Sensitive Frequency Bands Research Articles

A Sensitive Frequency Band Study for Distributed Acoustical Sensing Monitoring Based on the Coupled Simulation of Gas–Liquid Two-Phase Flow and Acoustic Processes

The sensitivity of gas and water phases to DAS acoustic frequency bands can be used to interpret the production profile of horizontal wells. DAS typically collects acoustic signals in the kilohertz range, presenting a key challenge in identifying the sensitive frequency bands of the gas and water phases in the production well for accurate interpretation. In this study, a gas–water two-phase flow–acoustic coupling model for a horizontal well is developed by integrating a gas–water separation flow model with a pipeline acoustic model. The model simulates the sound pressure level (SPL) and amplitude variations of acoustic waves under different flow patterns, spatial locations, and gas–water ratio schemes. The results demonstrate that within the same flow pattern, an increase in the gas–water ratio significantly elevates acoustic amplitude and SPL peaks within the 5–50 Hz frequency band. Analysis of oil field DAS data reveals that the amplitude response range for stages with a lower gas–water ratio falls within 5–10 Hz, whereas stages with a higher gas–water ratio exhibit an amplitude response range of 10–50 Hz.

A high sensitivity and wide frequency band vector hydrophone using PZT-based four spiral beam structure

The utilization of a high sensitivity and wide frequency band vector hydrophone exhibits significant potential in the exploration of underwater target. However, enhancing these performance remains a challenge due to limitations in materials and structures. Herein, we report a micro-electro-mechanical system (MEMS) vector hydrophone (FSPVH) built on lead zirconate titanate (PZT) film with four spiral beam structure and polyamide (PA) microsphere. The optimization of the structural dimensions and PZT thin film distribution has been the result of simulation. The FSPVH have been fabricated using MEMS technology, and the prototype has been assembled. The FSPVH was tested in a standing bucket wave calibration system. The results show that FSPVH achieve a sensitivity of −181.5 dB (0 dB = 1 V/μPa) at 1100 Hz, and the work bandwidth is 20–1330 Hz. Meanwhile, it exhibits a satisfactory “8″ shape directivity. This work provide theory and technique supports of vector hydrophone with high sensitivity and wide frequency band.

Performance prediction of industrial robot harmonic reducer via feature transfer and Gaussian process regression

This paper addresses the problem of identifying faults in the harmonic reducers of industrial robots by analysing their vibration signals. In order to solve the problem of obtaining fault data and rotation error from harmonic reducers in service, an accuracy performance prediction method based on transfer learning and Gaussian process regression (GPR) is proposed. The Euclidean distance between the spectral sequence of each component is proposed as the fitness index to optimise the transition bandwidth of the filter banks. The optimised empirical wavelet transform (OEWT) is used for signal decomposition to obtain sensitive frequency bands. A feature transfer method based on semi-supervised transfer component analysis (SSTCA) is proposed to achieve target domain feature transfer under missing data conditions. A prediction model based on GPR is established using the mapped features to predict the performance and accuracy of the harmonic reducer. The effectiveness of the proposed method is verified through model evaluation indicators and degradation experiments.

A Frangi filter aided deep learning approach for palaeochannel recognition

SUMMARY In view of the problem of identifying palaeochannels with high concealment and complex geological structures, this paper proposes an intelligent recognition method for palaeochannels based on Frangi filtering and deep learning. The methodology makes use of Maximum Entropy Wigner–Ville Distribution (MEWVD) method to process the original instantaneous amplitude data, which enhance the distinct features of micro palaeochannels in different sensitive frequency bands. The sample 2-D stratigraphic images generated from these data is labelled for the pre-training process of Attention R2U-Net neural network model. Subsequently, Frangi filter is used to identify and enhance the linear structures of river channels in target stratigraphic images, improving the identification effect of the neural network. Finally, RGB data fusion and 3-D visualization carving are performed on the identification data. This method not only eliminates redundant information using the Frangi filter but also proves that the Attention R2U-Net network model structure with attention mechanism and residual convolution structure can effectively improve the segmentation effect for river channels at different scales. Experimental examples show that this method achieves pixel-level feature segmentation of 3-D seismic data for palaeochannels, accurately depicting their shape, width, thickness, flow direction and other features, thus providing support for subsequent well deployment and horizontal well fracturing selection.

