High-frequency Signal Components Research Articles

Intra-layer and inter-layer monitoring of laser powder bed fusion defects based on airborne acoustic and gn-Res model: pore and deformation

ABSTRACT Macroscopic deformation and microscopic pores are the main defects affecting the comprehensive performance of laser powder bed fusion (LPBF) parts. The LPBF process involves highly coupled multiple physical mechanisms over a wide time scale, making it difficult to obtain strongly correlated defect characteristics and achieve online monitoring. Thus, this study proposes an acoustic-based macro–micro defect synchronous monitoring technology. It enables monitoring by analysing process information progressively from intra-layer to inter-layer. The research identifies that high energy density leads to deformation defects, with helical scan strategy energy fluctuations exacerbating deformation. A correlation between keyhole pores and high-frequency signal components allows quantitative pore analysis directly from acoustic signals. Furthermore, an intelligent diagnosis model (gn-Res_SC) is proposed, incorporating high-order information interaction and residual structure for in-depth process analysis. The model demonstrates superior performance with accuracy (90.70%), recall (90.70%) and F1-score (90.40%) in LPBF acoustic monitoring signal evaluation.

Experiments on two-phase flow in hydraulic jump on pebbled rough bed: Part 1–Turbulence properties and particle chord time and length

This study reported and discussed turbulence characteristics, such as turbulence intensity, correlation time scales, and advective length scales. The characteristic air–water time scale, including the particle chord time and length and their probability density functions (PDFs), was investigated. The results demonstrated that turbulence intensity was relatively greater on a rough bed in the roller length, whereas further downstream, the decay rate was higher. In addition, the relationship between turbulence intensity and dimensionless bubble count rate reflected an increase in turbulence intensity associated with the number of entrained particles. Triple decomposition analysis (TDA) was performed to determine the contributions of slow and fast turbulent components. The TDA results indicated that, regardless of bed type and inflow conditions, the sum of the band-pass (Tu′) and high-pass (Tu″) filtered turbulence intensities was equal to the turbulence intensity of the raw signal data (Tu). Tu″ highlighted a higher turbulence intensity and larger vorticities on the rough bed for an identical inflow Froude number. Additional TDA results were presented in terms of the interfacial velocity, auto- and cross-correlation time scales, and longitudinal advection length scale, with the effects of low- and high-frequency signal components on each highlighted parameter. The analysis of the air chord time indicated an increase in the proportion of small bubbles moving downstream. The second part of this research focused on the basic properties of particle grouping and clustering.

Effect of Variation in Sampling Time in Speed Regulation of a Synchronous Motor Drive that is Self-Controlled

This paper presents an analysis in the variation of sampling time of a synchronous motor drive that is self-controlled. The benefits of the synchronous motors are replacing classical induction and direct current (DC) motor drives. It is anticipated that they may be more significant in the imminent future. Because permanent magnet synchronous motors drives are preferred to perform vital tasks in most applications such as in the automotive industry and aerospace, the system reliability is usually an important consideration. Owing to these reasons, the sampling time of the drives have to be taken into consideration regarding how strong the impact of variation in sampling time can affect the drive performance. The drive is fed with three phase power and is utilized in speed regulation. Proportional integral (PI) regulatory approach is studied and is implemented in a PI-based speed regulatory system is presented. Simulation results of the motor current speed, and torque signals from the synchronous machine dynamics of the field flux are given. It was shown that an increase in sampling time results in the elimination of high frequency signal components.

Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis

Neuroprostheses have the potential to restore communication to people who cannot speak or type due to paralysis. However, it is unclear if silent attempts to speak can be used to control a communication neuroprosthesis. Here, we translated direct cortical signals in a clinical-trial participant (ClinicalTrials.gov; NCT03698149) with severe limb and vocal-tract paralysis into single letters to spell out full sentences in real time. We used deep-learning and language-modeling techniques to decode letter sequences as the participant attempted to silently spell using code words that represented the 26 English letters (e.g. “alpha” for “a”). We leveraged broad electrode coverage beyond speech-motor cortex to include supplemental control signals from hand cortex and complementary information from low- and high-frequency signal components to improve decoding accuracy. We decoded sentences using words from a 1,152-word vocabulary at a median character error rate of 6.13% and speed of 29.4 characters per minute. In offline simulations, we showed that our approach generalized to large vocabularies containing over 9,000 words (median character error rate of 8.23%). These results illustrate the clinical viability of a silently controlled speech neuroprosthesis to generate sentences from a large vocabulary through a spelling-based approach, complementing previous demonstrations of direct full-word decoding.

