Tracking Model Research Articles

Object Tracking Algorithm Based on Integrated Multi-Scale Templates Guided by Judgment Mechanism

The object tracking algorithm TransT, based on Transformer, achieves significant improvements in accuracy and success rate by fusing the extracted features of convolutional neural networks with the structure of Transformer. However, when dealing with the deformation of the object’s appearance, the algorithm exhibits issues such as insufficient tracking accuracy and drift, which directly affect the stability of the algorithm. In order to overcome this problem, this paper demonstrates how to expand scene information at the scale level during the fusion process and, on this basis, achieve accurate recognition and positioning. The predicted results are promptly fed back to the subsequent tracking process, from which temporal templates are embedded. Starting from both location and time can effectively improve the adaptive ability of the tracking model. In the final experimental comparison results, the algorithm proposed in this paper can adapt well to the situation of object deformation, and the overall performance of the tracking model has also been improved.

A high-performance ray tracing particle tracking model for the simulation of microplastics in inland and coastal aquatic environments

A high-performance ray tracing particle tracking model for the simulation of microplastics in inland and coastal aquatic environments

A Calculation Study on the Escape of Incident Solar Radiation in Buildings with Glazing Facades

More and more modern buildings are using glass curtain walls as their building envelope. The large area of window leads to a significant increase in solar heat gain, resulting in an increase in the cooling load and energy consumption of the building envelope. In the calculation of building cooling load, the thermal performance parameter of windows, the solar heat gain coefficient, is used to calculate the solar radiation heat gain of the windows. The window-to-wall ratio of buildings with glazing facades is large, and the phenomenon of escape of incident solar radiation cannot be ignored. In order to calculate the solar radiation escape rate, a dynamic model of solar radiation escape rate incorporating the solar path tracking model is developed in this research, which can achieve big data simulation analysis based on actual meteorological conditions. The model is programmed and simulated using MATLAB R2024a software. Five representative cities from different climate regions in China are selected and the variation rule of solar radiation escape rate are analyzed on three different time scales: day, month, and year. The influence of building orientation was also calculated and analyzed. The numerical calculation results indicate that the escape solar radiation rate varies with the incident angle of solar radiation at different times. It was found that the smaller the solar azimuth angle and solar altitude angle, the smaller the escape rate of solar radiation. The latitude of a city has a significant impact on the solar radiation escape rate. The weighted average of the solar radiation escape rates for each city were calculated for both summer and winter. Regardless of the season, the city’s location, and the orientation of the room, the value of solar radiation escape rate varies from 8.64% to 10.33%, which indicates that the solar radiation escape phenomenon cannot be ignored in glass curtain wall buildings. The results can be used as a reference value of solar radiation escape rate for the correction of actual solar heat gain of buildings in different climate regions.

Modeling of railway track integrating a shock-absorbing mat at the sleepers-ballast interface using eigenfunctions of displacement

Damage caused to railway tracks and the surrounding environment during trains passage have become a real concern for those involved in the railway sector over the years. To find a solution for this problem, several approaches have already been implemented on railway tracks in some countries. Despite the efforts made in this area, the problem of ground vibration induced by railway traffic still remains a concern. Therefore, many studies have been focused on solutions to attenuate, even cancel vibrations effects on the track and nearby environment. This work has presented a modeling of a ballasted railway track with a shock-absorbing mat or Under Sleepers Rubber Mat (USRm) at the sleepers/ ballast interface. Using the equation of wave propagation in the ground and the constitutive law, the eigenfunctions of the displacement vector were determined. By coupling the equations of the track and those of the ground, the displacement, the velocity, and the acceleration of each track component, as well as the stress on the ground surface were expressed. The Newmark numerical method was used to represent these variables and to highlight the (USRm) influence on the dynamic behavior of the different track component. Comparison of the results obtained with the proposed railway model shows a reduction in vibration amplitudes of 64.9% and 94%for the rail, 86.5% and 56.25% for the sleepers. At the ground surface, the attenuation of vibration is evaluated at 53% and 6.85%, respectively for linear and nonlinear cases. The insertion of the shock-absorbing mat (USRm) in the railway track structure also reduces stress at the ground surface. The configuration of the railway track with a damping mat (USRm) therefore significantly reduces the transmitted vibration to the ground and to the nearby structures. It also contributes to the stability of the track and the improvement of its performance. The proposed railway track model with a shock-absorbing mat can be considered as a solution to reduce vibrations generated on track and the ground during trains passages.

