High-speed Motion Research Articles

Entrainment probability of coal particle by bubble trailing vortex in coal flotation

Understanding the effect of bubble trailing vortex on particle trajectory is an important precursor for reducing the ash content of coal flotation. Using a high-speed motion acquisition system, we investigated the coal particle trajectory around the cylinder. Two typical trajectories of coal particles around the cylinder were measured. Some particles settled down out of the trailing vortex region while some ones flowed up. The effects of particle density, particle size and fluid flow velocity on the entrainment probability was analyzed. The entrainment probability gradually increased as the coal particle size decreased, increased as the particle density decreased, and increased as the fluid flow velocity increased. The entrainment probability obviously increased as the particle flowability increased independently of particle size, and the increase in the entrainment probability mainly concentrated in the range of particle flowability less than 12.5. The flotation of glass beads was conducted. The flotation recovery increased as the glass bead size decreased, which matched well with the predicted entrainment probability. Our results can provide a valuable insight into the development of flotation technology for cleaner mineral flotation.

Phase Measurement Method Based on Digital Dual Frequency Comb for High-Precision High-Speed Heterodyne Interferometry

The heterodyne interferometer is widely used as a precise displacement sensor in ultraprecision processing. However, there is a trade-off between measurement speed and measurement precision in the existing interference signal processing methods. In this work, a novel phase measurement method based on digital dual frequency comb is proposed. The proposed method uses the intrinsic frequency comb signal and a narrowband low-pass filter to form a band-pass filter bank, which divides the broadband brought by high-speed movement into multiple narrow bands. The signal-to-noise ratio (SNR) is improved for phase calculation. In addition, two groups of intrinsic frequency comb signals are used simultaneously for phase measurement and the rotation of the output, preventing the inaccurate phase measurement of the input signal whose frequency is the average of two frequencies in the frequency comb signal. The heterodyne laser interferometer can maintain high precision at high measurement speed. The simulation results show that the precision of the proposed method is more than 50% higher than that of the phase-locked loop method. The experiment with a circuit board combined with 100 Msps sampling 16-bit A/D converter shows that the phase measurement precision of the input signal frequency in the bandwidth range of 2.5–37.5 MHz can reach 0.77 mrad, equivalent to the displacement measurement precision of the double-pass interferometer (19 pm) at a measurement speed of less than 2.77 m/s.

A CSI Prediction Scheme for Satellite-Terrestrial Networks

Recently, low earth orbit (LEO) satellites have been suggested as an promising solution for global coverage of 5G networks. However, long propagation delay makes obtained channel state information (CSI) (which is employed for efficient spectrum utilization) outdated before usage. Note that CSI is closely related to elevation angle and relative location. Although elevation angle and relative location could be estimated by terrestrial device position and LEO ephemeris, maintaining these two information requires high cost for most IoT devices. Due to regular and high-speed orbital movement of LEO, for an arbitrary terrestrial device, LEOs repeat the same behavioral pattern (e.g., satellite rise and satellite set) again and again. Satellite rise and set result in different temporal correlations in elevation angle and relative location. By the aid of the temporal correlations mentioned above, we proposed a CSI prediction scheme for satellite-terrestrial networks without terrestrial device position and LEO ephemeris. Simulation results show that our scheme has rather low prediction error under various LEO altitude/obit, terrestrial device location/mobility, ground environments, weather and elevation angle when handover.

Towards Exoscope Automation in Neurosurgery: A Markerless Visual-Servoing Approach

Exoscopes are a promising tool for neurosurgeons, offering improved visualisation and ergonomics compared with traditional surgical microscopes. They consist of an external scope that projects the surgical field onto a 2D or 3D monitor, providing a wider field of view and better access to the surgical site. Despite the advantages, exoscopes present some limitations, such as the need for manual or foot joystick repositioning, which can disrupt the flow of the procedure and increase the risk of user error. In this study, a markerless visual-servoing approach for autonomous exoscope control is proposed to address these limitations and enhance the ergonomics and reduce the physical and cognitive load compared with traditional joystick control. The system uses visual information from the operating field to control the exoscope, eliminating the need for markers or additional tracking devices. The proposed approach was validated using a 7-DOF robotic manipulator with a stereo camera in an eye-in-hand configuration. Results showed that the system achieved 89 % accuracy in detecting the target and tracking its movement with a tracking error ranging from 0.50±0.17 cm for low-speed movements to 1.38±0.73 cm for high-speed movements. The proposed system also demonstrated improved efficiency, with a shorter execution time of 72.07±19.36 s compared with 106.52±18.50 s for the foot-joystick control. Additionally, the time out of the FoV was significantly higher in the joystick control mode and the frequency of appearance of the instrument in the centre of the image was higher when using the proposed system. The NASA TLX results indicated lower physical and cognitive load compared with the joystick control-based modality.

