Signal Noise Research Articles

Enhancing the Diagnostic Accuracy of Amyloid PET: The Impact of MR-Guided PET Reconstruction.

18 F-Florbetaben (FBB) uptake in the supratentorial cortex is indicative of amyloid positivity. Due to PET's low spatial resolution, image noise, and spill-over of signal from adjacent white-matter into gray-matter, there are inconsistencies in ratings among trained readers. A set of 264 18 F-Florbetaben (amyloid) PET/MRI exams were reconstructed using conventional ordered subset expectation maximization (OSEM) method and MR-guided block sequential regularized expectation maximization (MRgBSREM) method. Images from 264 patients reconstructed by OSEM method and rated by 3 trained readers. Fifty-three exams were rated inconsistently and were mixed with another 53 exams which were rated consistently. These 106 subjects were then rated by our readers using the MRgBSREM PET reconstruction method. Centiloids (CL) were measured using both reconstruction methods. Signal to noise ratio (SNR) was calculated in frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal regions for both reconstruction methods. There is significant correlation between CL measured by OSEM and MRgBSREM methods with R2=0.99. MRgBSREM enhanced the SNR in all regions by average of 21%. The number of inconsistent exams dropped by 64% using MRgBSREM method as compared with OSEM method. Using Fleiss-Kappa statistical test, the agreement between readers was raised from "Fair" to "Significant" in the 106-subjects subset. PET reconstruction with MR priors can significantly improve the consistency of ratings among trained readers. Given the prevalence of inconsistent ratings in amyloid PET, methods that enhance the ability to distinguish intermediate amyloid levels could be valuable for the widespread adoption of this modality.

Epilepsy EEG Seizure Prediction Based on the Combination of Graph Convolutional Neural Network Combined with Long- and Short-Term Memory Cell Network

With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure between the electrodes of the signal collection device nor the data structure of the Euclidean space to accurately reflect the interaction between signals. Graph neural networks can effectively extract features of non-Euclidean spatial data. Therefore, this paper proposes a feature selection method for epilepsy EEG classification based on graph convolutional neural networks (GCNs) and long short-term memory (LSTM) cells. While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. Therefore, experiments were conducted using the preprocessed Boston Children’s Hospital epilepsy EEG dataset, and input it into the GCN-LSTM model for deep feature extraction. The GCN network built by the graph convolution layer learns spatial features, then LSTM extracts sequence information, and the final prediction is performed by fully connected and softmax layers. The introduced method has been experimentally proven to be effective in improving the accuracy of epileptic EEG seizure detection. Experimental results show that the average accuracy of binary classification on the CHB-MIT dataset is 99.39%, and the average accuracy of ternary classification is 98.69%.

The simulation of MMV fuze echo characteristics in rainfall based on Monte Carlo

Abstract To enhance the adaptability of millimeter wave (MMV) fuzes in rainy conditions, the attenuation and scattering impacts of rainfall was conducted. Furthermore, a mathematical model was formulated to study the characteristics of MMV fuze echo signals under rainfall environments, and the MMV fuzes echo signals are simulated based on the Monte Carlo method. The results show that rainfall leads to a substantial increase in the bottom noise of the echo signal, and the rainfall backward scattering signal mainly exists in the low-frequency region, but even in the heavy rain and rainstorm environment, the MMV fuze proximity ranging effect is basically unaffected. The results of the study can provide a reference basis for the adaptability of MMV fuze to complex environments.

RFF Template Design: Adaptively Decreasing Both Doppler Shifts and Noise for Complex-Valued Signals

RFF Template Design: Adaptively Decreasing Both Doppler Shifts and Noise for Complex-Valued Signals

An online open circuit faults diagnosis method for converter using the lightweight two-channel deep network

Online diagnostic technology is crucial for ensuring the reliable operation of power converters in industrial applications. However, quickly diagnosing the faults of a power converter using intelligent algorithms is challenging due to the redundancy and noise of the sensor measurement signal. Hence, this paper proposes a lightweight two-channel deep network (LTCDN) to overcome it and achieve rapid fault diagnosis. It consists of a quadratic mean preprocessing layer (QMPL), a two-channel convolutional layer (TCCL), and a bidirectional gated recurrent unit (BGRU). The QMPL is designed to achieve denoising and lightening of output currents. Then, the TCCL is proposed to extract edge features and internal key information for improving diagnostic accuracy. Finally, the BGRU classifier completes the online fault diagnosis with a fast detection speed and high precision. It is evaluated through experiments with real-time open circuit faults for multiple switches of a four-level active neutral point clamped (4L-ANPC) converter, demonstrating superior performance with a false diagnosis rate of less than 1% and a diagnosis time of under 8ms. The method combines a novel data preprocessing layer with an efficient neural network to provide an effective fault detection method for a 4L-ANPC converter, which not only has high accuracy but also has significant computational efficiency. Its scalability for more complex multilevel converters is also verified.

