Real-time Velocity Research Articles

BiInNet: Bilateral Inversion Network for Real-Time Velocity Analysis

Most previous studies focus on using complex deep neural networks to learn diverse features of massive synthetic data. In more realistic situations with a limited number of data pairs, complex networks not only have higher computational complexity, increasing training time and reducing inference speed, but also tend to over-fit a small amount of training data, thus having weak generalization capability. To address the aforementioned problem, we propose a lightweight architecture for real-time velocity inversion in realistic situations, the bilateral inversion network (BiInNet). BiInNet uses lightweight ResNet18, ShuffleNetV2, and modified MobileNetV2 as backbones, taking into account the inversion accuracy and inference speed. To reduce the redundant information in common shot gathers and focus on graphical property features which are strongly correlated with velocity, the intermediate results of velocity analysis, semblances, and interval velocity models are prepared as data pairs. Numerical experiments show that BiInNet can infer interval velocity models in real-time, with frames per second (FPS) up to 76.90 when ResNet18 is used as the backbone. Moreover, BiInNet achieves the best inversion accuracy on more realistic fold models, fault models, salt models, and noisy dataset (NFOMD) when adopting ShuffleNetV2 as the backbone, which illustrates that BiInNet can be applied to velocity inversion tasks of different geological structures and is robust to noise. Adopting transfer learning to fine-tune pretrained model, BiInNet is effectively applicable to velocity reversal models and field data, which further demonstrates the reliability of the proposed method and provides a practical velocity inversion scheme when the field data pairs are insufficient.

Unraveling the role of ballast surface charge at floc growth behavior in ballasted flocculation

This study was emphasized on unraveling the concealed role of surface charge characteristics of employed ballasts at suspended solids interaction with employed ballasts during floc formation in ballasted flocculation (BF). The purpose was to investigate possibilities in surface charge modulation of employed ballasts leading to enhanced aggregation and ballast embedment during floc growth. The ballasts with varied surface charge (-37.8 mV to + 33.6 mV) and equivalent specific gravity (SG: 5.09) were employed in BF process in this study. This Ballasted flocculation study was conducted using a static mixer device in this study, and characteristics of ballasted flocs (size and settling velocity of aggregates) were analyzed by charge coupled device camera. These image analysis investigations revealed that ballast particles and suspended solids experience attractions and repulsions during aggregation subject to ballast surface charge. However, appropriate ballast selection (surface charge characteristics) corresponding to influent characteristics, and optimization of applied ballast concentration (2gL-1) corresponding to ballast surface charge characteristics (SC: +1.23) resulted in 99% clarification (1 NTU residual turbidity) accomplished in a quick settling interval (30 s) with efficient consumption of applied ballasts compared to previous studies. The real-time velocity observations (using CCD camera) also confirmed this rapid clarification exhibiting superficial sedimentation (102.4mh−1) of the aggregated flocs. Therefore, this study concluded that appropriate ballast selection and optimalization of applied ballast concentration corresponding to surface charge characteristics and influent properties can lead to rapid clarification of micropollutants, and instantaneous sedimentation with efficient ballast consumption. This can lead to efficient treatment facility design with shorter hydraulic retention time, compact space requirements, and capability to produce high quality effluent discharge from highly turbid influent.

Velocity vector reconstruction for real-time phase-contrast MRI with radial Maxwell correction.

