Influence Of Background Noise Research Articles

A probabilistic approach for acoustic emission based monitoring techniques: With application to structural health monitoring

It has been demonstrated that acoustic-emission (AE), inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an increased sensitivity to damage, as well as an ability to localise its source. There are, however, numerous challenges associated with the analysis of AE data. One issue is the high sampling frequencies required to capture AE activity. In just a few seconds, a recording can generate very high volumes of data, of which a significant portion may be of little interest for analysis. Identifying the individual AE events in a recorded time-series is therefore a necessary procedure for reducing the size of the dataset and projecting out the influence of background noise from the signal. In this paper, a state-of-the-art technique is presented that can automatically identify cluster the AE events from a probabilistic perspective. A nonparametric Bayesian approach, based on the Dirichlet process (DP), is employed to overcome some of the challenges associated with this task. Additionally, the developed model is applied for damage detection using AE data collected from an experimental setup. Two main sets of AE data are considered in this work: (1) from a journal bearing in operation, and (2) from an Airbus A320 main landing gear subjected to fatigue testing.

Influence of background noise on spatial audio localization for helicopter pilot headsets

A helicopter pilot is subjected to high workloads. A high noise level in the cockpit further increases stress. This stress can be reduced through headsets for hearing protection including active noise control (ANC). An actual research topic is the improvement of the human-machine-interface by 3-D-audio systems. These systems can improve pilot’s situational awareness and, therefore, reduce stress. This paper presents a 3-D-audio system for headphones. This binaural system uses head-tracking to process spatial information about the location of the sound source. The spatial sensation is achieved by convolving the source signal with head-related transfer functions. This sensation is influenced by background noise, which can be reduced by ANC. A first listening test with physical sound sources provides information on the localizability of sound sources at different elevation angles. This is compared to a second listening test with virtual sound sources generated by the 3-D-audio system. In a third listening test, the virtual sound source is located at variable azimuth and elevation angles. The system is evaluated with respect to sound source localization and the influence of background noise and ANC. This study highlights the potential of the 3-D-audio system to improve the helicopter pilot’s situational awareness in spatial acoustic environments.

MF-Net: Automated Muscle Fiber Segmentation From Immunofluorescence Images Using a Local-Global Feature Fusion Network.

Histological assessment of skeletal muscle slices is very important for the accurate evaluation of weightless muscle atrophy. The accurate identification and segmentation of muscle fiber boundary is an important prerequisite for the evaluation of skeletal muscle fiber atrophy. However, there are many challenges to segment muscle fiber from immunofluorescence images, including the presence of low contrast in fiber boundaries in immunofluorescence images and the influence of background noise. Due to the limitations of traditional convolutional neural network-based segmentation methods in capturing global information, they cannot achieve ideal segmentation results. In this paper, we propose a muscle fiber segmentation network (MF-Net) method for effective segmentation of macaque muscle fibers in immunofluorescence images. The network adopts a dual encoder branch composed of convolutional neural networks and transformer to effectively capture local and global feature information in the immunofluorescence image, highlight foreground features, and suppress irrelevant background noise. In addition, a low-level feature decoder module is proposed to capture more global context information by combining different image scales to supplement the missing detail pixels. In this study, a comprehensive experiment was carried out on the immunofluorescence datasets of six macaques' weightlessness models and compared with the state-of-the-art deep learning model. It is proved from five segmentation indices that the proposed automatic segmentation method can be accurately and effectively applied to muscle fiber segmentation in shank immunofluorescence images.

Hydrodynamic feature extraction and intelligent identification of flow regimes in vaneless space of a pump turbine using improved empirical wavelet transform and Bayesian optimized convolutional neural network

Hydrodynamic feature extraction of pressure pulsation signals (PPSs) and intelligent identification of flow regimes in vaneless space (VAS) of a pump turbine (PT) are crucial to the safe and stable operations of the pumped storage power station. In this work, the scheme based on an improved empirical wavelet transform (IEWT), energy feature vector (EFV) and Bayesian optimized convolutional neural network (BOCNN) is innovatively proposed. Firstly, the IEWT is proposed by introducing the least square method and mathematical morphology to improve the decomposition shortcomings of existing methods. The phenomenon of mode aliasing is eliminated and the influence of background noise is greatly reduced, as verified by both simulated and measured PPSs. Secondly, based on the IEWT, several significant mode components are obtained, and the energy feature indexes of them are calculated to extract the hydrodynamic feature information and construct the EFVs that can accurately reflect the features of different flow regimes in the VAS. Then, the BO algorithm is adopted to optimize the important hyperparameters of CNN, and the intelligent identification model of BOCNN is constructed and trained to identify four typical types of flow regimes in the VAS. Finally, the average identification accuracy of the proposed IEWT-EFV-BOCNN scheme can reach 99.15%, which is much higher than traditional schemes, illustrating that the proposed scheme has significant engineering application value.

