Velocity Information Research Articles

DopplerPTNet: Object Detection Network with Doppler Velocity Information for FMCW LiDAR Point Cloud

Abstract In the field of autonomous driving, LiDAR plays a crucial role in perception and detection. LiDAR based on Time-of-Flight (ToF) mode can only provide three-dimensional spatial coordinate information of point clouds. In point cloud object detection, the limited feature information of spatial coordinates to some extent restricts the further optimization and improvement of algorithm detection performance. However, LiDAR based on Frequency-Modulated Continuous-Wave (FMCW) mode can not only obtain the three-dimensional spatial coordinates of point clouds, but also directly measure the Doppler velocity information of points, effectively compensating for the limitation of relying solely on spatial coordinate information for object recognition. Therefore, based on the CARLA simulator, we construct the first FMCW LiDAR point cloud object detection simulation dataset, FMCWLidDet. What’s more, a novel 4D object detection algorithm, DopplerPTNet, is proposed based on the direct acquisition of point Doppler velocity information by FMCW LiDAR. The algorithm solves the problem of single spatial coordinate information feature in existing 3D object detection algorithms, which makes it difficult to further improve detection accuracy. The dataset is available at https://github.com/xilight123/FMCW-LiDAR-object-detection-dataset.

Polarization analysis and filtering of undersampled 3C seismic data in the frequency-slowness domain

Polarization filters have emerged as a useful tool for coherent noise denoising in multicomponent seismic data. By exploiting the polarization properties of seismic waves, these filters can significantly improve wave-type separation, which is a crucial step in seismic data processing. However, their effectiveness in land seismic data in complex areas can be impacted by the presence of scattering noise, and their applicability is limited by irregular spatial sampling, which hinders velocity information for wave-type discrimination. To address these limitations, we introduce a novel two-step method for distinguishing and separating polarized wave types in shot gathers captured from a spatially undersampled linear array of 3C vector sensors. The first step involves conducting a polarization analysis in the frequency-slowness domain using elliptical elements derived from the linear Radon transform of the seismic data. The second step isolates specific polarized waves using the 3C frequency-slowness polarization filter (3C-FSPF), a technique that uses tailored taper functions based on polarization analysis. Our method stands out from single-station filters that cannot use velocity information for wave-type discrimination and existing multistation filters that require regularly sampled data. To evaluate our method, comprehensive tests are conducted on synthetic and real data. The results consistently demonstrate the effectiveness of 3C-FSPF in separating diverse polarized wave types under conditions of irregular and sparse spatial sampling and noise. Our findings underscore the potential of this method for advancing exploration geophysics by enhancing the quality of seismic data, particularly in land regions with complex near-surface structures.

Localization of Moving Sound Stimuli under Conditions of Spatial Masking

The aim of this study was to investigate spatial masking of noise signals in the delayed motion paradigm. Spatial effects were created by interaural level differences (ILD). Stationary maskers were located laterally or near the head midline, while test signals moved at different velocities from the head midline towards the ears, or in the opposite direction. The masking effect was measured by shifts in the perceived azimuthal positions of the starting and final points of signal trajectories, compared to their positions in silence. The perceived trajectories of all test signals shifted in the opposite direction from the masker. The masking effect was most pronounced in the spatial regions closest to the maskers, and was stronger when the signal moved towards the masker, compared to moving away from it. The final points were perceptually shifted further than the starting points. Signal velocity and masker presentation side (left or right) did not change the degree of masking.

Coupling a torque rheometer with an ultrasonic velocity profiler for evaluating multiphase fluids in oscillatory shear flows

We propose a torque rheometer coupled with an ultrasonic velocity profiler (UVP) for evaluating multiphase fluids as bulk rheology in oscillatory shear flows. The rheometer mainly consists of wide-gap coaxial cylinders, where the outer cylinder is sinusoidally oscillated and the inner cylinder is fixed to a torque sensor for measuring the wall shear stress. Based on Cauchy’s equation of motion, the spatiotemporal distribution of the shear stress is obtained from the velocity information and the wall shear stress as a boundary condition. This rheometer was applied to a carboxymethyl cellulose aqueous solution and compared with a standard torque-type rheometer. The results of the evaluated viscoelastic properties agreed well with each other, indicating the validity of the proposed rheometry. To further investigate the applicability of the rheometer to multiphase fluids, suspensions of solid spherical particles with a diameter of 220μm with volume fraction of 0.8–3.4% were measured, which are out of the applicable ranges of the standard rheometer. For volume fractions up to 3% where the UVP measurement is available, the relative viscosity agrees well with a theoretical formula. This indicates the applicability of the method to examine multiphase fluids.

