Motion Model Research Articles

Stability of Northern Eurasia Based on Satellite Geodesy Data

The geodynamics of Northern Eurasia has been analyzed based on repeated satellite positioning with GNSS stations throughout the Russian Federation territory from 2015 to the present. The study utilized two sources of data: observations from the stations of the Russian Fundamental Astro-Geodetic Network (FAGN) and stations of the International GNSS Service (IGS) with permanent satellite tracking. This data set allowed to estimate correctness of the block kinematics of the Eurasian plate in three tectonic plate motion models: NUVEL-1A, NNR-MORVEL-56, and ITRF2014. The analysis of the misfits between the observed and model velocities has shown that these misfits have a systematic component in the vicinity of the East European Platform which differs for each of three models. In addition to analyzing the block kinematics of the Eurasian plate, we also evaluated its internal stability. To do this, we calculated the areal deformations of Northern Eurasia using the finite element method. For this purpose, we added the observations processing results from the global data set of the Nevada Geodetic Laboratory to the processing results from two original data sets. Besides interplate boundary deformations which are consistent with existing ideas of the geodynamics of Northern Eurasia, the strain field analysis also revealed intraplate deformations distributed consistently with the configuration of the Northern Eurasia cratons.

Breaking down walls: Continuous potential models for internal motions in NMR spin relaxation

Simple physical models for restricted diffusion in a potential, which provide important insights for NMR spin relaxation, usually are based on free diffusion within rigid boundaries or diffusion in relatively simple continuous potential energy surfaces. The diffusion-in-a-cone model is an example of the former and diffusion in an N-fold cosine potential is an example of the latter. The present work models restricted diffusion for arbitrary potential energy functions on the surface of a cone or a sphere, by expanding the potentials in Fourier or spherical harmonic series, respectively. The results exhibit simple relationships between generalized order parameters and effective correlation times, critical for analysis of experimental spin relaxation data, and illustrate the transition from diffusive-like to jump-like behavior in multi-well potentials.

A Computation Model for Coast Wave Motions with Multiple Breakings

This paper presents a computational model for coast wave motions with multiple wave breakings. In the Boussinesq model, the wave breaking judgment method is combined with the wave recovery judgment condition, which stops the wave breaking process when triggered. The energy dissipation of wave breaking is corrected, and the dissipation of wave energy is maintained at about 10% during the wave recovery stage, so that the dissipation caused by the residual turbulent motion of wave breaking and the increase in wave height caused by the shallowing of waves due to the water bottom slope are offset. By comparing the calculation results with the experimental results, it is proved that this model can be used to calculate multiple wave breakings. This model is applied to discuss the influence of wave incident angle and wave period on wave height and longshore current and gives the distribution characteristics of wave height and longshore current under multiple wave breakings.

YS-SLAM: YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots

Aiming at the problem of poor autonomous adaptability of mobile robots to dynamic environments, this paper propose a YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots. First, a light-weight YOLACT++ is utilized to detect and segment potential dynamic objects, and Mahalanobis distance is combined to remove feature points on active dynamic objects, also, epipolar constraint and clustering are employed to eliminate feature points on passive dynamic objects. Then, in terms of the semantic labels of dynamic and static components, the global semantic map is divided into three parts for construction. The semantic overlap and uniform motion model are chose to track moving objects and the dynamic components are added to the background map. Finally, a 3D semantic octree map is constructed that is consistent with the real environment and updated in real time. A series of simulations and experiments demonstrated the feasibility and effectiveness of the proposed approach.

Dynamics of a Double Pendulum with Viscous Friction at the Hinges. I. Mathematical Model of Motion and Construction of the Regime Diagram

Dynamics of a Double Pendulum with Viscous Friction at the Hinges. I. Mathematical Model of Motion and Construction of the Regime Diagram

In vivo biomechanical dynamic simulation of a healthy knee during the single-leg lunge and its experiment validation

Biomechanical modeling of the knee during motion is a pivotal component in disease treatment, implant designs, and rehabilitation strategies. Historically, dynamic simulations of the knee have been scant. This study uniquely integrates a dual fluoroscopic imaging system (DFIS) to investigate the in vivo dynamic behavior of the meniscus during functional activities using a finite element (FE) model. The model was subsequently validated through experiments. Motion capture of a single-leg lunge was executed by DFIS. The motion model was reconstructed using 2D-to-3D registration in conjunction with computed tomography (CT) scans. Both CT and magnetic resonance imaging (MRI) data facilitated the development of the knee FE model. In vivo knee displacements and rotations were utilized as driving conditions for the FE model. Moreover, a 3D-printed model, accompanied with digital imaging correlation (DIC), was used to evaluate the accuracy of the FE model. To a better inner view of knees during the DIC analysis, tibia and femur were crafted by transparent resin. The availability of the FE model was guaranteed by the similar strain distribution of the DIC and FE simulation. Subsequent modeling revealed that the compressive stress distribution between the medial and lateral menisci was balanced in the standing posture. As the flexion angle increased, the medial meniscus bore the primary compressive load, with peak stresses occurring between 60 and 80° of flexion. The simulation of a healthy knee provides a critical theoretical foundation for addressing knee pathologies and advancing prosthetic designs.

