Motion Model Research Articles

The MEMENTO code for modeling of macroscopic melt motion in fusion devices

The MEMENTO (MEtallic Melt Evolution in Next-step TOkamaks) code is a new numerical implementation of the physics model originally developed for the MEMOS-U code with the objective to self-consistently describe the generation of melt and its subsequent large scale dynamics in fusion devices and to assess the damage of metallic reactor armor under powerful normal and off-normal plasma events. The model has been validated in multiple dedicated EUROfusion experiments. MEMENTO solves the heat and phase transfer problem coupled with the incompressible Navier–Stokes equations in the shallow water approximation for the thin liquid film over the solid metal and with the current propagation equations on a domain that features a time-evolving deforming metal-plasma interface. The code utilizes non-uniform and adaptive meshing along with sub-cycling in time facilitated by the AMReX open-source framework as well as AMReX’s built-in parallelization capabilities.

Implementation of Manipulative Basic Movement Learning Model Development for Students aged 5-6 years

This research aims to know application of learning models motion base manipulative of participants educate aged 5-6 years . Loading experimental method pre test and post test as well as skills test results motion base manipulative . Population from study This participant educate 5-6 years old totaling 120 students . Experiment 12 meetings were held . Pretest and posttest were carried out with instrument motion base throwing , catching , dribbling , kicking and bouncing the ball. Based on the results of the data normality test stated that the data was not so that data analysis with using non- parametric statistics . Test results with using Wilcoxon states there is difference between group experiment and control with sig. 0.00 . With thereby can concluded that application of learning models with a motion model base manipulative effective For increase results ability motor rough child 5-6 years old .

A Performance Analysis of Stochastic Processes and Machine Learning Algorithms in Stock Market Prediction

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends of stock indices XLF (financial sector), XLK (technology sector), and XLV (healthcare sector). The results showed that stochastic processes achieved remarkable prediction performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index value to predict tomorrow’s value, which may overestimate their performance. In contrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value. Therefore, optimizing the hyperparameters of machine learning algorithms is crucial for further improving their performance.

Exploring the Macroevolutionary Signature of Asymmetric Inheritance at Speciation.

Popular comparative phylogenetic models such as Brownian Motion, Ornstein-Ulhenbeck, and their extensions, assume that, at speciation, a trait value is inherited identically by two descendant species. This assumption contrasts with models of speciation at a micro-evolutionary scale where descendants' phenotypic distributions are sub-samples of the ancestral distribution. Different speciation mechanisms can lead to a displacement of the ancestral phenotypic mean among descendants and an asymmetric inheritance of the ancestral phenotypic variance. In contrast, even macro-evolutionary models that account for intraspecific variance assume symmetrically conserved inheritance of ancestral phenotypic distribution at speciation. Here we develop an Asymmetric Brownian Motion model (ABM) that relaxes the assumption of symmetric and conserved inheritance of the ancestral distribution at the time of speciation. The ABM jointly models the evolution of both intra- and inter-specific phenotypic variation. It also infers the mode of phenotypic inheritance at speciation, which can range from a symmetric and conserved inheritance, where descendants inherit the ancestral distribution, to an asymmetric and displaced inheritance, where descendants inherit divergent phenotypic means and variances. To demonstrate this model, we analyze the evolution of beak morphology in Darwin finches, finding evidence of displacement at speciation. The ABM model helps to bridge micro- and macro-evolutionary models of trait evolution by providing a more robust framework for testing the effects of ecological speciation, character displacement, and niche partitioning on trait evolution at the macro-evolutionary scale.

Ship Autonomous Berthing Strategy Based on Improved Linear-Quadratic Regulator

There has been significant interest in the research field of ship automatic navigation, particularly in the area of autonomous berthing. To address the key challenges of path planning and control during ship berthing, we propose an enhanced Linear−Quadratic Regulator (LQR) control approach, reinforced by the Covariance Matrix Adaptation Evolution Strategy (CMA−ES), along with an adaptive berthing strategy decision model. This integrated framework encompasses ship motion control, path planning, and berthing strategy selection to facilitate adaptive and autonomous ship berthing. Initially, a dynamic mathematical model of ship motion is established, taking into account wind and current interference effects. Subsequently, an adaptive environment−aware berthing strategy model is introduced to enable automatic selection of berthing strategies based on spatial relationships between environmental factors and the berth. By utilizing the refined LQR method, autonomous motion control for ship berthing is achieved. To validate the effectiveness of our controller, comprehensive simulation analyses are conducted under varying operating conditions to encompass crucial factors such as large drift angle characteristics of ships, shallow water effects, and bank effects across seven diverse working conditions. The simulation results underscore the robustness of our proposed method in responding to environmental interference while demonstrating its capability to select appropriate berthing strategies based on varying operational scenarios.