Transverse emittance measurement in 2D and 4D performed on a Low Energy Beam Transport line: benchmarking and data analysis

2D and 4D transverse phase-space of a low-energy ion-beam is measured with two of the most common emittance scanners. The article covers the description of the installation, the setup, the settings, the experiment and the benchmark of the two emittance meters. We compare the results from three series of measurements and present the advantages and drawbacks of the two systems. Coupling between phase-space planes, correlations and mitigation of deleterious effects are discussed. The influence of background noise and aberrations of trace-space figures on emittance measurements and RMS calculations is highlighted, especially for low density beams and halos. A new data analysis method using noise reduction, filtering, and reconstruction of the emittance figure is described. Finally, some basic concepts of phase-space theory and application to beam transport are recalled.

OLSA level control in monaural speech audiometry in background noise for the evaluation of the CI fitting result

Speech audiometry measurements under the influence of background noise are a fundamental part of evaluating the outcome of hearing care. As yet far, there are no recommendations for selecting a suitable method for adaptive speech audiometry measurements in background noise in cochlear implant (CI) care, so either the choice the adaptive level change of the speech signal (S) with constant noise (N) or the adaptive level change of N with constant S. Do the measurement results of the monaural speechrecognition threshold in noise (SRT) with the Oldenburg Sentence Test (OLSA) depend on the choice of level control? A total of 50 series of measurements with OLSA in noise and the Freiburg speech intelligibility test in quiet (FBE) on middle-aged CIpatients from clinical routine. There is no significant difference in the measurement results with different level controls when the SRT is less than 5 [Formula: see text]. Below 55 % monosyllabic intelligibility in quiet, the SRT in noise becomes greater than 5 [Formula: see text]. From a clinical, audiological and methodological point of view, it is advisable to carry out the adaptive monaural speech intelligibility measurement with a constant speech signal at 65 [Formula: see text].

Research on State Recognition Technology of Elevator Traction Machine Based on Modulation Feature Extraction

Vibration signal analysis of the traction machine is an important part of the current rotating machinery state recognition technology, and its feature extraction is the most critical step. In this study, the time-frequency characteristics of the vibration of the traction machine under different elevator running directions, running speeds and load weights are analyzed. The novel demodulation method based on time-frequency analysis and principal component analysis (DPCA) is used to extract the periodic modulated wave signal. In order to compare different influence of background noise and unknown frequency influence, the Fast Fourier Transform (FFT) and Short Time Fourier Transform (STFT) methods are used to extract the characteristics of the traction machine vibration signal, respectively. Under different load conditions, it is difficult to observe the obvious differences and similarities of the vibration signals of the traction machine by time-frequency method. However, the DPCA demodulation method provides a guarantee for the reliability and accuracy of the state identification of the traction machine.

Evaluating acoustic signals to reduce avian collision risk.

Collisions with human-made structures are responsible for billions of bird deaths each year, resulting in ecological damage as well as regulatory and financial burdens to many industries. Acoustic signals can alert birds to obstacles in their flight paths in order to mitigate collisions, but these signals should be tailored to the sensory ecology of birds in flight as the effectiveness of various acoustic signals potentially depends on the influence of background noise and the relative ability of various sound types to propagate within a landscape. We measured changes in flight behaviors from zebra finches released into a flight corridor containing a physical obstacle, either in no-additional-sound control conditions or when exposed to one of four acoustic signals. We selected signals to test two frequency ranges (4–6 kHz or 6–8 kHz) and two temporal modulation patterns (broadband or frequency-modulated oscillating) to determine whether any particular combination of sound attributes elicited the strongest collision avoidance behaviors. We found that, relative to control flights, all sound treatments caused birds to maintain a greater distance from hazards and to adjust their flight trajectories before coming close to obstacles. There were no statistical differences among different sound treatments, but consistent trends within the data suggest that the 4–6 kHz frequency-modulated oscillating signal elicited the strongest avoidance behaviors. We conclude that a variety of acoustic signals can be effective as avian collision deterrents, at least in the context in which we tested these birds. These results may be most directly applicable in scenarios when birds are at risk of collisions with solid structures, such as wind turbines and communication towers, as opposed to window collisions or collisions involving artificial lighting. We recommend the incorporation of acoustic signals into multimodal collision deterrents and demonstrate the value of using behavioral data to assess collision risk.