Fuzzy adaptive cruise control with model predictive control responding to dynamic traffic conditions for automated driving

Traditional Adaptive Cruise Control (ACC) systems often struggle to dynamically adapt to rapidly changing traffic conditions, resulting in suboptimal performance. Additionally, with fuel consumption emerging as a critical consideration alongside safety, there is a pressing need for more advanced solutions. This paper presents a novel approach to address these challenges by integrating Fuzzy ACC with Model Predictive Control, denoted as FACMPC. This integration aims to enhance both the longitudinal safety of AVs and fuel efficiency by considering real-time traffic conditions. The FACMPC system utilizes fuzzy logic inside the MPC, adaptively generates controller's weighting factors, allowing the system to adapt instantly to varying traffic environments and driving circumstances. The findings show that this adaptation improves the balance between driving safety, efficiency, and comfort. Additionally, three interruption scenarios, Alpha, Beta and Gama, are examined. In Alpha, the study evaluates the sensitivity of the FACMPC to disturbances by applying band-limited white noise to the lead vehicle velocity. In Beta, the AV experiences a loss of the lead vehicle velocity signal for a defined period, prompting safety considerations and assumptions. The Gama scenario includes a sensitivity analysis to account for variations and uncertainties in parameters by considering a range of ±5% around the nominal values for four key parameters: road slope, wind speed, wind direction, and rolling resistance. The findings indicate that the proposed controller's mean fuel consumption is 8.110, only a 3.21% increase over the nominal, compared to a 7.03% increase for the conventional ACC, demonstrating greater robustness against uncertainties.

Enhancing routing and quality of service using AI-driven technique for internet of vehicles contexts

This paper introduces an Enhancing Routing and Quality of Service (QoS) Using AI-driven Technique for Internet of Vehicles (IoV) Contexts. The proposed approach aims to enhance QoS and overall network performance in connected vehicle networks. Vehicles utilize Road-Side Units and the Internet to aggregate data from neighboring vehicles, optimizing routing and data management. The proposed approach utilizes a graph traversal algorithm, a widely adopted technique in Artificial Intelligence for graph search, which facilitates traversal in routing. The proposed approach integrates a mobility score based on metrics such as velocity, acceleration, and neighboring information, ensuring optimal routing for dynamic vehicular networks. Objectives include improving QoS and network performance by reducing overheads, optimizing load balancing, and extending network lifetime. Implemented in NS-3 and MOVE simulators, results demonstrate significant performance enhancements compared to existing approaches. This approach addresses the challenges of extensive vehicle mobility and frequent topology changes in connected environments. Overall, the paper presents a comprehensive solution for IoV networks, combining AI-driven routing with mobility-aware strategies to advance network efficiency and QoS.

Research on multi-velocity measurement method of solid particles based on interdigital electrostatic sensor

Abstract In gas-solid two-phase flow, the velocities of solid particles will show different distributions as the flow pattern changes, and it is a challenging task to achieve the measurement of different velocity parameters in the flow field. To obtain the velocity information of solid particles in the flow field, a novel method to achieve multi-velocity measurement of solid particles is proposed by using the spatial filtering characteristics of interdigital electrostatic sensor with variational mode decomposition (VMD). Firstly, a three-dimensional model of the interdigital electrostatic sensor is established using COMSOL simulation software. Based on its spatial dynamic sensitivity, a theoretical analysis of its spatial filtering characteristics is conducted to obtain a mathematical relationship for velocity measurement. Subsequently, the VMD decomposition method is applied to an experiment involving charged wires, with the experimental results verifying the effectiveness of VMD in achieving multi-scale decomposition of electrostatic signals. Finally, the proposed measurement method is validated on a gravity feeding device and a pneumatic conveying system. The experimental results demonstrate that the method exhibits good feasibility.

