Motion Model Research Articles

An Imaging Method for Marine Targets in Corner Reflector Jamming Scenario Based on Time–Frequency Analysis and Modified Clean Technique

In the corner reflector jamming scenario, the ship target and the corner reflector array have different degrees of defocusing in the synthetic aperture radar (SAR) image due to their complex motions, which is unfavorable to the subsequent target recognition. In this manuscript, we propose an imaging method for marine targets based on time–frequency analysis with the modified Clean technique. Firstly, the motion models of the ship target and the corner reflector array are established, and the characteristics of their Doppler parameter distribution are analyzed. Then, the Chirp Rate–Quadratic Chirp Rate Distribution (CR-QCRD) algorithm is utilized to estimate the Doppler parameters. To address the challenges posed by the aggregated scattering points of the ship target and the overlapping Doppler histories of the corner reflector array, the Clean technique is modified by short-time Fourier transform (STFT) filtering and amplitude–phase distortion correction using fractional Fourier transform (FrFT) filtering. This modification aims to improve the accuracy and efficiency of extracting scattering point components. Thirdly, in response to the poor universality of the traditional Clean iterative termination condition, the kurtosis of the residual signal spectrum amplitude is adopted as the new iterative termination condition. Compared with the existing imaging methods, the proposed method can adapt to the different Doppler distribution characteristics of the ship target and the corner reflector array, thus realizing better robustness in obtaining a well-focused target image. Finally, simulation experiments verify the effectiveness of the algorithm.

Dynamical phases of a Bose-Einstein condensate in a bad optical cavity at optomechanical resonance

We study the emergence of dynamical phases of a Bose-Einstein condensate that is optomechanically coupled to a dissipative cavity mode and transversally driven by a laser. We focus on the regime close to the optomechanical resonance, where the atoms' refractive index shifts the cavity into resonance, assuming fast cavity relaxation. We derive an effective model for the atomic motion, where the cavity degrees of freedom are eliminated using perturbation theory in the atom-cavity coupling and benchmark its predictions using numerical simulations based on the full model. Away from the optomechanical resonance, perturbation theory in the lowest order (adiabatic elimination) reliably describes the dynamics and predicts chaotic phases with unstable oscillations. Interestingly, the dynamics close to the optomechanical resonance are qualitatively captured only by including the corrections to next order (nonadiabatic corrections). In this regime we find limit-cycle phases that describe stable oscillations of the density with a well-defined frequency. We further show that such limit-cycle solutions are metastable configurations of the adiabatic model. Our work sheds light on the mechanisms that are required to observe dynamical phases and predict their existence in atom-cavity systems where a substantial timescale separation is present. Published by the American Physical Society 2025

Visual collective behaviors on spherical robots

Abstract The implementation of collective motion, traditionally, disregard the limited sensing capabilities of an individual, to instead assuming an omniscient perception of the environment. This study implements a visual flocking model in a ``robot-in-the-loop'' approach to reproduce these behaviors with a flock composed of 10 independent spherical robots. The model achieves robotic collective motion by only using panoramic visual information of each robot, such as retinal position, optical size and optic flow of the neighboring robots. We introduce a virtual anchor to confine the collective robotic movements so to avoid wall interactions. For the first time, a simple visual robot-in-the-loop approach succeed in reproducing several collective motion phases, in particular, swarming, and milling. Another milestone achieved with by this model is bridging the gap between simulation and physical experiments by demonstrating nearly identical behaviors in both environments with the same visual model. To conclude, we show that our minimal visual collective motion model is sufficient to recreate most collective behaviors on a robot-in-the-loop system that is scalable, behaves as numerical simulations predict and is easily comparable to traditional models.

Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing.

Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes. Here, we study DW-MTJ artificial neurons capable of leaky integrate-and-fire (LIF) behavior and demonstrate geometry-dependent leaking dynamics that results in repeatable, tunable LIF operation. Studying the behavior of five different device designs, we show tuning the geometry, stimulating fields and currents, and location of electrical contacts results in a wide range of neuron behavior. Additionally, implementation of an asymmetric notch allows for nonlinear pinning which increased expressivity without sacrificing leaking. The measured behavior is implemented in a simulated spiking neural network that outperforms a 1D model of continuous DW motion and approaches the performance of an ideal LIF activation function. The results show that the analog LIF capability of DW-MTJ neurons combines many desirable neuron functions into a single device, which can result in varied forms of multifunctional neuromorphic computing.

Prostate motion in magnetic resonance imaging-guided radiotherapy and its impact on margins.

This study focused on reducing the margin for prostate cancer treatment using magnetic resonance imaging-guided radiotherapy by investigating the intrafractional motion of the prostate and different motion-mitigation strategies. We retrospectively analyzed intrafractional prostate motion in 77patients with low- to intermediate-risk prostate cancer treated with five fractions of 7.25 Gy on a1.5 T magnetic resonance linear accelerator. Systematic drift motion was observed and described by an intrafractional motion model. The planning target volume (PTV) margin was calculated in acohort of 77patients and prospectively evaluated for geometric coverage in aseparate cohort of 24patients. The intrafractional model showed that the prostate position starts out of equilibrium for the anterior-posterior (-1.8 ± 3.1mm) and superior-inferior (1.7 ± 2.6mm) directions, with relaxation times of12 and 15min, respectively. Position verification scans are acquired at 30min on average. At that time, the transient drift motion becomes indistinguishable from the residual random intrafractional motion. PTV margins can be reduced to 1.8 mm (left-right), 3.2 mm (anterior-posterior), and 2.9 mm (superior-inferior). Evaluation of the overlap with the clinical target volume (CTV) was performed for atotal of 120 fractions of 24patients. The overlap range between the CTV and the PTV was 93-100% and the applied 3‑mm PTV margin for the CTV had a99.5% averaged geometric overlap for all patients. APTV margin reduction to 3 mm is feasible. Apatient-specific approach could reduce the margins further.

Theoretical and experimental analysis of phase noise in semiconductor lasers biased below threshold

Abstract We report a theoretical and experimental study of phase noise in semiconductor lasers when the bias current is below the threshold value. The theoretical study is performed by using two types of rate equations, with additive and multiplicative noise terms. We find the conditions for which the evolution in those rate equations can be described by 1-dimensional and two dimensional Brownian motions, respectively. The main statistical differences between the additive and multiplicative noise models are then illustrated by using the simplified Brownian motion models. Additive and multiplicative noise models predictions are compared with measurements of the phase noise with a coherent receiver using a 90o optical hybrid. We develop a novel method to extract the phase noise directly from our measurements, that in contrast to the usual direct method is not based on the analysis of the phase noise difference. The method permits a direct visualization of the phase noise trajectories and a calculation of the averages and the distribution that is valid in the short-time limit. Our results are in very good agreement with the results obtained with the method based on the phase noise difference. Our experimental results show that the variance of the phase noise grows linearly in time and has Gaussian statistics, supporting the modelization of the phase noise statistics with the additive noise model.

Modeling Path Effects Due to 3D Velocity Structure for Nonergodic Ground-Motion Models: A Case Study Using Turkish Ground-Motion Data

ABSTRACT The objective of this study is to assess the performance of different path-effect models for developing nonergodic ground motion models (GMMs) using a Turkish ground-motion database. The cell-specific attenuation approach is widely used to capture path effects in the formulation of nonergodic GMMs. However, this approach can mainly capture anelastic attenuation effects associated with the spatial variation of the quality factor, and it is limited in capturing 3D velocity structure effects, which may be, in particular, relevant for long-period ground motions or short-distance and short-period ground motions. Recent efforts have introduced new models to incorporate 3D velocity structure effects; however, the assessment of these models in the context of instrumentally recorded ground motions is limited. This study assesses the performance of three path-effects models for Türkiye. Specifically, we consider the cell-specific attenuation approach and two additional models based on Gaussian processes but with a different parametrization on how they represent the spatial correlation of path effects. The results indicate that the models based on Gaussian processes outperform the cell-specific approach for long-period spectral accelerations and short-period ground motions at short distances, offering significant aleatory standard deviation reductions. The differences between the Gaussian process-based models are also discussed, highlighting how their parameterization is reflected in prediction patterns. This study contributes to the transition from ergodic to nonergodic approaches in performance-based earthquake engineering.

