Probabilistic Trajectory Model Research Articles

Predicting Trajectories of Plate-Type Wind-Borne Debris in Turbulent Wind Flow with Uncertainties

Debris poses multifaceted risks and jeopardizes various aspects of the environment, human health, safety, and infrastructure. The debris trajectory in turbulent wind flow is more dispersed due to the inherent randomness of the turbulent winds. This paper investigates the three-dimensional trajectories of plate-type wind-borne debris in turbulent wind fields via the method of numerical simulation. A 3D probabilistic trajectory model of plate-type wind-borne debris is developed. The debris trajectories are numerically calculated by solving the governing equation of debris motion and by introducing turbulent wind flows based on the near-ground wind field measured in the wind tunnel to account for the probability characteristics of the debris trajectory. The dimensionless velocities and displacements of the debris trajectory show good agreement with the experimental data in wind tunnel tests, confirming the rationality of the probabilistic trajectory model. Based on the validated trajectory model, the probability characteristics of the debris impact position, impact velocity, and kinetic energy, debris angular displacement, and angular velocity are analyzed in detail under five different wind attack angles. The proposed probabilistic model of plate-type debris in turbulent wind flow provides an accurate and effective method for predicting debris trajectory in three-dimensional space.

Variations in longitudinal sleep duration trajectories from infancy to early childhood

ObjectiveThis study investigates variations in night, day, and total sleep trajectories across infancy and childhood in Asian children. ParticipantsParticipants consisted of a subset of 901 children, within the Growing Up in Singapore Towards healthy Outcomes cohort, which recruited 1247 pregnant women between June 2009 and September 2010. DesignWe used a novel conditional probabilistic trajectory model: a probabilistic model for mixture distribution, allowing different trajectory curves and model variances among groups to cluster longitudinal observations. Longitudinal sleep duration data for the trajectory analyses were collected from caregiver-reported questionnaires at 3, 6, 9, 12, 18, 24, and 54 months. ResultsWe found 3 patterns of night sleep trajectories (n = 356): long consistent (31%), moderate consistent (41%), and short variable (28%); and 4 patterns of day sleep trajectories (n = 347): long variable (21%), long consistent (20%), moderate consistent (34%), and short consistent (25%). We also identified 4 patterns of total sleep trajectories (n = 345): long variable (19%), long consistent (26%), moderate consistent (28%), and short variable (27%). Short, moderate, and long trajectories differed significantly in duration. Children with consistent trajectories also displayed sleep patterns that were significantly more representative of typical developmental sleep patterns than children with variable trajectories. ConclusionsThis is the first study to describe multiple sleep trajectories in Singaporean children and identify between-individual variability within the trajectory groups. Compared to predominantly Caucasian samples, night/total sleep trajectories were generally shorter, while day sleep trajectories were longer. Future studies should investigate how these variations are linked to different developmental outcomes.

Mobility-Aware Participant Recruitment for Vehicle-Based Mobile Crowdsensing

Nowadays, vehicles have been increasingly adopted in mobile crowdsensing applications. Due to their predictable mobility trajectories, vehicles as participants bring new insight in improving the crowdsensing quality. The predictable mobility of vehicles provides not only the current locations of the vehicles, but also their future mobility trajectory. In this context, the existing participant recruitment solutions, which are mainly based on the current locations of the participants, cannot be directly used in vehicle-based mobile crowdsensing. Utilizing the predicted mobility trajectory of vehicles, this paper aims to propose efficient vehicle recruitment algorithms for mobile crowdsensing, so as to minimize the overall recruitment cost. Specifically, we study two mobility trajectory models of the vehicles, named deterministic and probabilistic models. We first prove that the vehicle recruitment problem under both models is NP-hard. Then, for the deterministic trajectory model, an efficient LP-relaxation-based heuristic algorithm is proposed, and an approximation ratio is analyzed. For the probabilistic trajectory model, we propose a greedy algorithm and analyze its performance with a guaranteed approximation ratio. Finally, we evaluate the performance of the proposed schemes through simulations.

Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control.

Human motor skill learning is driven by the necessity to adapt to new situations. While supportive contacts are essential for many tasks, little is known about their impact on motor learning. To study the effect of contacts an innovative full-body experimental paradigm was established. The task of the subjects was to reach for a distant target while postural stability could only be maintained by establishing an additional supportive hand contact. To examine adaptation, non-trivial postural perturbations of the subjects’ support base were systematically introduced. A novel probabilistic trajectory model approach was employed to analyze the correlation between the motions of both arms and the trunk. We found that subjects adapted to the perturbations by establishing target dependent hand contacts. Moreover, we found that the trunk motion adapted significantly faster than the motion of the arms. However, the most striking finding was that observations of the initial phase of the left arm or trunk motion (100–400 ms) were sufficient to faithfully predict the complete movement of the right arm. Overall, our results suggest that the goal-directed arm movements determine the supportive arm motions and that the motion of heavy body parts adapts faster than the light arms.

Numerical simulation of 3-D probabilistic trajectory of plate-type wind-borne debris

To address the uncertainty of the flight trajectories caused by the turbulence and gustiness of the wind field over the roof and in the wake of a building, a 3-D probabilistic trajectory model of flat-type wind-borne debris is developed in this study. The core of this methodology is a 6 degree-of-freedom deterministic model, derived from the governing equations of motion of the debris, and a Monte Carlo simulation engine used to account for the uncertainty resulting from vertical and lateral gust wind velocity components. The influence of several parameters, including initial wind speed, time step, gust sampling frequency, number of Monte Carlo simulations, and the extreme gust factor, on the accuracy of the proposed model is examined. For the purpose of validation and calibration, the simulated results from the 3-D probabilistic trajectory model are compared against the available wind tunnel test data. Results show that the maximum relative error between the simulated and wind tunnel test results of the average longitudinal position is about 20%, implying that the probabilistic model provides a reliable and effective means to predict the 3-D flight of the plate-type wind-borne debris.