Wavelet Soft Threshold Denoising Research Articles

The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer

Accurate wind speed prediction is crucial for effective wind power grid integration and energy dispatching. Recent research has explored the combination of decomposition algorithms with forecast models to form hybrid models, aiming to enhance wind speed prediction accuracy. However, these traditional decomposition techniques often lead to high time costs in practical applications, as they require new wind speed sequences to be appended to historical long sequences for decomposition before entering the forecast model. To overcome this challenge, this study introduces and improves the Autoformer model, applying it for the first time to long-term univariate wind speed forecasting. By incorporating decomposition technology as a sub-module of the forecast model, Autoformer not only solves the high time cost issue found in conventional hybrid models but also retains the benefits of decomposition technology in time series processing.Furthermore, in this paper, the decomposition module of Autoformer is replaced with the Mixture of Expert Decomposition Module (MOEDecomp) to better extract complex trend elements of wind speed series. Combined with the auto-correlation mechanism, sequential attention is paid to wind speed for extracting time dependencies in long series. Additionally, the Wavelet Soft Threshold Denoising (WSTD) algorithm is utilised for noise reduction in wind speed sequences. To evaluate the model's performance, two multi-step forecasting strategies were employed to predict wind speeds for the forthcoming 24, 48, 72, 96, and 120 h using four datasets. Experimental results demonstrate that the proposed model surpasses 15 comparative models in terms of prediction accuracy, generalization ability, and handling uncertain data.

RDTS Noise Reduction Method Based on ICEEMDAN-FE-WSTD

Aiming at the problem of temperature measurement error caused by the noise signal on the Raman-based distributed optical fiber temperature sensor (RDTS), a noise reduction method based on the improved complete ensemble empirical mode decomposition with adaptive noise, fuzzy entropy, and the wavelet soft threshold denoising (ICEEMDAN-FE-WSTD) is proposed. First, ICEEMDAN is used to decompose the input signal, and noise intrinsic mode functions (IMFs) are determined by calculating the FE value of each IMF. Then, noise IMFs are denoised by the WSTD method. Finally, the noise reduction results are obtained by combining the denoised IMFs with the remaining useful signal IMFs for reconstruction. The advantages of the proposed method in terms of denoising and character extraction are verified by the simulation signal experiment with different signal-to-noise ratios (SNRs). The temperature measurement experiments are carried out with heating areas set at 40 °C, 45 °C, 50 °C, 55 °C, and 60 °C. Taking the 40 °C temperature measurement experiment as an example, after the temperature measurement signal is denoised by the proposed method, the maximum temperature measurement error in the heating area is reduced by 1.07 °C, the SNR is increased by 4.83 dB, and the root-mean-square error (RMSE) is reduced by 0.77. The temperature drift of RDTS signals after noise reduction by the proposed method is also effectively suppressed, and the maximum temperature measurement error at 2.28 km is reduced by 1.43 °C. The denoising results of the proposed method are compared with the other six noise reduction methods in this article, and the proposed method has the best performance.

Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit

Accurate wind speed prediction is a premise that guarantees the reliable operation of the power grid. This study presents a combined prediction model that integrates data preprocessing, cascade optimization, and deep learning prediction to improve prediction performance. In data preprocessing, the wavelet soft threshold denoising (WSTD) is employed to filter the blurring noise of the original data. Then, the robust empirical mode decomposition (REMD) and adaptive variational mode decomposition (AVMD) are adopted to carry out a two-stage adaptive decomposition. Spearman correlation is used to quantify the mode that need to be decomposed for the second time. In the cascade optimization, the hybrid grey wolf algorithm (HGWO) is employed to optimize the parameters of the VMD and the gated recurrent unit (GRU), which overcomes the problem of empirical parameter adjustment. The HGWO is also adopted in the prediction strategy to optimize the GRU model to predict the grouped intrinsic mode functions (IMFs). Lastly, the final wind speed prediction result is obtained by superimposing the values of all the predicted models. The proposed model was validated with the measured wind speed data of the four quarters in the Bay area of China and was compared with 20 models of the classic method to further evaluate the effectiveness of the model. The results show that the whole process of the proposed model is adaptive, the final multi-step prediction performance is good, and high prediction accuracy can be attained.

