Characteristics Of Background Noise Research Articles

Analysis of GNSS-RTK Monitoring Background Noise Characteristics Based on Stability Tests

Analysis of GNSS-RTK Monitoring Background Noise Characteristics Based on Stability Tests

Effect of background noise characteristics on early warning indicators of thermoacoustic instability

In this work, we investigate the effects of background noise characteristics — specifically noise color (or correlation time) and intensity — arising from velocity-coupled and additive noise sources, on the early warning indicators (EWIs) of thermoacoustic instability. We employ a nonlinear reduced-order combustion dynamics model for our investigation. In the absence of noise, the nonlinear model undergoes a subcritical Hopf bifurcation, and our focus lies within the linearly stable region of the system (subthreshold region). The studied class of EWIs encompasses those derived from time series (variance), spectral analysis (coherence factor), Hurst exponent, and nonlinear methods (permutation entropy). We find that when the background noise is purely multiplicative, the trends in EWIs are primarily influenced by noise characteristics rather than the control parameter. Further, the EWIs cannot be estimated at low noise levels for large correlation times. In case of purely additive noise driven system, the coherence factor and variance are reliable EWIs across all range of investigated noise characteristics. The Hurst exponent can serve as effective EWI when the system features large noise correlation times, while permutation entropy is effective only when the system features small noise correlation time, i.e., where white noise assumption is acceptable. When the background noise includes contributions from both multiplicative and additive sources, coherence factor and variance emerges as the most reliable EWIs. These results provide insights for selecting appropriate EWIs to be employed in practical systems, considering potential variations in noise characteristics with simultaneous changes in combustor operating conditions.Novelty and significanceThis study conclusively shows that background noise (multiplicative and additive) characteristics – noise correlation time (color) and intensity – can significantly change the trends in early warning indicators (EWIs) for predicting the impending thermoacoustic oscillations. In gas turbine combustors, where thermoacoustic instability poses a critical challenge, inherent noise dynamics undergo variations with changing operating conditions and combustor designs. The development of effective EWIs to foresee the onset of thermoacoustic instability is crucial for preventing potential damage and ensuring the reliable operation of combustion systems. Previous works on stochastic dynamics of combustors simplify noise with the additive white noise assumption. Thus, our results demonstrated on a nonlinear combustion dynamics model advance the state-of-the-art with respect to the early prediction and control of thermoacoustic instability. The results provide valuable insights for selecting appropriate EWIs for engine monitoring in the absence of information on noise properties and their variation with operating conditions. This practical information is of direct relevance and interest to any gas turbine manufacturer/user.

National-scale nighttime high-temperature anomalies from Landsat-8 OLI images

Identifying and characterizing the distribution, abundance, and structural composition of national high-temperature anomalies (HTAs) are essential for mitigating natural disasters, regulating industrial emissions, and assessing societal impacts. The Landsat-8 Operational Land Imager (OLI) provides a unique capability for monitoring nighttime HTAs (N-HTAs) at high spatial resolutions without contamination from reflected solar radiation. However, the extraction of N-HTAs from nighttime OLI imagery poses significant challenges due to the interference of bright up-light, especially for smaller and cooler hotspots, and the absence of spectral information on backgrounds complicates the classification of N-HTAs at night. Here, we explored the background noise characteristics of Bands 2–7 in nighttime OLI imagery and developed an object-oriented approach for the enhanced detection of small and cool N-HTAs. Furthermore, we introduced a deep learning model that leverages high-resolution aerial imagery and land cover data to categorize N-HTAs into industrial sources, living sources, vegetation fires, and others. Applying these methodologies to nighttime OLI imagery from 2013 to 2020 allowed us to map and classify N-HTAs across the contiguous United States (CONUS), facilitating the creation of a detailed spatiotemporal inventory of industrial N-HTAs. Our analysis revealed that N-HTAs from living sources dominate the total N-HTAs, with a pronounced holiday effect, and gas flaring activities in oil and gas development areas account for >85 % of industrial N-HTAs. The outcomes of this national-scale investigation underscore the significant potential and practical utility of nighttime OLI imagery for N-HTA monitoring and advocate for expanding the spatial and temporal coverage of nighttime OLI observations globally.

Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network

When faults occur in rolling bearings, vibration signals exhibit sensitivity to periodic impact components, susceptibility to complex background noise, and non-stationary and nonlinear characteristics. Consequently, using traditional signal processing methods to effectively identify bearing faults presents significant challenges. To facilitate the accurate fault diagnosis of bearings in noisy conditions, we propose an intelligent fault diagnosis method using the Archimedes optimization algorithm (AOA), coupled with a one-dimensional multi-scale residual convolutional neural network (1D-MRCNN), to optimize the variational mode extraction (VME) parameters. First, we introduce a weighted correlated kurtosis (WCK) indicator, formulated using the correlation coefficient and correlated kurtosis as the objective function, to optimize the VME’s center frequency ω and penalty factor α, enabling targeted signal extraction. Second, deep learning techniques are employed to construct the 1D-MRCNN. The neural network then processes the extracted signal for feature extraction and automated fault-type identification. Our simulation results show that the WCK objective function effectively isolates impact components under fault conditions, and our experimental validation confirms that the proposed method accurately identifies diverse fault types across multiple noise levels.

The Effect of Noise on Initiation and Maintenance of Clear Speech and Associated Mental Demand.

The objectives of this study were to explore (a) the influence of different types of background noise and their informational content on the ability of speakers to initiate and maintain clear speech (CS), a widely utilized technique for enhancing speech intelligibility, and (b) the impact of background noise and CS usage on speakers' mental demand. Five adult females were asked to read sentences using both habitual and CS under four distinct noise conditions: quiet, multitalker (MT) noise, reversed multitalker (RevMT) noise, and speech-shaped (SS) noise. Following this, speakers rated their perceived level of mental demand for each speaking condition using the modified NASA Task Load Index scale. A two-part listening experiment with 48 listeners was conducted to evaluate the speakers' effectiveness in initiating and maintaining CS. Speakers initiated CS more successfully in noise than in quiet, with better performance observed in the presence of RevMT noise as compared to SS noise. Regarding the maintenance of CS, none of the speakers were successful in a quiet environment. Furthermore, the ability to maintain CS was most adversely affected in MT noise, followed by RevMT noise and SS noise. Our findings suggest that the effect of background noise on speech production is complex and multifaceted. The noise type affected speakers' ability to initiate and maintain CS as well as the mental demand associated with the speech task. The results underscore the importance of considering the characteristics of background noise and cognitive aspects of speech production when training and evaluating speakers' performance.

Oracle text positioning system based on improved YOLOv7-FC model

In recent years, how to use computer to efficiently and accurately recognize oracle characters on oracle rubbings has become the focus of research. Due to the severe damage caused by the natural environment and the characteristics of complex background and loud noise, the oracle bone rubbings are difficult to separate the foreground and back of the oracle bone inscriptions. Therefore, it will be the future research direction to eliminate the negative impact of complex background and accurately locate the Oracle bone inscriptions. This paper puts forward the YOLOv7-FC neural network model and realizes the specific application in the oracle topology data set, and presents the system of single oracle character positioning results.The experimental results show that on the HWOBC-A dataset, the mAP value of the YOLOv7-FC neural network model proposed in this paper reaches 0.937. Compared with the original YOLOv7 model, it has improved by 0.22, effectively balancing the relationship between running speed and average accuracy, and providing feasibility for Oracle text localization machine learning method.

Characteristics of Background Noise in the Oldest-1 Array Deployed on the Oldest Part of the Pacific Plate

ABSTRACT We characterize the sources of background noise recorded by an ocean-bottom seismometer array deployed on the oldest oceanic lithosphere of the western Pacific plate. This igneous oceanic crust is blanketed up to 1000 m cover of sediments, making it a unique place to investigate correlations between sediment thickness and background noise levels in the marine environment. Although the data are limited, we found that the thicker sediments attenuated short-period noise levels and amplified long-period noise levels, in agreement with observations made for seismic arrivals in onshore sedimentary basins. Comparison of the noise levels from our Oldest-1 deployment with those (1) in the western Pacific basin, (2) near the Mariana trench, and (3) offshore New Zealand indicates that the Oldest-1 has the highest noise levels in the double-frequency (secondary) microseism band by up to 7 dB. This result was unexpected given the greater water depths (averaging 5800 m) for the Oldest-1 array. However, even at these great depths, we find a strong correlation between significant wave heights and background noise levels in the short-period microseism band (2–5 s). In addition, the Oldest-1 array was subjected to a great number of intense close typhoons than the other arrays and was proximal to a great number of taller seamounts than the other arrays. Finally, tracking noise source azimuths using station pairs led to the identification of a possible new source for the single-frequency microseism band in the coastal areas of Papua New Guinea and Australia.