To develop an auto-calibrated image reconstruction for highly accelerated multi-directional phase-contrast (PC) MRI that compensates for (1) reconstruction instabilities occurring for phase differences near and (2) phase errors by concomitant magnetic fields that differ for individual radial spokes. A model-based image reconstruction for real-time PC MRI based on nonlinear inversion is extended to multi-directional flow by exploiting multiple flow-encodings for the estimation of velocity vectors. An initial smoothing constraint during iterative optimization is introduced to resolve the ambiguity of the solution space by penalizing phase wraps. Maxwell terms are considered as part of the signal model on a line-by-line basis to address phase errors by concomitant magnetic fields. The reconstruction methods are evaluated using simulated data and cross-sectional imaging of a rotating-disc, as well as in vivo for the aortic arch and cervical spinal canal at 3T. Real-time three-directional velocity mapping in the aortic arch is achieved at 1.8 × 1.8 × 6 mm3 spatial and 60 ms temporal resolution. Artificial phase wraps are avoided in all cases using the smoothness constraint. Inter-spoke differences of concomitant magnetic fields are effectively compensated for by the model-based image reconstruction with integrated radial Maxwell correction. Velocity vector reconstructions based on nonlinear inversion allow for high degrees of radial data undersampling paving the way for multi-directional PC MRI in real time. Whether a spoke-wise treatment of Maxwell terms is required or a computationally cheaper frame-wise approach depends on the individual application.

Real-Time Velocity Optimization for Energy-Efficient Control of Connected and Automated Vehicles

Abstract This paper presents a fast numerical algorithm for velocity optimization based on Pontryagin's minimum principle (PMP). Considering the difficulties associated with the application of the PMP when state constraints exist, a penalty function approach is proposed to convert the state-constrained problem into an unconstrained problem. Then, an iterative numerical algorithm in which the explicit solution is used to find the optimal solution is proposed. The proposed numerical algorithm is applied to velocity trajectory optimization for energy-efficient control of connected and automated vehicles (CAVs). The simulation results indicate that the algorithm can generate the optimal inputs in milliseconds and achieve a significant improvement in computational efficiency compared with traditional methods in a few seconds. A hardware-in-the-loop test for experimental validation is implemented to further verify the real-time performance of the proposed algorithm.

Earthquake Magnitude Estimation from High-Rate GNSS Data: A Case Study of the 2021 Mw 7.3 Maduo Earthquake

Peak ground displacement (PGD) and peak ground velocity (PGV) are critical parameters during earthquake early warning, as they can provide rapid magnitude estimation before rupture end. In this study, we used the high-rate Global Navigation Satellite System (GNSS) data from 55 continuous stations to estimate the magnitude of the 2021 Maduo earthquake in western China. We used the relative positioning method and variometric approach to acquire real-time GNSS displacement and velocity waveforms, respectively. The results showed the amplitude of displacement and velocity waveforms gradually decreased with increasing hypocentral distance. Our results showed that the fluctuation of PGD magnitudes over time is smaller than that of PGV magnitudes. Nonetheless, the earthquake magnitudes estimated from both methods were consistent with their counterparts (Mw 7.3) reported by the United States Geological Survey (USGS). The final magnitude estimated from the PGD and PGV methods were Mw 7.25 and Mw 7.31, respectively. In addition, our results highlighted how the number of high-rate GNSS stations could influence the stability and convergence time of magnitude estimation.

A sliding-bed particle solar receiver with controlling particle flow velocity for high-temperature thermal power generation

Various impeded flow particle receivers were proposed to prolong the particle residence time but always faced the risk of thermal deterioration and the difficulty of real-time particle velocity control. Herein, we reported a novel impeded flow particle receiver to solve above problems and further have the potential to control the particle velocity distribution, which could provide a heat source with better stability, better uniformity and higher temperature for subsequent thermal power generation and other thermal applications. In this receiver, the friction along the path and the gate valves at outlet act as the obstruction structures to allow the particles to move slowly and controllably in the form of a sliding-bed, which is why we call this receiver a sliding-bed receiver. The sliding-bed receiver's structural validity and operational characteristics at different working conditions were detailly investigated with both experimental and numerical methods. Experimental results showed that the outlet particle temperature and efficiency could reach 847 °C and 77.2% under a solar simulator of 4 kW. An optical and thermal coupling model was developed and revealed an improved particle temperature of 1350 °C and efficiency of 82% under higher incident power. The effects of the effects of the incident power distribution, the particle velocity distribution and the quartz glass on aperture on the receiver performance were detailly analyzed, which could also help optimize the design and operation of other particle receivers such as free-falling particle receivers.