A Robust Trajectory Multi-Bernoulli Filter for Superpositional Sensors

This paper proposes a trajectory multi-Bernoulli filter applied to the superpositional sensor model for multi-target tracking in the presence of unknown measurement noise. This filter can provide a Multi-Bernoulli approximation of the posterior density on a set of alive trajectories at the current time step. We also provide a Gaussian mixture (GM) implementation of this filter, employing a mixture of Gaussian and inverse Wishart distributions to represent the combined state of measurement noise and target information. Subsequently, the variational Bayesian (VB) method is employed to approximate the posterior distribution, ensuring its form remains consistent with the prior distribution. This method is capable of directly generating trajectory estimates and can jointly estimate both multi-object tracking and measurement noise covariance. The performance of this algorithm is verified through simulation. Finally, a computationally more efficient L-scan approximation is provided. The simulation results indicate that the filter can achieve robust tracking performance, adapting to unknown measurement noise.

Analysis of Erosion of Surfaces in Falling Particle Concentrating Solar Power

Abstract Next generation concentrating solar power (CSP) systems, which utilize solid particles for energy capture, transport, and storage, offer prospects for higher temperature operation, improved efficiency, and reduced overall costs. Nevertheless, the continuous impingement of particles on component materials can result in substantial erosion, significantly constraining the performance and longevity of the components. A comprehensive understanding of particle erosion on surfaces is essential for designing components and operational parameters or coatings that minimize wear. This study presents a computational physics-based particle tracking model of the erosion rate of incident surfaces under different geometric, operational, and particle parameters. The computational model is validated with experimental measurements conducted as part of the study. Computational simulations are presented to elucidate the effects of each parameter and further used to investigate erosion rates in a systematic design of experiments covering a wide range of parameters. Based on the simulation results, a generalized analytical model is developed to relate erosion wear to pertinent dimensionless groups governing the physics of the process. The analytical model is shown to be accurate to within 10% and its use in understanding surface erosion as well as designing wear-resistant coatings to limit erosion within acceptable values is presented.

Research on the multitarget 3D trajectory tracking method of Thalassodes immissaria in the thermal infrared region based on YOLOX-GMM and SORT-Pest.

Studying the behavioral responses and movement trajectories of insects under different stimuli is crucial for developing more effective biological control measures. Therefore, accurately obtaining the movement trajectories and behavioral parameters of insects in three-dimensional space is essential. This study used the litchi pest Thalassodes immissaria as the research object. A special binocular vision observation system was designed for nighttime movement. A thermal infrared camera was used for video recording of T. immissaria in a lightless environment. Moreover, a multi-object tracking method based on the YOLOX-GMM and SORT-Pest algorithms was proposed for tracking T. immissaria in thermal infrared images. By obtaining the central coordinates of the two T. immissaria in the video, target matching and 3D trajectory reconstruction in the parallel binocular system were achieved. Error analysis of the T. immissaria detection and tracking model, as well as the 3D reconstruction model, showed that the average accuracy of T. immissaria detection reached 89.6%, tracking accuracy was 96.9%, and the average error of the reconstructed 3D spatial coordinates was 15 mm. These results indicate that the method can accurately obtain the 3D trajectory and motion parameters of T. immissaria. Such data can greatly contribute to researchers' comprehensive understanding of insect behavioral patterns and habits, providing important support for more targeted control strategies.

Determination of the dynamic stiffness matrix of a rail damper and application to vibration decay rate

In this paper, the 6×6 dynamic stiffness matrix of a rail damper seen by a short section of rail is defined based on the force simplification theory, and a method to determine the matrix is presented. According to the method, frequency response functions (FRFs) between specific points on the rail as a rigid body are measured with hammer testing. These FRFs are then used to express the acceleration of the mass center of the rail as well as the rail's angular acceleration. As the third step, the equations of motion of the rail are established based on the laws of momentum and moment of momentum, from which the dynamic stiffness matrix can be worked out. The method is then applied to determine the dynamic stiffnesses of a typical damper, and the determined dynamic striffnesses are combined with an infinite periodic track model to stduy the effect of the damper on the verical and lateral vibration decay rates of the rail.

Organic Carbon Reaction Kinetics in Bioturbated Sediments

AbstractMost organic carbon delivered to the seafloor is degraded within the bioturbated layer. Theory and empirical evidence have shown that organic carbon reactivity relates to the age of a particle. However, due to particle mixing, the age‐depth linear relation induced by sediment accretion is obfuscated. Here we combine a Lagrangian particle tracking model that resolves the age distribution of particles in the bioturbated zone and couple it to age‐dependent organic carbon degradation. Depth profiles for organic carbon concentration, reactivity and degradation rate are presented for sediments receiving low and high reactivity organic carbon in coastal, continental slope and deep‐sea environments. Our results show that a simple first‐order kinetics model suffices for well‐mixed sediments and systems receiving pre‐processed materials. A reactive continuum approach is needed for poorly mixed sediments receiving highly reactive organic carbon and for sediments below the bioturbated layer.