Ball screw fault diagnosis based on continuous wavelet transform and two-dimensional convolution neural network

Due to extreme operating conditions such as high-speed and heavy loads, ball screws are prone to damages, that affect the accuracy and operational safety of the mechanical equipment. As strong background noise and weak fault characteristics, it is difficult to capture the inherent fault state only depending on the time-domain or frequency-domain information from the vibration signal. In this paper, a fault diagnosis method for the ball screw based on continuous wavelet transform (CWT) and two-dimensional convolutional neural network (2DCNN) is proposed. The noise-reducing vibration signal is obtained via CWT. The time-frequency graph of the noise reduction signal can more comprehensively reflect the fault information of the ball screw. The time-frequency graph is used as the input to train and test the 2DCNN. Finally, diagnosis results of different types of faults reveal that the proposed CWT-2DCNN fault diagnosis method can achieve an average recognition rate of 99.67%. Compared with one-dimensional convolutional neural network (1DCNN) and traditional BP neural network, the proposed method has fast network convergence and high recognition accuracy. Time-frequency graphs of the noise-reduced signal used as fault features for classification can effectively avoid the problem of uncertainty due to the manual extraction of features. The proposed method has high application potential in the field of ball screw pair fault diagnosis.

Active target classification using a shallow neural network with dimension reduction

Techniques for automatically identifying sonar targets using machine learning algorithms are being actively developed, and modern algorithms based on deep learning are often considered first. However, deep learning based algorithms require a lot of data to train a neural network, and if there is not enough data, overfitting easily occurs and the performance on real data can be degraded. In fact, in the case of active target detection where there is not enough data, it has been reported that using a shallow neural network after extracting appropriately designed features from the raw data showed better detection performance than applying deep learning directly to the raw data. With regard to this, we investigate the performance of a shallow neural network combined with dimensionality reduction techniques for active sonar target classification. In particular, several linear and nonlinear dimension reduction techniques are compared in terms of target classification performance, and the effects of the characteristics of background noise and the presence of reverberation on the target classification performance are discussed. [This work was supported by the research project PES4380 funded by KRISO.]

Effect of stimulus complexity on psychometric functions

Factors such as semantic and syntactic context, predictability, talker characteristics, background noise characteristics, and spatial differences between the target and maskers play major role in identifying speech in complex listening environments. Here, we present psychometric properties of two closed set speech corpus: (1) co-ordinate response measure (CRM) and (2) Boston University Corpus (BUC) obtained from a group of fifty young normal-hearing, listeners. CRM sentences are of the form “Ready CALLSIGN go to a COLOR NUMBER now” and BUC sentences are of the form “NAME VERB NUMBER ADJECTIVE NOUN.” Method of constant stimuli was used to obtain psychometric functions for the two corpora in the presence of speech and speech shaped noise. One half of the participants were presented the stimuli at a constant level (20 dB SL), while it was presented at comfortable listening levels for the half. The effect of the stimulus complexity, presentation level, and spatial configuration of the target and noise on the identification thresholds and the shape of the psychometric functions will be discussed.

Fault diagnosis of a planetary gearbox based on a local bi-spectrum and a convolutional neural network

The transmission paths of vibration signals in the planetary gearboxes are complex. The signals have the characteristics of strong background noise, instability and non-Gaussian. Bi-spectrums can suppress Gaussian colored noise and are suitable for vibration signal processing of planetary gearboxes. In the traditional fault diagnosis methods based on bi-spectrums, the amplitudes of fault characteristic frequency, or the other further quantitative calculations values, are generally used as the basis of fault diagnosis processes. It has been found that bi-spectrum images can directly characterize the faults of the planetary gearboxes. Convolutional neural networks (CNNs) have been used in mechanical fault diagnoses in recent years. One-dimensional original signals are converted into two-dimensional images as CNN input, which is an effective method for mechanical fault diagnoses. At the present time, there has not been any relevant research conducted using bi-spectral images as CNN input. In this study, a fault diagnosis method based on local bi-spectrum and CNN was proposed. A bi-spectral analysis of the vibration signals of the planetary gearbox was first carried out in order to reveal the fault information while retaining the non-Gaussian information. Then, according to the bi-spectrum symmetry, local images containing the main information were taken as the input of the CNN, which reduced the redundancy of the fault information. Then, in order to improve the diagnostic accuracy of the CNN, the key parameters of CNN architecture were optimized. Finally, a CNN diagnosis model was built to realize the classification diagnoses of different fault positions and different fault degrees of planetary gearboxes. This study’s comparison of the diagnosis results of the full bi-spectrum + CNN, original vibration signal + CNN, local bi-spectrum + (support vector machines), and local bi-spectrum + (stacked auto-encoder) showed that the proposed method in this study had achieved both accuracy and rapidity in the fault diagnoses of planetary gearboxes.