Relativistic Bistatic Scattering of a High-Speed Moving Plasma Coated Object

Accurate modeling of relativistic electromagnetic scattering characteristics from high-speed motion of plasma coated objects is crucial for the development of hypersonic aircraft and their applications in the identification and surveillance of moving stealth targets. Nevertheless, a solution for bistatic polarized radar cross sections (RCSs) from a 3-D object with plasma coated layer in motion has yet to be obtained. This manuscript proposes a solution to this problem by employing a combination of the auxiliary differential equation (ADE) method with Lorentz finite-difference time-domain (FDTD) method. Utilizing the Lorentz transformation, this paper presents the transformation of parameters of the incident plane wave and dimensions of the object between the laboratory system that remains static and the rest system that remains stationary relative to the object in high-speed motion. The near-zone electromagnetic fields near the object are computed using the ADE method in the rest system, after which the near-field to far-field (NF-FF) transformation is employed to obtain the far-zone polarized scattered field. By applying Lorentz transformation to the coordinates, this paper presents a solution for the polarized scattering from moving plasma coated objects. Especially, radial components of the polarized scatterings are analyzed. The proposed method is validated through several numerical experiments, demonstrating its efficiency and accuracy.

Data recovery of 2D lifetime-based phosphor thermometry using deep neural networks

Thermographic phosphor (TP) thermometry has been widely used as one of the newly developed non-contact surface temperature measurement methods. However, temperature information is frequently lost locally because chemical bonding coatings are easily damaged during the measurement. This limits its application, such as in the case of jet impact, high-speed motion, high vibration, etc. We proposed to use of deep neural networks (DNNs) as a tool for recovering lost data. In this study, we captured the dynamic two-dimensional (2D) temperature field of jet impingement cooling a high temperature plate by TP thermometry. Different parts of the temperature information, including jet impact area and non-impact area, were then removed to assume the coating was damaged. A prediction model was established by the DNNs using the 2D space and time coordinates as the input dataset and using the temperature information corresponding to the coordinates as the output dataset. The removed temperature information was then recovered using the developed regression model and compared with the raw temperature field to evaluate predicted results. The results showed that the removed data can be successfully recovered using the established DNN prediction model, in which the predicted accuracy was greater than 94.94%. While in the central and the boundary area of the jet, the model has a relatively poor performance, which is mainly due to the large temperature gradients. These results indicate that the established DNN model can be used to recover the lost temperature information but it is limited for applications in regions with drastic temperature changes.

Development of a simultaneous and noninvasive measuring method using high-frame rate videography and motion analysis software for the assessment of facial palsy recovery in a rat model

For the development of new therapeutic and reconstructive methods for facial nerve palsy, it is critical to validate them in animal models. This study developed a novel evaluation method using a high-speed camera and motion analysis software for rat facial paralysis models. The validity of the new method was verified using normal rats and rats with facial paralysis. The whisker movement was recorded using a high-frame video camera. The video files were processed using motion analysis software, and the angular velocities were measured. The score was calculated as the percentage of movement on the side that had palsy compared with the movement on the normal side. Normal rats were used to examine which of the four indices of angular velocity is appropriate for this evaluation method. Using this method, two types of facial nerve palsy models were compared. Furthermore, the three agents that were predicted to promote axon regeneration from previous studies were evaluated. The two averages of the protraction and retraction movement velocities of the whiskers were considered as the most appropriate indicators for this new method. Compared with the saline group, all agent groups showed significant differences in the improvement of facial palsy recovery. This method is an evaluation method for the effects of therapeutic intervention for facial nerve paralysis in real time without sacrificing animals.

Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach

In high-speed railways, the wireless channel and network topology change rapidly due to the high-speed movement of trains and the constant change of the location of communication equipment. The topology is affected by channel noise, making accurate channel estimation more difficult. Therefore, the way to obtain accurate channel state information (CSI) is the greatest challenge. In this paper, a two-stage channel-estimation method based on generative adversarial networks (cGAN) is proposed for MIMO-OFDM systems in high-mobility scenarios. The complex channel matrix is treated as an image, and the cGAN is trained against it to generate a more realistic channel image. In addition, the noise2noise (N2N) algorithm is used to denoise the pilot signal received by the base station to improve the estimation quality. Simulation experiments have shown the proposed N2N-cGAN algorithm has better robustness. In particular, the N2N-cGAN algorithm can be adapted to the case of fewer pilot sequences.

Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm

<span lang="EN-US">Mobile robots are typically depending only on robot kinematics control. However, when high-speed motions and highly loaded transfer are considered, it is necessary to analyze dynamics of the robot to limit tracking error. The goal of this paper is to present a new algorithm, chaotic-billiards optimizer (C-BO) to optimize internal controller parameters of a differential-drive mobile robot (DDMR)-based dynamic model. The C-BO algorithm is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. In addition, a comparison between the performance of C-BO and ant colony optimization (ACO) to determine the optimum controller coefficient that provides superior performance and convergence of the path tracking. The ISE criterion is selected as a fitness function in a simulation-based optimization strategy. For the point of accuracy, the velocity-based dynamic compensation controller was successfully integrated with the motion controller proposed in this study for the robot's kinematics. Control structure of the model was tested using MATLAB/Simulink. The results demonstrate that the suggested C-BO, with steady state error performance of 0.6 percent compared to ACO's 0.8 percent, is the optimum alternative for parameter optimizing the controller for precise path tracking. Also, it offers advantages of quick response, high tracking precision, and outstanding anti-interference capability.</span>

Research on Star Sensor-based Space Debris Detection and Positioning Technology

As an important part of space situational awareness, space-based surveillance of space debris is of great significance for understanding and protecting space resources. With the increasing number of spacecraft operating in the Earth's orbit, both the environment and the space security situation have gradually deteriorated. The accurate detection of small, dim targets in space is the technical basis of space-based debris monitoring; however, several difficulties exist in this field. Since space targets reflect low light energy, the number of pixels occupied by the target is small, and they do not reveal shape or texture characteristics. Space debris is therefore easily confused with background stars, leading to many false alarm targets, and making source detection, recognition, and tracking challenging. Accurate source positions for small and dim space targets are therefore important motivators in our project. However, the positioning accuracy of existing space-based monitoring platforms is limited by the position of the observation platform and the observable time period, due to the high-speed movements of the satellite platform and the observation target. The observation efficiency of existing space-based monitoring platforms is also low, which has certain limitations in practical applications. The high-precision measurement of target positions is thus inhibited when using existing space-based monitoring platforms. The aim of this research is to use an autonomous recognition algorithm based on a compound calibration star grid pattern for star identification. This study utilized a national advanced research project from the National High Technology Research and Development Program and used a star sensor for space debris monitoring, allowing dim and small space debris to be distinguished from stars. This research proposes a target detection technology path based on local sky reference star image comparison to detect weak and small targets in the background of star images. This technology enables the rapid, effective recognition and high-precision positioning of small, dim space targets.

Research on the method of searching and tracking of the time-sensitive target through the mega-constellation

The time-sensitive target has the characteristics of high-speed motion and an uncertain model, which present challenges to the observation process. Aiming at the problem of searching and tracking the time-sensitive target in a region, the scheduling method of the mega-constellation based on grids coding is proposed in this paper. Above all, the observation problem is assessed, after which the grids coding model of observation resources and tasks is constructed. Afterwards, the dynamic updating process of probability is established as a Markov state transition, taking into consideration the movement of the time-sensitive target during the searching phase, and the dynamic prediction model of target position for target tracking is founded, which takes into account the unpredictability of the time-sensitive target model. Ultimately, the mega-constellation searching technique is developed with the enhancement of information entropy of covering grids as the objective function. Based on the optimal acquisition probability of grids, a method for determining the field-of-view center grid code is established to realize cooperative tracking. It is proved by experiments that, the continuous tracking of time-sensitive targets can be realized and the percentage of searches achieved within half an hour is greater than 92%, which is 10% to 35% higher than the conventional methods.

Пути оптимизации дальних, местных и пригородных пассажирских перевозок в современных условиях

In this article, the authors consider the issue of optimizing passenger transportation by rail in terms of competition with other modes of transport that carry passengers both on the territory of Kazakhstan (local and suburban passenger transportation) and in the international direction. The analysis of the current state of passenger transportation in the Republic of Kazakhstan is carried out, divided by types of transport. The trends of changes in passenger traffic by the main modes of transport of the Republic of Kazakhstan are studied. The main priority directions of the development of railway passenger transport are highlighted, which can increase the demand for passenger transportation. The foreign experience of implementation and features of implementation of projects of high-speed and high-speed movement of passenger trains of developed countries are considered. Recommendations of the most cost-effective approach to increasing the speeds of passenger trains on the railway transport network are presented.