An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

Dynamic Programming-Based Track-before-Detect Algorithm for Weak Maneuvering Targets in Range–Doppler Plane

This paper focuses on detecting and tracking maneuvering weak targets in the range–Doppler (RD) plane with the track-before-detect (TBD) algorithm based on dynamic programming (DP). Traditional DP-TBD algorithms integrate target detection and tracking in their framework while searching the paths provided by a predefined model of the kinematic properties within the constraints allowed. However, both the approximate motion model used in the RD plane and the model mismatch caused when the target undergoes a maneuver can degrade the TBD performance sharply. To address these issues, this paper accurately describes the evolution of the RD equation based on Constant Acceleration (CA) and Coordinated Turn (CT) motion models with the process noise in the Cartesian coordinate system, and it also employs a recursive method to estimate the parameters in the equations for efficient energy accumulation and path searches. Facing the situation that targets energy accumulation during the DP iteration process will lead to an expansion of the target energy accumulation process. This paper designs a more efficient Optimization Function (OF) to inhibit the expansion effect, improve the resolution of the nearby targets, and increase the detection probability of the weak targets simultaneously. In addition, to search the trajectory more efficiently and accurately, we improved the process of DP multi-frame accumulation, thus reducing the computation amount of large-scale searches. Finally, the effectiveness of the proposed method for CA and CT motion target detection and tracking is verified by many of the simulation experiments that were conducted in this paper.

Indoor fixed-point hovering control for UAVs based on visual inertial SLAM

Purpose Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter. Design/methodology/approach The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms. Findings A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS. Originality/value A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

MotionTrack: Learning motion predictor for multiple object tracking

Significant progress has been achieved in multi-object tracking (MOT) through the evolution of detection and re-identification (ReID) techniques. Despite these advancements, accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains a challenge. This challenge arises from two main factors: the insufficient discriminability of ReID features and the predominant utilization of linear motion models in MOT. In this context, we introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor that relies solely on object trajectory information. This predictor comprehensively integrates two levels of granularity in motion features to enhance the modeling of temporal dynamics and facilitate precise future motion prediction for individual objects. Specifically, the proposed approach adopts a self-attention mechanism to capture token-level information and a Dynamic MLP layer to model channel-level features. MotionTrack is a simple, online tracking approach. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT, characterized by highly complex object motion.

Stopping power and range estimations in proton therapy based on prompt gamma timing: motion models and automated parameter optimization

Objective. Particle therapy treatments are currently limited by uncertainties of the delivered dose. Verification techniques like Prompt-Gamma-Timing-based Stopping Power Estimation (PGT-SPE) may allow for reduction of safety margins in treatment planning. Approach. From Prompt-Gamma-Timing measurements, we reconstruct the spatiotemporal distribution of prompt gamma emissions, which is linked to the average motion of the primary particles. The stopping power is determined by fitting a model of the average particle motion. Here, we compare a previously published implementation of the particle motion model with an alternative formulation and present two formulations to automatically select the hyperparameters of our procedure. The performance was assessed using Monte-Carlo simulations of proton beams (60 MeV–219 MeV) impinging on a homogeneous PMMA phantom. Main results. The range was successfully determined within a standard deviation of 3 mm for proton beam energies from 70 MeV to 219 MeV. Stopping power estimates showed errors below 5% for beam energies above 160 MeV. At lower energies, the estimation performance degraded to unsatisfactory levels due to the short range of the protons. The new motion model improved the estimation performance by up to 5% for beam energies from 100 MeV to 150 MeV with mean errors ranging from 6% to 18%. The automated hyperparameter optimization matched the average error of previously reported manual selections, while significantly reducing the outliers. Significance. The data-driven hyperparameter optimization allowed for a reproducible and fast evaluation of our method. The updated motion model and evaluation at new beam energies bring us closer to applying PGT-SPE in more complex scenarios. Direct comparison of stopping power estimates between treatment planning and measurements during irradiation would offer a more direct verification than other secondary-particle-based techniques.