Improved Complete Ensemble Robust Local Mean Decomposition With Adaptive Noise for Slewing Bearings Performance Degradation Assessment

Signal de-noising is one of challenging tasks in the slewing bearing performance degradation assessment due to the strong background noise and early weak fault characteristics. Adaptive digital driven methods like local mean decomposition (LMD) and robust local mean decomposition (RLMD) are a promising approach to implement signal-noise separation. However, the mode mixing problem limits its practical applications. Based on noise-assisted approach, complete ensemble robust local mean decomposition with adaptive noise (CERLMDAN) is used to solve this problem. Then, statistic detection of kernel principle component analysis (KPCA) is employed to select fault components for reconstruction and de-noising. After that, square prediction error (SPE) is utilized for performance degradation assessment model establishment. In the decomposition process of CERLMDAN, moth-flame optimization (MFO) is proposed to optimize the noise amplitude and ensemble trials for the improvement of signal decomposition ability. Meanwhile, a comparison is conducted between ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), LMD, RLMD, CERLMDAN and MFO-CERLMDAN. The effectiveness of the proposed method is validated using numerical as well as experimental signals obtained through a slewing bearing highly accelerated life test. The results illustrate that MFO-CERLMDAN gets better result in signal de-noising, and SPE based on MFO-CERLMDAN-KPCA can assess the performance degradation of slewing bearing effectively.

Weak fault detection in rotating machineries by using vibrational resonance and coupled varying-stable nonlinear systems

In the era of Industry 4.0, weak faults on a critical component must be detected timely to ensure the reliability of a critical machine. However, in general, a weak fault signal cannot be easily detected due to the presence of strong background noise and weak fault characteristics. Thus, in this work, we investigate a new weak fault detection method based on the vibrational resonance (VR) and coupled varying-stable nonlinear systems. In contrast to most existing noise cancellation fault detection methods, the proposed method can be regarded as a noise utilization method. Furthermore, in contrast to the stochastic resonance (SR) technique, which is a noise utilization fault detection method, the proposed method is easily controllable and exhibits a better performance. Moreover, resonance can be easily and adaptively achieved, and the varying-stable systems can be controlled to exhibit monostable, bistable or tristable states, by tuning the system parameters. In this paper, the nonlinear systems are coupled; therefore, the nonlinear systems are dependent on each other, and the interactions among them are extensively considered. The proposed method is validated by considering an implanted gear fault on a planetary gearbox and several naturally developed bearing faults on a fixed axis gearbox. The mechanism of noise utilization is illustrated, and different nonlinear systems with different configurations are considered. The corresponding results are compared to validate the effectiveness and advantages of the proposed method.

Development of a Bidirectional-Excitation Eddy-Current Sensor With Magnetic Shielding: Detection of Subsurface Defects in Stainless Steel

To reduce the effects of surface characteristics of materials and background noise of a magnetic excitation field, a new sensor with magnetic shielding was developed for eddy-current (EC) testing. Based on the analysis of EC theory, the performances of different cores, including air core, solid cylindrical ferrite core, hollow cylindrical ferrite core, and magnetic shielding ferrite core, were analyzed. The simulation results show that the magnetic shielding core can increase the EC density in the test material. External magnetic field shielding can effectively improve the defects’ detection sensitivity and the detection depth of subsurface defects. According to the simulation results of finite-element excitation, a bidirectional-excitation EC sensor with magnetic shielding was prepared and optimized according to the orthogonal experiment. Finally, the experimental results show that it can effectively detect the notch defects of a 10-mm-thick stainless steel plate surface at a distance of 7 mm. A linear relationship was observed between the change of impedance amplitude, phase difference, and depth of subsurface defects.

Tools for assessing efficacy of hearing loss compensation

We are developing a real-time, wearable, Open-source Speech-Processing platform (OSP) that can be configured by audiologists and Hearing Aid (HA) researchers for lab and field studies. This contribution describes OSP tools to (i) control the HA state, such as amplification parameters in each subband, (ii) provide stimuli to the user, and (iii) get feedback from the user. The system is based on a web server with interfaces to the HA state, the environment state (e.g., background noise characteristics, reverberation conditions, GPS location, etc.) and the user state (as inferred from ecological momentary assessments). We describe Application Programmer’s Interfaces (APIs) used in Hypertext Markup Language (HTML) and server side scripting language (e.g., PHP) to create web applications aimed at assessing efficacy of various Hearing Loss (HL) compensation approaches. HTML and PHP are versatile tools and lot easier to develop functional skills compared with software tools used in the OSP. We provide example sc...