Joint event location and velocity model update in real-time for downhole microseismic monitoring: A deep learning approach

Accurate event location in downhole microseismic monitoring depends largely on how accurate the velocity model is reconstructed. On the flipside, construction of an accurate velocity model is always an uphill task marred with numerous uncertainties. However, with proper inversion approach, both the model and the location of the events can be jointly evaluated. We propose, in this study, a deep learning approach for locating microseismic events and performing velocity model update, in real-time, efficiently and accurately. Both tasks were considered as a multi-dimensional and non-linear regression problem and a multi-layer two-dimensional (2D) convolutional neural network (CNN) was designed to perform the inversions. In training the neural network, low signal-to-noise ratio (SNR) synthetic microseismic data were used to mimic field data. Overall results indicate that the CNN is capable of learning the relationship between the microseismic waveform data and the events locations and update the velocity model, in real-time, to a high degree of precision compared to classical methods. The errors in the inversion results are less than a few percent. In addition, deep learning offers a number of benefits for automated and real-time microseismic event location and velocity model update, including minimal preprocessing, continuous improvement in performance as more training data is obtained, as well as low computational cost after the network has been trained.

An Efficient Congestion Avoiding Approach for Optimal Path Finding for Emergency Vehicle

Background: Emergency vehicles required a quick clearance so as to reach the destination with minimum delay and the human life could be saved. Emergency vehicle required a dedicated path for clearance. The dedicated path creates a chaos by blocking the entire route for others vehicles and it is not always possible to create a dedicated path. So there is an imperative need for a smart road navigation system which adapts to the traffic congestion in real-time velocity of vehicle, count of vehicle, number of lanes and distance from source to destination. Objective: The objective of this paper is to find an optimal route for providing a least congested optimal route for emergency vehicle with least delay considering various issues such as congestion, numbers of vehicles, average traffic flow on the roads and width of the lane between source and destination. Proposed approach: Real-time traffic data like number of vehicles, velocity of the vehicles, and the width of the road and distance of the route are used to determine the congestion factor on all possible route. Congestion factor is used for finding the shortest route to requesting emergency vehicle. Result: Experimental results establish that the travel time of a vehicle is reduced by approximately 26%, when the vehicle uses the optimized route. This is beneficial for any emergency vehicle as the optimal path is provided on a real-time basis. Conclusion: This research work proposes an analytical approach that provides the least congested optimal route on-demand based on real-time traffic.

Self-sustainable flow-velocity detection via electromagnetic/triboelectric hybrid generator aiming at IoT-based environment monitoring

Harvesting energy from the environment has aroused widespread interest and it can supply renewable power to achieve sustainable environment monitoring for the Internet of things (IoT). In this paper, a tri-cylinder-like hybrid generator composed of triboelectric nanogenerator (TENG) and electromagnetic generator (EMG) is presented to harvest the mechanical energy produced by various fluids as well as sense the real-time flow velocity ecofriendly. This generator can be driven to rotate through a windmill or waterwheel, leading to the reciprocating sliding of the internal spring-magnet structure. By arranging coil and foldable TENG structure, it can generate hybrid signals along with the rotation while gear pairs are further utilized for both boosting the output and expanding the sensing range. The experimental result shows more than twice and six times output can be realized by gear pairs for TENG and EMG, respectively, and the recognizable rotational frequency reaches 10 Hz with a transmission ratio of 5:1. The rotation speed and direction detection are realized based on the output voltage frequency and value of TENG caused by the difference of triboelectric layer number. Combined with two hybrid generators in a vertical layout, the wind directions are also investigated to be distinguished by computing the signal characteristics. Based on these sensing mechanisms, this hybrid generator is able to achieve wireless sensing as EMG boosted by gear pairs is sufficient to supply the energy for wireless transmission. This self-sustained sensory system shows its great potential for unmanned environment monitoring, disaster warning as well as the meteorological record in IoT applications.

State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing.

In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.

