High-speed Frame Research Articles

The Discovery of Accelerating Expansion of Universe 8.8 Billion Years after the Big Bang Vindicates M1 World One Out of the Six Solutions of Einstein’s General Theory of Relativity

This paper gives the mathematical methodology (Tensor mathematics) of describing the surfaces of our Universe in General Theory of Relativity. It expresses the mass energy equivalence formula. It introduces the concept of time dilation, length contraction and mass increment in high speed frame of reference. Minowski space time 4D manifold is illustrated for representing the flat spacetime fabric Reimannian Metric is enumerated. Schwarzschild metric and how mass is accounted for is given.

Virtual track irregularity establishment for load prediction of heterogeneous bogie frames for virtually coupled trainsets

In this paper, we establish the virtual track irregularity (VTI) considering suspension system dynamics for load prediction of heterogeneous bogie frames for virtual coupled train sets (VCTS). At first, the dynamic characteristics of axlebox spring set and primary suspension vertical damper of A/B-type high-speed trains are analyzed and compared for establishment of frequency-varying parameter simplified vehicle dynamic models (FVP-SVDMs). Then, a frequency-segmented time-domain inversion (FSTIM) of VTI suitable for FVP-SVDMs is conducted, and a quantitative evaluation method for VTI consistency is established. Finally, the consistency of VTIs based on measured loads of A/B-type high-speed train bogie frames is quantitatively assessed, and the agreement between the measured and predicted load spectra based on VTI is evaluated. Based on the FVP-SVDM and the corresponding VTI, the equivalent load amplitude error between the predicted and measured bouncing load is 7%. The VTI technology provides the possibility for VCTS to eliminate the need for conducting the main test before applying the new vehicles on the line. In addition, for the fleet is running on the same track through the VCTS communication, the VTI will provide the theoretical support for implementing active control across the entire fleet.

Dynamic fatigue damage calculation for a high-speed train bogie frame considering the three-dimensional gear meshing

A three-dimensional rigid-flexible coupling model of a high-speed train has been developed incorporating both the elastic bogie frame and the gear meshing within the drivetrain system. Notably, both the wheel polygonisation and dynamic nonlinear internal excitation forces induced by the time-varying gear meshing stiffness and errors of the gear meshing are systematically taken into account. By virtue of the finite element scheme, the real-time and time-varying cumulative fatigue damage of the bogie frame is achieved based on the rainflow method and Miner’s damage summation rule. Numerical results demonstrate that the impacts of nonlinear internal excitation forces and wheel polygonisation on the fatigue damage of the bogie frame are significant. Compared to the case without nonlinear internal excitation forces, the fatigue damage at some critical positions of the bogie frame drastically increases, specifically, the damage of the most dangerous position increases by approximately 106.09%. Whether the dynamic internal excitation forces are exit or not, the fatigue damage of the bogie frame increases with the increasing amplitude of the polygonal wheel. The effect of nonlinear internal excitation forces on the fatigue damage of the bogie frame is more pronounced at lower amplitudes of the polygonal wheel.

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

An experimental array setup to study the integration of biosonar and maneuvering flight in bats

Understanding biosonar-based flight control in bats remains a challenge for fundamental bioacoustics and holds promise for engineered flight-control systems, especially for highly maneuverable drones. To accomplish this, detailed data on bat flight is required that captures the variability across flight situations, individuals, and species. Hence, a flight tunnel has been constructed that provides enough space for natural flight maneuvers. Reconstructing the detailed geometry of freely maneuvering bats requires capturing a flying bat from many different angles to ensure freedom from occlusions. Ideally, this would also be done without placing artificial markers on the bat. Hence, a flight tunnel has been instrumented with arrays of 50 high-speed video cameras and 28 ultrasonic microphones that were all integrated within a modular canvas setup. The optical properties of each camera have been determined from multiple images taken of a panel with a calibration grid seen from different orientations. The spatial relationship between the cameras was estimated based on a large set of images containing random points created with an LED. Reconstructing the 3D geometries of the bats from large numbers of high-speed video frames requires an automated method due to the problem's complexity. To this end, deep-learning methods are currently under development.

Temporal compressive holographic microscopy based on alternating deep denoisers and total variation prior

We present temporal compressive holographic microscopy to achieve high-speed phase imaging with a low-speed camera. In this method, alternating deep denoising network and total variation denoiser are combined into a plug-and-play framework to reconstruct ten high-speed holographic frames from a single-shot measurement. Therefore, we can observe a 500 frame per second (fps) holographic scene when the camera works at 50 fps. This technique provides an effective tool to detect dynamic phase change scenes.