The direction vector assisted INS/spectral redshift integrated navigation system for spacecraft

Abstract A reliable and autonomous navigation system is crucial for the achievement of high survivability of spacecraft. As a novel autonomous navigation system, inertial/spectral redshift(INS/SRS) integrated navigation system can correct the navigation error of INS through redshift and direction vector information from celestial body. However, Since SRS need to obtain the position measurement by integrating the velocity information of the spacecraft, the position error from INS/SRS is diverged. Thus, this paper investigates a direction vector assisted INS/SRS integrated navigation system for the spacecraft. In this paper, the relationship among the position of spacecraft, redshift and direction vector from celestial body are investigated. And then a direction vector assisted SRS is proposed which can solve the position information of spacecraft directly by redshift and direction vector information. Finally, the direction vector assisted SRS has been combined with INS as the direction vector assisted INS/SRS integrated navigation system. Simulations and comprehensive analysis have demonstrated the proposed integrated navigation system has better navigation performance than INS/SRS integrated navigation system.

Predictive posture stabilization before contact with moving objects: equivalence of smooth pursuit tracking and peripheral vision.

Postural stabilization is essential to effectively interact with our environment. Humans preemptively adjust their posture to counteract impending disturbances, such as those encountered during interactions with moving objects, a phenomenon known as anticipatory postural adjustments (APAs). APAs are thought to be influenced by predictive models that incorporate object motion via retinal motion and extraretinal signals. Building on our previous work that examined APAs in relation to the perceived momentum of moving objects, here we explored the impact of object motion within different visual field sectors on the human capacity to anticipate motion and prepare APAs for contact between virtual moving objects and the limb. Participants interacted with objects moving toward them under different gaze conditions. In one condition, participants fixated on either a central point (central fixation) or left-right of the moving object (peripheral fixation), whereas in another, they followed the moving object with smooth pursuit eye movements (SPEMs). We found that APAs had the smallest magnitude in the central fixation condition and that no notable differences in APAs were apparent between the SPEM and peripheral fixation conditions. This suggests that the visual system can accurately perceive motion of objects in peripheral vision for posture stabilization. Using Bayesian model averaging, we also evaluated the contribution of different gaze variables, such as eye velocity and gain (ratio of eye and object velocity) and showed that both eye velocity and gain signals were significant predictors of APAs. Taken together, our study underscores the roles of oculomotor signals in the modulation of APAs.NEW & NOTEWORTHY We show that the human visuomotor system can detect motion in peripheral vision and make anticipatory adjustments to posture before contact with moving objects, just as effectively as when the eye movement system tracks those objects with smooth pursuit eye movements. These findings pave the way for research into how age-induced changes in spatial vision, eye movements, and motion perception could affect the control of limb movements and postural stability during motion-mediated interactions with objects.

Cruise Speed Model Based on Self-Attention Mechanism for Autonomous Underwater Vehicle Navigation

This study proposes a cruise speed model based on the Self-Attention mechanism for speed estimation in Autonomous Underwater Vehicle (AUV) navigation systems. By utilizing variables such as acceleration, angle, angular velocity, and propeller speed as inputs, the Self-Attention mechanism is constructed using Long Short-Term Memory (LSTM) for handling the above information, enhancing the model’s accuracy during persistent bottom-track velocity failures. Additionally, this study introduces the water-track velocity information to enhance the generalization capability of the network and improve its speed estimation accuracy. The sea trial experiment results indicate that compared to traditional methods, this model demonstrates higher accuracy and reliability with both position error and velocity error analysis when the used Pathfinder DVL fails, providing an effective solution for AUV combined navigation systems.