Scenario-based seismic hazard for horizontal and vertical ground motions in central Italy

We propose an innovative methodology for seismic emergency planning and earthquake risk mitigation in central Italy by integrating three prototypal earthquake scenarios. The different scenarios derive maximum earthquake magnitudes from different input data. The first scenario utilizes local rheological, geological, and geophysical conditions; the second scenario considers the study area fault characteristics, while the third scenario relies on the cluster analysis of historical and instrumental earthquake records. The magnitudes and related uncertainties are combined using a conflation method to derive the expected ground motions for a grid of sites in central Italy. The resulting scenarios include peak ground acceleration and spectral ordinates, presented as maps and spectra for two selected localities. The vertical component of ground motion is also presented, because it is essential for accurately assessing the response of short-period structures. Our methodology complements the more classic seismic hazard analyses, offering additional insights for earthquake contingency planning and loss analysis. The proposed methodology is flexible; multiple models and ongoing advancements in scenario practice (near-field effects, vertical ground motion, and the choice of ground motion models) can be easily incorporated, increasing the effectiveness of seismic scenario modeling in seismic emergency planning and risk mitigation.

Balance and Walking Control for Biped Robot Based on Divergent Component of Motion and Contact Force Optimization

This paper presents a complete planner and controller scheme to achieve balance and walking for a biped robot, which does not need to distinguish the robot’s dynamic model parameters. The high-level planner utilizes model predictive control to optimize both the foothold location and step duration based on the Divergent Component of Motion (DCM) model to enhance the robustness of generated gaits. For low-level control, we use quadratic programming (QP) to optimize the contact force distribution under the contact constraints to achieve the virtual wrench exerted on the base of the robot. Then, the joint torques sent to the robot are derived from three parts: first, the torques mapped from the contact force; second, the swing leg tracking; and third, the stance foot stabilization. The simulation and experiment on BRUCE, a miniature bipedal robot from Westwood Robotics (Los Angeles, CA, USA), testify to the performance of the control scheme, including push recovery, Center of Mass (CoM) tracking, and omnidirectional walking.

A Simple Model for Plate Motion and Topography

A Simple Model for Plate Motion and Topography

Marine management and monitoring system based on AHP cost-benefit analysis with integrated evaluation and genetic algorithms for recurrent searches

Marine management and monitoring systems need to develop a safety procedure for submersibles that predicts changes in the position of the submersible over time. This would effectively prevent the submersible from losing communication with the host and would allow it to react to possible deficiencies. In this paper, a random walk model is used to predict the position of the submersible at each moment on the seafloor. The probability of the highest point on each plane is 0.87-0.92. The error in the position of the submersible caused by the uncertainty on the seafloor is corrected by the Kalman filter model. In this paper, we use AHP cost-benefit analysis to comprehensively evaluate and select the best additional search equipment. In this paper, the genetic algorithm based on cyclic search is used to determine the optimal initial deployment point and search pattern. After a large number of iterations, the shortest search path length of 817.273 is obtained, and the shortest search path is taken as the optimal path for search and rescue. In this paper, the model is extended using particle swarm algorithm and environment modelling. The predictive model of the submersible is combined with the environmental model of different sea areas. Therefore, the actual motion model of the submersible is approximated. After simulation, it was found that the accuracy of the submersible probability increased by 23.72%.

Research on relative reachable domain in target orbit for maneuvering spacecraft

PurposeLots of successful space missions require that the maneuvering spacecraft can reach the target spacecraft. Therefore, research on relative reachable domain (RRD) in target orbit for maneuvering spacecraft is particularly important and is currently a hot-debated topic in the field of aerospace. This paper aims at analyzing and simulating the RRD in target orbit for maneuvering spacecrafts with a single fixed-magnitude impulse and continuous thrust, respectively, to provide a basis for analyzing the feasibility of spacecraft maneuvering missions and improving the design efficiency of spacecraft maneuvering missions.Design/methodology/approachBased on the kinematics model of relative motion, RRD in target orbit for maneuvering spacecraft with a single fixed-magnitude impulse can be calculated via analyzing the relationship between orbital elements, position vector and velocity vector of spacecrafts, and relevant studies are introduced to compare simulation results for the same case and validate the method proposed in the paper. With analysis of the dynamic model of relative motion, the calculation of RRD in target orbit for maneuvering spacecraft with continuous thrust can be transformed as the solution of the optimal control problem, and example emulations are carried out to validate the method.FindingsFor the case with a single fixed-magnitude impulse, simulation results show preliminarily that the method is in agreement with the method in Ref. (Wen et al., 2016), which treats the same case and thus is plausibly correct and feasible. For the case with continuous thrust, analysis and simulation results confirm the validity of the proposed method. The methods based on relative motion in this paper can efficiently determining the RRD in target orbit for maneuvering spacecraft.Originality/valueBoth theoretical analyses and simulation results indicate that the method proposed in this paper is comparatively simple but efficient for determine the RRD in target orbit for maneuvering spacecraft swiftly and precisely.