An Intelligent Method Based on Time–Frequency Analysis and Deep Learning Semantic Segmentation for Investigating the Electromagnetic Pulse Features of Engine Digital Controllers

A novel intelligent extraction method based on time–frequency analysis and deep learning semantic segmentation were proposed for detailing electromagnetic pulse (EMP) sensitive frequency bands of engine digital controllers. In this method, wavelet transformation was performed to convert EMP-coupled signals into image data sets. Two semantic segmentation networks, namely fully convolutional networks and DeepLabV3+, were used to train and verify the obtained image data sets. The performance of the two models was compared using the confusion matrix and Kappa coefficient. Threshold segmentation, binarization, and average pooling operations were sequentially performed on the feature map output of the networks, and EMP characteristic pixel percentage was calculated using the time–frequency map. The original image was segmented by using the result of the binarization of the feature map output by the last layer of the network as a mask. This technique is immune to the interference caused by noise pixels when calculating the spectrum index. The proposed method has improved the EMP feature extraction accuracy of the model, and the overall performance of the DeepLabV3+ network model is proper. It was performed to obtain a theoretical basis for subsequent targeted EMP protection.

Capsule-Like Smart Aggregate with Pre-Determined Frequency Range for Impedance-Based Stress Monitoring.

In this article, a new capsule-like smart aggregate (CSA) is developed and verified for impedance-based stress monitoring in a pre-determined frequency range of less than 100 kHz. The pros and cons of the existing smart aggregate models are discussed to define the requirement for the improved CSA model. The conceptual design and the impedance measurement model of the capsule-like smart aggregate (CSA) are demonstrated for concrete damage monitoring. In the model, the interaction between the CSA and the monitored structure is considered as the 2-degrees of freedom (2-DOF) impedance system. The mechanical and impedance responses of the CSA are described for two conditions: during concrete strength development and under compressive loadings. Next, the prototype of the CSA is designed for impedance-based monitoring in concrete structures. The local dynamic properties of the CSA are numerically simulated to pre-determine the sensitive frequency bands of the impedance signals. Numerical and experimental impedance analyses are performed to investigate the sensitivity of the CSA under compressive loadings. The changes in the impedance signals of the CSA induced by the compressive loadings are analyzed to assess the effect of loading directions on the performance of the CSA. Correlations between statistical impedance features and compressive stresses are also made to examine the feasibility of the CSA for stress quantification.

Enhancing Strain-Sensing Properties of the Conductive Hydrogel by Introducing PVDF-TrFE

Conductive hydrogels have attracted attention because of their wide application in wearable devices. However, it is still a challenge to achieve conductive hydrogels with high sensitivity and wide frequency band response for smart wearable strain sensors. Here, we report a composite hydrogel with piezoresistive and piezoelectric sensing for flexible strain sensors. The composite hydrogel consists of cross-linked chitosan quaternary ammonium salt (CHACC) as the hydrogel matrix, poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT: PSS) as the conductive filler, and poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) as the piezoelectric filler. A one-pot thermoforming and solution exchange method was used to synthesize the CHACC/PEDOT: PSS/PVDF-TrFE hydrogel. The hydrogel-based strain sensor exhibits very high sensitivity (GF: 19.3), fast response (response time: 63.2 ms), and wide frequency range (response frequency: 5-25 Hz), while maintaining excellent mechanical properties (elongation at break up to 293%). It can be concluded that enhanced strain-sensing properties of the hydrogel are contributed to both greater change in the relative resistance under stress and wider response to dynamic and static stimulus by adding PVDF-TrFE. This has a broad application in monitoring human motion, detecting subtle movements, and identifying object contours and a hydrogel-based array sensor. This work provides an insight into the design of composite hydrogels based on piezoelectric and piezoresistive sensing with applications for wearable sensors.