A machine learning-based approach for automatic motion/constraint and mobility analysis of parallel robots

AbstractMotion and constraint identification are the fundamental issue throughout the development of parallel mechanisms. Aiming at meaningful result with heuristic and visualizable process, this paper proposes a machine learning-based method for motions and constraints modeling and further develops the automatic software for mobility analysis. As a preliminary, topology of parallel mechanism is characterized by recognizable symbols and mapped to the motion of component limb through programming algorithm. A predictive model for motion and constraint with their nature meanings is constructed based on neural network. An increase in accuracy is obtained by the novel loss function, which combines the errors of network and physical equation. Based on the predictive model, an automatic framework for mobility analysis of parallel mechanisms is constructed. A software is developed with WebGL interface, providing the result of mobility analysis as well as the visualizing process particularly. Finally, five typical parallel mechanisms are taken as examples to verify the approach and its software. The method facilitates to attain motion/constraint and mobility of parallel mechanisms with both numerical and geometric features.

Prostate intra-fraction motion recorded by transperineal ultrasound.

Infra-fraction motion of the prostate was recorded during 3.423 fractions of image guided radiotherapy (IGRT) in 191 patients, 14 of which were treated by intensity modulated radiation therapy (IMRT), and 177 of which were treated by volumetric arc therapy (VMAT). The prostate was imaged by three-dimensional and time-resolved transperineal ultrasound (4D-US) of type Clarity by Elekta AB, Stockholm, Sweden. The prostate volume was registered and the prostate position (center of volume) was recorded at a frequency of 2.0 samples per second. This raw data set contains a total of 1.985.392 prostate and patient couch positions over a time span of 272 hours, 52 minutes and 34 seconds of life radiotherapy as exported by the instrument software. This data set has been used for the validation of models of prostate intra-fraction motion and for the estimation of the dosimetric impact of actual intra-fraction motion on treatment quality and side effects. We hope that this data set may be reused by other groups for similar purposes.

Prescribed Performance Fault-Tolerant Attitude Tracking Control for UAV with Actuator Faults

This paper proposes a prescribed performance fault-tolerant control based on a fixed-time extended state observer (FXTESO) for a carrier-based unmanned aerial vehicle (UAV). First, the attitude motion model of the UAV is introduced. Secondly, the proposed FXTESO is designed to estimate the total disturbances including coupling, actuator faults and external disturbances. By using the barrier Lyapunov function (BLF), it is proved that under prescribed performance control (PPC), the attitude tracking error is stable within the prescribed range. The simulation results for tracking the desired attitude angle show that the average overshoot and stabilization time of PPC-FXTESO is 0.00455rad and 6.2s. Comparatively, the average overshoots of BSC-ESO and BSC-FTESO are 0.035rad and 0.027rad, with stabilization times of 14.97s and 12.56s, respectively. Therefore, the control scheme proposed in this paper outperforms other control schemes.

A Dyson Brownian Motion Model for Weak Measurements in Chaotic Quantum Systems

We consider a toy model for the study of monitored dynamics in many-body quantum systems. We study the stochastic Schrödinger equation resulting from continuous monitoring with a rate Γ of a random Hermitian operator, drawn from the Gaussian unitary ensemble (GUE) at every time t. Due to invariance by unitary transformations, the dynamics of the eigenvalues {λα}α=1n of the density matrix decouples from that of the eigenvectors, and is exactly described by stochastic equations that we derive. We consider two regimes: in the presence of an extra dephasing term, which can be generated by imperfect quantum measurements, the density matrix has a stationary distribution, and we show that in the limit of large size n→∞ it matches with the inverse-Marchenko–Pastur distribution. In the case of perfect measurements, instead, purification eventually occurs and we focus on finite-time dynamics. In this case, remarkably, we find an exact solution for the joint probability distribution of λ’s at each time t and for each size n. Two relevant regimes emerge: at short times tΓ=O(1), the spectrum is in a Coulomb gas regime, with a well-defined continuous spectral distribution in the n→∞ limit. In that case, all moments of the density matrix become self-averaging and it is possible to exactly characterize the entanglement spectrum. In the limit of large times tΓ=O(n), one enters instead a regime in which the eigenvalues are exponentially separated log(λα/λβ)=O(Γt/n), but fluctuations ∼O(Γt/n) play an essential role. We are still able to characterize the asymptotic behaviors of the entanglement entropy in this regime.