Signal processing implementation of low-cost target speed detection of CW radar using FPGA

The low-cost Continuous Wave (CW) radar architecture for object speed detection is presented in this research. An FPGA (Field-Programmable Gate Array) processor is utilized to implement the CW radar dependably and optimally. The suggested approach uses a bank of filters made expressly to extract velocity information from radar signals to compute the target velocity. Depending on the needs of the particular application, the acquired data has either undergone additional processing or been presented in an approachable manner. The implemented design is checked in real-time with a chip scope, functionally simulated, and then inspected with lab instruments to compare all simulated and implemented outcomes. Numerous applications, such as industrial automation, surveillance, and automotive systems, where accurate velocity detection is crucial and low-cost design is required, could benefit from this concept.

Modeling kinematic variability reveals displacement and velocity based dual control of saccadic eye movements.

Noise is a ubiquitous component of motor systems that leads to behavioral variability of all types of movements. Nonetheless, systems-based models investigating human movements are generally deterministic and explain only the central tendencies like mean trajectories. In this paper, a novel approach to modeling kinematic variability of movements is presented and tested on the oculomotor system. This approach reconciles the two prominent philosophies of saccade control: displacement-based control versus velocity-based control. This was achieved by quantifying the variability in saccadic eye movements and developing a stochastic model of its control. The proposed stochastic dual model generated significantly better fits of inter-trial variances of the saccade trajectories compared to existing models. These results suggest that the saccadic system can flexibly use the information of both desired displacement and velocity for its control. This study presents a potential framework for investigating computational principles of motor control in the presence of noise utilizing stochastic modeling of kinematic variability.

Calibration to Differentiate Power Output by the Manual Wheelchair User from the Pushrim-Activated Power-Assisted Wheel on a Force-Instrumented Computer-Controlled Wheelchair Ergometer

To examine the biomechanical demands of manual wheelchair propulsion, it is crucial to determine the wheelchair user’s (WCU) force for propulsion technique parameter calculation. When using a pushrim-activated power-assisted wheelchair (PAPAW) on a wheelchair ergometer, a combined propulsion force from the WCU and PAPAW is exerted. To understand PAPAW’s assistance and distinguish the WCU’s force application from the force exerted by the PAPAW, both propulsion components must be assessed separately. In this study, a calibration of the PAPAW on an ergometer was developed to achieve this separation. The calibration consists of five steps: (I) Collecting data on force and velocity measured from the ergometer, along with electrical current and velocity from the PAPAW. (II) Synchronizing the velocity signals of the wheelchair ergometer and PAPAW using cross-correlation. (III) Calibrating the PAPAW’s electromotors to convert electrical current (mA) to force (N). A product-specific motor constant of 0.30, provided an average ICC of 0.563, indicating a moderate agreement between the raw ergometer data (N) and the motor constant-converted drive-rim (PAPAW) data (from mA to N). (IV) Subtracting the PAPAW’s force signal from the ergometer’s measured force to isolate forces generated by the WCU. (V) Using markerless motion capture to determine and validate the phase of hand contact with the handrim. This technical note provides an example of PAPAW calibration for researchers and clinicians. It emphasizes the importance of integrating this calibration into the development of PAPAW devices to reveal the complex interaction between PAPAW and WCU during wheelchair propulsion.

Turbulent Channel Flow Diagnostics By Means Of Shake-The-Box (STB): Flow Structures And Sublayer Profiles

Time-resolved Shake-the-Box (STB) experiments have been conducted in the channel-flow facility at the Institute of Fluid Mechanics (ISTM) at f = 30 kHz repetition rate to study the near-wall velocity field at a viscous Reynolds number of Reτ = 350 (centerline velocity Ucl = 8.4 m/s, channel half-height h = 12.6 mm, viscous unit δv ≈ 36 μm) to testify the STB method for the applicability to provide high-resolution velocity information of the given flow field including the sublayer region of the channel flow. In addition to the extraction of Lagrangian particle tracks and corresponding Eulerian iso-surfaces of vortical structure the major emphasis of the study centers around an evaluation of the resulting near wall velocity profile. Particularly, it is discussed how the choice of data-processing steps and strategies affects the resulting velocity estimates in the immediate vicinity of the wall, where the major impact for the current study has been identified to result from image-arithmetic efforts during data pre-processing. The achieved results are discussed and further compared with direct numerical simulations (DNS) of similar Reτ and earlier experimental efforts at ISTM of the same flow configuration by means of stereoscopic particle image velocimetry (stereo PIV) and a laser-Doppler velocimetry profile sensor (LDV-PS). Finally, future perspectives of the experimental efforts for near-wall shear flow measurements are outlined on the grounds of the achieved insights.