Pseudo-Signal Interference Regularity of Single-Frequency Electromagnetic Radiation to Stepped-Frequency Radar

When typical radar equipment is subjected to single-frequency electromagnetic radiation, the radar display interface forms a pseudo-signal, resulting in the misjudgment of real targets. Based on the working principle of stepped-frequency radar ranging, the effect mechanism of radar equipment pseudo-signal interference is revealed. Taking a Ku band stepped-frequency ranging radar as the test object, the pseudo-signal interference effect test of single-frequency electromagnetic radiation is carried out in this study. The pseudo-signal level value of 6 dBmV is selected as the sensitive criterion of the pseudo-signal interference effect. Through experiments, the variation curves of the pseudo-signal level values of the sensitive frequency bands and the typical frequency points inside and outside the band with the field strength of the single-frequency interference are obtained. Based on the nonlinear distortion analysis of the receiving circuit, the variation laws of the pseudo-signal level values inside and outside the band are explained, respectively. The experimental results show that there are at least seven pseudo-signal interference-sensitive bands in the tested radar, and the first pseudo-signal strength is only related to the interference signal strength. The essence of the second type of pseudo-signal interference is intermodulation interference, and the pseudo-signal level is related to the interference signal and the useful signal strength.

Polarization angle requirements for CMB B-mode experiments. Application to the LiteBIRD satellite

A methodology to provide the polarization angle requirements for different sets of detectors, at a given frequency of a CMB polarization experiment, is presented. The uncertainties in the polarization angle of each detector set are related to a given bias on the tensor-to-scalar ratio r parameter. The approach is grounded in using a linear combination of the detector sets to obtain the CMB polarization signal. In addition, assuming that the uncertainties on the polarization angle are in the small angle limit (lower than a few degrees), it is possible to derive analytic expressions to establish the requirements. The methodology also accounts for possible correlations among detectors, that may originate from the optics, wafers, etc. The approach is applied to the LiteBIRD space mission. We show that, for the most restrictive case (i.e., full correlation of the polarization angle systematics among detector sets), the requirements on the polarization angle uncertainties are of around 1 arcmin at the most sensitive frequency bands (i.e., ≈ 150 GHz) and of few tens of arcmin at the lowest (i.e., ≈ 40 GHz) and highest (i.e., ≈ 400 GHz) observational bands. Conversely, for the least restrictive case (i.e., no correlation of the polarization angle systematics among detector sets), the requirements are ≈ 5 timesless restrictive than for the previous scenario. At the global and the telescope levels, polarization angle knowledge of a few arcmins is sufficient for correlated global systematic errors and can be relaxed by a factor of two for fully uncorrelated errors in detector polarization angle. The reported uncertainty levels are needed in order to have the bias on r due to systematics below the limit established by the LiteBIRD collaboration.

Noise Reduction Characteristics of Macroporous Asphalt Pavement Based on A Weighted Sound Pressure Level Sensor.

Based on the manual of macroporous noise-reducing asphalt pavement design, the indoor main drive pavement function accelerated loading test system was applied to investigate the impact of speed, loading conditions (dry and wet) and structural depth on the noise reduction of macroporous Open Graded Friction Course (OGFC) pavement, as well as its long-term noise reduction. Combined with the noise spectrum of the weighted sound pressure level, the main components and sensitive frequency bands of pavement noise under different factors were analyzed and compared. According to experimental results, the noise reduction effect of different asphalt pavements from strong to weak is as follows: OGFC-13 > SMA-13 > AC-13 > MS-III. The noise reduction effect of OGFC concentrates on the frequency of 1–4 kHz when high porosity effectively reduces the air pump effect. As the effect of wheels increases and the depth of the road structure decreases, the noise reduction effect of OGFC decreases. It indicates the noise reduction performance attenuates at a later stage, similar to the noise level of densely graded roads.