Filtering Deep Convolutional Features for Image Retrieval

In image retrieval, highlighting target object and reducing the influence of background noise remains challenging. To address this problem, we propose a novel weighting method that aggregates deep convolutional features based on filtering, called filtering on spatial channel weighting (FSCW) factors, to represent image contents, and utilize it for image retrieval. There are three main contributions of this study. First, the designed filter can effectively remove the influence of background noise. Second, we propose a new channel selection and spatial weighting method, which can accurately distinguish target object from the background noise. Finally, we designed a new channel weighting strategy to suppress intra-image visual burstiness. Experimental results on benchmark datasets demonstrate that the proposed method effectively enhances discriminative power and outperforms some existing state-of-the-art methods in terms of the mAP metric. Furthermore, the proposed method is superior to some existing algorithms in distinguishing background noise and target object.

Passive acoustic monitoring for detecting the Yellow-bellied Glider, a highly vocal arboreal marsupial.

Passive acoustic monitoring (PAM) is increasingly being used for the survey of vocalising wildlife species that are otherwise cryptic and difficult to survey. Our study aimed to develop PAM guidelines for detecting the Yellow-bellied Glider, a highly vocal arboreal marsupial that occurs in native Eucalyptus forests in eastern and south-eastern Australia. To achieve this, we considered the influence of background noise, weather conditions, lunar illumination, time since sunset and season on the probability of detecting vocalisations. We deployed Autonomous Recording Units (ARUs) at 43 sites in the Central Highlands of Victoria during two periods: spring/summer (October 2018 to January 2019), and autumn/winter (May to August 2019). ARUs were programmed to record for 11 hours from sunset for 14 consecutive days during each period. Background noise resulted from inclement weather (wind and rain) and masked vocalisations in spectrograms of the recordings, thus having the greatest influence on detection probability. Vocalisations were most common in the four hours after sunset. Rainfall negatively influenced detection probability, especially during the autumn/winter sampling period. Detection of Yellow-bellied Gliders with PAM requires deploying ARUs programmed to record for four hours after sunset, for a minimum of six nights with minimal inclement weather (light or no wind or rain). The survey period should be extended to 12 nights when rain or wind are forecast. Because PAM is less labour intensive than active surveys (i.e., spotlighting and call playbacks with multiple observers and several nights’ survey per site), its use will facilitate broad-scale surveys for Yellow-bellied Gliders.

BER and Channel Capacity Performance of an FSO Communication System over Atmospheric Turbulence with Different Types of Noise.

The propagation performance of a free-space optical (FSO) communication system in an atmospheric environment is restricted and degraded due to the influence of atmospheric turbulence. In this paper, both the lognormal and Gamma–Gamma channel models are employed to characterize this turbulence under weak-to-strong conditions. In addition, the average bit error rate and average channel capacity of an FSO communication system under the influence of background noise, thermal noise and quantum noise (resulting from the environment, the device, manual operation, etc.) are considered. Moreover, the comparison of system performance under different turbulence conditions and various noises are conducted. Simulation results reveal that thermal noise has a dominant effect on the FSO system. In addition, both the channel parameters and the system parameters have a significant influence on the performance of an FSO communication system.

High‐Performance Voice Recognition Based on Piezoelectric Polyacrylonitrile Nanofibers

AbstractAcoustic sensors show a wide application in diverse fields. However, it is still a challenge to develop highly sensitive transducing materials for the detection of low‐to‐middle decibel sounds. Herein, for the first time the remarkable property of an acoustic sensor based on electrospun polyacrylonitrile (PAN) nanofibers in the recognition of human voices is reported. The nanofiber device shows a sensitivity as high as 23 401 mV P−1 to 70 dB sound, which is much higher than most of the acoustic sensors reported in the literature. First the sounds from different musical instruments are used as a standard sound source to show the high accuracy of the nanofiber device to distinguish different sounds, e.g., from both the same and different instruments, and then people's voices are tested. Experimental results show that the device can distinguish people's voices with high resolution and the influence of background noise on speech recognition is very small. The acoustic sensor is stable and can be used in sound detection for a long time. Electrospun PAN membranes may be useful for the development of voice recognition systems for security, environmental protection, scientific research, and other high‐tech applications.