GEOMETRIC BROWNIAN MOTION WITH JUMP DIFFUSION AND VALUE AT RISK ANALYSIS OF PT BANK NEGARA INDONESIA STOCKS

Investments in stocks are made to make a profit, where the higher the expected profit, the greater the risk undertaken. The return on investing in stocks can be influenced by changes in the price of stocks that are difficult to predict, which can lead to uncertainty in the value of the return and the risk of the stock. The application of the Geometric Brownian Motion (GBM) model with Jump Diffusion is crucial for enhancing the accuracy of stock price forecasting and risk analysis by incorporating price jumps resulting from external events within complex market dynamics. The data used in this study are the closing price data of the daily stock of PT Bank Negara Indonesia for the period 1 December 2022 to 31 January 2024, where the stock return data has a kurtosis value greater than 3 (leptokurtic) so that the data indicates a jump. The GBM with Jump Diffusion model was implemented to predict the stock price with a simulation repetition of 1000 times. The analysis shows that the GBM model with Jump Diffusion has an excellent accuracy rate with the smallest MAPE value of 0.86%. The average value of the VaR with Monte Carlo simulation obtained at the reliability levels of 80%, 90%, 95%, and 99% in a row is 0.96%, 1.53, 1.97%, and 2.64%. This result shows that the higher the confidence level used, the greater the risk.

Research on Underground Track Tracking of a New Type Shaft Boring Machine

A trajectory tracking control scheme is proposed in this paper to achieve unmanned underground construction of a new type of shaft boring machine. The body motion model is constructed, and the control relationship between the driving system and the body motion is deduced. MATLAB and Simulink simulate the joint of the track tracking process in the new shaft boring machine. The actual effect of the track tracking control scheme is validated by the track tracking field test of the new shaft boring machine. The results show that the new shaft boring machine can complete various complex track tracking tasks, with the maximum position error being less than 0.005 m. The running track and excavation areas of the boring machine are observed along with the track tracking field test of the new shaft boring machine, demonstrating that the track tracking control scheme makes the new shaft boring machine realize unmanned construction and verifies the co-simulation accuracy. The PID (proportional–integral–derivative control) algorithm can complete complex trajectory tracking tasks and promote the development of unmanned construction technology.

Optimal Planning Target Volume Margins to Account for Intra-Fractional Prostate Motion Relative to Treatment Duration: A Study Using Real-Time Transperineal Ultrasound Guidance.

Prostate motion during external beam radiotherapy (EBRT) is common and typically managed using fiducial markers and cone beam CT (CBCT) scans for inter-fractional motion correction. However, real-time intra-fractional motion management is less commonly implemented. This study evaluated the extent of intra-fractional prostate motion using transperineal ultrasound (TPUS) and examined the impact of treatment time on prostate motion. Patients undergoing prostate EBRT with TPUS at a single institution from August 2016 to August 2021 were analysed. Pre-treatment daily CBCT corrected inter-fractional prostate shift. Continuous intra-fractional prostate motion was recorded at two frames per second in three dimensions, with three-dimensional (3D) displacement calculated as a vector. Motion data were modelled to determine the probability of the prostate remaining within pre-specified PTV margins relative to treatment delivery time. The study analysed 3364 fractions delivered to 122 patients. The mean treatment delivery time was 3.8 min. The prostate remained within a 5 mm margin with high frequencies in the superior-inferior (SI) and left-right (LR) directions, 97.8% and 98.4% of fractions respectively while 5.5% of fractions had deviations greater than 5 mm in the anterior-posterior (AP) direction. By contrast, the 3D vector exceeded a 5 mm margin in 14.5% of fractions. Drift motion modelling indicated a 99% probability of the vector staying within a 3 mm margin for 2 min, while for a 5 mm margin, the duration extended to 3.4 min. Intra-fractional prostate motion monitoring is increasingly important as SABR with reduced PTV margins are utilised in prostate radiotherapy. Smaller PTV margins and longer treatment time require real-time monitoring to avoid geographical miss.