Terahertz wave up-conversion detection based on organic nonlinear optical crystals

Laser pumped terahertz (THz) wave up-conversion detection with high sensitivity, fast responsivity and wide frequency band is achieved at room temperature, based on home-made organic nonlinear crystals 4-N,N-dimethylamino-4′-N′-methyl-stilbazolium tosylate (DAST). Green laser pulses pumped KTiOPO<sub>4</sub> optical parametric oscillators are utilized as the sources of dual-wavelength near-infrared (NIR) beams (1.3–1.6 μm, for THz-wave difference frequency generation (DFG)) and a single NIR beam (1.2–1.4 μm, for up-conversion detection). The nonlinear medium for both THz-DFG and detection is DAST (grown by CETC-46). A nanosecond-time-resolved THz pulse is obtained with an InGaAs p-i-n photo-diode. The spectrum of the up-converted NIR light is acquired, which allows us to measure the THz frequency indirectly. The sensitivity (also at room temperature) is 4 orders better at 19 THz than the sensitivity of a commercial thermal detector (Golay Cell). The wide frequency band operation is realized with different sets of band-pass filters, which cover the entire range from 3.15 to 29.82 THz except 8.4 THz of the strong absorption peak of DAST. The dynamic range of a THz source based on DFG can be commonly improved by 2–3 orders, by changing the traditional thermal detector with the up-conversion detection. The presented technology can promote the applications of DFG THz source in the fields of high-resolution spectroscopy and imaging.

Research on Bearing Fault Monitoring Technology Based on the Acoustic Array in Working Environment

At present the most widely used method in mechanical fault diagnosis is feature extraction based on vibration signal analysis, but the test results are not accurate. Fault diagnosis method based on acoustic measurement can overcome this shortcoming, although traditional acoustic measurement based on single channel is susceptible to interference and it is difficult to obtain effective signals under strong background noise or complex testing environment. Directional noise measurement technology based on acoustic array and beamforming method can effectively suppress background noise. In this paper, the 3D constant beam width beam forming array is used to extract the broadband radiated noise from the monitored equipment and the sensitive frequency bands corresponding to different fault stages of bearings are preprocessed to obtain the corresponding eigenvectors. BP Neural Network is used to identify the fault of the tested bearings. The results show that the 3D microphone array based on constant beam width beam forming can effectively suppress the interference of environmental noise to ensure the validity of monitoring data and improve the reliability of fault diagnosis.

Frequency content of environmental variability and extinction risk of age-structured populations: Chinook salmon (Oncorhynchus tschawytscha) as an example

Investigations into the influence of environmental variability on population extinction have assessed the role of noise “color” (e.g., “white” or “reddened”), but the influence of spectral sensitivity of age-structured populations on extinction risk is not well understood. Recent findings indicate that age-structured populations are more sensitive to environmental variability that occurs on time scales near their mean age of spawning and on long time scales, particularly when populations are at low abundance. This raises the question of how variability at those sensitive time scales would influence extinction risk. We used an age-structured population model with density-dependent recruitment based on the life history of spring-run Chinook salmon, as an example, to investigate how the spectrum of environmental variability influences (1) variability of spawning female abundance, (2) the probability of quasi-extinction, and (3) the distribution of times to quasi-extinction. Population simulations were run with different spectra to investigate the influence of: (a) low frequencies, (b) frequencies equal to the inverse of the mean age of spawning (generational time scales), (c) both of these at once, and (d) “reddened” white noise. Results showed that even though variability on generational time scales causes greater population variability than other time scales, by itself, it does not increase probability of quasi-extinction. However, environmental variability at the other peak in sensitivity at low frequencies increased extinction in a way similar to the reddened signal. Environmental variability distributed over both sensitive frequency bands (i.e., low frequency and cohort frequencies) actually had less effect on extinction than low frequency variability alone.

Design and Implementation of T-type MEMS heart sound sensor

In view of low sensitivity of traditional stethoscope and valuableness of existing intelligent electronic stethoscope, a T-type MEMS heart sound sensor with high sensitivity, low cost and high SNR is designed, which is combined with MEMS technology, bionic principle, piezoresistive principle and Wheatstone bridge detection principle. Firstly, working principle of T-type MEMS heart sound sensor is introduced; Secondly, theoretical model of T-type sensitive microstructure is established, and its performance is simulated and analyzed by ANSYS finite element simulation software, then the size of T-type sensitive microstructure is determined and T-type MEMS heart sound sensor is made. Finally, T-type MEMS heart sound sensor prototype is tested. The results of tests show that: sensitivity and working frequency band of T-type MEMS heart sound sensor are respectively -183dB@500 Hz and 0–1000 Hz, which have verified correctness and practicability of T-type microstructure. In addition, the acoustic properties of S1 and S2 are analyzed from time domain and frequency domain, which indicates that heart sound signals detected by T-type MEMS heart sound sensor are highly consistent with that of 3200-type electronic stethoscope of 3 M company. Moreover, SNR of T-type MEMS heart sound sensor is 7.9 dB higher than 3200-type electronic stethoscope of 3 M company. The above shows that the T-type MEMS heart sound sensor can provide a powerful basis for early accurate diagnosis of cardiovascular disease and has broad prospects in the field of heart sound detection.