Uncertainty study of two-phase flow distribution characteristics measurement based on bubble trajectory tracking method

Bubbles undergo complex motions such as nonlinear trajectories in two-phase flow, where the position, velocity magnitude, and direction of bubble centers continuously change as they evolve under the influence of forces. The complexity of bubble trajectory increases the difficulty in measuring the flow field distribution characteristics, which has been understudied in the previous. The bubble trajectory tracking model based on the Monte Carlo method is constructed to study the uncertainty of two-phase flow distribution characteristics measurement by the conductivity probe. The Monte Carlo method is adopted to randomly generate the bubble velocity at the initial moment and the bubble force characteristics are embedded to simulate the three-dimensional motion of the bubble. The influence of various factors on the distribution of effective bubble numbers and velocity relative error at the local point is quantitatively analyzed. It is found that the lateral spacing of the sensors has the greatest influence on the measurement results of small-sized bubbles, the accuracy is greatly reduced when the space is larger than the radius of the measured bubbles. Moreover, the measurement accuracy of the probe for medium-sized bubbles is high, and the effect of bubble lateral velocity and aspect ratio on the effective bubble ratio and velocity relative error is negligible. In addition, the maximum measurement range of the probe at local points is investigated which is found to be 4 mm in the lateral direction and 2 mm in the longitudinal direction. Finally, uniformly distributed bubbles are generated in a 20 mm × 10 mm transverse plane and the distribution of the effective bubble ratio of the probe is found to be consistent at different measurement points, which verifies the reliability of the probe measurement.

Advancements, Challenges, and Future Prospects in Clinical Hyperpolarized Magnetic Resonance Imaging: A Comprehensive Review

Hyperpolarized (HP) magnetic resonance imaging (MRI) is a groundbreaking imaging platform advancing from research to clinical practice, offering new possibilities for real-time, non-invasive metabolic imaging. This review explores the latest advancements, challenges, and future directions of HP MRI, emphasizing its transformative impact on both translational research and clinical applications. By employing techniques such as dissolution Dynamic Nuclear Polarization (dDNP), Parahydrogen-Induced Polarization (PHIP), Signal Amplification by Reversible Exchange (SABRE), and Spin-Exchange Optical Pumping (SEOP), HP MRI achieves enhanced nuclear spin polarization, enabling in vivo visualization of metabolic pathways with exceptional sensitivity. Current challenges, such as limited imaging windows, complex pre-scan protocols, and data processing difficulties, are addressed through innovative solutions like advanced pulse sequences, bolus tracking, and kinetic modeling. We highlight the evolution of HP MRI technology, focusing on its potential to revolutionize disease diagnosis and monitoring by revealing metabolic processes beyond the reach of conventional MRI and positron emission tomography (PET). Key advancements include the development of novel tracers like [2-13C]pyruvate and [1-13C]-alpha-ketoglutarate and improved data analysis techniques, broadening the scope of clinical metabolic imaging. Future prospects emphasize integrating artificial intelligence, standardizing imaging protocols, and developing new hyperpolarized agents to enhance reproducibility and expand clinical capabilities particularly in oncology, cardiology, and neurology. Ultimately, we envisioned HP MRI as a standardized modality for dynamic metabolic imaging in clinical practice.

ECDG-DST: A dialogue state tracking model based on efficient context and domain guidance for smart dialogue systems

ECDG-DST: A dialogue state tracking model based on efficient context and domain guidance for smart dialogue systems

Crops Feed Rain to Drylands in Northwest China

AbstractAs a key region supplementing China's limited croplands, Northwest China has undergone rapid cropland expansion over the past decades to satisfy rising food demand from population growth and socio‐economic development. Although cropland expansion may overconsume local water resources in general, in Northwest China, increased precipitation and enhanced glacier melt have increased the water available for croplands. Counterintuitively, the enhanced evapotranspiration (ET) resulting from this cropland expansion could benefit remote ecologically vulnerable natural vegetation through atmospheric moisture recycling. In this study, we used a moisture tracking model to quantify contributions of croplands and cropland expansion to local precipitation and the consequent precipitation supply to natural vegetation in Northwest China. We found that the croplands contributed 27.69 billion m3/year (2.13%) of regional total precipitation and supplied 17.30 billion m3/year (2.39%) of precipitation over natural vegetation, and the cropland expansion resulted in a net increase of 80.25 million m3/year (1.07% of the total increase) in regional precipitation and 36.23 million m3/year (4.56% of the total increase) in precipitation supply for natural vegetation. Among different types of natural vegetation, grasslands received the most precipitation supply due to its vast area, followed by forests and shrublands. The more arid regions experienced not only faster rates of cropland expansion but also obtained a greater increase in regional precipitation and precipitation supply to natural vegetation. Our study quantifies the ecological impacts of cropland expansion through moisture recycling in Northwest China. This shows the complexities of water competition between agricultural development and ecological conservation in drylands and elsewhere.