Seismic and GNSS strain-based probabilistic seismic hazard evaluation for northern Chile using DAS Magnitude Scale

BackgroundProbabilistic Seismic Hazard Assessment (PSHA) is a leading methodology for determining key ground motion parameters such as Peak Ground Acceleration (PGA) and spectral acceleration (SA), essential for structural design. This approach uses extensive earthquake data, typically spanning over a century, leveraging frequency and magnitude statistics. However, long-term ground shaking probabilities may not always be accurately captured by traditional data-driven methods. To address these limitations, this study develops a PSHA map for Northern Chile using both seismic and GNSS (Global Navigation Satellite System) data. A curated homogeneous earthquake catalog, based on the advanced seismic moment magnitude scale Mwg(Das Magnitude Scale), replaces the traditional Mw scale to ensure superior accuracy, particularly for intermediate and smaller earthquakes.ResultsUsing the earthquake catalog, seismicity parameters ‘a’ and ‘b’ from the Gutenberg-Richter relationship were derived. Seismogenic modeling and Ground Motion Models (GMMs) were applied to estimate ground motion probabilities for a 475-year return period. Additionally, a PSHA map was constructed using GNSS strain rates, translating velocity-derived strain rates into seismic moment rates and ground shaking probabilities for seismic source zones. Comparative analyses revealed higher PGA values from GNSS strain data compared to seismic catalog data. GNSS strain data proved invaluable for refining seismic segmentation in Northern Chile, enhancing the precision of PSHA calculations.ConclusionsA PSHA map for Northern Chile, synthesizing seismic catalog data and GNSS strain rates using a Logic Tree-based algorithm, has been developed for a 475-year return period. This map provides a critical tool for generating seismic hazard assessments aligned with building codes and emergency planning protocols. By integrating GNSS strain rates and seismic data, this study advances the reliability and accuracy of long-term ground shaking predictions.

A novel critical velocity model for the incipient motion of non-spherical particles on cuttings bed in extended reach wells

A novel critical velocity model for the incipient motion of non-spherical particles on cuttings bed in extended reach wells

Non-parametric ground motion model for displacement response spectra and Fling for Himalayan region using machine learning

Non-parametric ground motion model for displacement response spectra and Fling for Himalayan region using machine learning

Advancing dynamic quantum crystallography: enhanced models for accurate structures and thermodynamic properties.

X-ray diffraction (XRD) has evolved significantly since its inception, becoming a crucial tool for material structure characterization. Advancements in theory, experimental techniques, diffractometers and detection technology have led to the acquisition of highly accurate diffraction patterns, surpassing previous expectations. Extracting comprehensive information from these patterns necessitates different models due to the influence of both electron density and thermal motion on diffracted beam intensity. While electron-density modelling has seen considerable progress [e.g. the Hansen-Coppens multipole model and Hirshfeld Atom Refinement (HAR)], the treatment of thermal motion has remained largely unchanged. We have developed a novel method that combines the strengths of the advanced charge-density models [Aspherical Atom Models (AAMs), such as HAR or the Transferable Aspherical Atom Model (TAAM)] and the thermal motion model (normal modes refinement, NoMoRe). We denote this approach AAM_NoMoRe, wherein instead of refining routine anisotropic displacement parameters (ADPs) against single-crystal X-ray diffraction data, we refine the frequencies obtained from periodic density functional theory (DFT) calculations. In this work, we demonstrate the effectiveness of this model by presenting its application to model compounds, such as alanine, xylitol, naphthalene and glycine polymorphs, highlighting the influence of our method on the H-atom positions and shape of their ADPs, which are comparable with neutron data. We observe a significant decrease in the similarity index for H-atom ADPs after AAM_NoMoRe in comparison to only AAM, aligning more closely with neutron data. Due to the use of aspherical form factors (AAM), our approach demonstrates better fitting performance, as indicated by consistently lower wR2 values compared to the Independent Atom Model (IAM) refinement and a significant decrease compared to the traditional NoMoRe model. Furthermore, we present the estimation of a key thermodynamic property, namely, heat capacity, and demonstrate its alignment with experimental calorimetric data.

