Motion Model Research Articles

A back propagation neural network based respiratory motion modelling method.

This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases. Internal and external respiratory data from four-dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement. The BP-RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP-RMM for 75 marked points is 1.819mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511mm. The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.

Quantum tunnelling and thermally driven transitions in a double-well potential at finite temperature

We explore dissipative quantum tunnelling, a phenomenon central to various physical and chemical processes, utilizing a model based on a double-well potential. This paper aims to bridge gaps in understanding the crossover from thermal activation to quantum tunnelling, a domain still shrouded in mystery despite extensive research. By numerically investigating a model derived from Caldeira–Leggett’s work on quantum Brownian motion, examining both Lindblad and stochastic Schrödinger dynamics, we offer new insights into the transition states in the crossover region. Contrary to a common belief that temperature strongly dampens all quantum effects, our findings reveal that under certain conditions temperature instead alters the nature of tunnelling from a deterministic and periodic process to a stochastic yet still very quantum phenomenon. This underscores the profound influence of quantum effects on transition rates and the critical role of temperature in modulating tunnelling behaviours. Additionally, we introduce a new model for quantum Brownian motion that takes Lindblad form and is formulated as a modification of the widely known model found in Breuer and Petruccione. In our approach, we remove the zero-temperature singularity resulting in a better description of low-temperature quantum Brownian motion near a potential minima. Despite these advancements, we recognize persistent challenges in accurately simulating the dynamics at extremely low temperatures for arbitrary potentials, particularly those that cannot be closely approximated locally by a quadratic function.

Kinematics analysis of planetary roller screw mechanism with misalignment

The misalignment is unavoidable for planetary roller screw mechanism (PRSM) in practical application due to many uncertainties. The misalignment has a strong effect on the kinematic characteristics of PRSM. However, the relevant research has been rarely reported in the past. In order to clearly present the motion characteristics, a novel kinematics model of PRSM with misalignment is established in this article. The rotational tensor theory is introduced to obtain the coordinate transformation equations. The position vectors of PRSM with misalignment are acquired to calculate the position of the contact point. The motion model of PRSM with misalignment is derived by analyzing the velocity of the contact point. Compared with some published literature, the kinematic model of PRSM is verified. Moreover, a relationship between the misalignment variates, which are the misalignment angle, the misalignment azimuth angle, and the offset distance of the nut's ending point, respectively, and the kinematic characteristics of PRSM are investigated. The results indicate that those misalignment variates have a great influence on the kinematics of PRSM and lead to fluctuations in the PRSM's transmission velocity and angular velocity ratio. The fluctuation amplitude of transmission velocity is positively correlated with misalignment angle.

Using Brownian model to study the effect of temperature on diffusion coefficient

This study employs molecular simulation techniques, particularly the Brownian motion model, to investigate the influence of temperature on diffusion coefficients. Leveraging Einsteins relationship for calculating diffusion coefficients and a one-dimensional random walk model, we systematically explore the impact of simulation direction, particle quantity, and simulation duration on diffusion behavior. The research extends its scope to three-dimensional Brownian motion simulations, offering insights into the stochastic motion of particles in a fluid. The primary objective is to analyze the relationship between temperature and diffusion coefficients. Through rigorous linear regression analysis, a significant and strong linear association is identified, demonstrating that an increase in temperature correlates with an increase in the diffusion coefficient. The research not only contributes to the fundamental understanding of molecular motion but also provides practical recommendations for simulation parameters, especially in resource-constrained computational environments. The study acknowledges certain limitations in the current Brownian motion algorithm and proposes avenues for future research to enhance computational efficiency and precision. This abstract encapsulates the key methodologies, findings, and implications of the research, laying the foundation for a comprehensive exploration of temperature-dependent diffusion coefficients in molecular systems.

The importance of corner frequency in site‐based stochastic ground motion models

The importance of corner frequency in site‐based stochastic ground motion models

Continuous-discrete extended Kalman filtering based on the neural ordinary differential equations method

In bearings-only tracking (BOT), the accuracy of state estimation significantly diminishes when confronted with an unknown target motion model. Therefore, this study proposed a novel continuous-discrete filtering algorithm, the neural ordinary differential equations-based continuous-discrete extended Kalman filtering (NODE-CD-EKF). The approach utilizes the neural ordinary differential equation (NODE) to build the target's system model, which is then used in the time update to precisely predict the target state at the next time point within the continuous-discrete Kalman framework. The NODE concisely and accurately describes the motion model in the form of ordinary differential equations in a data-driven way. This approach addresses the limitations of traditional Kalman filters, which suffer from degraded performance due to a lack of a precise mathematical model of target states. Our method is evaluated and compared to existing multiple-model and other data-driven algorithms in simulations of BOT and another scenario using the Accumulated Root Mean Square Error (ARMSE) of position. The ARMSE of our method is only 16.30 % of the multiple-model methods at least. Overall, this approach outperforms other multiple-model and data-driven algorithms and possesses the potential for solving other nonlinear tasks in real-world applications.

Using the Buckingham π Theorem for Multi-System Transfer Learning: A Case-Study with 3 Vehicles Sharing a Database

Many advanced driver assistance schemes or autonomous vehicle controllers are based on a motion model of the vehicle behavior, i.e., a function predicting how the vehicle will react to a given control input. Data-driven models, based on experimental or simulated data, are very useful, especially for vehicles difficult to model analytically, for instance, ground vehicles for which the ground-tire interaction is hard to model from first principles. However, learning schemes are limited by the difficulty of collecting large amounts of experimental data or having to rely on high-fidelity simulations. This paper explores the potential of an approach that uses dimensionless numbers based on Buckingham’s π theorem to improve the efficiency of data for learning models, with the goal of facilitating knowledge sharing between similar systems. A case study using car-like vehicles compares traditional and dimensionless models on simulated and experimental data to validate the benefits of the new dimensionless learning approach. Preliminary results from the case study presented show that this new dimensionless approach could accelerate the learning rate and improve the accuracy of the model prediction when transferring the learned model between various similar vehicles. Prediction accuracy improvements with the dimensionless scheme when using a shared database, that is, predicting the motion of a vehicle based on data from various different vehicles was found to be 480% more accurate for predicting a simple no-slip maneuver based on simulated data and 11% more accurate to predict a highly dynamic braking maneuver based on experimental data. A modified physics-informed learning scheme with hand-crafted dimensionless features was also shown to increase the improvement to precision gains of 917% and 28% respectively. A comparative study also shows that using Buckingham’s π theorem is a much more effective preprocessing step for this task than principal component analysis (PCA) or simply normalizing the data. These results show that the use of dimensionless variables is a promising tool to help in the task of learning a more generalizable motion model for vehicles, and hence potentially taking advantage of the data generated by fleets of vehicles on the road even though they are not identical.

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.

A Computation Model for Coast Wave Motions with Multiple Breakings

A Computation Model for Coast Wave Motions with Multiple Breakings

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

AbstractAiming 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

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

Abstract Motion 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.

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.