Improvements to infrasonic detection capabilities through application of a generalized least squares beamformer

A common challenge in seismoacoustic research and analysis is detection of low signal-to-noise ratio (SNR) signals in a variable and often coherent background. Previously, the application of an adaptive F-detector to infrasonic data has successfully reduced false detection rates attributed to coherent noise across array elements; however, the detector is applied post-processing after analysis using a standard (Bartlett) beamformer, which raises the detection threshold and can lead to missed detections. Application of a generalized least squares (GLS) beamformer can enhance processing of transient infrasonic signals, particularly in the presence of correlated noise, by adaptively accounting for the background noise characteristics. The GLS beamformer enhancement leads to improved capabilities for accurately detecting low amplitude infrasonic events of interest that would not normally produce a sufficiently high F-statistic to be declared a detection. A statistical analysis of GLS beamformer performance in ...

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil.

Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method.

Knowledge base operator support system for nuclear power plant fault diagnosis

A high-tech, high-performance system such as the nuclear power plant needs a wide range of support for operators to efficiently operate the plant, interpret and manage the volume of information available, and detect and diagnose fault in a timely manner. Increasingly, application of artificial neural networks and its variants for fault detection and isolation has moved from toy examples to real-world systems. However, different network architectures respond to different data set in different ways, and the complex, dynamic, high background noise, overlapping patterns and non-linear characteristics of Nuclear Power Plants (NPP) requires a careful selection of a suitable neural network architecture that reflects these traits. This work presents a pilot scheme towards the development of a comprehensive knowledge base for the operator support system of the Chinese Qinshan II NPP, using Principal Component Analysis (PCA) and artificial neural networks. In this work, we utilize the PCA method for noise filtering in the pre-diagnostic stage, and evaluate the predictive/regression capability of two different recurrent neural networks – The Elman neural network and the Radial Basis Network – on a representative data from Qinshan II NPP. The process was validated using data from different fault scenarios simulated on a desktop Pressurized Water Reactor simulator, and the fault signatures were used as the input. The predictive outputs required are the location and sizes of the faults. The result shows that the Radial Basis network gives better prediction and diagnoses the faults faster than Elman neural network. Some of the important diagnostic results obtained from the networks are presented in this paper, and they serve as the pilot study for the development of knowledge base for the computerized NPP operator support system.

Comprehensive Detection of Gas Plumes from Multibeam Water Column Images with Minimisation of Noise Interferences.

Multibeam echosounder systems (MBES) can record backscatter strengths of gas plumes in the water column (WC) images that may be an indicator of possible occurrence of gas at certain depths. Manual or automatic detection is generally adopted in finding gas plumes, but frequently results in low efficiency and high false detection rates because of WC images that are polluted by noise. To improve the efficiency and reliability of the detection, a comprehensive detection method is proposed in this paper. In the proposed method, the characteristics of WC background noise are first analyzed and given. Then, the mean standard deviation threshold segmentations are respectively used for the denoising of time-angle and depth-angle images, an intersection operation is performed for the two segmented images to further weaken noise in the WC data, and the gas plumes in the WC data are detected from the intersection image by the morphological constraint. The proposed method was tested by conducting shallow-water and deepwater experiments. In these experiments, the detections were conducted automatically and higher correct detection rates than the traditional methods were achieved. The performance of the proposed method is analyzed and discussed.

A Report on Upgraded Seismic Monitoring Stations in Myanmar: Station Performance and Site Response

ABSTRACT Myanmar is in a tectonically complex region between the eastern edge of the Himalayan collision zone and the northern end of the Sunda megathrust. Until recently, earthquake monitoring and research efforts have been hampered by a lack of modern instrumentation and communication infrastructure. In January 2016, a major upgrade of the Myanmar National Seismic Network (MNSN; network code MM) was undertaken to improve earthquake monitoring capability. We installed five permanent broadband and strong‐motion seismic stations and real‐time data telemetry using newly improved cellular networks. Data are telemetered to the MNSN hub in Nay Pyi Taw and archived at the Incorporated Research Institutions for Seismology Data Management Center. We analyzed station noise characteristics and site response using noise and events recorded over the first six months of station operation. Background noise characteristics vary across the array, but indicate that the new stations are performing well. MM stations recorded more than 20 earthquakes of M ≥4.5 within Myanmar and its immediate surroundings, including an M 6.8 earthquake located northwest of Mandalay on 13 April 2016 and the M w 6.8 Chauk event on 24 August 2016. We use this new dataset to calculate horizontal‐to‐vertical spectral ratios, which provide a preliminary characterization of site response of the upgraded MM stations.