A new tightly coupled method for high-rate seismogeodesy: a shake table experiment and application to the 2016 Mw 6.6 central Italy earthquake

SUMMARY High-rate global navigation satellite system (GNSS) has emerged as an effective method to recover seismic waveforms without saturation and drifts, but it has the limitation of relatively lower sampling rate and higher noise level compared to seismic instruments. In this study, we present a new seismogeodetic method by integrating GNSS and accelerometer data to obtain optimal real-time seismic waveforms. Unlike traditional integration methods based on GNSS techniques of relative positioning or precise point positioning, the new method uses a GNSS time difference technique and inherits its unique advantage in real-time and high-accuracy velocity solutions. Furthermore, by incorporating the tightly coupled structure, it can overcome the cascading problem and provide more accurate and robust waveforms compared to its loosely coupled counterpart. The performance of this method is first compared with the traditional loosely coupled approach in challenging environments through a set of shake table experiments. With three GNSS satellites, this approach method can improve the accuracy of velocities and displacements by 42 and 87 per cent, respectively. With four or more GNSS satellites, the average improvements of the method reach 25 and 41 per cent for the velocities and displacements, respectively. We then validate the full performances of the method through simulated shake table experiments and collocated GNSS and accelerometer data during the 2016 Mw 6.6 central Italy earthquake. The simulated and real-event analyses demonstrate that the new integration method can take full advantage of the complementary characteristics of GNSS and accelerometer sensors. By providing more accurate and broad-band velocity and displacement waveforms in a real-time or near-real-time manner, this method is quite promising in earthquake early warning and rapid source inversion.

A Lubricating Oil Condition Monitoring System Based on Wear Particle Kinematic Analysis in Microfluid for Intelligent Aeroengine.

Lubricating oil monitoring technology is a commonly used method in aeroengine condition monitoring, which includes particle counting technology, as well as spectral and ferrography technology in offline monitoring. However, these technologies only analyze the characteristics of wear particles and rely on physical and chemical analysis techniques to monitor the oil quality. In order to further advance offline monitoring technology, this paper explores the potential role of differences in wear particle kinematic characteristics in recognizing changes in wear particle diameter and oil viscosity. Firstly, a kinematic force analysis of the wear particles in the microfluid was carried out. Accordingly, a microfluidic channel conducive to observing the movement characteristics of particles was designed. Then, the wear particle kinematic analysis system (WKAS) was designed and fabricated. Secondly, a real-time tracking velocity measurement algorithm was developed by using the Gaussian mixture model (GMM) and the blob-tracking algorithm. Lastly, the WKAS was applied to a pin–disc tester, and the experimental results show that there is a corresponding relationship between the velocity of the particles and their diameter and the oil viscosity. Therefore, WKAS provides a new research idea for intelligent aeroengine lubricating oil monitoring technology. Future work is needed to establish a quantitative relationship between wear particle velocity and particle diameter, density, and oil viscosity.

Acoustic emission source location from P-wave arrival time corrected data and virtual field optimization method

Acoustic emission (AE) event location plays an important role in structural safety assessments. However, accurately locating an AE event is usually difficult, especially for a small structure. Interestingly, a pencil-lead break (PLB) experiment shows that the P-wave arrival time of a sensor is always significantly different (~260 µs) from that of the other sensors. This time difference is much greater than the P-wave travel time in the range of the experimental sample. Therefore, it can be inferred that there is a P-wave arrival time system error (PATSE) for each sensor. The PATSE may be due to a combined result of the sensor site effect and signal transfer delay time from the sensor to the signal storage. To handle this, a Bayesian inversion framework was built to estimate PATSEs. A synthetic test demonstrated the effectiveness of the proposed Bayesian method for noisy P-wave arrival time data. Then, Bayesian inversion was applied to 15 PLB events, which confirmed the existence of PATSE in an AE experiment for the first time. The average PATSE reached 1.47 µs without considering the P-wave arrival time significantly different sensor. The average location error of 25 PLB events was 14.30 mm and 6.58 mm for PATSE unremoved and removed data, respectively. To achieve this, a high-precision virtual field optimization location method (VFOM) was used. This demonstrates the necessity of removing the PATSEs. Finally, the AE event location performance for the PATSE unremoved and removed data was compared, where the AE events were obtained from the uniaxial compression of a red sandstone sample. The results indicated that there was a higher location detection success rate for the corrected data. The AE locations based on the corrected data were in a better correlation with the rock sample failure mode than that without correction. Moreover, increase the signal sampling frequency for AE event identification, use a real-time inverted 3D velocity model and update the PATSEs in real time could be used to further improve the AE event location accuracy.

