Influence Of Noise Research Articles

Crack analysis of tall concrete wind towers using an ad-hoc deep multiscale encoder–decoder with depth separable convolutions under severely imbalanced data

An accurate and timely cracking assessment, including the presence, location and crack geometric feature measurement, is crucial for evaluating concrete wind towers. Therefore, the early identification of cracks is a critical procedure in promptly evaluating structural integrity. This study proposed an ad-hoc encoder–decoder network based on DeepLabv3+ with depth separable convolutions to automatically segment cracks from real-world images captured from various concrete wind towers. The combined advantages of the improved DeepLabv3+ and the lightweight MobileNet v2 are suitable as a benchmark due to their high performance and universality. Four experiments were conducted to determine the model design choice and crack feature measurement capability: (1) six parametric tests using various pre-trained base networks and algorithm optimisers, (2) the influence of complex background noise (i.e., handwriting script) on crack segmentation performance, (3) comparative studies with cutting-edge pixel-wise segmentation models and (4) crack feature measurement (i.e., length and width). The research outcome demonstrated that DeepLabv3+ with MobileNet v2 can potentially be applied for efficient and accurate crack segmentation in concrete wind towers with complex backgrounds.

Indices of vertebral vein discirculation in workers in noise occupaions

Introduction. Occupational sensorineural hearing loss (OSNHL) is a gradual bilateral hearing loss developing over several years as a result of exposure to constant or non-constant noise (mainly high-frequency) at the workplace as one of the main causes in etiology, researchers name a violation of blood supply and vascular stenosis. The purpose of the study is to evaluate the indices of venous dyscirculation under the influence of industrial noise at the early stages of the development of OSNHL. Materials and methods. Twenty two male patients who have been working in conditions exceeding the maximum permissible noise levels for more than 5 years were examined, and the comparison group included 29 healthy male volunteers from 27 to 55 years old who do not work in the production sector. All the subjects were examined by an otorhinolaryngologist, a neurologist, and a therapist. Audiometry was performed on two axes for everyone. Air and bone conduction were performed separately for each ear. All patients underwent a triplex scan of the arteries and veins of the neck and brain in the supine and standing positions. Results. In comparison with the control group of healthy volunteers who did not work in industrial noise conditions, workers in noise occupations had a significant increase in blood flow, which averaged 25.2 cm/sec. When measuring the diameters of the vertebral veins, a tendency to vasodilation was revealed. Limitations. The limitation was the small sample size, due to the low patient turnover at the preclinical stage. Conclusion. The complex effect of noise on the body leads to changes in the wall of the venous network, a violation of venous outflow and can manifest itself not only with clinical complaints, but also in the form of episodic dizziness, cephalgia, which in turn can worsen the course of the development of OSNHL. It is necessary to perform ultrasound dopplerography of the neck veins for all workers of noise occupations at the preclinical stage to assess the risk of OSNHL occurrence.

Determination of the resolution intervals in time and frequency under the influence of multiplicative noise based on the Woodworth criterion

Issues related to estimating the influence of quasi-deterministic and fluctuating multiplicative noise on the delay resolution and frequency resolution of systems processing radio signals based on the Woodward criterion for narrow-band and broadband signals are considered. The problem of resolution when detecting or measuring signal parameters can be considered both for useful signals alone and for cases when interfering signals are present as well. It is pointed out that the effect of multiplicative noise on the signal almost always leads to a resolution problem. It is shown, that under the influence of significant broadband multiplicative noise, the time resolution interval is determined only by the signal envelope and does not depend on its phase structure. For instance, for signals with a rectangular or bell-shaped envelope, it is equal to the equivalent signal duration. Examples of calculating resolution intervals under the influence of multiplicative noise are given. Taking into account the effect of multiplicative noise on signal resolution leads to an increase in the efficiency of radio systems, detection of objects being an example.

Wavelet denoising analysis on vacuum-process monitoring signals of aerospace vacuum vessel structures

Abstract The monitoring of micro-defects or external actions induced vacuum degradation in aerospace vacuum vessels is an important challenge. A vacuum-process monitoring method based on quasi-distributed fiber Bragg grating (FBG) sensing technology is proposed. Due to the influence of environmental noise and vacuum pump operation noise, the raw signals measured by FBG sensors contain a large amount of noises, which affects the measurement accuracy and data analysis. Therefore, the wavelet threshold (WT) denoising method is proposed to analyze the influence of noises on the monitoring signals measured by two different kinds of FBG sensors. The evaluation of the monitoring signals after denoising indicates that the proposed method can effectively remove the noise and significantly improve signal quality. The highest signal-to-noise ratio of the processed signals can reach 37.61 dB and the mean square error is 3.68 × 10−7, while retaining the key features of the original signal. The proposed WT denoising method demonstrates better performance and feasibility compared with moving average filtering and Kalman filtering methods. The study provides critical supports for improving the performance and reliability of the vacuum vessel monitoring system.