Fatigue damage assessment of high-speed train bogie frame load spectra based on phase reconstruction

This study is focused on the issue of fatigue failure in the bogie frame during high-speed train operation, and is aimed to accurately predicting the fatigue life of the frame using measured load data. To minimize the discrepancy between the damage calculated by the load spectrum and the measured damage, a load phase matrix reconstruction method is proposed in this paper. The load-strain transfer matrix and load-stress transfer matrix are derived through calibration of the frame and auxiliary equipment, while the load and stress time history is obtained by track test. The correlation between the key stress measurement points in various load systems, as well as the relationship between the stress and load, are analyzed. Furthermore, a load prediction stress model is established using a phase matrix by reconstructing the load phase information lost during rain-flow counting through bench loading tests. The error between the calculated equivalent stress and the measured equivalent stress indicates that the load spectrum that incorporates a phase matrix more closely resembles the actual fatigue damage than the load spectrum without a phase matrix.

Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing.

Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture sequential video frames and compress them into a single measurement. Various reconstruction methods have been developed to recover the high-speed video frames from the snapshot measurement. However, most existing reconstruction methods are incapable of efficiently capturing long-range spatial and temporal dependencies, which are critical for video processing. In this paper, we propose a flexible and robust approach based on the graph neural network (GNN) to efficiently model non-local interactions between pixels in space and time regardless of the distance. Specifically, we develop a motion-aware dynamic GNN for better video representation, i.e., represent each node as the aggregation of relative neighbors under the guidance of frame-by-frame motions, which consists of motion-aware dynamic sampling, cross-scale node sampling, global knowledge integration, and graph aggregation. Extensive results on both simulation and real data demonstrate both the effectiveness and efficiency of the proposed approach, and the visualization illustrates the intrinsic dynamic sampling operations of our proposed model for boosting the video SCI reconstruction results. The code and model will be released.

ARTIFICIAL NEURAL NETWORKS TO ENHANCE THE RING MACHINE EFFICIENCY AND YARN QUALITY BY DETERMINATION AND OPTIMIZATION OF DYNAMIC YARN TENSION

The yarn spinning process involves the interaction of large varieties of variables. The relation between the dynamic yarn tension (DYT), yarn quality, and production efficiency of the spinning frame cannot be established conclusively. Artificial neural network (ANN) is a promising step in this filed. In this research work, ANNs simulation and modeling is applied for the optimization of the DYT n to improve the production efficiency and quality of yarn. The research to date in DYT is insufficient to meet the developmental requirement of the high-speed and efficient ring spinning frame. One of the major problems facing the effective use of the ANN is the correct selection of the input parameters to be fed for the training of ANNs. Data of various input variables such as count, traveler no., spindle speed and dynamic yarn tension etc., was used for ANN modeling and simulation. DYT plays a significant role in the determination of yarn quality and its productivity in terms of end breakage rate. However, it has never been explained in terms of displacement from the original yarn path. This work is aimed to the determination and optimization of DYT at ring spinning frame. The influence of different yarn geometry parameters on DYT, measured by the tensiometer was investigated. The optimized DYT values for the machines, running at different speed and different counts were determined using ANN modeling. It is found that the optimized values predicted from ANN resulted in better quality, high production, and decreased end-breakage at industrial ring spinning frames. By the implementation of ANNs the optimum speed and effective utilization of textile raw materials can be achieved.

A Method for Determining the Density and Velocity of Pulse Gas Flows of Millisecond Duration

An experimental setup for studying pulse gas flows within short periods (up to 1 ms) was developed and an experimental data processing method is presented. Based on high-speed frame interferometry data and the results of dynamic pressure measurements, the spatial and temporal distributions of the helium flow density and velocity are determined. The optimal method for reconstructing the spatial density distributions with consideration for the experimental errors is described. The presented method allows characterization of the gas flows with a density of more than 0.0001 kg/m3 and a velocity of more than 400 m/s.