Non-invasive decoding of hand movements from electroencephalography based on a hierarchical linear regression model.

A non-invasive brain-computer interface (BCI) is an assistive technology with basic communication and control capabilities that decodes continuous electroencephalography (EEG) signals generated by the human brain and converts them into commands to control external devices naturally. However, the decoding efficiency is limited at present because it is unclear which decoding parameters can be used to effectively improve the overall decoding performance. In this paper, five subjects performed experiments involving self-initiated upper-limb movements during three experimental phases. The decoding method based on a hierarchical linear regression (HLR) model was devised to investigate the influence of decoding efficiency according to the characteristic parameters of brain functional networks. Then the optimal set of channels and most sensitive frequency bands were selected using the p value from a Kruskal-Wallis test in the experimental phases. Eventually, the trajectories of free movement and conical helix movement could be decoded using HLR. The experimental result showed that the Pearson correlation coefficient (R) between the measured and decoded paths is 0.66 with HLR, which was higher than the value of 0.46 obtained with the multiple linear regression model. The HLR from a decoding efficiency perspective holds promise for the development of EEG-based BCI to aid in the restoration of hand movements in post-stroke rehabilitation.

Design of high performance copyright protection watermarking based on lifting wavelet transform and bi empirical mode decomposition

This paper developed new and efficient image watermarking scheme for copyright protection based on Lifting wavelet transform (LWT) and Bi- dimensional Empirical Mode Decomposition (BEMD). A LWT has been selected because it is fast, less computational cost and maintains the integrity of the recovered watermark. The BEMD transform can separate the image from the most robust to the least sensitive or fragile frequency bands. This advantage is utilised in this study for the purpose of embedding the watermark in the robust part of BEMD, i.e. the residue (r). In addition, the embedding process has been performed in the low sub-band of LWT decomposed image as the low sub-band is more robust to image processing such as JPEG compression. The robust watermark which is grey scale image is decomposed using DWT to enhance the security and select only high sub-band as it has less impact on the quality of the watermarked image. As a result, the original image’s visual quality can be preserved and the concealed watermark could be successfully retrieved even if the watermarked images have undergone severe attacks like JPEG, rotation, Gamma correction, filtering, additive noise, translation, shearing, and scaling. Furthermore, the improved scheme offers greater robustness against many image processing operations, in comparison to the current schemes about copyright protection.

High-frequency random vibration analysis of a high-speed vehicle–track system with the frequency-dependent dynamic properties of rail pads using a hybrid SEM–SM method

ABSTRACTA hybrid Spectral Element Method (SEM)–Symplectic Method(SM) method for high-efficiency computation of the high-frequency random vibrations of a high-speed vehicle–track system with the frequency-dependent dynamic properties of rail pads is presented. First, the Williams-Landel-Ferry (WLF) formula and Fractional Derivative Zener (FDZ) model were, respectively, applied for prediction and representation of the frequency-dependent dynamic properties of Vossloh 300 rail pads frequently used in China's high-speed railway. Then, the proposed hybrid SEM–SM method was used to investigate the influence of the frequency-dependent dynamic performance of Vossloh 300 rail pads on the high-frequency random vibrations of high-speed vehicle–track systems at various train speeds or different levels of rail surface roughness. The experimental results indicate that the storage stiffness and loss factors of Vossloh 300 rail pad increase with the decrease in dynamic loads or the increase in preloads within 0.1–10,000 Hz at 20°C, and basically linearly increase with frequency in a logarithmic coordinate system. The results computed by the hybrid SEM–SM method demonstrate that the frequency-dependent viscous damping of Vossloh 300 rail pads, compared with its constant viscous damping and frequency-dependent stiffness, has a much more conspicuous influence on the medium-frequency (i.e. 20–63 Hz) random vibrations of car bodies and rail fasteners, and on the mid- (i.e. 20–63 Hz) and high-frequency (i.e. 630–1250 Hz) random vibrations of bogies, wheels and rails, especially with the increase in train speeds or the deterioration of rail surface roughness. The two sensitive frequency bands can also be validated by frequency response function (FRF) analysis of the proposed infinite rail–fastener model. The mid and high frequencies influenced by the frequency-dependent viscous damping of rail pads are exactly the dominant frequencies of ground vibration acceleration and wheel rolling noise caused by high-speed railways, respectively. Even though the existing time-domain (or frequency-domain) finite track models associated with the time-domain (or frequency-domain) fractional derivative viscoelastic (FDV) models of rail pads can also be used to reach the same conclusions, the hybrid SEM–SM method in which only one element is required to compute the high-order vibration modes of infinite rail is more appropriate for high-efficiency analysis of the high-frequency random vibrations of high-speed vehicle–track systems.