Research on Application of High-Frequency Pulse Vibration in Ship Electric Propulsion System

Aiming at the problems existing in the method of using sensors to detect the rotor position of marine main propulsion motor at low speed, a sensorless rotor position identification method based on high-frequency pulse vibration signal is proposed. In this method, the low-pass filter is used to separate the high-frequency signal component, and the current component of d-axis to q-axis is demodulated. The control mode of PI action law is used to phase-locked processing of the demodulated signal, in which purpose is to obtain the rotor position signal. The design method is simulated and verified by MATLAB/Simulink software. The error between the estimated position and the actual position of the rotor in the simulation waveform is small, and the accuracy is high. The simulation results show that this method can achieve good results when applied to the ship electric propulsion system without position sensor, which provides a theoretical reference for the design of marine main propulsion motor control system.

Analysis of short-term soil moisture effects on the ASCAT backscatter-incidence angle dependence

The incidence angle dependence of C-band backscatter is strongly affected by the presence of vegetation in the sensor footprint. Many studies have shown the suitability of this dependence for studying and monitoring vegetation dynamics. However, short-term dynamics in the backscatter-incidence angle dependence remain unexplained and indicate that secondary effects might be superimposed on the vegetation component. In this study, we hypothesize that the observed short-term dynamics are caused by soil moisture. We investigate the effect by exploring relationships between the slope of the backscatter-incidence angle dependence (σ′) from the Advanced Scatterometer (ASCAT) and soil moisture, rainfall, temperature, and leaf area index. We carry out the analysis over six study regions in Portugal, Austria, and Russia with different climate, land cover, and vegetation cycles. Our results indicate that soil moisture has an effect on σ′. Spearman correlations of σ′ anomalies with soil moisture anomalies are stronger than with any other variable in most study regions and range from −0.38 to −0.70. Even when accounting for effects of water on canopy, correlations between σ′ and soil moisture remain relatively strong, ranging from −0.14 to −0.46. These results confirm the presence of secondary effects in the dynamic σ′, which need to be corrected for when applying σ′ in studies of vegetation dynamics. A correction may be achieved by the application of a suitable smoothing on σ′ (i.e., removing high frequency signal components), by masking observations taken under wet conditions, or by the use of models that explicitly account for the effect of soil moisture on σ′.

Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data Analytics.

To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D multimodal medical image segmentation algorithm using reinforcement learning and big data analytics is proposed. Bayesian maximum a posteriori estimation method and improved wavelet threshold function are used to design wavelet shrinkage algorithm to remove high-frequency signal component noise in wavelet domain. The low-frequency signal component is processed by bilateral filtering and the inverse wavelet transform is used to denoise the 3D multimodal medical image. An end-to-end DRD U-Net model based on deep reinforcement learning is constructed. The feature extraction capacity of denoised image segmentation is increased by changing the convolution layer in the traditional reinforcement learning model to the residual module and introducing the multiscale context feature extraction module. The 3D multimodal medical image segmentation is done using the reward and punishment mechanism in the deep learning reinforcement algorithm. In order to verify the effectiveness of 3D multimodal medical image segmentation algorithm, the LIDC-IDRI data set, the SCR data set, and the DeepLesion data set are selected as the experimental data set of this article. The results demonstrate that the algorithm's segmentation effect is effective. When the number of iterations is increased to 250, the structural similarity reaches 98%, the SNR is always maintained between 55 and 60 dB, the training loss is modest, relative overlap and accuracy all exceed 95%, and the overall segmentation performance is superior. Readers will understand how deep reinforcement learning and big data analytics test the effectiveness of 3D multimodal medical image segmentation algorithm.