Sub-micro-g resolution test of accelerometers based on tilt dynamic modulation in gravity field

Abstract Strategic-grade accelerometers play a pivotal role in various application fields and fundamental research. As the performance of accelerometers are improving rapidly, the requirements for test method have been raised. However, due to the influence of environmental noise, the test facility for accelerometers, especially when the resolution reaches sub-μg level, remains a challenge.
To address this limitation, this study introduces a resolution test facility based on tilt modulation in gravity field, driven by piezoelectric ceramic stacks. The input acceleration ranges from 30ng to 10μg, and the frequency is adjustable from 0.01Hz to 20Hz. Since the input acceleration oscillates at certain frequency, modulation & demodulation technology enables us to extract ultra weak signal out of relatively noisy background. With this facility, we test the resolution of commercial strong motion accelerograph, quartz flexure accelerometers, and homemade accelerometer based on levitated optomechanics, proving the reliability of this method. Our advancement in test method lay solid foundation for strategic-grade accelerometers, not only provides a minimal resolution test solution in the ng~μg range, but also provides a possible scheme of evaluating dynamic response characteristic with an ultra-low acceleration input in a considerable frequency range.

Unconventional Photon Blockade in a Hybrid Optomechanical System with an Embedded Spin‐Triplet

AbstractThis research investigates the unconventional photon blockade in a hybrid optomechanical system with an embedded spin‐triplet state. The self‐homodyning interference between squeezed quantum fluctuations produced by the emitter and the coherent fraction from the driving laser results in two‐photon suppression. Analytical solutions of the correlator equation and numerical simulations of the master equation reveal that modulated mechanical dissipation plays a crucial role in achieving strong single‐photon blockade. In contrast to conventional cavity optomechanical systems, a second‐order correlation function of can be achieved with weak single‐photon optomechanical coupling. By combining unconventional and conventional antibunching, the hybrid system achieves the convergence of maximal photon population, two‐photon interference, and suppression of higher‐order correlations. Additionally, the influence of the thermal noise on photon blockade is investigated, demonstrating greater robustness of the second‐order correlation under weaker phonon‐spin coupling.

Frequency selective illumination for high aperture coherence scanning interferometry

Abstract Coherence scanning interferometry is a widely used optical topography measurement technique, which can achieve axial resolutions in the sub-nanometer regime. Nevertheless, in the lateral dimension it is inherently diffraction limited and multiple problems arise when approaching this limit. Especially for challenging surface topographies like steep slopes or small grating periods measurement artifacts start to cause massive deviations in the 3D reconstruction of the surface due to the increased influence of noise on increasingly weak signals. In this study we present an illumination approach for Linnik-type CSI, which highlights oblique incident angles of the illuminating light cone in high numerical aperture (0.95) systems. It is demonstrated, that this approach can significantly improve the signal-to-noise ratio for measurements at steep surface slopes and therefore increase the subsequent surface reconstruction.

Machine Learning-Based Randomness Analysis For a Auantum Random Number Generator

Abstract Random numbers are critical to information security. Quantum random numbers are theoretically truly random and unpredictable. However, the measurement process of a quantum random number generator (QRNG) can be affected by environmental disturbances that compromise the integrity of the generated random numbers. A machine learning model is proposed to assess the stochasticity of the continuous variable QRNG under the influence of vacuum noise. The model is designed to detect the correlation between the randomness of the QRNG being corrupted under the influence of classical noise (electrical noise). In addition, our model detects a decrease in the randomness of the QRNG random numbers when the electrical noise intensity increases to a certain correlation. The results show that machine learning (ML) can be used as a measure of the quality of QRNG.

