Motion Model Research Articles

Nonparametric ground motion models of arias intensity and significant duration for the Italian dataset

Abstract In the field of ground motion simulation, the stochastic site-based methodology relies on the existing database of ground shaking. Based on these methodologies, several properties of seismic signals are used to simulate seismic waves. These parameters could be evaluated either parametrically via linear or nonlinear regression techniques or non-parametrically via sophisticated machine-learning algorithms. Nonetheless, parametric models, which consist of a particular mathematical formulation, can be a source of large bias. In this study, machine learning techniques are employed to develop predictive models for two main input parameters of a stochastic site-based ground motion model: Arias intensity and significant duration, which control the time variation of the simulated ground shakings. The Arias intensity, defined by the integral of the square of the acceleration time series, and the significant duration, which is related to the strong shaking phase of an earthquake, are also of particular interest in structural and geotechnical engineering fields. For this purpose, the random forest approach is employed to develop prediction models for the Italian database. To guarantee the prediction accuracy of the models also for unseen future data, only 80 percent of the data is used for training, and the rest is reserved for testing the trained model. The model hyperparameters are tuned to control bias and variance trade-offs by k-fold cross-validation. For each model, a set of hyperparameters is selected, and a possible range is given. Then, a Bayesian optimization technique is implemented to find the best set of these hyperparameters among the given range. All these models provided promising results compared to the prior models in the literature.

MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model.

Human motion modeling is important for many modern graphics applications, which typically require professional skills. In order to remove the skill barriers for laymen, recent motion generation methods can directly generate human motions conditioned on natural languages. However, it remains challenging to achieve diverse and fine-grained motion generation with various text inputs. To address this problem, we propose MotionDiffuse, one of the first diffusion model-based text-driven motion generation frameworks, which demonstrates several desired properties over existing methods. 1) Probabilistic Mapping. Instead of a deterministic language-motion mapping, MotionDiffuse generates motions through a series of denoising steps in which variations are injected. 2) Realistic Synthesis. MotionDiffuse excels at modeling complicated data distribution and generating vivid motion sequences. 3) Multi-Level Manipulation. MotionDiffuse responds to fine-grained instructions on body parts, and arbitrary-length motion synthesis with time-varied text prompts. Our experiments show MotionDiffuse outperforms existing SoTA methods by convincing margins on text-driven motion generation and action-conditioned motion generation. A qualitative analysis further demonstrates MotionDiffuse's controllability for comprehensive motion generation.

Calculation method for natural frequency and damping ratio of hydropower units based on motion model separation

Abstract In recent years, the research on characteristic extraction methods for non-stationary and non-linear vibration signals of hydroelectric units has been a hot topic in the fault diagnosis of hydroelectric units. The effectiveness of feature extraction directly affects the accuracy of fault diagnosis. This article proposes a vibration signal feature extraction method for hydroelectric units based on motion model separation and logarithmic attenuation rate method. The under-damped decay oscillation motion trajectory is obtained from the signal after motion model separation. And then, the logarithmic attenuation rate method in the time domain analysis is used to calculate the system damping ratio and natural frequency. In this process, the principle of selecting the envelope interpolation function is proposed, and the effectiveness of the cubic Hermite interpolation function is determined. Based on the calculation effectiveness and accuracy requirements, the effective step length of the vibration signal is determined to be Δt = 0.001, and the effective calculation interval for extracting the decay oscillation characteristics is [ts , tend ]. The results show that the proposed method is more straightforward, efficient, has higher computational accuracy and better signal-to-noise ratio adaptability, and can provide a theoretical basis for practical engineering applications.