Condition monitoring of deep-hole drilling process based on improved empirical wavelet de-noising and high multiple frequency components of rotation frequency

The machining condition monitoring is an important task to improve product quality and avoid economic losses in deep-hole drilling. Out-of-roundness-tolerance is one of major defects in deep-hole drilling process. In this work, the spindle vibration signals generated in deep-hole drilling process are studied to seek the characteristics which can reveal the roundness error change. Considering the influence of background noise on monitoring effect and the sensitivity of high multiple frequency components of spindle vibration signal towards the roundness error change, an improved empirical wavelet de-noising method is proposed to remove the noise of the spindle vibration signal. The high multiple frequency components of the rotation frequency are extracted from the de-noised spindle vibration signal. Firstly, the spindle vibration signal is decomposed by empirical wavelet transform into a set of wavelet coefficients, and then an improved thresholding function is proposed to process the Fourier spectrum of wavelet coefficients for removing the noise from the wavelet coefficients. Secondly, the wavelet coefficients are reconstructed based on the de-noised wavelet coefficients, and the wavelet coefficients which contain high multiple components of rotation frequency are selected. Finally, the energy entropy of the selected wavelet coefficients is calculated as a roundness error monitoring indicator to detect the roundness error of deep hole. The proposed method is verified with well-designed deep-hole drilling tests. The test results show that the monitoring indicator can effectively reflect the roundness error change in deep-hole drilling process.

Improved empirical wavelet denoising algorithm with application to whirling detection in deep hole drilling process

This paper investigates the empirical wavelet denoising algorithm, to extract the high multiple frequency signals of rotation frequency from the spindle vibration signal in deep hole drilling. With the characteristic of adaptive frequency partition, empirical wavelet transform is introduced to decompose the vibration signal. Considering the influence of background noise on the vibration signal, an improved threshold denoising method is proposed to remove the noise of the spindle vibration signal. Thus, an improved empirical wavelet denoising algorithm is proposed for the high multiple frequency signals of rotation frequency extraction. The improved empirical wavelet denoising algorithm is combined with energy entropy to detect whirling in deep hole drilling process.

Human-robot-interaction using cloud-based speech recognition systems

Progress in natural speech processing has enabled significantly more powerful speech processing systems, primarily due to the use of machine learning technologies. In order to integrate cloud-based speech recognition systems for human-robot interaction, an interface for the voice control of a lightweight robot was developed. The main contribution of this work is the design and implementation of a software interface to recognize commands via cloud-based speech processing and the subsequently conversion into machine-readable code. Requirements for the evaluation of different cloud-services for the control of robots are determined. Furthermore, the control architecture for the robot is modeled and implemented. An example application, which enables users to control robot movements via speech, is realized as a proof of concept and for additional studies. This application includes the basic features of cloud-based speech processing: intent recognition from utterances, slot filling and dialogue-based interaction. Lastly, the influence of background noise on process safety was examined within an experiment. It turns out that a feasible process reliability can be achieved with the system despite the presence of background noises.

Calibration issues for a three-band microwave radiometric system with background noise compensation

For microwave radiometric remote sensing systems in natural environments, the important issue is calibration, i.e. determination-one correspondence of values of the output signal value of the brightness temperature of the sensed region, the largest of which evaluated physical parameters. Microwave radiometric systems operate under the influence of external interference, for example, background noise – radiothermal radiation of the space surrounding the system, received through the scattering region of the antenna pattern. This article examines the influence of background noise on the external calibration of a microwave radiometric system (based on an external “deterministic” noise source) and analyzes the conditions for performing such calibration in a system with compensation for the influence of background noise. The analysis of the output signal of the microwave radiometric system showed a significant influence of background noise on the parameter determined during calibration of the system – the beginning of the receiver scale, due to a change in the value of the contribution of background noise to the output signal when forming two reference levels during calibration from two angular directions. The possibility of reducing the interference effect of background noise on the results of measurements in a microwave radiometric system with a special two-channel antenna operating on two modes of a circular waveguide, with the formation of an additional compensation signal at the antenna output, is shown. The proposed version of the analytical evaluation of the degree of compensation of the influence of background noise on the output differential signal of the system and presents results of its application to numerical analysis of error in external calibration a tri-band microwave radiometric system with compensation of background noise when receiving thermal radiation onthe total aperture of the antenna.