Vision-Based Control of an Industrial Vehicle in Unstructured Environments

Most of the previously proposed vehicle controllers are developed with the no-skid assumption, also presume that full states of the vehicle are available, which are usually violated for industrial tractors. This article dedicates to building a practical vision-based antiskid tracking control framework for industrial tractors. For that, we first present a novel nonlinear visual-inertial estimator (VIE) to estimate the real-time position and velocity of the tractor in unstructured environments (GPS-denied, no prior feature maps, etc.). Subsequently, VIE-based kinematic and dynamic controllers are designed for trajectory tracking. The proposed kinematic controller is easy to implement. It works well even if the vehicle is towing payloads since the sideslip angle can be reactively estimated and compensated. Besides, by fully considering vehicle dynamics, the VIE-based dynamic controller can be compatible with occasions that require higher control performance. It is proved by Lyapunov theory that the asymptotic stability of the closed-loop estimation-control system is guaranteed. Extensive full-scale experiments on an autonomous tractor are implemented to show the validity of the VIE-based control system in unstructured environments.

Inversion of vehicle-induced signals based on seismic interferometry and recurrent neural networks

Vehicle-induced vibrations provide useful signals for passive seismic exploration. Such signals are repeatable and environmentally friendly; hence, they can provide an economical way to analyze subsurface structures. We have developed a new workflow to monitor roads or railways by producing 1D subsurface S-wave velocities in real time. This workflow consists of two steps: seismic interferometry and recurrent neural networks (RNN). Seismic interferometry can efficiently retrieve surface waves by crosscorrelating vehicle-induced vibrations. RNN was designed to first encode the picked dispersion curve into a fixed-length vector and then decode the vector into 1D S-wave velocities. To simulate the railway vibrations, we first analyze the time-dependent characteristic of the high-speed-train source and verify its mathematical expression by comparing the frequency spectrums of the real and synthetic data. We then evaluate the RNN-based surface-wave dispersion inversion method and validate the designed network structure using the 3D SEG/EAGE overthrust model. Finally, seismic interferometry and RNN-based surface-wave inversion are applied to a synthetic train-induced data set, a 33 min field record of railway vibrations and a 76 min field data of road vibrations, respectively. The synthetic and field data tests indicated that our workflow can be a feasible and cost-effective tool for real-time monitoring of subsurface media along roads and railways.

Using Drifter Velocity Measurements to Assess and Constrain Coarse-Resolution Ocean Models

AbstractProperly fitting ocean models to observations is crucial for improving model performance and understanding ocean dynamics. Near-surface velocity measurements from the Global Drifter Program (GDP) contain valuable information about upper-ocean circulation and air–sea fluxes on various space and time scales. This study explores whether GDP measurements can be used for usefully constraining the surface circulation from coarse-resolution ocean models, using global solutions produced by the consortium for Estimating the Circulation and Climate of the Ocean (ECCO) as an example. To address this problem, a careful examination of velocity data errors is required. Comparisons between an ECCO model simulation, performed without any data constraints, and GDP and Ocean Surface Current Analyses Real-Time (OSCAR) velocity data, over the period 1992–2017, reveal considerable differences in magnitude and pattern. These comparisons are used to estimate GDP data errors in the context of the time-mean and time-variable surface circulations. Both instrumental errors and errors associated with limitations in model physics and resolution (representation errors) are considered. Given the estimated model–data differences, errors, and signal-to-noise ratios, our results indicate that constraining ocean-state estimates to GDP can have a substantial impact on the ECCO large-scale time-mean surface circulation over extensive areas. Impact of GDP data constraints on the ECCO time-variable circulation would be weaker and mainly limited to low latitudes. Representation errors contribute substantially to degrading the data impacts.