A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California

After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.

Improved Variable Structure Interacting Multimodels for Target Trajectory Tracking and Extrapolation

To improve the lengthy computation time of conventional variable structure interacting multiple model (VSIMM) algorithm and increase the precision of target prediction and extrapolation, the target state and flight intent information captured by the Automatic Dependent Surveillance-Broadcast (ADS-B) are used as the model’s prior information; combining this information with VSIMM theoretical framework, we purpose an intent variable structure interacting multiple model (INT-VSIMM) algorithm. Firstly, the motion pattern of the target in the flight phase of the flight path is decomposed, and complete sets of motion models are established. Secondly, according to the principle of directed graph switching, a model set switching method is designed, which is mainly based on “hard” switching and supplemented by “soft” switching. Finally, the INT-VSIMM algorithm is used to track the trajectory of the target aircraft, and short-term trajectory extrapolation is performed based on the target state estimation. The simulation results show that the target tracking performance computational time based on the INT-VSIMM algorithm is superior to the comparative existing methods, and the extrapolated trajectory has less error in the short term, which can satisfy the needs of conflict detection.

Optimizing Camera Motion with MCTS and Target Motion Modeling in Multi-Target Active Object Tracking

In this work, we are dedicated to multi-target active object tracking (AOT), where the goal is to achieve continuous tracking of targets through real-time control of camera. This form of active camera control can be applied to unmanned aerial vehicles (UAV), intelligent robots, and sports events. Our work is conducted in an environment featuring multiple cameras and targets, where our goal is to maximize target coverage. Contrasting with previous research, our work introduces additional degrees of freedom for the cameras, allowing them not only to rotate but also to move along boundary lines. In addition, we model the motion of target to predict the future position of the target in environment. With target’s future position, we use Monte Carlo Tree Search (MCTS) method to find the optimal action of camera. Since the action space is large, we propose to leverage the action selection from multi-agent reinforcement learning (MARL) network to prune the search tree of Monte Carlo Tree Search method, so as to find the optimal action more efficiently. We establish a multi-target 2D environment to simulate several sports games, and experimental results demonstrate that our method can effectively improve the target coverage. The code is available at: http://github.com/HopeChanger/ActiveObjectTracking .

Mechanical properties and microstructure evolution of T2 copper in multimodal ultrasonic vibration assisted micro-compression

Multimodal ultrasonic vibration (UV) assisted micro-forming has been widely investigated for its advantages of further reducing forming loads and improving forming quality. However, the influence mechanism of different UV modes on microstructure evolution and mechanical properties was still unclear. Multimodal UV assisted micro-compression tests on T2 copper with different grains and sample sizes were conducted in this study. The microstructure evolution for different UV modes was observed by EBSD. The results showed that the true stress reduction caused by UV was increased sequentially with tool ultrasonic vibration (TV), mold ultrasonic vibration (MV) and compound ultrasonic vibration (CV). The region of grain deformation was shifted along the direction of UV, and the MV promoted the uniform distribution of deformation stress. The grain refinement, fiber streamline density, grain deformation and rotation degree were further enhanced under CV, due to the synergistic effect of TV and MV. Additionally, a coupled theoretical model considering both acoustic softening effect and size effect was proposed for describing the mechanical properties. And a physical model of dislocation motion in different UV modes was developed for describing the microstructure evolution. The maximum error between the theoretical and experimental results was only 2.39 %. This study provides a theoretical basis for the optimization of UV assisted micro-forming process.

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression.

The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.

Order–disorder phase transitions in front of the exit during human crowd evacuations

Human crowds often exhibit collective behaviors through self-organization, such as orderly queueing and disordered congestion at exits. Understanding the dynamic mechanisms behind these behaviors is crucial for pedestrian traffic management and the handling of mass crowds. Although current models and experiments have provided profound insights into ordered and disordered pedestrian groups separately, the transition mechanism from order to disorder within pedestrian crowds in front of exits remains unclear. In this study, a pedestrian queueing evacuation experiment was presented to demonstrate the phase transition process of pedestrian movement states as urgency levels in the environment change. We discovered the migration pattern of the phase transition point under evacuation control and identified different propagation modes of pedestrian velocity waves. Furthermore, a pedestrian motion model based on experimental data and observed phenomena was established. This model not only replicates the observed phenomena and regularities from the experiments but also reveals the quantitative phase transition mechanism of pedestrian movement under the influence of a single factor (i.e., urgency level or queueing ratio). Our findings hold significant implications for various domains, including pedestrian management, traffic control, and pedestrian dynamics.