Composite error event-triggered-based finite-time safe formation control of underactuated vessels

For the problems of limited communication bandwidth and frequent triggering of actuators during the formation navigation, comprehensively considering the safety performance and coordinated control performance, a novel composite error event-triggered-based formation control strategy is proposed. Firstly, design an event-triggered-based finite-time extended state observer, which can not only quickly estimate the compound disturbances and velocity information simultaneously, but also reduce the communication frequency between sensor and observer. Secondly, considering the communication frequency and coordinated control performance of the whole formation system, a composite error based on the state information of adjacent underactuated vessels is defined, and it is used as a factor of the event-triggered mechanism. Then, by combining the exponential potential energy function, the composite error event-triggered-based formation controller is designed. And through stability analysis, we known that all signals of the system are uniformly ultimately bounded. Furthermore, the Zeno phenomenon that exists in event-triggered control can also be avoided completely. Finally, through simulation results and comparative analysis, the effectiveness of the composite error event-triggered-based formation control scheme for safe navigation control and reducing communication frequency and triggering frequency has been verified.

Accurate Method for Estimating Wall-Friction Based on Analytical Wall-Law Model

A novel method is proposed for accurately determining the local wall friction through the near-wall measurement of time-average velocity profile in a Type-A turbulent boundary layer (TBL). The method is based on the newly established analytical wall-law in Type-A TBL. The direct numerical simulations (DNS) data of turbulence on a zero-pressure-gradient flat-plate (ZPGFP) is used to demonstrate the accuracy and the robustness of the approach. To verify the reliability and applicability of the method, a two-dimensional particle image velocimetry (PIV) measurement was performed in a ZPGFP TBL with a low-to-moderate Reynolds number (Re). Via utilizing the algorithm of single-pixel ensemble correlation (SPEC), the velocity profiles in the ZPGFP TBL were resolved at a significantly improved spatial resolution, which greatly enhanced the measurement accuracy and permitted us to accurately capture the near-wall velocity information. The accuracy of the approach is then quantitatively validated for the high Reynolds number turbulence using the ZPGFP TBL data. The research demonstrates that the current method can provide the precise estimation of wall friction with a mean error of less than 2%, which not only possesses the advantage of its insensitivity to the absolute wall-normal distance of the measuring point, but also its capability of providing an accurate prediction of wall shear stress based on fairly sparse experimental data on the velocity profile. The current study demonstrates that the wall shear stress can be accurately estimated by a velocity even at a single-point either measured or calculated in the near-wall region.

Research on fixed-time time-varying formation of heterogeneous multi-agent systems based on tracking error observer under DoS attacks

In this paper, the fixed-time time-varying formation of heterogeneous multi-agent systems (MASs) based on tracking error observer under denial-of-service (DoS) attacks is investigated. Firstly, the dynamic pinning strategy is used to reconstruct the communication channel for the system that suffers from DoS attacks to prevent the discontinuous transmission information of the communication network from affecting MASs formation. Then, considering that the leader state is not available to each follower under DoS attacks, a fixed-time distributed observer without velocity information is constructed to estimate the tracking error between followers and the leader. Finally, adaptive radial basis function neural network (RBFNN) is used to approximate the unknown ensemble disturbances in the system, and the fixed-time time-varying formation scheme is designed with the constructed observer. The effectiveness of the proposed control algorithm is demonstrated by the numerical simulation.