Modified stochastic diffusion particle-tracking model (MSDPTM) incorporating energy cascade theory and eddy intermittency for suspended sediment transport in open channel flow.

This study presents a modified stochastic diffusion particle tracking model (MSDPTM) that incorporates energy cascade theory to more accurately simulate suspended sediment transport. The impact of turbulent eddies on sediment particles is an intermittent process, which is also considered in this study. The study examines the time correlation between eddies using eddy turnover time and finds that closer-scale eddies exhibit higher correlations than those farther apart. The statistical properties of particle movement, such as the ensemble mean and variance of particle trajectories, have been calculated and compared with the stochastic diffusion particle tracking model (SDPTM) results. Notably, MSDPTM with intermittency demonstrates a significantly larger ensemble mean of particle trajectories in the streamwise direction than other particle tracking models. The proposed model is validated through comparison with available data, showing its enhanced performance. The results of the simulation indicate that MSDPTM outperforms SDPTM, especially when the intermittency effect of eddies is considered.

An optimized method for AUV trajectory model in benthonic hydrothermal area based on improved slime mold algorithm

The optimization of the desired autonomous underwater vehicle (AUV) trajectory modeling and AUV trajectory tracking control in the benthonic hydrothermal area were studied. In the conventional trajectory tracking model construction methods, the time points were roughly combined with the position points of the planned path, making it difficult to produce a smooth trajectory. Although the spline interpolation method was an ideal option for smooth curves, a great number of points were needed for a complex desired trajectory mode. In response to the demanding requirements of AUV trajectory tracking control in the benthonic hydrothermal area, an under-actuated test platform was first established, and the cubic spline interpolation was adopted to process the preset path points for a smooth desired trajectory. An improved slime mold algorithm (SMA) was put forward to optimize the interpolating points used in the trajectory modeling. The Levy flight technology and the compactness technique to speed up the search process and increase the search accuracy. The simulation experiments were conducted in comparison with the artificial fish swarm algorithm (AFSA), the particle swarm optimization (PSO), and the compact cuckoo search (CCS). The results showed that the improved SMA shortened the search process, effectively avoided the local extreme values, and generated a high-precision desired trajectory model in a shorter time. The pool test also verified the feasibility and effectiveness of the proposed method. The method proposed in this study can satisfy the modeling of benthonic hydrothermal trajectory with a fewer number of nodes, faster search progress and search accuracy.

Enhancing BDS-3 PPP-B2b real-time positioning performance by tightly integrating MEMS IMU and LiDAR in GNSS-Degraded environment

Currently, BDS-3 can provide satellite-based real-time precise point positioning (PPP) service via its PPP-B2b signal. However, in a complex Global Navigation Satellite System (GNSS)-degraded environment, the BDS-3 PPP-B2b positioning performance is severely influenced. A tightly coupled PPP/microelectromechanical system inertial measurement unit (MEMS IMU)/Light Detection and Ranging (LiDAR) combination is proposed, in which the original pseudorange and carrier observations, IMU measurements, and LiDAR planar features are fused via an extended Multi-State Constraint Kalman Filter (MSCKF). Moreover, the Galileo satellites are further utilized using the broadcast ephemerides to increase the number of available satellites. And to improve the real-time computing efficiency, a planar feature tracking model based on ground segmentation is adopted. The model consists of point cloud segmentation, planar feature extraction and fusion, and data correlation. To evaluate the effectiveness of the proposed method, the experiment is conducted at both a campus and a park. The result shows that approximately sub-meter-level positioning accuracy can be achieved using tightly coupled BDS-3 PPP-B2b, IMU, and LiDAR, while the positioning errors of BDS-3 PPP-B2b and BDS-3 PPP-B2b/IMU are both larger than 5 m. Furthermore, the MEMS IMU and LiDAR measurements show the capability of bridging the gaps in the GNSS data, which enables continuous and stable positioning. Moreover, compared to the state-of-the-art A-LOAM, LEGO-LOAM, and LIO-SAM frameworks, absolute trajectory errors of less than 1.0 and 2.5 m are achieved in the campus and park, respectively, using the proposed algorithm, with improvements of more than 3.40 and 1.58 times.