Real-time turning rate estimation in urban networks using floating car data

Real-time information about the dynamic variation of turning rates at intersections is essential for achieving a more efficient traffic control and management system. Limitations in obtaining measurements of turning movements have drawn attention to the need for alternative methods for estimation of turning rates. In a large-scale urban network, characteristics such as disparate signal plans and various intersection layouts make defining a generic model for the entire network challenging. For this reason, most of the proposed methods are impractical for real-field implementation. The application of existing optimization-based methods is also limited by computational complexity, the need for pre-defined assumptions and insufficient stationary traffic measurements (e.g. loop detectors and plate recognition cameras) in urban networks. In this study, we take advantage of probe vehicles to propose a hybrid method that noticeably reduces the need for stationary measurements in the network. This hybrid method consists of two core stages: signal processing, and physical data-driven modeling to consecutively estimate turning rates. In the signal processing phase, we use wavelet transformation to extract fundamental components of inferred floating car turning rates. These components are then used as inputs to a turning rate estimation model. Using these decomposed components of floating car turning rates instead of raw input helps to balance the effect of low and high-frequency signal components and leads to a more accurate estimation. Investigation of the proposed method in a microscopic simulation environment reveals the ability of the hybrid model to detect turning rate variations even when a severe incident occurs.

Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Three-directional analytic signal analysis and interpretation of magnetic gradient tensor

Compared to conventional magnetic data, magnetic gradient tensor data contain more high-frequency signal components, which can better describe the features of geological bodies. The directional analytic signal of the magnetic gradient tensor is not easily interfered from the tilting magnetization, but it can infer the range of the field source more accurately. However, the analytic signal strength decays faster with depth, making it difficult to identify deep field sources. Balanced-boundary recognition can effectively overcome this disadvantage. We present here a balanced-boundary identification technique based on the normalization of three-directional analytic signals from aeromagnetic gradient tensor data. This method can effectively prevent the fast attenuation of analytic signals. We also derive an Euler inversion algorithm of three-directional analytic signal derivative. By combining magnetic-anomaly model testing with the traditional magnetic anomaly interpretation method, we show that the boundary-recognition technology based on a magnetic gradient tensor analytic signal has a greater advantage in identifying the boundaries of the geological body and can better reflect shallow anomalies. The characteristics of the Euler equation based on the magnetic anomaly direction to resolve the signal derivative have better convergence, and the obtained solution is more concentrated, which can obtain the depth and horizontal range information of the geological body more accurately. Applying the above method to the measured magnetic-anomaly gradient data from Baoding area, more accurate field source information is obtained, which shows the feasibility of applying this method to geological interpretations.

Hybrid Pattern Recognition and Multi-resolution Analysis (MRA) Based Fault Location in Power Transmission Lines

This paper proposes a fault (line-to-line) location on Ikeja West – Benin 330kV electric power transmission lines using wavelet multi-resolution analysis and neural networks pattern recognition abilities. Three-phase line-to-line current and voltage waveforms measured during the occurrence of a fault in the power transmission-line were pre-processed first and then decomposed using wavelet multi-resolution analysis to obtain the high-frequency details and low-frequency approximations. The patterns formed based on high-frequency signal components were arranged as inputs of the neural network, whose task is to indicate the occurrence of a fault on the lines. The patterns formed using low-frequency approximations were arranged as inputs of the second neural network, whose task is to indicate the exact fault type. The new method uses both low and high-frequency information of the fault signal to achieve an exact location of the fault. The neural network was trained to recognize patterns, classify data and forecast future events. Feed forward networks have been employed along with back propagation algorithm for each of the three phases in the Fault location process. An analysis of the learning and generalization characteristics of elements in power system was carried using Neural Network toolbox in MATLAB/SIMULINK environment. Simulation results obtained demonstrate that neural network pattern recognition and wavelet multi-resolution analysis approach are efficient in identifying and locating faults on transmission lines as the average percentage error in fault location was just 0.1386%. This showed that satisfactory performance was achieved especially when compared to the conventional methods such as impedance and travelling wave methods.

The Minimum Audible Movement Distance for Localization of Approaching and Receding Broadband Noise with a Reduced Fraction of High-Frequency Spectral Components Typical of Prebyscusis

The minimum audible movement distance was estimated for approaching and receding sound images with a reduced fraction of high-frequency spectral components which reflects an age-related deterioration of auditory perception (presbycusis). Such a reduction can lead to a partial shutdown of the high-frequency binaural auditory mechanism and changes in distance estimation for sound sources positioned at egocentric distances up to 5 m, i.e. in case of making a contact (collision) decision. This study was carried out in free-field conditions using a sound source movement model. The movement was modelled by noise bursts with linearly changing amplitudes, which were emitted by two loudspeakers spaced 3 m apart. The minimum audible movement distance in estimating moving sound images with a reduced fraction of high-frequency signal components was found to be twice as low as for sound images without such a reduction. The data obtained indicate that, provided that the suprathreshold signal encoding mechanisms remain intact, high-frequency level lowering does not increase the minimum audible movement distance.