A bionic mechanical foot with adaptive variable postures travelling on sand

In this study, an adaptive variable posture bionic mechanical foot is designed, which enables the transformation of different postures during the touchdown period. At the same time, the bolts at the joints are tightened to enable the non-variable configuration function of the bionic mechanical foot. A test rig was used to test the travelling and traction performance of the bionic mechanical foot at different speeds on sandy and hard surfaces. The results show that on sandy surfaces, at both high and low speeds, the variable posture mechanical foot outperforms the non-variable posture mechanical foot, especially at high speeds, indicating that the variable mechanical foot is suitable for movement at higher speeds on sandy ground. On hard ground, the traction and pedaling forces generated by the variable posture mechanical foot are essentially the same as those generated by the non-variable posture mechanical foot at low and high speeds, indicating that the travelling and traction performance of both mechanical feet on hard ground is the same. The variable posture mechanical foot is suitable for high-speed movement on sandy ground, providing a theoretical and technical basis for the design of future legged robots for efficient movement on desert surfaces and deep space soft surface environments.

An Improved S-Plane Controller for High-Speed Multi-Purpose AUVs with Situational Static Loads

The classic S-plane control method combines PD structure with fuzzy control theory, with the advantages of a simple control structure and fewer parameters to be adjusted. It has been proved as a practical method in an autonomous underwater vehicle (AUV) motion control at low and medium speeds, but it takes no account of the situational static load and varying hydrodynamic forces which influence the control quality and even result in a “dolphin effect” at the time of high-speed movement. For this reason, an improved S-plane controller is designed based on the sliding mode variable structure, sliding mode surface, and control items in order to respond to the situational static load and high-speed movement. The improved S-plane controller is verified by Lyapunov stability analysis. The thrust allocation strategies are also discussed with constraints introduced in accordance with task requirements. In order to verify the practicability and effectiveness of the improved S-plane controller, both simulation experiments and field trials of AUV motion control, long-range cruise, and path point following were carried out. The results have demonstrated the superiority of the improved S-plane controller over the classic S-plane controller.

PHYSICAL ACTIVITY FORMATION BY MEANS OF DANCE SPORT

The article reveals the problems of special physical training of athletes-dancers. To master competitive programs, dancers need special physical training, which includes various parameters of locomotor skills and non-standard skills for performing complexly coordinated dance elements. Dance is not only a physical exercise, but also one of the forms of psychological relief. Unlike other physical exercises, dancing has no age restrictions. By examining the structure of dance movements, it can be stated that they have the dynamics of movement abilities, an emotional component, and the rhythmic content of music, which in general create a deep and interesting artistic image. High-speed dance movements performed with maximum amplitude require increased physical effort, strength, endurance, flexibility, and coordination of movements. Athletes- dancers of different age groups and skill levels need not only to improve, but also to maintain their physical form, which allows them to successfully perform at competitions. Therefore, when building the training process, it is worth paying attention to both the complex of general physical exercises and the complex of special exercises.
 Nowadays, it is impossible to achieve success in dance sport without a high level of development of these qualities since a decrease in their level in athletes-dancers leads to a deterioration in sports results.

The optical measurement speed of moving bodies and the observer's position effect

From ancient times to the Newtonian mechanics system, people defaulted to the speed of light as infinity. People usually measure the speed of a moving object directly with the help of optical or electronic equipment and take the measured speed as the actual speed of the object. In fact, the speed of light is limited. When an object moves at high speed, we must correct the measured speed of the object. However, at present, people only use Einstein's special theory of relativity to deal with the high-speed motion of objects, and there is no theory to correct the measured speed of objects under the condition of high-speed motion. Here, we report that the optical measurement speed of an object is affected by the observer's position effect and obtain the relationship between the optical measurement speed and the actual speed. On this basis, this paper explains the reason why Newton's classical mechanics is suitable for the case of low-speed motion, and there is a large deviation between the optical measurement speed and the actual speed under the condition of high-speed motion. This paper also explains the velocity limit of high-energy particles and the phenomena of superluminal and negative velocity and analyzes the observation of microparticle motion. This paper provides a theory to explain and deal with the high-speed motion of objects, which is of great scientific significance.