Application of the hybrid integration displacement algorithm based on CEEMDAN and wavelet threshold denoising in vibration screening equipment

Abstract Vibration screening equipment has an extensive application profile in material screening, in which the displacement parameters can reveal the motion state of the material and affect the screening efficiency. These displacement parameters can be obtained by integrating the acceleration signal of the equipment. In this paper, to prevent the noise in the acceleration signal from further amplifying its negative effects on the subsequent integration, the acceleration signal is preprocessed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold denoising. Besides, a hybrid integration displacement algorithm is utilized to mitigate the influence of integration errors. The consistent results between simulation and platform experiments demonstrate that CEEMDAN in combination with wavelet threshold denoising can effectively remove noise while retaining the main frequency signal. In addition, the displacement signal obtained by the hybrid integration algorithm proposed in this paper is closer to the original displacement signal. Compared with the 2nd time-domain integration, the 2nd frequency-domain integration, and the empirical mode decomposition (EMD) integration methods, the integration method proposed in this paper achieves a smaller peak error, mean absolute error (MAE), and root mean square error (RMSE). The experimental results corroborate the superiority of this method in the application of vibration screening equipment.

Weak ultrasonic guided wave signal recognition based on one-dimensional convolutional neural network denoising autoencoder and its application to small defect detection in pipelines

Pipeline failures are often caused by the expansion of small defects. Structural damage to pipelines can lead to major safety accidents. When ultrasonic guided wave (UGW) technology is used for pipeline failure detection, the echoes produced by small defects manifest as weak UGW signals amidst significant noise. The low amplitude of these signals or complete drowning by noise makes them difficult to recognize. This study innovatively introduces a one-dimensional convolutional neural network denoising autoencoder (1DCNN-based DAE) for noise reduction in UGW signals using deep learning. To improve the conventional DAE, the model incorporated the Parametric Rectified Linear Unit (PReLU) activation function and a CNN for enhanced feature extraction, resulting in the proposed 1DCNN-based DAE. The model is trained on an extensive dataset of mixed signals with strong noise and their corresponding clean signals, enabling autonomous denoising in an unsupervised manner. Additionally, this paper proposes the application of the window-shifted power spectrum method for analyzing the denoised signals to identify and locate pipeline defects. The method involves traversing the signal with a window to intercept fragments, calculating their power, and plotting the power spectrum curve. Defects are then located based on the peak positions of this curve. Numerical simulation and experimental signals were used to validate the proposed method. Simulation results showed that the proposed 1DCNN-based DAE effectively improved the signal-to-noise ratio (SNR) of UGW mixed signals from −9 dB to 21.63 dB, representing an improvement of up to 30.63 dB. Experimental results demonstrated that the method accurately detected weak UGW signals from small defective pipes with a 2 % cross-section loss rate, achieving over 90 % recognition confidence and less than 1.5 % axial positioning error rate. In summary, the proposed 1DCNN-based DAE can effectively improve the SNR of the signal, reduce the noise in the UGW detection signal, and improve the sensitivity of defect identification; the window-shifted power spectrum method has a advantage in the accurate localization of defects.

Wall thinning quantification with a lift-off distance for ferromagnetic structures using pulsed ECT equipped with ICA-Gauss filter and Hough Transform

Determining the thickness of ferromagnetic materials with a lift-off distance poses a significant challenge for current non-destructive testing (NDT) techniques. Pulsed eddy current (PEC) testing is deemed as a powerful candidate to evaluate this type of defect. However, the signal-to-noise ratio (SNR) of the PEC response signal obtained with large lift-off distance is very poor, so that the signal feature can hardly be extracted. To improve the SNR of PEC response signals and capture the signal feature adaptively, this paper proposed a novel PEC signal processing algorithm based on ICA-Gauss filter and Hough Transform (HT). Firstly, the principle of the proposed method was introduced. Then, two case studies, a comparison experiment and an application experiment were conducted to verify the effectiveness and accuracy of this method. Results from these experiments show that (a) the ICA-Gauss filter can effectively suppress the power-line noises and random noises in PEC signals, (b) the ICA-Gauss filter outperforms traditional filters in feature robustness and computing efficiency, including double-logarithmic median filter and Savitzky-Golay filter, and (c) HT is an adaptive and accurate method to extract the PEC signal feature, thus achieving a small detection error.