Across-scale process similarity based interpolation for image super-resolution

A pivotal step in image super-resolution techniques is interpolation, which aims at generating high resolution images without introducing artifacts such as blurring and ringing. In this paper, we propose a technique that performs interpolation through an infusion of high frequency signal components computed by exploiting ‘process similarity’. By ‘process similarity’, we refer to the resemblance between a decomposition of the image at a resolution to the decomposition of the image at another resolution. In our approach, the decompositions generating image details and approximations are obtained through the discrete wavelet (DWT) and stationary wavelet (SWT) transforms. The complementary nature of DWT and SWT is leveraged to get the structural relation between the input image and its low resolution approximation. The structural relation is represented by optimal model parameters obtained through particle swarm optimization (PSO). Owing to process similarity, these parameters are used to generate the high resolution output image from the input image. The proposed approach is compared with six existing techniques qualitatively and in terms of PSNR, SSIM, and FSIM measures, along with computation time (CPU time). It is found that our approach is the fastest in terms of CPU time and produces comparable results.

Impact of a single distance phase retrieval algorithm on spatial resolution in X-ray inline phase sensitive imaging.

A single-projection based phase retrieval method based on the phase attenuation duality principle (PAD) was used to compare the spatial resolution of the acquired phase sensitive and PAD processed phase retrieved images. An inline phase sensitive prototype was used to acquire the phase sensitive images. The prototype incorporates a micro-focus x-ray source and a flat panel detector with a 50 μm pixel pitch. A phantom composed of a 2 cm thick 50-50 adipose-glandular mimicking slab sandwiched with a 0.82 cm thick slanted PMMA sharp edge was used. Phase sensitive image of the phantom was acquired at 120 kV, 3.35 mAs with a 16 μm tube focal spot size under a geometric magnification (M) of 2.5. The PAD based method was applied to the acquired phase sensitive image for the retrieval of phase values. With necessary data processing, modulation transfer function (MTF) curves were determined for the estimation and comparison of the spatial resolution. The PAD processed phase retrieved values of the phantom were in good agreement with the theoretically calculated values. Phase sensitive images showed higher spatial resolution at all spatial frequencies compared to the phase retrieved images. It was noted that the high-frequency signal components in the retrieved image were suppressed that resulted in lower MTF values. When compared to the phase sensitive image, the cutoff resolution (10% MTF) for phase retrieved image dropped 32% from 15.6 lp/mm (32μm) to 10.6 lp/mm (47μm). The resolution offered by this phase sensitive prototype is radiographically enough to detect breast cancer.

Time Domain Synchronous Moving Average and its Application to Gear Fault Detection

Periodic signal detection methods are widely used in applications including human detection and machinery fault diagnosis. Averaging is one of the most powerful filtering techniques for periodic signals extraction. Time domain synchronous average (TSA) and moving average (MA) are the most commonly used average techniques in engineering. TSA has the advantage at periodic signal detection by depressing noises and asynchronous signal components. MA is effective to remove noises while keeping signal periodicity. However, the TSA signal is not periodic as a measurement signal, and signal spectrum resolution degrades seriously; meanwhile, the MA filters out high-frequency signal components of interests. Detection of periodic signal among noises while keeping signal periodicity and high-frequency signal components become a challenge. To address this problem, time-synchronous moving average (TSMA) method is proposed as an improvement on TSA inspired by MA in this paper. Influences of signal overlap and properties of TSMA are investigated. Furthermore, a practical average times optimization method is given for reference. The correctness of theoretical deviations and effectiveness of the proposed method on periodic signal detection are validated using numerical simulations. At last, the proposed method is validated by an application on fault detection of the gearbox.