Temperature gradient effect on high-speed railway frame structure yard based on fluid-solid coupling analysis

As the frame structure can effectively save the lower space and optimize overall layout of the yard, it was first applied in the railway double-layers yard in Beijing Fengtai, including the elevated high-speed yard and the ground common-speed yard. To identify the most unfavorable temperature gradient effect on railway frame structure yard, the present study combined with CFD (Computational Fluid Dynamics) and FEM (Finite Element Method) to analyze the temperature gradient and corresponding temperature stress of the structure, and utilizes ray tracing method to analyze the shielding effect between components of structure under the sunlight. The results show that, the temperature gradients of the slab need to be focused. Both positive and negative temperature gradients are observed in the vertical direction of the slab at the end of September (autumn equinox), which are 80.95℃/m and −29.85℃/m, respectively. Under the most unfavorable temperature gradient, the maximum principal tensile stress of the structure lies at the junction of positive and negative temperature gradients of the slab, accounting for 45.6% of the concrete tensile strength, which is the controlling index of the frame structure.

Investigation of Track Condition Effects on Fatigue of High-Speed Train Bogie Frames

Investigation of Track Condition Effects on Fatigue of High-Speed Train Bogie Frames

Jetting Dynamics of Viscous Droplets on Superhydrophobic Surfaces.

We investigated the dynamics of liquid jets engendered by the impact of droplets on a fractal superhydrophobic surface. Depending on the impact conditions, jets emanate from the free liquid surface with several different shapes and velocities, sometimes accompanied by droplet ejection. Experimental outcomes exhibit two different regimes: the singular jet and columnar jet. We found that droplet impacts at a lower impact velocity and low viscosity result in singular jets, attaining a maximum velocity nearly 20-fold higher than the impact velocity. The high-speed video frames reveal that the formation and subsequent collapse of the cylindrical air cavities within the droplet favor the formation of these high-speed singular jets. In contrast, the capillary wave focusing engenders columnar jets at a moderate to high impact velocity. With an increase in viscosity, singular jets are suppressed at lower impact velocities, whereas columnar jets are seen regularly. The columnar jets ascend and grow over time, feeding a bulbous mass, and subsequently the bulb separates itself from the parent jet due to capillary pinch-off phenomena. The quantitative analysis shows that columnar jets' top jet drop size varies nonmonotonically and is influenced by preceding jetting dynamics. At moderate viscosity, the drop size varies with jet velocity, following a power-law scaling. At very high viscosities, both singular and columnar jetting events are inhibited. The results are relevant to several recent technologies, including microdispensing, thermal management, and disease transmission.

Fatigue life assessment of high-speed train’s bogie frame due to dynamic loads under the influence of wheel flat

A bogie frame is the most important railway vehicle’s component that supports almost the weight of the system, and it is subjected to various excitations and loads that can result in the failure of the structure. The dynamic forces influenced by various excitations, both internal and external, can speed up the system's fatigue failure. This paper aims to analyse the impact of wheel flats on the life of a high-speed bogie frame. To accomplish this study, a Chinese railway high-speed (CRH2) bogie frame, has been chosen for the analysis. The required input data for CRH2 have been collected from literature; the wheel flat defect models under the wheel flat lengths of 20 mm, 40 mm, and 60 mm have been developed with a MATLAB tool, and the results have been inputted into a multibody model with the SIMPACK tool. The dynamic response loads for each case have been exported and applied to the finite element model in ANSYS, where stresses and strain have been used to estimate the fatigue life. The results from the study show that as the wheel flat length increases, it increases the dynamic forces and stresses that decrease the life of the bogie frame; thus, the influence of wheel flat should be considered in the life prediction of a bogie frame.

SuperFast: 200× Video Frame Interpolation via Event Camera.

Traditional frame-based video frame interpolation (VFI) methods rely on the linear motion assumption and brightness invariance assumption, which may lead to fatal errors confronting the scenarios with high-speed motions. To tackle the above challenge, inspired by the advantages of event cameras on asynchronously recording brightness changes at each pixel, we propose a Fast-Slow joint synthesis framework for event-enhanced high-speed video frame interpolation, named SuperFast, in this paper, which can generate high frame rate (5000 FPS, 200× faster) video from the input low frame rate (25 FPS) video and the corresponding event stream. In our framework, the task is divided into two sub-tasks, i.e., video frame interpolation for the contents with and without high-speed motions, which are tackled by two corresponding branches, i.e., the fast synthesis pathway and the slow synthesis pathway. The fast synthesis pathway leverages a spiking neural network to encode the input event stream, and combines boundary frames to generate intermediate results through synthesis and refinement, targeting on contents with high-speed motions. The slow synthesis pathway stacks the two input boundary frames and the event stream to synthesize intermediate results, focusing on relatively slow-motion contents. Finally, a fusion module with a comparison loss is utilized to generate the final video frame interpolation results. We also build a hybrid visual acquisition system containing an event camera and a high frame rate camera, and collect the first 5000 FPS High-Speed Event-enhanced Video frame Interpolation (THU[Formula: see text]) dataset. To evaluate the performance of our proposed framework, we have conducted experiments on our THU[Formula: see text] dataset and the existing HS-ERGB dataset. Experimental results demonstrate that our proposed framework can achieve state-of-the-art 200× video frame interpolation performance under high-speed motion scenarios.