Sensitivity Analysis on the Impact of Input Parameters on Seismic Hazard Results: A Case Study of Central America

We present a sensitivity analysis on the impact of input parameters and methods used on the results of a probabilistic seismic hazard assessment (PSHA). The accurate estimation of the parameters in recurrence models (declustering and fitting methods), along with the selection of scaling relationships for determining maximum magnitude and the selection of ground motion models (GMMs), enhance control over epistemic uncertainties when constructing the logic tree, minimizing final calculation errors and producing credible results for the study region. This study focuses on Central America, utilizing recent data from seismic, geological, and geophysical studies to improve uncertainty analyses through classic statistical methods. The results demonstrate that proper fitting of the recurrence model can stabilize acceleration variations regardless of the declustering method or b-value fitting method used. Regarding scaling relationships, their low impact on the final results is noted, provided the models are tailored to the tectonic regime under study. Finally, it is shown that the GMM contributes the most variability to seismic hazard results; therefore, their selection should be conditioned on calibration with observed data through residual analysis where region-specific models are not available.

Ensembled Pretrained Model-Based Adaptive Sequential Trace Recognition for Moving Objects

In this paper, we propose an adaptive continuous trajectory recognition method for badminton sports scenarios. First, we adopt an integrated pre-training model with high generality and expressive ability as the infrastructure. By pre-training large-scale badminton game data, the model can learn the features of badminton sports from rich and diverse trajectory data. Second, to adapt to the trajectory differences in different scenarios, we introduce an adaptive mechanism. By extracting and encoding features from real-time acquired badminton motion trajectories, the model can adjust its own parameters and weight assignments according to the changes in the current scene to optimally recognize and track the motion targets. The algorithm also employs the Kalman filter aggregation Enhanced Correlation Coefficient (ECC) method of the motion model to improve the prediction accuracy. Finally, a series of experiments and comparisons are conducted to validate the effectiveness of the approach. The results show that our proposed adaptive continuous trajectory recognition method for badminton achieves better performance in different scenarios and various complexities, and has higher accuracy and robustness than the traditional method. The FDA-SSD model operates at 28.7[Formula: see text]fps, which is about 16.5% faster than the traditional SSD. In the target tracking experiments, the target tracking mean squared error is about 1.0%. In the target tracking experiments based on the FDA and geometrically constrained localization method, the root-mean-square error of the target tracking is less than 4.72[Formula: see text]cm.

Ground Motion Modeling and Adaptive Joint Control for Large-Scale UAVs

Aiming at the problem of lateral deviation of large-scale long-endurance solar-powered UAVs relative to the runway during takeoff or landing, a UAV ground motion control structure based on the combination of engine differential and rudder was proposed. According to the structural characteristics of large-scale long-endurance solar-powered UAVs, a ground motion model of a three-point layout UAV including landing gear was established, and the ground rolling dynamics and modal characteristics were analyzed. In order to accurately correct the trajectory error, the outer loop designs a trajectory correction control law and gives the inner loop desired control instructions. In order to solve the problem of environmental disturbance and small heading damping, the inner loop adopts the adaptive back-stepping control method. The disturbance signal is estimated through the adaptive law and compensated into the control system to achieve balanced control of speed and rolling correction. Finally, medium-speed and high-speed sliding tests were designed to verify the rationality of the proposed control scheme and control structure, as well as the efficiency of the control law design method adopted.

