavior share the same space. For this, we applied a technique that can be divided into clear types for normal and attack by discrete wavelet transformation and detected anomalies. As a result, we confirmed that the outliers can be identified through One-Class SVM in the data reconstructed by discrete wavelet transform.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.815-820
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• 2000

The early damage detection of large structures is very important to prevent the disaster due to its global failure. In this paper, a damage detection method of the beam-analogy structure based on the wavelet transformation of mode shape is presented. This can effectively detect the singularity of mode shape caused to the inconsistency of bending moment and shear force at the damaged part using the discrete wavelet and its inverse transforms. To investigate the validity and the applicability of the presented damage detection method, numerical simulation and experiment are carried out for the idealized beam and the real ship structures.

• 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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.3
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• pp.291-297
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• 2012

Conventional Fourier analysis can give only limited information about the dynamic characteristics of nonstationary signals. Instead, time-frequency analysis is widely used to investigate the nonstationary signal in detail. Several time-frequency analysis methods are compared for a typical acoustic emission burst generated during the impact between a ferrite ceramic and aluminum plate. This AE burst is inherently nonstationary and random containing many frequency contents, which leads to severe interference between cross terms in bilinear convolution type distributions. The smoothing and reassignment processes can improve the readability and resolution of the results. Spectrogram and scalogram of the AE burst are obtained and compared to get the characteristics information. Renyi entropies are computed for various bilinear time-frequency transforms to evaluate the randomness. These bilinear transforms are reassigned by using the improved algorithm in discrete computation.

• Journal of the Society of Naval Architects of Korea
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• v.39 no.2
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• pp.19-27
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• 2002

The early damage detection of large structures is very important to prevent the disaster due to its global failure. In this paper, a crack detection method of the beam-analogy structure based on the wavelet transformation of mode shape is presented. The method can effectively detect the singularity of mode shape caused to the inconsistency of bending moment and shear force at the damaged part using the discrete wavelet of mode shape and its inverse transforms of detail components. To investigate the validity and the applicability of the presented damage detection method, numerical simulation and experiment are carried out for the idealized beam and the real ship structures.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2292-2298
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• 2009

In this paper, the lossy image compression algorithm using the quad tree and the bandlets is proposed. The proposed algorithm transforms input images by the discrete wavelet transform (DWT) and represents the geometrical structures of high frequency bands using the bandlets with a 8 block- size. In addition, the proposed algorithm searches the position information of the significant coefficients by using the quad tree algorithm and computes the magnitude and the sign information of the significant coefficients by using the Embedded Image Coding using Zerotrees of Wavelet Coefficients (EZW) algorithm. The compression result by using the quad tree algorithm improves the PSNR performance of high frequency images up to 1 dB, compared to that of JPEG-2000 algorithm and that of S+P algorithm. The PSNR performance by using DWT and bandlets improves up to 7.5dB, compared to that by using only DWT.