State and Parameter Estimation of a Nonlinear Servo System Handling Noises and Varying Payloads

Payload estimators have many parameters, which are trained using the recorded position, velocity and known payload information. To use these payload estimators in the real-time applications, accurate position and velocity information of the system are required. In this paper, first recently proposed sliding-mode observers (SMOs) are designed and compared for velocity estimation of a nonlinear servo system. Second, a parameter estimation based on sliding-mode super-twisting approach is designed to estimate unknown and varying parameter for a class of nonlinear systems. The convergence property of observers is considered using Lyapunov stability method. In the applications, the constant and varying payloads of the servo system have been estimated using the designed method and compared with Extended-Kalman Filter (EKF). In the final section, artificial noises with different SNR are applied to the measurement signal. When the less amplitude of noise signal is applied, second order SMO estimated the states and the payload better than EKF. However, EKF provides much better estimation results than second order SMO for large amplitude of noise signals. For the sake of generalization, second order SMO is a fast and robust observer for small noise cases. In addition, the filtering property of EKF has still importance for large noise cases.

Cover Picture: Real‐Time Plume Velocity Measurement of Solid Propellant Rocket Motors Using TDLAS Technique (Prop., Explos., Pyrotech. 4/2021)

The cover picture is based on the Full Paper Real-Time Plume Velocity Measurement of Solid Propellant Rocket Motors Using TDLAS Technique by Anchen Song et al., https://doi.org/10.1002/prep.202000091. The Full Paper can be found on page 636 ff.

Single Output and Algebraic Modal Parameters Identification of a Wind Turbine Blade: Experimental Results

This paper describes the evaluation of a single output, online, and time domain modal parameters identification technique based on differential algebra and operational calculus. In addition, an analysis of the frequency response function (FRF) of the system is conducted in a specific set up, emulating its nominal or operational conditions to determine the influence of the non-linearities over the dynamic behavior of the system in those particular magnitudes of deformations; thus, this influence is quantified by a numerical index. This methodology is applied to a wind turbine blade submitted to wind tunnel experiments. The natural frequencies and modal damping ratios of six bending modes associated with the blade are estimated using real-time velocity measurements from one single point of the blade. A comparison with the usual impact hammer modal testing is performed to evaluate and establish the proposed approach’s main contributions. The developed modal parameter identification algorithms are implemented to run into a standard personal computer (PC) where the data acquisition system’s measurements are conditioned and processed. The results show the performance and the fast parametric estimation of the proposed algebraic identification approach.

Radio-frequency positioning for airborne wind energy systems

Conventional GPS and encoder-based solutions for the real-time position and velocity tracking of tethered aircraft in airborne wind energy (AWE) systems are known to have some practical limitations. As an alternative, the main contribution of this paper is to present a setup based on 2.4 GHz radio-frequency (RF) devices, for which there are still no reported experimental results in the literature. To this end, four estimation algorithms are formulated to a lateration problem: linear and nonlinear least squares (kinematic approaches), as well as the extended and the unscented Kalman filters (dynamic approaches). The Cramér–Rao lower bound is computed to serve as a benchmark for the filtering performance in terms of the position estimate covariance. Drawbacks of the two kinematic algorithms are discussed, followed by the advantages of using the two Kalman filters in a sensor fusion scheme that uses not only the RF rangings but also line angles and length measurements obtained from rotary encoders at the ground station. Estimates computed from field experimental data acquired with a small-scale AWE prototype are validated against the encoder measurements. The results show that setups based on RF ranging devices have many advantages over more conventional positioning techniques such as those based on line-following mechanisms with encoders, GPS and IMU, and computer vision, representing a promising solution for AWE applications.