Design standardisation and seismic protection of SMRs through modular metafoundations

This paper investigates the seismic protection of the Nuward™ small modular reactor (SMR) building, focusing on design loading and beyond design basis earthquake (bDBE) conditions. The study aims to achieve two primary objectives: i) to enhance seismic mitigation of a SMR building under bDBE conditions, through the use of the innovative modular single-layer (SLM) and multi-layer (MLM) metafoundations (MFs); ii) to effectively standardise and harmonise SMR building designs in locations prone to beyond design basis conditions. To accomplish these goals and demonstrate the protective capabilities of the MFs, the study employs non-linear time-history analyses (NLTHAs) for both DBE and bDBE conditions. Along these lines, a reduced-order model was developed from a refined finite element (FE) model of the SMR building using the Craig-Bampton mode synthesis technique. Then, finite locally resonant modular MFs were designed and analysed using NLTHAs. Specifically, physics-based ground motion models (GMMs) were used to generate and select seismic triplets that mimicked DBE and bDBE scenarios for NLTHAs. Successively to achieve improved seismic performance, the optimization of the MFs was pursued by targeting the optimal number of columns, resonator parameters, and unit cell dimensions. Additionally, the deployment of inerters was considered, to significantly reduce the size of the MFs and enable their application in multiple layers for ultra-low frequency attenuation. The overall findings suggest that modular MFs meet seismic protection requirements, and positively contribute to the standardization process of SMR buildings, even in areas characterized by beyond-design seismic conditions.

Deck motion prediction using neural kernel network Gaussian process regression

Deck motion prediction and compensation are critical technologies for carrier-based aircraft automatic landing. Traditional deck motion prediction methods rely on precision of motion models and parameter adjustments, facing challenges in adaptability to complex sea conditions, different types of carriers, changes in flight conditions, and limitations in prediction duration, as well as reliability issues. This paper proposes a deck motion prediction method based on the neural kernel network Gaussian process regression (NKN-GPR) model. The NKN-GPR model can utilize a neural kernel network (NKN) to automatically construct the Gaussian process regression (GPR) model′s composite kernel, effectively addressing the limitations of the automated kernel search (ACKS) algorithm, which heavily depends on manual prior knowledge. Simulation data is generated using a combination of sine wave and power spectrum models, and the NKN-GPR model is compared with an autoregressive (AR) model based on least squares in a simulated validation. The simulation results demonstrate that the NKN-GPR model exhibits significant advantages in motion prediction accuracy, smoothness, and prediction duration, which confirms the effectiveness of the proposed algorithm. This study provides theoretical support for safe automatic landing of carrier-based aircraft.

Event-specific ground motion anomalies highlight the preparatory phase of earthquakes during the 2016–2017 Italian seismicity

Although physical models are improving our understanding of the crustal processes that lead to large earthquakes, observing their preparatory phases is still challenging. We show that the spatio-temporal evolution of the ground motion of small magnitude earthquakes can shed light on the preparatory phase of three main earthquakes that occurred in central Italy between 2016 and 2017. We analyze systematic deviations of peak ground accelerations generated by each earthquake from the values predicted by a reference ground motion model calibrated for background seismicity and refer to such deviations as event-specific ground motion anomalies (eGMAs). The eGMA temporal behavior indicates that during the activation phase of the main earthquakes, the ground shaking level deviates, positively or negatively, from the values expected for the background seismicity. eGMA can be exploited as beacons of stress change and help to monitor the mechanical state of the crust and the nucleation of large earthquakes.

Dynamic synchronization optimization of beef atmosphere packaging system

Modified atmosphere packaging (MAP) of beef is a key tool to ensure its post-production shelf life. However, it is still a technical challenge to realize intelligent, dynamic, modified-atmosphere packaging of beef products on the production line to improve production and packaging efficiency. In this paper, we propose a dynamic and synchronized modified atmosphere packaging optimization strategy for beef based on admittance control. The purpose of synchronized, accurate, and efficient modified-atmosphere packaging of beef is achieved. Firstly, particle swarm optimization was designed to optimize the conductance parameters, which did not achieve good optimization results, so a whale optimization algorithm based on inertia weights as well as teaching strategy improvement was designed for optimization, and simulation analysis was carried out. The most important parts of the packaging process were also tested, such as the effect of synchronous following motion, the stability and ability to block interference, the accuracy of the Cartesian Coordinate Robot's repeated positioning, and the system's overall operation performance. The results show that the synchronous following error of the system is ±1%, and after the optimization of the improved whale optimization algorithm, the conduction model is reduced in both overshooting amount and regulation time, which improves the performance of the closed-loop following motion model. And the beef dynamic modified atmosphere packaging system, with the application of the algorithm, can complete the modified atmosphere packaging work stably, accurately, continuously, and efficiently.