Transmission line image defect diagnosis based on YOLOv3 and SNIPER cascade lifting algorithm

Deep learning classifies information and combines the underlying features to form more abstract high-level features. Its ability of autonomous learning also provides favorable conditions for industrial application. Aiming at the problem of insufficient performance of current small target detection, this paper proposes a deep learning algorithm using One-stage fusion two-stage. The influence of background noise on network training is reduced. The algorithm combines multi-scale training methods and achieves sample equilibrium adjustment based on sample distribution and stratified sampling. The high efficiency of YOLOv3 method for large target detection is well used, and the multi-scale training advantages of SNIPER algorithm are also played out, which makes the intelligent detection effect of pin defect of transmission line get some performance improvement.

Decoupling of Multiple Concurrent Faults for Diagnosing Coal Cutter Gearboxes: An Extensive Experimental Investigation With Multichannel Sensor Measurements

Abstract Due to harsh operating environments in underground coal seams, the key components (e.g., gear pairs and bearings) in the power transmission systems of coal cutters suffer from extreme wear and functional damages. To guarantee the safe and reliable operation of the coal cutters, it is important to monitor the condition of their transmission systems and detect possible faults in a timely manner. A challenging task here is to diagnose multiple concurrent faults. A literature review indicates that the current interests lie on the decoupling of multiple co-existing faults and that the very limited work has been done to deal with the dependence/correlation between the fault signals. To address this issue, this work extends our previous work on gear crack detection using the bounded component analysis (BCA) and proposes an improved BCA-based approach for decoupling hybrid faults with high dependence/correlation in coal cutter transmission systems. The proposed approach incorporates the Vold–Kalman order tracking and spectral kurtosis into an improved BCA framework (OTBCA-SK). Owing to the uniform sampling of order tracking, the influence of background noise and rotational speed variation on vibration signals can be effectively reduced. Since BCA is capable of handling vibration sources that are statistically dependent, OTBCA-SK can decouple both independent and dependent source signals. As a result, the vibration sources excited by hybrid faults, although maybe dependent/correlated, can be fully decoupled into single-fault vibration source signals. Three specially designed case studies were used to evaluate the effectiveness of the proposed OTBCA-SK approach in decoupling hybrid gear faults. The analysis results demonstrate better performance of hybrid fault decoupling using OTBCA-SK than that of three representative techniques, i.e., Erdogan's BCA (E-BCA), joint approximate diagonalization of eigen matrices (JADE) and fast independent component analysis (FastICA). These case studies also suggest that the proposed OTBCA-SK approach can retain the physical meaning of the original vibration and is hence suitable for hybrid fault diagnosis in practical applications.

Micro‐Doppler separation of multi‐target based on ACO in midcourse

To solve the problem of mutual superposition and interference of multi-target micro-Doppler in midcourse, a method based on modified ant colony optimisation (ACO) is proposed. On the basis of building 3D time–frequency map, pheromone on each spot in the workspace can be updated, according to the changing rule of micro-Doppler. The pheromone leads ant to seek for required optimal micro-Doppler path gradually; thus single micro-Doppler curve corresponding to each scattering point can be separated. Simulation results indicate that the proposed method can overcome the coupling between micro-Doppler of different scattering point and the influence of background noise better, and implement effective separation of multi-target.

Dynamic monitoring of a super high-rise structure based on GNSS-RTK technique combining CEEMDAN and wavelet threshold analysis

To capture the dynamic characteristics of a super high-rise structure under construction (i.e. Tianjin 117 Tower), multi-constellation GNSS-RTK (global navigation satellite system – real time kinematic) sensors are employed to derive the displacement information of the structure. A combined approach (CEEMDAN–WT) of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold (WT) technique is put forward to weaken the influence of background noise of sensors. For the sake of testing the effect of noise reduction via employing CEEMDAN–WT, a nonlinear signal with additive noise is introduced. Subsequently, the natural frequencies and the corresponding damping ratios of the structure are extracted based on the fast Fourier transform (FFT) and random decrement technique. In addition, the finite element model (FEM) of the structure is built to forecast the natural frequencies via modal analysis. The results show that: (1) The dynamic displacement information of super high-rise structures can be effectively determined based on GNSS-RTK technique; (2) The presented CEEMDAN–WT approach outperforms the single CEEMDAN and WT approaches; (3) The result obtained via the field measurement coincides with the FEM.