Heuristic wavelet shrinkage for denoising

Liu, Chan-Cheng; Sun, Tsung-Ying; Tsai, Shang-Jeng; Yu, Yu-Hsiang; Hsieh, Sheng-Ta

doi:10.1016/j.asoc.2009.11.016

Cited by 32 publications

(19 citation statements)

References 24 publications

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“…The purpose of the thresholding procedure is to eliminate or suppress small value coefficients which mainly represent the noise content. As soft-thresholding has slightly smooth property for a signal and better quality than hard-thresholding in most of application [29], the soft-thresholding, decomposition level-dependent thresholds chosen by SURE selection rule is applied in this paper. Figs.…”

Section: Wavelet Packet Based Filtering Schemementioning

confidence: 99%

Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge

2013

Measurement

242

129

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Section: Wavelet Packet Based Filtering Schemementioning

confidence: 99%

Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge

2013

Measurement

242

129

View full text Add to dashboard Cite

“…Specifically, a direct comparison with the noise-riding DCV signal and the de-noised signal at level five enables us to make sure the effectiveness of the proposed work. Different levels of noise with the signal-to-noise ratio (SNR) values are used to test the denoising capability [14]. Each SNR value of all denoised signals linked to the level-based thresholding is listed in Table I.…”

Section: Implementation Of the Proposed Work By Achieving Noiseredmentioning

confidence: 99%

Application of wavelet transform-based discharging/charging voltage signal denoising for advanced data-driven SOC estimator

Kim

Cho

2015

2015 IEEE Applied Power Electronics Conference and Exposition (APEC)

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Unexpected sensing of noisy discharging/charging voltage of a Li-Ion cell may result in erroneous state-of-charge (SOC) estimation and low battery management system (BMS) performance. Therefore, this study gives insight to the design and implementation of the discrete wavelet transform (DWT)-based denoising technique for noise reduction of the DCV. The steps of denoising of noisy DCV for proposed study are follows. Firstly, by using the multi-resolution analysis (MRA), the noiseriding DCV signal is decomposed into different frequency subbands. Specifically, the signal processing considering high frequency component that focuses on short-time interval is absolutely necessary in order to reduce noise of the DCV. Secondly, the hard-thresholding based denoising technique is used to adjust the wavelet coefficients of the DWT for a clear separation between the signal and the noise. Thirdly, the desired de-noised DCV signal is reconstructed by taking the inverse DWT on filtered detailed coefficients. Consequently, this signal is applied to the equivalent circuit model (ECM)-based SOC estimation algorithm using the extended Kalman filter (EKF). Experimental results indicate the robustness of the proposed work. I.INTRODUCTION Because of high specific energy density and long cycle life as well as low self-discharge, Li-Ion cell has been more increasingly recognized as a promising solution for electricpowered transportation such as electric vehicle (EV) and hybrid electric vehicle (HEV) application [1]. Specifically, with the increased interest in EV and HEV, the need for accurate and reliable knowledge in order to guarantee the overall system performance, namely a battery management system (BMS) is significantly increased together. Therefore, numerous studies have investigated to the design an improved BMS, in particular, state-of-charge (SOC) which is considered as one of the key factors in BMS for supporting optimal performance [2]. Precise SOC information is very critical in practical applications where it is necessary to determine how long the cell will last for predicting a reliable operating range,

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“…An ascending value implies that its own component is more than component of noise. Hence, the restructured signal has a good recovery [33]. r 22 , hr 21 and r 11 are defined as follows:…”

Section: Flow Of the Proposed Denosing Methodsmentioning

confidence: 99%

Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

Wang

Tan

et al. 2016

Applied Sciences

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Abstract:As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA) is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT) to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA) was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

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Heuristic wavelet shrinkage for denoising

Cited by 32 publications

References 24 publications

Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge

Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge

Application of wavelet transform-based discharging/charging voltage signal denoising for advanced data-driven SOC estimator

Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

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