Development of numerical and experimental performance evaluation techniques for high-reliability turbine flow meters of ice-making water-dispenser

A turbine flowmeter for sensing the water supply in an ice-making system of a refrigerator requires high sensitivity to the flow rate. This research aims to develop a comprehensive performance evaluation technique by constructing an experimental setup and establishing a numerical technique to simulate the rotational motion of the impeller in response to the injection of water into the flowmeter. An experimental rig was built to secure the measurement accuracy according to the fluid temperature and pressure changes and verify the reliability under various flow conditions from low to high. A numerical method based on the six-degree of freedom method was used to confirm its validity, which is different from a moving reference frame, which numerically analyzes the rotational speed of the turbine by backtracking the rotational speed to correspond to the flow conditions by giving it a constant rotational speed. By simulating water injection into the flowmeter, generating kinetic energy in the impeller according to the flow rate, and subsequently rotating the impeller due to the generated torque, the numerical analysis cost was reduced, allowing direct numerical analysis of the impeller’s acceleration motion. The results were evaluated in terms of angular velocity and pulse signal, and the applicability and reliability of the numerical technique were verified by comparing the numerical results with experimental results. The aerodynamic performance of the impeller was analyzed to understand the phenomena as the impeller rotates. The results of this study can be utilized to conduct future research to improve the performance of flow meters, thus proving its value as a preliminary study.

An improved correction of radial velocity systematics for the SOPHIE spectrograph

High-precision spectrographs can on occasion exhibit temporal variations in their reference velocity or nightly zero point (NZP). One way to monitor the NZP is to measure bright stars, whose intrinsic radial velocity variation is assumed to be much smaller than the instrument precision. The variations of these bright stars, which is primarily assumed to be instrumental, are then smoothed into a reference radial velocity time series (master constant) that is subtracted from the observed targets. While this method is effective in most cases, it does not fully propagate the uncertainty arising from NZP variations. We present a new method for correcting for NZP variations in radial velocity time series. This method uses Gaussian processes based on ancillary information to model these systematic effects. Moreover, it enables us to propagate the uncertainties of this correction into the overall error budget. Another advantage of this approach is that it relies on ancillary data that are collected simultaneously with the spectra and does not solely depend on dedicated observations of constant stars. We applied this method to the SOPHIE spectrograph at the Haute-Provence Observatory using a few instrument housekeeping data, such as the internal pressure and temperature variations. Our results demonstrate that this method effectively models the red noise of constant stars, even with a limited number of housekeeping data, while preserving the signals of exoplanets. Using simulations with mock planets and real data, we found that this method significantly improves the false-alarm probability of detections. It improves the probability by several orders of magnitude. Additionally, by simulating numerous planetary signals, we were able to detect up to 10% more planets with small-amplitude radial velocity signals. We used this new correction to reanalyse the planetary system around HD158259 and to improve the detection of the outermost planets. We propose this technique as a complementary approach to the classical master-constant correction of the instrumental red noise. We also suggest to decrease the observing cadence of the constant stars to optimise the telescope time for scientific targets.

Predicting pressure fields from incomplete velocity fields based on deep convolutional neural network

Acquisition of an instantaneous pressure field in a flow field is essential for experimental fluid dynamics research because pressure is associated with various flow phenomena. Machine learning has performed well in various fluid mechanics studies in recent years due to its nonlinearity. In this paper, we use the machine learning method to predict the pressure fields from incomplete velocity fields while reconstructing the missing velocity information, as exemplified by the stationary circular cylinder flow and circular cylinder undergoing forced oscillating problems, in which the incompleteness of the velocity field is caused by the optical path occlusion in experiments. We propose a network model based on convolutional neural networks. The experimental results show that the error of machine learning methods is around 4%, and even if the resolution of the training set is reduced, it does not affect the prediction accuracy of the network. For statistical averaging of prediction results, the network has high accuracy in predicting first-order quantities, but its ability to predict second-order quantities is limited. We also explore the effects of the amount of noise on the performance of networks. When applying the model to the forced oscillating cylinder problem, we use transfer learning to train the network given the similarity of the two problems, and the training error yields faster convergence and more minor convergence results. In addition to this, we use flow field data with a single vibration frequency to construct a single dataset, and flow field data with multiple vibration frequencies to construct a mixed dataset, and study the effect of different datasets on the prediction accuracy, which shows that it is difficult for neural networks to achieve both accuracy and generalization.