Explore the vast range of titles available.

In shallow flow conditions, turbulence effects appear on a water surface as a form of irregularity of surface shape composed of a large number of fluctuating ripples. The intensity of such a fluctuation increases with the Froude number and also with the Reynolds number as can be observed in flooding river flow. In such a flow condition, surface irregularities are viewed as surface features or textures moving with the flow. Although there has been a discussion in terms of the traceability of surface features, the advection speed of surface features agrees well with the surface velocity from a practical point of view. Based on the assumption about the traceability of surface features, image-based techniques have been developed in the past decades. The space–time image velocimetry (STIV) is one of those techniques developed by Fujita et al. (Int J River Basin Man 5(2):105–114, 2007), with success of measuring river surface velocity distributions without seeding the flow. However, there is still some room for improvement in determining accurate surface velocity from a space–time image (STI) used in STIV. For that purpose, a novel technique was developed that utilizes the two dimensional auto-correlation function of the image intensity in an STI together with quality indices of STI. The performance of the new technique was verified using synthetic images as well as its application to the measurement of snowmelt flood.

In this study, a data processing based on the empirical mode decomposition (EMD) of Hilbert-Huang Transform (HHT) is developed at Chung-Li VHF radar to identify and remove the aircraft clutter for improving the atmospheric wind measurement. The EMD decomposes the echo signals into the so-called intrinsic mode functions (IMFs) in the time domain, and then the aircraft clutter that is represented by a number of specific IMFs can be identified in the radar returns and separated from the clear air echoes that are observed concurrently by the VHF radar. The identified clutter is validated by using the aircraft information collected by the Automatic Dependent Surveillance-Broadcast (ADS-B) receiver. It shows that the proposed algorithm can detect the aircraft echoes that are mixed with the clear air echoes. After implementing the algorithm on the experimental data, the atmospheric horizontal wind velocities are estimated after the aircraft clutter is removed. In order to evaluate the degree of the improvement of the horizontal wind measurement, a comparison in the horizontal wind velocities between Chung-Li VHF radar and a co-located UHF wind profiler radar is made. The results show that the use of EMD and the proposed data processing can effectively reduce the uncertainty and substantially improve the precision and reliability of the horizontal wind measurement.

To improve the performance of wind sensors in the high velocity range, this paper proposes a wind measurement strategy for thermal wind velocity sensors that combines the constant power and constant temperature difference driving modes of the heating element. Based on the airflow distribution characteristics from fluid dynamics, sequential measurement and correction is proposed as a method of measuring wind direction. In addition, a wind velocity and direction measurement instrument was developed using the above-mentioned approaches. The test results showed that the proposed instrument can obtain large dynamic wind velocity measurements from 0 to 60 m/s. The wind velocity measurement accuracy was ±0.5 m/s in the common velocity range of 0–20 m/s and ±1 m/s in the high velocity range of 20–60 m/s. The wind direction accuracy was ±3° throughout the 360° range. The proposed approaches and instrument are not only practical but also capable of meeting the requirements of wide-range and large dynamic wind vector measurement applications.

Recently, mass finishing processes have attracted renewed interests due to their feasibility on complex and rough additively manufactured components. In mass finishing, media sliding velocity ( V ) is a key parameter that directly influences material removal rate from a component. Experimental measurements of V are crucial in validating simulation models and improving fundamental process understanding. Thus far, measurement attempts have been conducted externally — with the image acquisition system located outside of the mass finishing bowl. In this work, a submersible media tracker is developed for internal V measurements within a stream finishing bowl. The velocity estimation results from two data post-processing methods are compared: (i) manual frame-by-frame tracking and (ii) particle image velocimetry (PIV). It is shown that with an appropriate evaluation frame rate, internal measurements could reliably estimate local media velocities at various locations within the stream finishing bowl. Media sliding velocities are most sensitive to changes in relative media approach angle in stream finishing. The velocity measurement results aid in fundamental process understanding and are useful in validating process simulation models.

This paper presents a Doppler lidar system based on a mode-locked semiconductor laser (ML-SL) source. The ML-SL consists of two sections: a Fabry–Pérot (F-P) cavity and a saturable absorber (SA) region. The system utilizes multiple phase-correlated modes of the optical frequency comb to acquire multiple Doppler shift signals; through cross-referencing of these signals, the robustness of the velocimetry system is enhanced. Experimental validation of precise velocity measurements for moving objects was conducted within the speed range of 0.005 m/s to 0.5 m/s. For target speeds of 0.563 m/s and 0.00563 m/s, the maximum and minimum absolute errors were 0.00064 m/s and 0.00003 m/s, respectively, with relative errors consistently below 1%. Comparative experiments demonstrated that utilizing multiple comb teeth reduces the maximum absolute error from 0.001286 m/s (observed when using a single tooth) to 0.000833 m/s. Furthermore, the velocity resolution of the system was analyzed: a frequency resolution of 30 Hz corresponds to a velocity resolution of 0.1117 m/s, while improving the frequency resolution to 1 Hz yields a velocity resolution of 0.0037 m/s.