The efficient method to get better raw brain signal on rat anesthetics experiment

This paper introduces an efficient methodology for conducting rat anesthesia experiments, aimed at enhancing the quality of raw brain signals obtained. The proposed approach enables the acquisition of animal brain signals during experiments without the confounding influence of muscle noise. Initially, the use of alpha-chloralose (a-c) in conjunction with Isoflurane is introduced to induce anesthesia in rats. Subsequently, Dexdomitor is administered to prevent muscular movements during the collection of brain signals, further refining the signal quality. Experimental outcomes conclusively demonstrate that our anesthesia method produces cleaner raw signals and exhibits improved robustness during data acquisition, outperforming existing methods that rely solely on Isoflurane or the Ketamine-Xylazine combination. Notably, this improved performance is achieved with minimal alterations to vital physiological parameters, including body temperature, respiration, and heart rates. Moreover, the efficacy of a-c in maintaining anesthesia for up to 7 h stands in contrast to the shorter durations achievable with continuous Isoflurane administration or the 30-min window offered by Ketamine-Xylazine, highlighting the practical advantages of our proposed method. Finally, post-experiment observations confirmed that the animals gradually returned to normal behavior without any signs of distress or adverse effects, indicating that our method was both effective and safe.

Differential Lucy-Richardson-Rosen algorithm for near diffraction-limited image restoration assisting by a wavefront sensor

Lucy-Richardson-Rosen algorithm (LR2A) is one of the most important tools for image restoration, especially in computer vision, target imaging and so on. However, the performance of the LR2A can be affected by large optical aberration and imaging noise and even failure under severe environment. To address the mentioned problem, we proposed a new Differential Lucy-Richardson-Rosen Algorithm(D-LR2A) to reduce the influence of optical aberrations and detection noise with advantages of faster speed of convergence and better restoration quality. In the modified optical configuration, a Hartmann-Shack wavefront sensor is employed to measure optical aberration and estimate accurately the Point Spread Function (PSF) of the optical imaging system. The numeric and experimental results show that the proposed method can be used to restore images with large optical aberration and worse noise level. Besides that, it also has faster speed, better restoration quality, and thus easier application as compared to those of LR2A

Weak signal detection in chaotic noise background - based on VMD-EEMD and self-attention mechanisms

Abstract In this paper, a method for detection and estimation of weak harmonic signals in chaotic noise background is proposed. Firstly, the observed signal is smoothed by local weighted regression model to reduce the influence of noise. Then, the smoothed signals are decomposed into the intrinsic mode functions by Variational Mode Decomposition and Ensemble Empirical Mode Decomposition (VMD-EEMD). The components are reconstructed in the phase space, and the one-step prediction is carried out by adding a layer of the self-attention mechanism in the Long Short-Term Memory (LSTM). The one-step prediction error is obtained by summing up the prediction results of the components and the weak harmonic signals will be detected from the one-step prediction error. Finally, the periodogram method is used to detect whether the observed signals contain harmonic signals or not. If the observed signals contain harmonic signals, the amplitude and phase are estimated by the least squares method. Simulation experiments show that the method proposed in this paper has good performance. In detection of single harmonic signal, the Mean Absolute Error (MAE) and Mean Square Error (MSE) of the one-step prediction are 0.0212 and 0.00068 respectively; MAE and MSE of the signal estimation are 0.001977 and 0.0007 respectively. In detection of multiple harmonic signals, MAE and MSE of the one-step prediction are 0. 0248 and 0. 0006 respectively; MAE and MSE of the signal estimation are 0. 0035 and 1.681×10−5 respectively. The method proposed in this paper is able to efficiently detect weak harmonic signals from chaotic noise backgrounds and estimate the harmonic signals.

Robust sensing matrix design for the Orthogonal Matching Pursuit algorithm in compressive sensing

In compressive sensing, Orthogonal Matching Pursuit (OMP) is a greedy algorithm used for recovering sparse signals from their incomplete linear measurements. Conventionally, the OMP algorithm relies on both the measurement matrix and the measurement signal to reconstruct sparse signals. A sensing matrix can be designed to have a small mutual coherence with respect to (w.r.t.) the measurement matrix, which is used to boost the performance of the OMP algorithm in sparse signal reconstruction. Nevertheless, sensing matrices designed by current methods are vulnerable to measurement noises. In this paper, we begin by examining the underlying cause of the non-robustness to measurement noises exhibited by these sensing matrices. Subsequently, we propose a novel approach to design a robust sensing matrix capable of withstanding the influence of measurement noises. Finally, we conduct numerical simulations to demonstrate the effectiveness and robustness of the sensing matrix designed by the proposed method.