A Self-Powered Sensor Mimicking Slow- and Fast-Adapting Cutaneous Mechanoreceptors.

Highly efficient human skin systems transmit fast adaptive (FA) and slow adaptive (SA) pulses selectively or consolidatively to the brain for a variety of external stimuli. The integrated analysis of these signals determines how humans perceive external physical stimuli. Here, a self-powered mechanoreceptor sensor based on an artificial ion-channel system combined with a piezoelectric film is presented, which can simultaneously implement FA and SA pulses like human skin. This device detects stimuli with high sensitivity and broad frequency band without external power. For the feasibility study, various stimuli are measured or detected. Vital signs such as the heart rate and ballistocardiogram can be measured simultaneously in real time. Also, a variety of stimuli such as the mechanical stress, surface roughness, and contact by a moving object can be distinguished and detected. This opens new scientific fields to realize the somatic cutaneous sensor of the real skin. Moreover, this new sensing scheme inspired by natural sensing structures is able to mimic the five senses of living creatures.

Assessment of noise level and noise propagation generated by light-lift helicopters in mountain natural environments.

The use of helicopter rises discussion about environmental noise propagation especially when it operates in proximity of environmentally sensitive areas (ESAs) for an extended period because of its potential implications in wildlife behaviours. In order to support decisions on helicopter logging operation management in proximity of ESAs, this study focused on (i) analysing the noise spectrum of a light-lift helicopter during logging operations and on (ii) assessing the noise propagation in the surrounding environments. This study investigated a helicopter logging operation for wood fuel extraction in the eastern part of the Italian Alps. The potential disturbance area covered for the entire helicopter logging operation was evaluated by a specific GIS application according to hearing sensitivity of the most sensitive wildlife species in the study area (different strigiform species). The noise level at the ground appeared to be affected by the location regardless both the use of equivalent continuous sound pressures level dB(A) (LAeq) and the single-event level (SEL) noise metrics. The lowest values were recorded when the helicopter was flown over the sound meter level located under the forest canopy, while the highest was recorded when the helicopter was unhooking the loads at the landing. The GIS application highlighted the consistent of the exceeded noise area (weighted to strigiform hearing range and sensitivity) for the lower frequency bands (0.016-0.250kHz). A more restricted exceeded noise area concerned instead the most sensitive frequency bands" for the strigiform (1-2kHz). Graphical abstract ᅟ.

Constructing the lie detection system with fuzzy reasoning approach

Current approaches to lie detection generally rely on specialized instrumentation or environmental conditions which can be time- and cost-intensive to secure and produce questionable results. This study uses electroencephalographic (EEG) variability and fuzzy theory to develop a lie detection model and rule set, identifying sensitive and useful EEG frequency bands to accurately measure lying states based on spectral analysis. Twenty subjects participated in card tests accompanied by EEG recording to evaluate the performance of the proposed model against other data mining methods. The result shows that our proposed model has a lie detection accuracy rate of 89.5% and compares well with other data mining methods. A mobile prototype for real-time lie detection is developed by integrating commercial brainwave measurement instruments with mobile devices. The proposed device can facilitate real-time and accurate polygraphy, while reducing the disadvantages of conventional lie detection approaches.