CNN Intelligent diagnosis method for bearing incipient faint faults based on adaptive stochastic resonance-wave peak cross correlation sliding sampling

As a representative of deep learning networks, convolutional neural networks (CNN) have been widely used in bearing fault diagnosis with good results. However, the signal length and segmentation of the input CNN can have a significant impact on diagnostic accuracy. In addition, the signal-to-noise ratio of early bearing faults is usually very low, which makes it difficult for traditional CNNs to accurately identify and classify these faults. To solve this problem, this paper proposes an adaptive stochastic resonance wave peak cross-correlation sliding sampling method. Firstly, the adaptive stochastic resonance is used to reduce the noise of the original signal, and then the data is divided from the position of the signal wave peak, the correlation coefficient between the divided signals is calculated, and the maximum value is found to determine the size of the division window. Finally, it is converted into a 2D image by Gramian Angular Field and input into CNN for diagnostic classification. The design methodology was validated using the Case Western Reserve University bearing dataset. Subsequently, three validation strategies were established on a self-built platform, including mixed diagnosis of 10 different bearing states, variable speed diagnosis, and low sampling data diagnosis. The proposed method outperforms the conventional CNN by 10 % in the Case Western Reserve University dataset test set. The variable speed test set is 24.67 % and 31.17 % higher, respectively. It is 30 % higher in low sampling data diagnosis.

Practice and validation of inversion of seismic source localisation based on genetic algorithm

As the mining depth of coal resources increases, resulting in frequent mine earthquakes during mining. In this study, the rolling window ratio method is firstly chosen as the seismic phase recognition method to read the mine earthquake data received by the microseismic sensor. Secondly, the improved genetic algorithm is used as the optimization algorithm of the objective function to build the algorithmic framework of accurate inverse localization of mine earthquake. Finally, the accuracy of this algorithm for seismic source localization is validated using actual engineering cases. Results show that the first arrival time extraction by the rolling window ratio method has the advantages of high accuracy and fast algorithm operation speed. The Fast Fourier Transform-Butterworth joint noise reduction method has a good noise reduction effect, which successfully suppressing noise outside the mine earthquake signal and effectively improving the issue of excessive noise in the mine earthquake signal. Compared to microseismic monitoring data, the localization error for mine earthquakes remains within 5 %.

Microseismic Signal Reconstruction From Strong Complex Noise Using Low-Rank Structure Extraction and Dual Convolutional Neural Networks.

Microseismic signal reconstruction from complex nonrandom noise is challenging, especially when the signal is disrupted or completely covered by strong field noise. Various methods often assume that signals are laterally coherent or the noise is predictable. In this article, we propose a dual convolutional neural network preceded by a low-rank structure extraction module to reconstruct signals hidden by strong complex field noise. Preconditioning by low-rank structure extraction is the first step in removing high-energy regular noise. The module is followed by two convolutional neural networks with different complexity to achieve better signal reconstruction and noise removal. In addition to the combination of synthetic and field microseismic data, natural images are also used in the training due to their correlation, complexity, and completeness, which contributes to increasing the generalization of the networks. The results from synthetic and real datasets demonstrate superior signal recovery, which cannot be achieved by using solely deep learning, low-rank structure extraction, or curvelet thresholding. Algorithmic generalization is demonstrated using independently acquired array data excluded during training.

Improvement of multiscale decomposition for space-based gravitational wave signal processing technology.

During the process of detecting gravitational waves in space, addressing noise issues caused by terrestrial vibrations, natural environmental changes, and the factors intrinsic to the detectors, this paper proposes a multiscale variational mode adaptive denoising algorithm based on momentum gradient descent. This algorithm integrates momentum factors and multiscale concepts into the variational mode algorithm to resolve the issue of multiple local optima encountered during operation, reduce oscillations in regions with large or unstable gradient changes, and improve convergence speed. Additionally, the algorithm combines the least mean squares algorithm to automatically adjust weights, thereby mitigating the impact of noise, addressing the issue of noise from multiple and random sources, effectively suppressing noise in the gravitational wave signal, and enhancing the quality and reliability of the gravitational wave signal. Experimental results demonstrate that this algorithm performs better than other algorithms in noise suppression, effectively reducing noise in gravitational wave signals and meeting the noise suppression requirements for space-based gravitational wave detection.

