• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.283-286
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• 2002

본 논문에서는 보다 효과적이고 강인한 워터마크 은닉을 위한 방법으로 웨이브릿 변환 영역에서 영상의 통계적 특성에 기초한 비정상상태(non-stationary)에서와 정상상태(stationary) 일반화 가우스(generalized Gaussian: GG)모델을 이용한 적응 워터마크 은닉 기술을 제안한다. 워터마크는 고주파 영역에서 연속 부대역 양자화(successive subband quantization: SSQ)를 이용하여 다해상도 영상의 웨이브릿 계수 중에서 시각적 중요 계수(perceptual significant coefficients: PSC)를 선택하여 삽입한다. 워터마크 은닉을 위한 지각 모델은 NVF(noise visibility function)함수에 의해 계산된다. 이것은 비정상상태와 정상상태의 통계적 특성을 이용하고, 국부영상 특성을 가진다. 은닉모델은 다해상도내의 각 부대역별 분산과 형상계수(shape parameter)를 사용한다. Stirmark benchmark test에 근거하여 여러 가능한 왜곡에 대한 실험에서 강인성과 비가시성에서의 우수함을 확인하였고, 비정상상태의 경우와 정상상태의 경우를 비교하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.157-162
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• 2010

In this paper we propose a fractal analysis based on the discrete wavelet transform. It is well known that Fourier Transform is widely used for frequency analysis of random signal. However, the frequency domain is not used for expressing the sudden signal change and non-stationary signal at the time-axis by this method. Maximum value in the wavelet modules can be expressed by the Lipschitz exponent, which is useful to represent the characteristics of signal or the edge of an image. It is possible to reconstruct the original image only by using the few maximum points. The v possible image It iusing oil was acquired to interpret the maximum value. ufter that, it was applied to the v possible image of a ship model. In addition, the fractal dimens by by the conlapse process of the sediment particle was examined. In this paper, the fractal dimens by has been obtained by the maximum value and the experiment obtained from the visualized image also acquired the same result as existing methods.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Wind and Structures
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• v.31 no.3
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.

• Wind and Structures
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• v.13 no.6
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• pp.529-556
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• 2010

This paper investigates the correlation of wind characteristics monitored on a cable-stayed bridge. Total five anemoscopes are implemented into the bridge. Two out of 5 anemoscopes in inflow and two out of 5 anemoscopes in wake-flow along the longitudinal direction of the bridge are installed. Four anemoscopes are respectively distributed at two cross-sections. Another anemoscope is installed at the top of the tower. The correlation of mean wind speed and direction, power spectral density, the turbulent intensity and integral length of wind in flow at two cross-sections are investigated. In addition, considering the non-stationary characteristics of wind, the spatial correlation in time-frequency is analyzed using wavelet transform and different phenomenon from those obtained through FFT is observed. The time-frequency analysis further indicates that intermittence, coherence structures and self-similar structures are distinctly observed from fluctuant wind. The flow characteristics around the bridge deck at two positions are also investigated using the field measurement. The results indicate that the mean wind speed decrease when the flow passing through the deck, but the turbulence intensity become much larger and the turbulence integral lengths become much smaller compared with those of inflow. The relationship of RMS (root mean square) of wake-flow and the mean wind speed of inflow is approximately linear. The special structures of wake-flow in time-frequency domain are also analyzed using wavelet transform, which aids to reveal the forming process of wake-flow.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.941-953
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• 2012

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1140-1152
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• 2012

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.51-53
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• 2004

In this paper, we are going to propose the new algorithms to detect, classify, discriminate the transient and the reflected signal from noise and thus discriminate the fault section and locale the fault accurately on underground power cable system. Actually, at this system, it's very difficult to discriminate the transient because of the reflected signal including many noises. Therefore, how to solve the noise interference is a big problem. In this paper, authors present a solution based on multiple scales correlation of the transient using stationary wavelet transform. It's simple, quick and straightforward. For applying all algorithms, we just use the signal captured in single end.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.373-389
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• 2023

The downstream submergence damages caused during the flood season in 2020, around the Yongdam-dam and five other sites, were analyzed using related dam management data. Hourly- and daily-data were collected from public national websites and to conduct various analyses, such as autocorrelation, partial-correlation, stationary test, trend test, Granger causality, Rescaled analysis, and principal statistical analysis, to find the cause of the catastrophic damages in 2020. The damage surrounding the Yongdam-dam in 2020 was confirmed to be caused by mis-management of the flood season water level. A similar pattern was found downstream of the Namgang- and Hapcheon-dams, however the damage caused via discharges from these dams in same year is uncertain. Conversely, a different pattern from that of the Yongdam-dam was seen in the areas downstream of Sumjingang- and Daecheongdams, in which the management of the flood season water level appeared appropriate and hence, the damages is assumed to have occurred via the increase in the absolute discharge amount from the dams and flood control capacity leakage of the downstream river. Because of the non-stationarity of the management data, we adapted the wavelet transform analysis to observe the behaviors of the dam management data in detail. Based on the results, an increasing trend in the discharge amount was observed from the dams after the year 2000, which may serve as a warning about similar trends in the future. Therefore, additional and continuous research on downstream safety against dam discharges is necessary.