A High-Efficiency Side-Scan Sonar Simulator for High-Speed Seabed Mapping.

Side scan sonar (SSS) is a multi-purpose ocean sensing technology, but due to the complex engineering and variable underwater environment, its research process often faces many uncertain obstacles. A sonar simulator can provide reasonable research conditions for guiding development and fault diagnosis, by simulating the underwater acoustic propagation and sonar principle to restore the actual experimental scenarios. However, the current open-source sonar simulators gradually lag behind mainstream sonar technology; therefore, they cannot be of sufficient assistance, especially due to their low computational efficiency and unsuitable high-speed mapping simulation. This paper presents a sonar simulator based on a two-level network architecture, which has a flexible task scheduling system and extensible data interaction organization. The echo signal fitting algorithm proposes a polyline path model to accurately capture the propagation delay of the backscattered signal under high-speed motion deviation. The large-scale virtual seabed is the operational nemesis of the conventional sonar simulators; therefore, a modeling simplification algorithm based on a new energy function is developed to optimize the simulator efficiency. This paper arranges several seabed models to test the above simulation algorithms, and finally compares the actual experiment results to prove the application value of this sonar simulator.

Enhancing OLSR protocol in VANETs with multi-objective particle swarm optimization

The Optimized Link State Routing Protocol (OLSR) is envisioned as one of the key components to the operation of future vehicular ad hoc networks (VANETs). Its performance is however fairly constrained in the vehicular communication environment due to the limited coverage of wireless transmission and high-speed movement of nodes. In this brief, we propose a multi-objective particle swarm optimization (MOPSO) framework to enhance the performance of OLSR in VANETs. Specifically, we formulate a multi-objective optimization problem (MOP) considering not only the quality of service (QoS), including throughput, delay and packet loss rate, but also the cost of service, i.e., routing load. We solve this MOP with MOPSO and obtain the Pareto front corresponding to the optimal equilibrium of performance and cost. In the experiment, our optimization framework is used in a general setting of VANETs to obtain the optimal parameters of OLSR, i.e., Hello and TC intervals, and these parameters are further validated in realistic VANET scenarios. Our optimization framework can be naturally extend to other dynamic routing protocols in networks.

CFSR: Coarse-to-Fine High-Speed Motion Scene Reconstruction with Region-Adaptive-Based Spike Distinction

As a novel bio-inspired vision sensor, spike cameras offer significant advantages over conventional cameras with a fixed low sampling rate, recording fast-moving scenes by firing a continuous stream of spikes. Reconstruction methods including Texture from ISI (TFI), Texture from Playback (TFP), and Texture from Adaptive threshold (TFA) produce undesirable noise or motion blur. A spiking neural model distinguishes the dynamic and static spikes before reconstruction, but the reconstruction of motion details is still unsatisfactory even with the advanced TFA method. To address this issue, we propose a coarse-to-fine high-speed motion scene reconstruction (CFSR) method with a region-adaptive-based spike distinction (RASE) framework to reconstruct the full texture of natural scenes from the spike data. We utilize the spike distribution of dynamic and static regions to propose the RASE to distinguish the spikes of different moments. After distinction, the TFI, TFP, and patch matching are exploited for image reconstruction in different regions, respectively, which does not introduce unexpected noise or motion blur. Experimental results on the PKU-SPIKE-RECON dataset demonstrate that our CFSR method outperforms the state-of-the-art approaches in terms of objective and subjective quality.

A Real-Time Planning and Control Framework for Robust and Dynamic Quadrupedal Locomotion

Legged locomotion poses significant challenges due to its nonlinear, underactuated and hybrid dynamic properties. These challenges are exacerbated by the high-speed motion and presence of aerial phases in dynamic legged locomotion, which highlights the requirement for online planning based on current states to cope with uncertainty and disturbances. This article proposes a real-time planning and control framework integrating motion planning and whole-body control. In the framework, the designed motion planner allows a wider body rotation range and fast reactive behaviors based on the 3-D single rigid body model. In addition, the combination of a Bézier curve based trajectory interpolator and a heuristic-based foothold planner helps generate continuous and smooth foot trajectories. The developed whole-body controller uses hierarchical quadratic optimization coupled with the full system dynamics, which ensures tasks are prioritized based on importance and joint commands are physically feasible. The performance of the framework is successfully validated in experiments with a torque-controlled quadrupedal robot for generating dynamic motions.