An efficient noise reduction technique in underwater acoustic signals using enhanced optimization-based residual recurrent neural network with novel loss function

An important process for underwater acoustic signal is noise reduction. In ocean exploration and the military, the minimization of noise in the underwater environment is essential to provide a significant impact in our society. While considering the different acoustic channels and complexity of marine environment, the noise reduction process in acoustic signals is always difficult. Owing to these complexities, an advanced noise reduction mechanism for underwater acoustic signal denoising is performed using adaptive deep learning method is developed. The proposed model involves finding and analyzing the noises present in the underwater acoustic signal, which is helpful for underwater target detection, recognition and acoustic communication quality. Here, a Advanced Recurrent Neural Network with Novel Loss Function (ARRNN-NLF) is implemented for reducing noises in underwater acoustic signals. Hence, the input signal is given to the reduction process where the ARRNN-NLF network is utilized for densification. The high-order nonlinear features from the original signal are extracted and converted into subvectors with fixed lengths based on temporal dimension. Finally, the denoised signal is obtained from the developed ARRNN with a minimum loss between the predicted and target output. Here, the parameters from ARRNN-NLF are optimized by the Enhanced Osprey Optimization Algorithm (EOOA) for enhancing the denoising model. The resultant results are evaluated by diverse conventional denoising models to prove efficiency.

Dynamic Temporal Denoise Neural Network with Multi-Head Attention for Fault Diagnosis Under Noise Background.

Fault diagnosis plays a crucial role in maintaining the operational safety of mechanical systems. As intelligent data-driven approaches evolve, deep learning (DL) has emerged as a pivotal technique in fault diagnosis research. However, the collected vibrational signals from mechanical systems are usually corrupted by unrelated noises due to complicated transfer path modulations and component coupling. To solve the above problems, this paper proposed the dynamic temporal denoise neural network with multi-head attention (DTDNet). Firstly, this model transforms one-dimensional signals into two-dimensional tensors based on the periodic self-similarity of signals, employing multi-scale two-dimensional convolution kernels to extract signal features both within and across periods. Secondly, for the problem of lacking denoising structure in traditional convolutional neural networks, a temporal variable denoise (TVD) module with dynamic nonlinear processing is proposed to filter the noises. Lastly, a multi-head attention fusion (MAF) module is used to weight the denoted features of signals with different periods. Evaluation on two datasets, Case Western Reserve University bearing dataset (single sensor) and Real aircraft sensor dataset (multiple sensors), demonstrates that the DTDNet can reduce the useless noises in signals and achieve a remarkable improvement in classification performance compared with the state-of-the-art method. DTDNet provides a high-performance solution for potential noise that may occur in actual fault diagnosis tasks, which has important application value.

Noise reduction method for mine wind speed sensor data based on CEEMDAN-wavelet threshold

The mine wind speed sensor is an important intelligent sensing equipment in the mine intelligent ventilation system that can provide accurate and key wind speed parameters for the intelligent ventilation system. The turbulent pulsation characteristics of the airflow in the underground tunnel are a major factor for the inaccurate measurement of mine wind speed. Therefore, according to the random non-stationary characteristics of a turbulent pulsation signal, a denoising method based on adaptive complete ensemble empirical mode decomposition (CEEMDAN) combined with the wavelet threshold is proposed for suppressing the turbulent pulsation noise in the wind speed signal. First, the CEEMDAN algorithm is used for decomposing the wind speed signal into a series of IMF components. Second, the continuous mean square error criterion is used for determining the high-frequency IMF components with more noise. The wavelet threshold denoising method is used for denoising the high-frequency IMF components with more noise. Finally, the denoised IMF components and remaining low-frequency IMF components are reconstructed for obtaining the denoised signal. The results of the denoising analysis of measured turbulent pulsation signals, comparative analysis of denoising of simulated turbulent pulsation signals by different joint denoising methods, and denoising analysis of actual mine wind speed sensor data indicate that the joint denoising method proposed in this study has a higher signal-to-noise ratio and lower root mean square error of the wind speed signal after denoising. Compared with the EMD-wavelet threshold and EEMD-wavelet threshold denoising methods, the denoising method proposed in this study is better and has higher denoising accuracy, which provides a new method for processing actual mine wind speed sensor data.