Theoretical research of impact vibration of multi-section serial pipeline inspection gauge in large drop pipeline

When operating in large drop pipelines, pipeline inspection gauge may experience severe impact vibration at elbows, potentially leading to its failure. This paper focuses on serial pipeline inspection gauges with multiple sections in large drop pipelines, analyzes the force acting on the pipeline inspection gauge during the descent phase, and establishes a multi-body dynamics model of the gauge under pipe-soil coupling conditions. In addition, a correction formula is developed for the pressure changes in the medium at the front and rear of the pipeline inspection gauge. To solve the model, MSC/ADAMS and MATLAB/Simulink are used for the bi-directional fluid–structure-interaction joint simulation. Moreover, the results are compared with the pipeline inspection gauge motion model established by OLGA. The study investigates the dynamic responses, vibration superposition and laws of dynamic evolution at the upper and lower elbows of the large drop pipeline. The results show that the speed of the gauge increases suddenly during the descent in large drop sections and then stabilizes, with different effects of the fluid medium pressure difference at various stages. The vibration responses along the axial, horizontal radial, and vertical directions at the elbows vary, with the second section experiencing stronger vibrations due to superposition effects. Compared to fixed boundaries, the acceleration extremes of the gauge are lower under the constraints of pipe-soil model, indicating significant soil buffering and vibration damping effects.

Directionality characteristics of horizontal response spectra from the 2022 Mw 6.9 Chihshang, Taiwan earthquake

Horizontal earthquake ground motion intensity, and specifically response spectral ordinates, vary with orientation. This phenomenon is usually referred to as ground motion directionality and can be separated into two aspects: the orientation where the maximum spectral response occurs and the variation of response spectral ordinates as the orientation moves away from the orientation of maximum spectral response. This work studies both aspects using the recent 2022 Mw 6.9 Chihshang, Taiwan earthquake, which was recorded by a dense network of strong motion stations with various geological and topographical settings. The mean variation of response spectral ordinates with orientation is found to be slightly more significant than that of previous shallow crustal earthquakes in active tectonic regimes. Moreover, the orientation of maximum spectral response is found to be close to the transverse orientation, which is perpendicular to the orientation at a given site that points to the earthquake epicenter, confirming prior observations made for strike-slip earthquakes. These results suggest that the location of a site relative to the seismic source could be used to modify the outputs of ground motion models to estimate spectral responses at specific horizontal orientations.

HME-KG: A method of constructing the human motion encoding knowledge graph based on a hierarchical motion model

The diversity, infinity, and nonuniform description of human motion make it challenging for computers to understand human activities. To explore and reuse captured human motion data, this work defines a more comprehensive hierarchical theoretical model of human motion and proposes a standard human posture encoding scheme. We construct a domain knowledge graph (DKG) named the human motion encoding knowledge graph (HME-KG) based on posture codes and action labels. Community detection, similarity analysis, and centrality analysis are used to explore the potential value of motion data. This paper conducts an evaluation and visualization of HME-KG.

Estimation of Geometrical Spreading Factor and Coda-Wave Attenuation Characteristics for the Saurashtra Horst in Western Deccan Volcanic Province, Gujarat, India

ABSTRACT In the present study, we have estimated the coda Q (QC) and the geometrical spreading factor (gamma) for the Saurashtra region that is located in the western part of the Deccan Volcanic Province, Gujarat, India. The waveform data from the seismic network operated by the Institute of Seismological Research, Gandhinagar, are used for the analysis. We have used records of a total of 221 local earthquakes with epicentral distances less than 70 km from the seismic stations. The moment magnitude range of the earthquakes is 2.5–5.3, and the depth range is 1–22 km. The QC has been estimated at five central frequencies (1.5, 3, 6, 12, and 20 Hz) using 20, 30, and 40 s window lengths for vertical, north–south, and east–west components, respectively. The obtained QC values show that the attenuation in the region has a strong dependence on frequency and points toward a heterogeneous medium. The geometrical spreading factors are also estimated for two cases, that is, with and without Q at the same central frequencies and window lengths. The average geometrical spreading factors with Q are 1.37, 1.48, 1.40, 1.08, and 1.04, at central frequencies 1.5, 3, 6, 12, and 20 Hz, respectively, whereas it is 1.59, 1.53, 1.47, 1.19, and 1.14, respectively, in the absence of Q. The estimated values also indicate that the medium is heterogeneous in the Saurashtra region. The estimated region-specific coda Q and the geometrical spreading factor may help in the modeling of strong ground motions and assessment of the seismic hazard for the Saurashtra region.