Improved Wavelet Denoising Research Articles

Broiler sound signal filtering method based on improved wavelet denoising and effective pulse extraction

There is little attention paid to signal filtering in existing broiler health monitoring or broiler sound signal classification research, and the only few studies still have issues such as lack of specific, in-depth, and specialized details. In response to these problems, the authors conducted a depth analysis of the signal characteristics of effective signal components and noises, and proposed a broiler sound signal filtering method based on improved wavelet denoising and effective pulse extraction. This method consists of two parts. The first part is wavelet denoising, which is used to remove the widely distributed noises in broiler sound signals. Specifically, a wavelet denoising algorithm based on continuous differentiable threshold function and scale threshold has been proposed. The disadvantages in existing hard threshold function, soft threshold function, and semi-soft threshold function have been effectively addressed, and the smoothness of broiler sound signals has been ensured. The second part is pulse extraction processing, which is used to remove sudden noises that appears as independent pulses in broiler sound signals. Specifically, a two-stage variable multi-threshold pulse extraction algorithm has been proposed. The main advantages of existing three-threshold pulse extraction algorithm and double-threshold pulse extraction algorithm have been referenced, the threshold setting standard has been clarified, the continuous multi-pulse problem has been given special attention, and the effective signal components in broiler sound signals have been accurately extracted. In this way, the noises in broiler sound signals were removed, and the effective signal components were highlighted. The signal-to-noise ratio (SNR) and root mean square error (RMSE) from signal detection field, as well as the classification accuracy and F1-score from machine learning field, were used to comprehensively evaluate the effect of the signal filtering method proposed in this paper. A large number of tests shown that, compared to existing signal filtering methods, the broiler sound signal processed by the signal filtering method proposed in this paper achieved the maximum SNR and the minimum RMSE. On feature data sets created from broiler sound signals processed by different signal filtering methods, three classifiers all achieved the highest classification accuracy and F1-score on the feature data set corresponding to the signal filtering method proposed in this paper. The feasibility, practicality, and superiority of the signal filtering method proposed in this paper have been widely verified. This study is an important supplement to the broiler health monitoring and broiler sound signal classification research, providing important references for signal filtering research in similar fields.

The Improved Wavelet Denoising Scheme Based on Robust Principal Component Analysis for Distributed Fiber Acoustic Sensor

The Improved Wavelet Denoising Scheme Based on Robust Principal Component Analysis for Distributed Fiber Acoustic Sensor

Welding Penetration Monitoring for Ship Robotic GMAW Using Arc Sound Sensing Based on Improved Wavelet Denoising

The arc sound signal is one of the most important aspects of information related to pattern identification regarding the penetration state of ship robotic GMAW; however, arc sound is inevitably affected by noise interference during the signal acquisition process. In this paper, an improved wavelet threshold denoising method is proposed to eliminate interference and purify the arc sound signal. The non-stationary random distribution characteristics of GMAW noise interference are also estimated by using the high-frequency detail coefficients in different domains after wavelet transformation, and a mode of measuring scale that is logarithmically negatively correlated with the wavelet decomposition scale is created to update the threshold. The gradient convergent threshold function is established using the natural logarithmic function structure and concave–convex gradient to enable the nonlinear adjustment of the asymptotic rate. Further, some property theorems related to the optimized threshold function are proposed and theoretically proven, and the effectiveness and adaptability of the improved method are verified via the denoising simulation of speech synthesis signals. The four traditional denoising methods and our improved version are applied in the pretreatment of the GMAW arc sound signal, respectively. Statistical analysis and short-time Fourier transform are used to extract eight-dimensional time and frequency domain feature parameters from the denoised signals with randomly time-varying characteristics, and the extracted joint feature parameters are used to establish a nonlinear mapping model of penetration state identification for ship robotic GMAW using the pattern classifiers of RBFNN, PNN and PSO-SVM. The simulation results yielded by visual penetration classification and the multi-dimensional evaluation index of the confusion matrix indicate that the improved denoising method proposed in this paper achieves a higher accuracy in the extraction of penetration state features and greater precision in the identification of pattern classification.

Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory

The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.

Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

The signal corresponding to English speech contains a lot of redundant information and environmental interference information, which will produce a lot of distortion in the process of English speech translation signal recognition. Based on this, a large number of studies focus on encoding and processing English speech, so as to achieve high-precision speech recognition. The traditional wavelet denoising algorithm plays an obvious role in the recognition of English speech translation signals, which mainly depends on the excellent local time-frequency domain characteristics of the wavelet signal algorithm, but the traditional wavelet signal algorithm is still difficult to select the recognition threshold, and the recognition accuracy is easy to be affected. Based on this, this paper will improve the traditional wavelet denoising algorithm, abandon the single-threshold judgment of the original traditional algorithm, innovatively adopt the combination of soft threshold and hard threshold, further solve the distortion problem of the denoising algorithm in the process of English speech translation signal recognition, improve the signal-to-noise ratio of English speech recognition, and further reduce the root mean square error of the signal. Good noise reduction effect is realized, and the accuracy of speech recognition is improved. In the experiment, the algorithm is compared with the traditional algorithm based on MATLAB simulation software. The simulation results are consistent with the actual theoretical results. At the same time, the algorithm proposed in this paper has obvious advantages in the recognition accuracy of English speech translation signals, which reflects the superiority and practical value of the algorithm.

Air quality prediction method based on improved wavelet denoising and LSTM

in order to optimize the traditional wavelet de-noising algorithm threshold function in continuity and noise reduction effect, an improved soft hard threshold compromise method and deep learning model are proposed to analyze the prediction performance of different deep learning models and improved wavelet algorithm. RNN, GRU and LSTM neural networks are constructed to predict the original data, the traditional wavelet denoising data and the improved wavelet denoising data. The air quality data of Chengdu were used for simulation experiment. The results of quantitative analysis showed that: for AQI, PM2.5 and O3_ Oh denoising will cause the loss of useful signals. The improved wavelet threshold denoising for PM10, SO2, Co, NO2 data can improve the prediction effect of the model. The results show that LSTM model has good applicability in air quality prediction, the absolute percent error is 5.867%, and the mean square error is 4.870. The prediction performance of LSTM model is about 10%, the absolute percent error is 5.176%, and the mean square error is 5.314. The traditional threshold denoising will affect the prediction results of the model for the actual data due to the loss of high-frequency signal and pseudo Gibbs phenomenon. The results show that the improved soft hard threshold compromise method can better deal with air quality data, and the LSTM model based on improved wavelet denoising has strong applicability for air quality prediction.

Weak Moving Target Detection Based on Improved Wavelet De-noising

When the weak moving target is far away, the signal of the detecting system is very weak, and the background noise has a great influence on the detection results. In this paper, a method based on kernel theory is proposed, this method can distinguish target signal from background noise signal. To suppress background noise, a new threshold function based on wavelet packet decomposition is proposed. In the high-speed camera, the above two methods are used to detect weak moving targets. The experimental results show that the method has great advantages in the detection of small moving targets.

Improved Wavelet Denoising by Penalty Function and Regularization Parameter

Wavelet-based convex optimization in sparse signal processing has attracted extensive research interest in recent years. Research demonstrates that the penalty function promotes the sparsity and improves the accuracy better than L1 norm method in dealing with convex and sparse signal reconstruction issues. This paper presents an improved denoising model which utilizes a penalty function to induce stronger sparsity in wavelet domain. In this paper, we have redefined the relationship model between signal noise levels and the regularization parameter to enhance sparsity caused by penalty functions in a wavelet domain. And three penalty functions are employed for wavelet coefficients to induce strong sparse wavelet coefficients under the wavelet domain. We then apply the improved wavelet denoising algorithm for image denoising to get the values of the PSNR are analysed under different noise level conditions. Experimental results clearly show that the improved wavelet denoising model is strength in terms of both quantitative measure and structural similarity quality, and outperforms many widely used denoising algorithms.