Analysis of Artifacts on Chipless RFID Backscatter Tag Signals for Real World Implementation

Chipless radio-frequency identification (RFID) has been studied as an alternative technique to conventional chipped RFID. However, chipless RFID tag backscatter signals are affected by motion and especially when they are attached to different objects. In this paper, we performed a comprehensive analysis of the backscattered chipless tag signal in two different stages. At the first stage, we performed an analysis of the backscattered signal in the nonanechoic environment when the frequency-coded tags were moving at different stepped positions. Then we continued to identify the response of signal where the chipless tag was attached to highly scattering objects. The results showed the variation in amplitude and shifting in the frequency of tag resonant when the tag was moving at different stepped positions. The variation was more prominent when the tag was attached to random objects. In order to address the issue, we conducted an experiment on orientation insensitive ground plane tag to solve the frequency shifting problem and proposed a robust detection method based on mother wavelet for the detection of tag IDs. The overall results show that the proposed technique reduces uncertainty in frequency-based chipless RFID tag signal response due to the variation in amplitude. The technique also helps in detecting the tag data bits with better resolution and has a good arrangement between time and frequency resolution for high-frequency signal components. The throughput of 90% is obtained even when the tag is attached to highly scattering objects. Finally, to validate the performance, a ground plane tag is implemented and the proposed algorithm is experimentally demonstrated.

Detection of convulsive seizures using surface electromyography.

Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequencyEMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection.

Denoising Projection Data with a Robust Adaptive Bilateral Filter in Low-Count SPECT

Low-count SPECT images are well known to be smoothed strongly by a Butterworth filter for statistical noise reduction. Reconstructed images have a low signal-to-noise ratio (SNR) and spatial resolution because of the removal of high-frequency signal components. Using the developed robust adaptive bilateral filter (RABF), which was designed as a pre-stage filter of the Butterworth filter, this study was conducted to improve SNR without degrading the spatial resolution for low-count SPECT imaging. The filter can remove noise while preserving spatial resolution. To evaluate the proposed method, we extracted SNR and spatial resolution in a phantom study. We also conducted paired comparison for visual image quality evaluation in a clinical study. Results show that SNR was increased 1.4 times without degrading the spatial resolution. Visual image quality was improved significantly (p < 0.01) for clinical low-count data. Moreover, the accumulation structure became sharper. A structure embedded in noise emerged. Our method, which denoises without degrading the spatial resolution for low-count SPECT images, is expected to increase the effectiveness of diagnosis for low-dose scanning and short acquisition time scanning.

The simple technique to restore temperature impulse shape using deconvolution

The technique to restore temperature impulse shape from thermocouple measurements is proposed.In most cases to measure temperature the contact techniques are used. Thermocouples are one of the most widely used temperature measurement devices due to their robustness, ease of manufacturing and low cost. However, thermocouple using in harsh environment requires the increasing of wire diameters and consequently the measurement bandwidth is reduced. In many applications signal restoration techniques are needed to recover the true temperature from the attenuated measurements. Also, many applications require the availability of fast response temperature sensors.To remove the thermocouple effect on the measurement results the compensation method is proposed. Compensation involves two stages: thermocouple identification is followed by the temperature reconstruction. Reconstruction is a process of restoring the unknown temperature from thermocouple outputs using deconvolution technique. Deconvolution is made in time domain.This paper describes compensation technique based on deconvolution to address loss of high-frequency signal components using the measurements from thermocouple. In particular, filtration of thermocouple output signal and restored signal is proposed. The result clearly shows that the inclusion of signal filtration increases signal to noise ratio. This technique does require a priori knowledge of the thermocouple impulse response function.The quality estimation of the thermocouple input signal reconstruction is obtained.The effectiveness of deconvolution method in time domain for solving the inverse problem of determining the temperature impulse shape is demonstrated.

Multiplexed coded time domain sampling with metamaterials.

The far infrared region of the electromagnetic spectrum often necessitates the use of thermal detectors that, by nature, typically have poor response times and diminished sensitivities, at least compared to adjacent bands. However, many signals of interest contain frequency components far too fast to be reliably measured with such detectors, and hence expensive and inefficient alternatives are brought to bear. Here we propose and experimentally validate a new method leveraging the speed and scalability of dynamic metamaterial modulators to encode high-frequency signal components at a lower frequency, making them reliably measurable with thermal detectors that would otherwise be too slow. An optimal weighing scheme design in the time domain is realized, the result being an imaging system whose time resolution is independent of detector speed and is rather limited only by the speed of the modulator and the reproducibility of the signal of interest.