Modeling and Simulation of Reel Motion in a Foxtail Millet Combine Harvester

Due to the high plant height, heavy ear, and easy forward tilt of millet during harvesting, the reel of a traditional combine harvester is often difficult to adapt to the special growth characteristics of millet, resulting in serious grain loss. Therefore, optimizing the design of the reel is important to improve the harvesting efficiency of millet and reduce the grain header loss. In order to determine the optimal reel speed ratio(λ), kinematics simulation experiments and analysis were carried out under different combinations of forward speed and reel revolution speed. The results showed that the supporting effect of the reel is insufficient when λ ≤ 1, and the trochoidal trajectory of the reel can provide a backward driving force when λ > 1, the optimum speed ratio of the reel should be controlled between 1.5 and 1.6. Field experiments results showed that the grain header loss rate was the lowest (0.9%) when λ = 1.6. This study provides key guidance for the adjustment of the combine harvester, effectively reducing the grain header loss rate in harvesting millet, and improving the harvesting efficiency.

On the river flow motion in the bend channel cross-section

At the river bed curves, secondary flow normal to the main flow direction are formed. Depending on the channel geometry, there may be several secondary flows in the cross-section, and they may have different scales. Even a small secondary cross-section flow affects the parameters of the hydrodynamic flow and this influence must be taken into account when modeling riverbed processes and researching coast deformations of the channel. Three-dimensional modeling of such multi-scale processes requires large computational costs and is currently possible only for small model channels. Therefore, a reduced-dimensional model is proposed in this paper to study coastal processes. The performed reduction of the problem from a three-dimensional model of river flow motion to a two-dimensional one in the plane of the channel cross-section assumes that the hydrodynamic flow is quasi-stationary and the hypotheses on the asymptotic behavior of the flow along the flow coordinate are fulfilled for it. Taking into account these limitations, a mathematical model of the problem of a stationary turbulent calm river flow in a channel cross-section is formulated in this work. The problem is formulated in a mixed velocity–vortex–stream function formulation. Specifying of the boundary conditions on the flow free surface for the velocity field determined in the normal and tangential directions to the cross-section axis is required as additional conditions for the problem reduction. It is assumed that the values of this velocity field should be determined from the solution of auxiliary problems or obtained from data of natural or experimental measurements. The finite element method in the Petrov–Galerkin formulation is used for the numerical solution of the formulated problem. A discrete analog of the problem is obtained and an algorithm for its solution is proposed. The performed numerical studies showed generally good agreement between the obtained solutions and the known experimental data. The authors associate the errors in the numerical results with the need for a more accurate determination of the radial component of the velocity field in the cross-section by selecting and calibrating a more suitable model for turbulent viscosity calculating and a more accurate determination of the boundary conditions on the cross-section free boundary.

Enhanced USV path planning through integrated Bi-RRT and DWA algorithms considering environmental factors

This paper presents a novel hybrid dynamic path planning algorithm termed GIBi-RRT-IDWA, which enhances global path planning by amalgamating the Bi-RRT algorithm with the local path planning capabilities of the refined DWA algorithm. Through enhancements in node expansion techniques, Improvements in Tree Expansion to eliminate redundancy, and improvements in Sample Point Generation to augment the Bi-RRT algorithm, the algorithm achieves heightened search efficiency and path security, ultimately leading to the derivation of globally optimal trajectories. The motion model for USV is refined to encompass environmental factors such as winds and currents, pertinent to marine contexts. Furthermore, the evaluation function of the DWA algorithm undergoes enhancements, incorporating elements from heuristic functions within the A* algorithm and radar-based assessment of search range. These improvements facilitate expedited Goal point tracking and fortify the USV's capability to navigate around hazards. Experimental findings corroborate the efficacy of the GIBi-RRT-IDWA algorithm, showcasing its superiority over conventional DWA algorithm and IDWA algorithm. Notably, the algorithm demonstrates prowess in generating shorter and safer trajectories, adeptly navigating through intricate and dynamic waterside environments. Consequently, it improves path planning strategies' adaptability and resilience, allowing USVs to navigate more safely and efficiently.