Feasibility of loop-gain tuning for general measurement systems inspired by quantum locking for DECIGO

Abstract A series of quantum locking theories have been proposed to enhance the quantum-noise-limited target sensitivity of the DECi-hertz Interferometer Gravitational wave Observatory. The quantum locking that uses a square completion optimizes the sensitivity across all frequencies. However, a substantial amount of data-series must be post-processed since the square completion is a form of signal processing technique. This paper approaches the optimal sensitivity across all frequencies from an alternative perspective: by optimizing the frequency dependence of a servo gain in a feedback loop. The optimal servo gain is formulated by comparing the alternative method with the square completion method for the same optical setup. This will be shown in general noise issues extending the framework of the quantum locking. We find that the optimal servo gain forms a non-feasible filter but has certain characteristics. We also find that the noise of the measurement signal deteriorates proportionally to the noise measured in the feedback loop when the servo gain is slightly imperfect.

EI-ISOA-VMD: Adaptive denoising and detrending method for nuclear circulating water pump impeller

As a key component of the nuclear circulating water pump, it is difficult to extract effective information from low-quality impeller signal because of the interference noise and non-stationary trend, such as water flow impact, vibrations from unrelated components, and sensor noise. Traditional signal denoising methods, particularly those utilizing variational mode decomposition, often require manual parameter tuning, resulting in unsatisfactory outcome. To address these issues, this paper proposes a signal denoising and detrending method based on the evaluation index driven the improved seagull algorithm optimized for variational mode decomposition (EI-ISOA-VMD). During the optimization process, sinusoidal chaotic mapping is utilized for initializing the seagull population, while a tanh function is employed to design nonlinearly decreasing inertia weights. Furthermore, the Levy flight mechanism enhances the randomness of position updates and optimizes seagull individuals beyond the boundary range in order to form the improved seagull optimization algorithm. The denoising, detrending process, and evaluation index proposed in this paper are utilized as the fitness function for the improved seagull optimization algorithm to optimize key parameters of variational mode decomposition. During the denoising and detrending process, the trend component is initially selected using the mean ratio method. The useful, low noise and high noise intrinsic mode functions are screened by correlation coefficient and weighted permutation entropy. Subsequently, the low noise components are denoised by wavelet thresholding method, and the high noise components are discarded to finally reconstruct the signal. The experimental results demonstrate that the proposed method effectively eliminates noise and trend components in low-quality signal while preserving more valuable information.

A Fast iterative shrinkage and threshold algorithm-based least-squares deconvolution technique with application to bearing fault signals

Bearings are commonly subjected to load, transmission, and impact during operation, resulting in bearing failure that ultimately leads to mechanical breakdown. The low energy level of fault-induced impulsive signals, however, renders it vulnerable to being masked by environmental noise and other disturbances present in the vibration signal. To cope with this issue, this paper investigates the application of the finite impulse response (FIR) filter for bearing fault detection. In this work, the estimation of the FIR is formulated as a least squares optimization problem, aiming to minimize the l 2 norm of the FIR and effectively suppress noise and interference sources. The solutions of the least squares optimization problem are obtained using the fast iterative shrinkage–thresholding (FISTA) algorithm. Furthermore, to better select the regularization parameter in our method, an improved gray wolf optimizer (I–GWO) is employed to conduct the parameter selection. Based on these improvements, the proposed FISTA–LSD technique enables rapid and precise detection of fault characteristics upon their occurrence, thereby mitigating the propagation of faults and preventing accidents from happening. Furthermore, with this automated selection process, engineers can save valuable time that would otherwise be spent on trial-and-error approaches. Through its rapid response capabilities and precise fault detection abilities enabled by the FISTA–LSD technique along with I–GWO integration for automatic parameter selection; this FISTA–LSD technique greatly enhances its practical value. The proposed technique is validated through the utilization of both numerical and experimental data.

Study of Millimeter-Wave Fuze Echo Characteristics under Rainfall Conditions Using the Monte Carlo Method

Due to the similarity in wavelength between millimeter-wave (MMW) signals and raindrop diameters, rainfall induces significant attenuation and scattering effects that challenge the detection performance of MMW fuzes in rainy environments. To enhance the adaptability of frequency-modulated MMW fuzes in such conditions, the effects of rain on MMW signal attenuation and scattering are investigated. A mathematical model for the multipath echo signals of the fuze was developed. The Monte Carlo method was employed to simulate echo signals considering multiple scattering, and experimental validations were conducted. The results from simulations and experiments revealed that rainfall increases the bottom noise of the echo signal, with rain backscatter noise predominantly affecting the lower end of the echo signal spectrum. However, rain conditions below torrential levels did not significantly impact the detection of strong reflection targets at the high end of the spectrum. The modeling approach and findings presented offer theoretical support for designing MMW fuzes with improved environmental adaptability.

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.