Tag-Array-Based UHF Passive RFID Tag Attitude Identification of Tracking Methods.

Attitude information is as important as position information in describing and localizing objects. Based on this, this paper proposes a method for object attitude sensing utilizing ultra-high frequency passive RFID technology. This method adopts a double tag array strategy, which effectively enhances the spatial freedom and eliminates phase ambiguity by leveraging the phase difference information between the two tags. Additionally, we delve into the issue of the phase shift caused by coupling interference between the two tags. To effectively compensate for this coupling effect, a series of experiments were conducted to thoroughly examine the specific impact of coupling effects between tags, and based on these findings, a coupling model between tags was established. This model was then integrated into the original phase model to correct for the effects of phase shift, significantly improving the sensing accuracy. Furthermore, we considered the influence of the object rotation angle on phase changes to construct an accurate object attitude recognition and tracking model. To reduce random errors during phase measurement, we employed a polynomial regression method to fit the measured tag phase information, further enhancing the precision of the sensing model. Compared to traditional positioning modes, the dual-tag array strategy essentially increases the number of virtual antennas available for positioning, providing the system with more refined directional discrimination capabilities. The experimental results demonstrated that incorporating the effects of inter-tag coupling interference and rotation angle into the phase model significantly improved the recognition accuracy for both object localization and attitude angle determination. Specifically, the average error of object positioning was reduced to 12.3 cm, while the average error of attitude angle recognition was reduced to 8.28°, making the method suitable for various practical application scenarios requiring attitude recognition.

Quantification of ocean microplastic fragmentation processes in the Sea of Japan using a combination of field observations and numerical particle tracking model experiments

This study estimated the fragmentation rate of microplastics (MiPs) in the Sea of Japan by analyzing MiP size over time following their generation from macroplastics (MaPs). A 5-year particle-tracking model was used to simulate the MaP and MiP motions driven by ocean currents, Stokes drift, the windage of MaPs, beaching, re-drifting, the conversion process from MaPs to MiPs, and the removal of MiPs from the upper ocean. MiP sizes decreased downstream in the Tsushima Current flowing northeastward. The highest correlation between MiP size and elapsed time occurred in the simulation where MiP fragmentation also occurred in the ocean, at 20 % of the rate on beaches. The apparent fragmentation rate in nature was estimated to approximately 1.0 mm/100 days. This study demonstrated that incorporating spatiotemporal information from the simulation into observed size results could further our understanding of fragmentation of MiPs degraded in marine environments.

Multiturn simulation of radiative Bhabha scattering in the equivalent photon approximation

In this paper, we present a Monte Carlo event generator for radiative Bhabha scattering, based on the method of equivalent photons, and optimized for multiturn tracking simulations in the framework. We demonstrate the accuracy of the event generator with successful benchmarks of the luminosity and event cross section against currently existing alternative tools. Consequently, we use it to investigate existing estimates of the radiative Bhabha scattering beam lifetime at the FCC-ee, as well as the impact of radiative Bhabha scattering on the beam dynamics, using full nonlinear tracking models. Published by the American Physical Society 2024

Exploring the use of deep learning models for accurate tracking of 3D zebrafish trajectories.

Zebrafish are ideal model organisms for various fields of biological research, including genetics, neural transmission patterns, disease and drug testing, and heart disease studies, because of their unique ability to regenerate cardiac muscle. Tracking zebrafish trajectories is essential for understanding their behavior, physiological states, and disease associations. While 2D tracking methods are limited, 3D tracking provides more accurate descriptions of their movements, leading to a comprehensive understanding of their behavior. In this study, we used deep learning models to track the 3D movements of zebrafish. Videos were captured by two custom-made cameras, and 21,360 images were labeled for the dataset. The YOLOv7 model was trained using hyperparameter tuning, with the top- and side-view camera models trained using the v7x.pt and v7.pt weights, respectively, over 300 iterations with 10,680 data points each. The models achieved impressive results, with an accuracy of 98.7% and a recall of 98.1% based on the test set. The collected data were also used to generate dynamic 3D trajectories. Based on a test set with 3,632 3D coordinates, the final model detected 173.11% more coordinates than the initial model. Compared to the ground truth, the maximum and minimum errors decreased by 97.39% and 86.36%, respectively, and the average error decreased by 90.5%.This study presents a feasible 3D tracking method for zebrafish trajectories. The results can be used for further analysis of movement-related behavioral data, contributing to experimental research utilizing zebrafish.