A novel deep learning ensemble model with data denoising for short-term wind speed forecasting

Wind speed forecasting plays a pivotal role in the security and economy of the power system operation. However, accurate prediction on the wind speed value is still quite challenging due to three features of wind speed: randomness, fluctuation and unpredictability. Though many previous works have studied decomposition-based methods for preprocessing, these methods are difficult to use in actual prediction, since the newly acquired data will greatly affect the values of the initial decomposed subsequences. To solve this problem, a good solution referred as the wavelet soft threshold denoising (WSTD) is proposed herein, which is also systematically introduced for the first time. Moreover, a novel deep learning method gated recurrent unit (GRU) is successfully developed to be combined with WSTD to forecast the wind speed series in this paper. In the ensemble WSTD-GRU model, the WSTD is applied to filter the redundant information from the original data of wind speed series; GRU, which is particularly advantageous for learning the features in case of cooperating with WSTD, is adopted to predict the future multi-step wind speed values. Also, the parameters of the GRU are adjusted by cross-validated grid-search, and they are visualized in the paper. In experiments, thirty-five commonly used models are involved for comparison to evaluate the forecasting performance of the proposed model, through the datasets from four sites in the United States covering a wide range of longitudes. Finally, a superior performance of this methodology with relatively high accuracy, fast forecasting speed, small volatility of results and good adaptability is verified by the test results of case studies aforementioned.

Data Driven Intelligent Diagnostics for Parkinson’s Disease

Parkinson's disease (PD) is a common chronic occult neurological degeneration disease which is most likely to occur among middle-aged and old-aged people. Neuroprotective methods can significantly slow the PD progression, therefore early diagnosis and the treatment of the PD is crucial. As the second most common neurodegenerative disorder, Parkinson's disease has its identification based on clinical diagnoses. Early diagnosis is especially important due to the lack of radical treatment. In all early diagnostic methods, results of imaging diagnosis based on positron emission tomography (PET) imaging has the most outstanding accuracy. Deep learning methods based on convolutional neural networks performed well in diagnosing different diseases. To address the demand of early diagnosis, this paper applies several preprocessing methods to image data such as gray level transformation, histogram equalization, improved wavelet soft-threshold denoising and image enhancement, and proposes a deep learning model based on U-Net architecture with deformable convolution kernels. After comparing the results of both methods, we found out that deformable U-Net outperformed the previously built improved VGG-Net. These results show that U-Net with deformable convolution structure has a good diagnostic capability and a better performance than the improved VGG-Net.

Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising.

Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), effort-to-compress complexity (ETC), refined composite multiscale dispersion entropy (RCMDE) and wavelet threshold denoising is proposed. Firstly, the original signal is decomposed into several IMFs by CEEMDAN and noise IMFs can be identified according to the ETC of IMFs. Then, calculating the RCMDE of remaining IMFs, these IMFs are divided into three kinds of IMFs by RCMDE, namely noise-dominant IMFs, real signal-dominant IMFs, real IMFs. Finally, noise IMFs are removed, wavelet soft threshold denoising is applied to noise-dominant IMFs and real signal-dominant IMFs. The denoised signal can be obtained by combining the real IMFs with the denoised IMFs after wavelet soft threshold denoising. Chaotic signals with different signal-to-noise ratio (SNR) are used for denoising experiments by comparing with EMD_MSE_WSTD and EEMD_DE_WSTD, it shows that the proposed algorithm has higher SNR and smaller root mean square error (RMSE). In order to further verify the effectiveness of the proposed method, which is applied to noise reduction of real underwater acoustic signals. The results show that the denoised underwater acoustic signals not only eliminate noise interference also restore the topological structure of the chaotic attractors more clearly, which lays a foundation for the further processing of underwater acoustic signals.

Applications Of Compressive Sensing Over Wireless Fading Channels

Abstract Wireless fading channels exist mixed noise, the most common noises are gaussian noise and impulse noise, to ensure the quality of the received signal, removing the noise in the channel is very important and necessary. WSDN (wavelet soft-threshold de-noising) can suppress low-intensity gaussian noise well; unfortunately, the denoising effect in removing impulse noise using WSDN is not obvious, especially when noise intensity is relatively high. BPDN (Basis Pursuit Denoising) which is a reconstruction algorithm of compressive sensing can control high-intensity gaussian noise in the channels well preceding WSDN, but also the effect of BPDN denoising the common impulse noise is not obvious, to denoise impulse noise, we adopt IBPDN (Improved Basis Pursuit Denoising) that changes L2 form to noise to L1 form, which was proposed in 2006 by Guosheng Bing. The experimental data show that IBPDN has good anti-noise ability to both gaussian noise and impulse noise; furthermore, more satisfactory results are obtained using IBPDN to mixed noise than those with BPDN.

Optical Aperture Synthesis Object's Information Extracting Based on Wavelet Denoising

Wavelet denoising is studied to improve OAS(optical aperture synthesis) object's Fourier information extracting. Translation invariance wavelet denoising based on Donoho wavelet soft threshold denoising is researched to remove Pseudo-Gibbs in wavelet soft threshold image. OAS object's information extracting based on translation invariance wavelet denoising is studied. The study shows that wavelet threshold denoising can improve the precision and the repetition of object's information extracting from interferogram, and the translation invariance wavelet denoising information extracting is better than soft threshold wavelet denoising information extracting.