Mathematical Modeling of Refraction Characteristics of Electromagnetic Radiation at Stochastic Gravitational Field

Numerical-analytical modeling is used for estimation of gravitational noise influence on propagation of electromagnetic radiation at gravitational field of a group of astrophysical objects. The method bases on numerical integrate of system of light differential equations in the Euler’s form, added differential equations for calculation of statistic moments of side fluctuations of light at a picture plane of observer. The influence of gravitation was taken into account by introducing of the effective refraction index of vacuum, expressed through a gravitational potential. Visions about spatial correlation function of inhomogeneities of effective refraction index of vacuum are used as model. Calculating of gravitation effects of group astrophysical objects are performed under assumption additive contribution of field of every objects in general gravitational field. Modeling of the refraction characteristics of electromagnetic radiation is performed for a various configurations of gravitation field. Results of calculating are shown at a picture plane of observer. The blurring effects of gravitational lensing are investigated at dependence from position of localized area of gravitational noise and astrophysical objects. It’s showed that for case of single-component gravitational field full coverage of object by area of gravitational noise match maximum blurring of lensing effect. The features of gravitational focusing are determined at dependence from position of area noise for asymmetrical twocomponent gravitational field, where one of objects coverage by area of gravitational noise. Offered tool of modeling can be used for both investigation of gravitational lensing of multi-component gravitational field in presence variety of limited area of gravitational noise and under conditions uniform spatial distribution of stochastic gravitational inhomogeneities. Also it can use for recovering of gravitational potentials of non-radiating (hidden) objects by characteristics of received electromagnetic radiation of remote space sources.

Joint local smoothness and low-rank tensor representation for robust multi-view clustering

Low-rank tensor representation (LRTR) has become a significant method for achieving improved multi-view clustering (MVC) performance. Generally, most LRTR methods impose a tensor low-rank constraint (TLRC) on a tensor, which is spliced by the representation matrix of each view, to explore the low-rank prior hidden in these representation matrices. However, two problems remain unsolved. On the one hand, these representation matrices still contain noise since the raw data usually possess noise; on the other hand, and more importantly, is there any other prior information that can be explored among these representation matrices? Since samples located in the same cluster are similar, the coefficients of their representation matrices should exhibit similar. That is, these representation matrices should be local smoothness (LS), which is also verified by our numerical tests. In addition, the use of the LS prior helps to denoise the noise contain in the data. Then, in this paper, we propose a joint LS and LRTR for robust MVC method (LS-LRTR), which can simultaneously exploit the low-rank and LS priors. Specifically, we utilize a TLRC to explore the low-rank prior in the representation matrices. Subsequently, to mine the LS prior and further reduce the influence of noise, we introduce the Total Variation (TV) norm to the constraint representation matrices. Then, we fuse the TLRC and TV norm into a unified framework. Additionally, we apply an Augmented Lagrange Multiplier to solve the optimization problem of LS-LRTR. Experiments conducted on several datasets indicate that LS-LRTR outperforms the state-of-the-art clustering methods.

Structural damage identification based on Bayesian updating

Due to factors such as model accuracy, sensor arrangement, noise influence, eigenvalue selection and the influence of local damage on the overall response, the structural damage identification process inevitably has uncertainties, so it is necessary to introduce probabilistic statistical methods to improve its accuracy and robustness. In this paper, structural damage identification is carried out based on Bayesian updating. Bayesian updating is combined with real-time monitoring data (e.g. displacement, acceleration), and the real-time data reflect the current state of the structure and contain damage information. The experimental results show that the update results of the Bayesian method correlate well with the actual data, which verifies the feasibility of Bayesian updating in structural damage identification. The structural prior probability can be effectively updated by this method, which shows high accuracy and robustness in structural damage identification and is able to maintain stable performance under different working conditions and environments. Based on Bayesian updating, this structural damage identification method provides a new technical means for structural health monitoring and damage assessment, with broad application prospect and development space.

Detection of Beaded Karst Caves in Subway Works by Mixed-Source Surface Wave Survey: A Case Study

Abstract Karst cave causes geological disasters in tunneling engineering, which brings great risks to subway construction. The microtremor survey has become one of the main methods for karst cave detection in urban areas due to its convenience and great detection depth. However, the microtremor survey lacks high-frequency signals, resulting in poor detection accuracy on the shallow layer. An advanced method called mixed-source surface wave (MSW) method is proposed by imposing active sources during the continuous passive surface wave survey. MSW is applied to the detection of beaded karst caves (i.e. two or three karst caves at different depths in the same location) in Ji'nan, China. The influences of ambient noise, array type, and other factors on the detection accuracy were studied. Experimental results show that the detection depth and accuracy of MSW are much better than those of passive surface wave method. The inversion accuracy of linear array perpendicular to road is better than the triangle array and L-shaped array. The accuracy of the linear array along the road is worst. The detection accuracy of MSW is similar to that of combined active–passive surface wave methods. However, the basic principle of MSW is easier to understand, and its operation is more convenient. The two-dimensional measurement results show that the MSW method can well reflect the size and location of the beaded karst cave and provide important references for the practical engineering.