The radial variation of the LMC-induced reflex motion of the Milky Way disc observed in the stellar halo

ABSTRACT We measure the kinematic signature arising from the Milky Way (MW) disc moving with respect to the outer stellar halo, which is observed as a dipole signal in the kinematics of stellar halo tracers. We quantify how the reflex motion varies as a function of Galactocentric distance, finding that (i) the amplitude of the dipole signal increases as a function of radius, and (ii) the direction moves across the sky. We compare the reflex motion signal against a compilation of published models that follow the MW–LMC interaction. These models show a similar trend of increasing amplitude of the reflex motion as a function of distance, but they do not reproduce the direction of the disc motion with respect to the stellar halo well. We also report mean motions for the stellar halo as a function of distance, finding radial compression in the outer halo and non-zero prograde rotation at all radii. The observed compression signal is also present in MW–LMC models, but the rotation is not, which suggests that the latter is not induced by the LMC. We extensively validate our technique to measure reflex motion against idealized tests. We discuss prospects for directly constraining the mass and orbital history of the LMC through the impact on the motion of the MW stellar disc, and how the modelling of the reflex motion can be improved as more and better data become available.

Evaluation of the applicability of ground motion models (GMMs) for South Korea

Evaluation of the applicability of ground motion models (GMMs) for South Korea

Liver respiratory-induced motion estimation using abdominal surface displacement as a surrogate: robotic phantom and clinical validation with varied correspondence models

PurposeThis work presents the implementation of an RGB-D camera as a surrogate signal for liver respiratory-induced motion estimation. This study aims to validate the feasibility of RGB-D cameras as a surrogate in a human subject experiment and to compare the performance of different correspondence models.MethodsThe proposed approach uses an RGB-D camera to compute an abdominal surface reconstruction and estimate the liver respiratory-induced motion. Two sets of validation experiments were conducted, first, using a robotic liver phantom and, secondly, performing a clinical study with human subjects. In the clinical study, three correspondence models were created changing the conditions of the learning-based model.ResultsThe motion model for the robotic liver phantom displayed an error below 3 mm with a coefficient of determination above 90% for the different directions of motion. The clinical study presented errors of 4.5, 2.5, and 2.9 mm for the three different motion models with a coefficient of determination above 80% for all three cases.ConclusionRGB-D cameras are a promising method to accurately estimate the liver respiratory-induced motion. The internal motion can be estimated in a non-contact, noninvasive and flexible approach. Additionally, three training conditions for the correspondence model are studied to potentially mitigate intra- and inter-fraction motion.

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.

Design and Implementation of a Hybrid-Driven Soft Robot

Currently, soft robots alone cannot obtain the same operating speed as rigid robots, while rigid robots are not safe enough for human-robot interaction. To address this problem, this paper describes a hybrid robot system that combines both rigid and flexible systems for unknown domain exploration. The system consists of a four-wheeled robot chassis and a cylindrical pneumatic soft actuator, and finally, a computer is used to coordinate and control both. The hardware of the robot system is designed, a bending motion model is proposed, and SOFA framework is used to carry out finite element simulation (FEM) to verify the reasonableness of the design; linear motion speeds of up to 0.5 m/s, higher than the existing soft robots investigated, were verified experimentally separately after carrying the new module, and steering ability was retained; and the robot carrying the navigation module is verified to have a certain map building and localization function through the construction of the simultaneous localization and mapping (SLAM) experimental platform. The hybrid robot introduced in this paper can move quickly on flat terrain and can use its soft part to avoid wear and tear.

Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM

Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition, based on the output of the attention learning module, the expectation maximization (EM) algorithm is used to estimate the system model parameters online, and a Kalman filter is used to obtain state estimation. This paper was validated using the GPS trajectory path dataset, and the experimental results showed that the proposed parameter adaptive model free state estimation method has better estimation accuracy than other models, providing an effective method for using deep learning networks for state estimation.