Snapshot temporal compressive light-sheet fluorescence microscopy via deep denoising and total variation priors.

We present a snapshot temporal compressive light-sheet fluorescence microscopy system to capture high-speed microscopic scenes with a low-speed camera. A deep denoising network and total variation denoiser are incorporated into a plug-and-play framework to quickly reconstruct 20 high-speed video frames from a short-time measurement. Specifically, we can observe 1,000-frames-per-second (fps) microscopic scenes when the camera works at 50 fps to capture the measurement. The proposed method can potentially be applied to observe cell and tissue motions in thick living biological specimens.

Load Expansion Method of High-speed Train Frame Based on Fatigue Limit

Load Expansion Method of High-speed Train Frame Based on Fatigue Limit

Phase Reconstruction Method of High-speed Train Frame Load Based on Correlation Analysis

Phase Reconstruction Method of High-speed Train Frame Load Based on Correlation Analysis

Experimental investigation on the cuttings formation process and its relationship with cutting force in single PDC cutter tests

The single polycrystalline diamond compact (PDC) cutter test is widely used to investigate the mechanism of rock-breaking. The generated cuttings and cutting force are important indexes reflecting the rock failure process. However, they were treated as two separate parameters in previous publications. In this study, through a series of rock block cutting tests, the relationship between them was investigated to obtain an in-depth understanding of the formation of cuttings. In addition, to validate the standpoints obtained in the aforementioned experiments, rock sheet cutting tests were conducted and the rock failure process was monitored by a high-speed camera frame by frame. The cutting force was recorded with the same sampling rate as the camera. By this design, every sampled point of cutting force can match a picture captured by the camera, which reflects the interaction between the rock and the cutter. The results indicate that the increase in cutting depth results in a transition of rock failure modes. At shallow cutting depth, ductile failure dominates and all the cuttings are produced by the compression of the cutter. The corresponding cutting force fluctuates slightly. However, beyond the critical depth, brittle failure occurs and chunk-like cuttings appear, which leads to a sharp decrease in cutting force. After that, the generation of new surface results in a significant decrease in actual cutting depth, a parameter proposed to reflect the interaction between the rock and the cutter. Consequently, ductile failure dominates again and a slight fluctuation of cutting force can be detected. As the cutter moves to the rock, the actual cutting depth gradually increases, which results in the subsequent generation of chunk-like cuttings. It is accompanied by an obvious cutting force drop. That is, ductile failure and brittle failure, one following another, present at large cutting depth. The transition of rock failure mode can be correlated with the variation of cutting force. Based on the results of this paper, the real-time monitoring of torque may be helpful to determine the efficiency of PDC bits in the downhole.

Plug-and-Play Algorithms for Video Snapshot Compressive Imaging.

We consider the reconstruction problem of video snapshot compressive imaging (SCI), which captures high-speed videos using a low-speed 2D sensor (detector). The underlying principle of SCI is to modulate sequential high-speed frames with different masks and then these encoded frames are integrated into a snapshot on the sensor and thus the sensor can be of low-speed. On one hand, video SCI enjoys the advantages of low-bandwidth, low-power and low-cost. On the other hand, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging and one of the bottlenecks lies in the reconstruction algorithm. Existing algorithms are either too slow (iterative optimization algorithms) or not flexible to the encoding process (deep learning based end-to-end networks). In this paper, we develop fast and flexible algorithms for SCI based on the plug-and-play (PnP) framework. In addition to the PnP-ADMM method, we further propose the PnP-GAP (generalized alternating projection) algorithm with a lower computational workload. We first employ the image deep denoising priors to show that PnP can recover a UHD color video with 30 frames from a snapshot measurement. Since videos have strong temporal correlation, by employing the video deep denoising priors, we achieve a significant improvement in the results. Furthermore, we extend the proposed PnP algorithms to the color SCI system using mosaic sensors, where each pixel only captures the red, green or blue channels. A joint reconstruction and demosaicing paradigm is developed for flexible and high quality reconstruction of color video SCI systems. Extensive results on both simulation and real datasets verify the superiority of our proposed algorithm.