Efficient Microbubble Trajectory Tracking in Ultrasound Localization Microscopy Using a Gated Recurrent Unit-Based Multitasking Temporal Neural Network.

Ultrasound Localization Microscopy (ULM), an emerging medical imaging technique, effectively resolves the classical trade-off between resolution and penetration inherent in traditional ultrasound imaging, opening up new avenues for noninvasive observation of the microvascular system. However, traditional microbubble tracking methods encounter various practical challenges. These methods typically entail multiple processing stages, including intricate steps like pairwise correlation and trajectory optimization, rendering real-time applications unfeasible. Furthermore, existing deep learning-based tracking techniques neglect the temporal aspects of microbubble motion, leading to ineffective modeling of their dynamic behavior. To address these limitations, this study introduces a novel approach called the Gated Recurrent Unit (GRU)-based Multitasking Temporal Neural Network (GRU-MT). GRU-MT is designed to simultaneously handle microbubble trajectory tracking and trajectory optimization tasks. Additionally, we enhance the nonlinear motion model initially proposed by Piepenbrock et al. to better encapsulate the nonlinear motion characteristics of microbubbles, thereby improving trajectory tracking accuracy. In this study, we perform a series of experiments involving network layer substitutions to systematically evaluate the performance of various temporal neural networks, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), GRU, Transformer, and its bidirectional counterparts, on the microbubble trajectory tracking task. Concurrently, the proposed method undergoes qualitative and quantitative comparisons with traditional microbubble tracking techniques. The experimental results demonstrate that GRU-MT exhibits superior nonlinear modeling capabilities and robustness, both in simulation and in vivo dataset. Additionally, it achieves reduced trajectory tracking errors in shorter time intervals, underscoring its potential for efficient microbubble trajectory tracking. Model code is open-sourced at https://github.com/zyt-Lib/GRU-MT.

FLK: A filter with learned kinematics for real-time 3D human pose estimation

There is a growing interest in adopting 3D human pose estimation in safety-critical systems, from healthcare to Industry 5.0. Nevertheless, when applied in such settings, these neural networks may suffer from estimation inaccuracy. Besides imprecise or inconsistent annotations in the training dataset, the inaccuracy is caused by poor image quality, rare poses, dropped frames, or heavy occlusions in the scene. In addition, these scenarios often require the software results to have temporal constraints, such as real-time and zero- or low-latency, which make many of the filtering solutions proposed in the literature inapplicable. This paper proposes FLK, a Filter with Learned Kinematics, to refine 3D human motion data in real-time and at zero/low latency. The temporal core combines a Kalman filter and a low-pass filter, which learns the motion model through a recurrent neural network. The spatial core takes advantage of the biomechanical constraints of the human body to provide spatial coherency between keypoints. The combination of the cores allows the filter to adequately address different types of noise, from jittering to dropped frames. We test the filter on motion data from multiple datasets and seven 3D human pose estimation backbones, improving accuracy up to 140 mm with non-Gaussian noise and 53 mm with missing information.

Localizing Mobile Target using Kalman Filtering and Least Square Boosting Ensemble Learning based Approach

Trilateration-based target localization using received signal strengths (RSS) within wireless sensor networks (WSN) often results in inaccurate location estimates due to the considerable fluctuations in RSS measurements encountered in indoor environments. Enhancing the precision of RSS-based localization systems has been a primary area of interest in extensive research efforts. This study introduces two range-free algorithms, Ensemble Learning (EL) and EL+KF, which leverage RSS measurements for localization. In contrast to trilateration, the EL- based localization approach enables the direct estimation of target locations based on field measurements, eliminating the need for distance calculations. Notably, unlike other cutting-edge localization and tracking (L&T) scheme like the support vector regression (SVR), the LSBoost (Least Squares Boosting) EL based localization architecture can be trained very quickly using RSS measurements to determine the mobile target's position. Furthermore, the proposed EL-based localization scheme incorporates the Kalman filter (KF) to achieve additional refinement in target location estimates. To assess the localization effectiveness of these proposed algorithms in the presence of noisy RF channels and dynamic target motion models, rigorous simulations were conducted. Thanks to the robust generalization capabilities of EL, the simulation results reveal that the presented EL-based localization algorithms exhibit superior performance compared to trilateration and the SVR-based localization scheme, particularly in terms of indoor localization accuracy.

A Novel Joint Motion Compensation Algorithm for ISAR Imaging Based on Entropy Minimization.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk-Nelder-Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

Research on effect of anode microstructures on mass transfer and electrochemical reaction in SOFCs based on a fractional Brownian motion model

Research on effect of anode microstructures on mass transfer and electrochemical reaction in SOFCs based on a fractional Brownian motion model