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.

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.

ДИНАМІЧНА МОДЕЛЬ ІТЕРАЦІЙНОГО ЕЛЕКТРОПРИВОДА ПОДАЧІ З ДВОМА ГВИНТОВИМИ ПЕРЕДАЧАМИ ДЛЯ ПРЕЦИЗІЙНИХ ВЕРСТАТІВ ТА ОБРОБНИХ ЦЕНТРІВ

A variant of a simplified design diagram is proposed and the corresponding kinematic diagram of a two-motor gearless drive mechanism with two screw gears (SG) is given for an iterative two-channel electric drive for the longitudinal feed of the working tool (worktable with a product) of a coordinate multi-purpose metal-cutting machine of especially high precision, model 24K70AF4. Compensators of the negative dynamic interaction of load control channels have been determined. A generalized dynamic model of the cutting process has been constructed, taking into account both the inertia of the cutting process and the influence of the machine “WT-cutter” elastic system dynamics. A dynamic model of a conditional compensator for the cutting process is determined and calculated. A refined mathematical model of motion in steady-state feed modes of the two-channel electric drive has been obtained, taking into account the compensation of the dynamic interaction of the channels on the load and the influence of friction nonlinearities in the drive mechanism and the working tool of the machine. A structural-algorithmic diagram of an iterative two-channel compensated feed electric drive with two SGs and subordinated adjustment of control channels has been designed. The diagram includes a generalized dynamic model of the cutting process and a dynamic model of the corresponding conditional compensator for the cutting process, and also takes into account the main static moments of resistance and nonlinearity of friction in the drive load. References 13, tables 2, figures 5.

Applicability Study of Euler–Lagrange Integration Scheme in Constructing Small-Scale Atmospheric Dynamics Models

The atmospheric flow field and weather processes exhibit complex and variable characteristics at small scales, involving interactions between terrain features and atmospheric physics. To investigate the mechanisms of these process further, this study employs a Lagrangian particle motion model combined with a Euler background field approach to construct a small-scale atmospheric flow field model. The model streamlines the modeling process by combining the benefits of the Lagrangian dynamics model and the Eulerian integration scheme. To verify the effectiveness of the Euler–Lagrange hybrid model, experiments using the Fluent wind field model were conducted for comparison. The results show that both models have their advantages in handling terrain-induced wind fields. The Fluent model excels in simulating the general characteristics of wind fields under specific terrain, while the Euler–Lagrange hybrid model is better at capturing the upstream and downstream disturbances of the terrain on the atmospheric flow field. These findings provide powerful tools for in-depth diagnostic analysis of atmospheric flow simulation and convective precipitation processes. Notably, the Euler–Lagrange hybrid model demonstrates excellent computational efficiency, with an average computation time of approximately 2 s per time step in a Python environment, enabling rapid simulation of 40 time steps within approximately 90 s.

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

AbstractSynthetic ground motions (GMs) play a fundamental role in both deterministic and probabilistic seismic engineering assessments. This paper shows that the family of filtered and modulated white noise stochastic GM models overlooks a key parameter—the high‐pass filter's corner frequency, . In the simulated motions, this causes significant distortions in the long‐period range of the linear‐response spectra and in the linear‐response spectral correlations. To address this, we incorporate as an explicitly fitted parameter in a site‐based stochastic model. We optimize by individually matching the long‐period linear‐response spectrum (i.e., for ) of synthetic GMs with that of each recorded GM. We show that by fitting the resulting stochastically simulated GMs can precisely capture the spectral amplitudes, variability (i.e., variances of ), and the correlation structure (i.e., correlation of between distinct periods and ) of recorded GMs. To quantify the impact of , a sensitivity analysis is conducted through linear regression. This regression relates the logarithmic linear‐response spectrum () to 7 GM parameters, including the optimized . The results indicate that the variance of observed in natural GMs, along with its correlation with the other GM parameters, accounts for 26% of the spectral variability in long periods. Neglecting either the variance or correlation typically results in an important overestimation of the linear‐response spectral correlation.

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.