Leakage detection of HVAC pipeline network based on pressure signal diagnosis

In order to further improve the accuracy and application range of pipeline network leakage detection, this paper simulates the complex water distribution system to set up an experimental platform. On this basis, a leakage detection method using improved wavelet denoising and short time Fourier transform is proposed, which can be used to monitor the leakage of the pipeline network in real time. This method is compared with the six methods of Empirical Mode Decomposition (EMD), wavelet decomposition, Gauss model, recurrence plot, Wigner-Ville distribution (WVD) and Wigner-Hough transformation (WHT). The results show that this method can accurately diagnose the leakage of the pipeline network. This method is verified by the field measured pressure signals of other literature. It is concluded that this method is effectual for the leakage detection of the actual complex pipeline network. In order to test the universality of this method, it is also used to analyze the pressure signal of the heating pipeline network and the gas pipeline network. The results show that the method proposed in this paper is also applicable to other complex pipeline networks.

Improved Wavelet Denoising by Non-Convex Sparse Regularization Under Double Wavelet Domains

This paper presents a double wavelet denoising (DWAD) method, which can preserve more details of an original signal. Although the noise removal method based on wavelet transform has been widely used, it still performs poorly for the signals with a low signal-to-noise ratio (SNR) or frequency overlap. Different from the wavelet denoising methods based on a single basis function, the DWAD considers filtering the wavelet coefficients of the noisy signal by threshold functions under two different wavelet domains, simultaneously. It considers using the difference of wavelet coefficient distribution and forcing the denoised signals under two wavelet domains to be the same to achieve more retention of details. In addition, the arctangent function is employed as a penalty function for wavelet coefficients to induce strong sparse wavelet coefficients. The DWAD is applied to one-dimensional signals and it is found that some wavelet coefficients which are smaller than the threshold could be retained during noise removal. The experiment results show that the average SNR of different noise levels is improved by at least 4.2 and 2.1 dB compared with the classical soft threshold method for the one-dimensional and image signals, respectively. Besides, the DWAD tends to obtain better performance on the details of original signals.

An Improved Wavelet Denoising Algorithm Based on Principal Component Analysis

In order to eliminate the noise of nonlinear non-stationary signal and restore the pure signal, an improved wavelet denoising algorithm based on principal component analysis is provided in this paper. This novel algorithm improves the threshold function of the commonly used denoising algorithm and can eliminate noise based on useful signal energy ratio. The experimental results show that the improved algorithm can effectively improve the signal-to-noise ratio of the denoising signal and reduce the difference of variance, and then it can achieve a better effect of eliminating noise.

An Improved Wavelet Denoising Algorithm for Wideband Radar Targets Detection

A novel denoising technique based on wavelet transform modulus maxima (WTMM) is proposed for processing wideband radar spread targets detection signal in a clutter environment. Combined with the improved adaptive Bayes–Shrink threshold and Lipschitz exponents, we propose the path pruned approach at each scale terms as full-scale to split the signal. The estimation of WTMM over each scale has been optimized, thus, the signal and the noise can be split effectively. Additionally, to improve the computational efficiency, a fast method based on a piecewise polynomial interpolation algorithm is applied for the split signal reconstruction. Statistical results are quite promising and perform better than the conventional denoising algorithms: compared with the classical WTMM algorithm, the improved WTMM full-scale denoising algorithm not only increases the signal-to-noise (SNR) ratio by over 10 % but also reduces the processing time by 88 % and reduces the root-mean-square-error (RMSE) by over 35 %. More generally, the proposed algorithm has better performance than that of several typical algorithms in its denoising quality and singularity detection.

Improved Wavelet Denoising Using Neighboring Coefficients and Its Application to Machinery Fault Diagnosis

Improved Wavelet Denoising Using Neighboring Coefficients and Its Application to Machinery Fault Diagnosis