An active protection method based on superimposed phase currents for active distribution systems

To enhance the fault current amplitude of active distribution systems during the weak fault period and decrease the influence of the measurement equipment noise, this paper proposes an active protection method based on superimposed phase currents for active distribution systems. This method is shown as follows. First, the reference value of the filter inductor current of each distributed generator (DG) is set as different values when the fault intensity of the active distribution system changes. Second, the superimposed phase current in each relay is obtained by subtracting each sample of phase current from its corresponding sample of phase current in the previous cycle. Third, the fundamental phase angle of the superimposed phase current is extracted. This is because the fundamental current component of the superimposed current cannot be affected by the active control strategy, which is proved in this paper in detail. Meanwhile, the feature direction is built by fundamental phase angles at both ends of the line to detect faulty zone. Finally, the proposed protection method has been validated in a modified IEEE 13-bus test system, and the test results prove the effectiveness of the proposed active protection method that can enhance the faulty feature to detect faults.

A reference learning network for fault diagnosis of rotating machinery under strong noise

The strong noise often masks the fault characteristics of equipment, which reduces the accuracy of fault diagnosis and even leads to the inability of intelligent fault diagnosis algorithms to be applied in industrial environments. This has always been a challenge in the field of mechanical fault diagnosis. As known that equipment failure results from the continuous degradation of the equipment’s state, with the failure state evolving from the healthy state. Considering that both healthy signals and fault signals contain similar noise, this paper proposes a Reference Learning Network (RLNet) model. The model aims to enhance the distinguishing features between healthy and faulty samples through reference units, thereby eliminating the influence of noise on feature distribution. Firstly, the impact of variable speed on the model’s robustness is mitigated using the computed order tracking method. Then, the difference features between healthy samples and a class of fault samples are extracted through the binary classification reference learning unit (RLU). Next, the extracted differential features are used to train the state classifier. Finally, membership weights are employed to effectively combine the feature recognition results, reducing the influence of fault features from mismatched RLUs. The robustness and superiority of the proposed method were verified by comparing it with five other intelligent fault diagnosis methods on the gear and bearing datasets. RLNet is of great significance for the engineering application of intelligent fault diagnosis methods in industrial noise environments.

A Semi-Supervised Active Learning Method for Structured Data Enhancement with Small Samples

In order to solve the problems of the small capacity of structured data and uneven distribution among classes in machine learning tasks, a supervised generation method for structured data called WAGAN and a cyclic sampling method named SACS (Semi-supervised and Active-learning Cyclic Sampling), based on semi-supervised active learning, are proposed. The loss function and neural network structure are optimized, and the quantity and quality of the small sample set are enhanced. To enhance the reliability of generating pseudo-labels, a Semi-supervised Active learning Framework (SAF) is designed. This framework redistributes class labels to samples, which not only enhances the reliability of generated samples but also reduces the influence of noise and uncertainty on the generation of false labels. To mine the diversity information of generated samples, an uncertain sampling strategy based on spatial overlap is designed. This strategy incorporates the idea of spatial overlap and uses global and local sampling methods to calculate the information content of generated samples. Experimental results show that the proposed method performs better than other data enhancement methods on three different datasets. Compared to the original data, the average F1macro value of the classification model is improved by 11.5%, 16.1%, and 19.6% relative to compared methods.

Model-based humidity observer for vehicle proton exchange membrane fuel cell based on a new sliding mode observer estimation technique

ABSTRACT The internal water content of the proton exchange membrane fuel cell (PEMFC) stack significantly affects its performance and duration. To achieve optimal humidity level, real-time monitoring of the humidity of the cathode and the anode is crucial for the PEMFC. As far as we know, the relative humidity of the PEMFC can be estimated via an observer based on the measurable output voltage. However, the rapid changes in vehicle operating conditions pose a challenge to the estimation accuracy. Different from the existing sliding mode observers (SMOs), this study pays more attention to the testing noise of voltage measuring sensor. To reduce the noise influence and improve the estimation accuracy, a new sliding mode observer (NSMO) combined with an online Kalman filter is presented based on the dimensionless model of the PEMFC in this study. The applicability of the proposed NSMO in estimating the cathode relative humidity and the anode relative humidity is illustrated by the simulation results. At the same time, comparisons between the NSMO and the online Kalman filter are provided which show a substantially higher estimation accuracy and lower cost for the NSMO. Furthermore, the final results indicate the mean absolute deviations of the cathode and the anode relative humidity for the NSMO are, respectively, reduced to 0.39% and 0.28%.