In this paper, the problem of distributed finite time six-degree-of-freedom (6-DOF) synchronization control for spacecraft formation flying (SFF) with the external disturbances and parameter uncertainties is investigated. Firstly, a continuous adaptive finite time distributed control protocol with full state feedback is proposed, which can overcome the chattering problem and reduce the convergence time in the reaching phase. Subsequently, an adaptive sliding mode observer with finite time convergence is designed to estimate the velocity information. Then a new observer-based continuous adaptive finite time distributed control protocol is designed. Rigorous proofs show that these two distributed controllers both can guarantee that the attitude and relative position tracking errors can converge to the origin within finite time rather than the bounded regions around the origins. Finally, the effectiveness of the designed distributed control protocols is demonstrated by simulation results.

In the autonomous navigation of drones, improving positioning accuracy is of significant importance to obtain highly accurate information on flight velocity. Traditional microwave and acoustic velocity measurement methods have the disadvantages of poor precision and susceptibility to interference. In this study, an unmanned aerial vehicle (UAV)-mounted two-dimensional laser Doppler velocimeter was developed and investigated, and a relevant drone flight navigation and positioning experiment was carried out. The UAV-mounted two-dimensional laser Doppler velocimeter (LDV) prototype developed in this study applies a scheme of dual-beam measurement light, sharing a focusing lens group. After process integration, the performance of the prototype was measured. It shows that a velocity measurement effect with a high signal-to-noise ratio can be achieved by using two measurement probe beams within a working distance range of 40 m–60 m. In the flight experiment, the flight trajectory calculated using the LDV-measured velocity data was compared with the global navigation satellite system (GNSS)-recorded trajectory. The result shows that LDV can achieve an odometer accuracy of 4.8‰. This study has validated the feasibility of the laser Doppler velocimeter in drone navigation and positioning, providing a novel method for reliable and high-precision velocity measurement in autonomous drone navigation.

Spacecraft separation safety is the key characteristic of flight safety. Obtaining the velocity and distance curves of spacecraft and booster at the separation time is at the core of separation safety analysis. In order to solve the separation velocity measurement problem, this paper introduces the YOLOv8_n target detection algorithm and the circle fitting algorithm based on random sample consistency (RANSAC) to measure the separation velocity of space targets according to a space-based video obtained by a monocular camera installed on the spacecraft arrow-shaped body. Firstly, MobileNetV3 network is used to replace the backbone network of YOLOv8_n. Then, the circle fitting algorithm based on RANSAC is improved to improve the anti-interference performance and the adaptability to various light environments. Finally, by analyzing the imaging principle of the monocular camera and the results of circle feature detection, distance information is obtained, and then the measurement results of velocity are obtained. The experimental results based on a space-based video show that the YOLOv8_n target detection algorithm can detect the booster target quickly and accurately, and the improved circle fitting algorithm based on RANSAC can measure the separation speed in real time while maintaining the detection speed. The ground simulation results show that the error of this method is about 1.2%.

To address the energy limitations, long-term operation demands, and load imbalance in fragment velocity measurement wireless sensor networks, this paper proposes an Energy-Balanced and Stability-Oriented Grey Wolf Optimization (EBSIGWO) algorithm. The algorithm employs a multi-objective fitness function that jointly considers residual energy, intra-cluster load balance, and long-term communication cost, ensuring both energy efficiency and clustering stability. A dynamic elite ratio strategy is further introduced to adaptively balance global exploration and local exploitation, thereby mitigating cluster-head overload and slowing energy depletion. Simulation results show that EBSIGWO significantly extends network lifetime compared with LEACH, HEED, GWO, and FIGWO, improving the half-node-death (HND) round by 518.0%, 200.1%, 111.2%, and 30.5%, respectively. Moreover, EBSIGWO reduces energy variance and slows energy consumption, demonstrating superior energy balance and overall efficiency. These results indicate that EBSIGWO provides an effective solution for reliable fragment velocity measurement applications.

In detecting the rotation velocity of an object employing the rotational Doppler effect of vortex beams, atmospheric turbulence can easily cause phase distortion and spiral spectrum dispersion, consequently reducing velocity measurement accuracy. This study combines adaptive optical intelligence algorithms with polarization compensation information to propose a novel approach, the Stokes–Particle swarm optimization Gerchberg–Saxton (Stokes-PSO GS) algorithm, which integrates Stokes polarization information assistance and PSO for GS phase retrieval. The algorithm adjusts the phase and amplitude of the pre-compensated phase screen of the GS algorithm utilizing Stokes information of polarized vortex beam (with lL = 5 and lR = −5) before and after distortion. The PSO is then employed to optimize the pre-compensated phase screen and perform compensations. Simulation results at zS-T = 200 m and Cn2 = 1 × 10−14 m−2/3, demonstrate that the Stokes-PSO GS algorithm exhibits strong stability (small angular spectrum purity deviation, σp, Stokes-PSO GS = 0.005675% < σp, GS = 11.62%), superior optical field recovery (well-recovered Stokes optical field, up to 33.76% improvement in angular spectrum purity), and high-velocity measurement accuracy (25.93% improvement) compared to the GS algorithm. This approach enables precise measurement of the rotation velocity of the vortex beam, demonstrating its potential in practical applications.