2,460,132 research outputs found
Online Learning for Ground Trajectory Prediction
This paper presents a model based on an hybrid system to numerically simulate
the climbing phase of an aircraft. This model is then used within a trajectory
prediction tool. Finally, the Covariance Matrix Adaptation Evolution Strategy
(CMA-ES) optimization algorithm is used to tune five selected parameters, and
thus improve the accuracy of the model. Incorporated within a trajectory
prediction tool, this model can be used to derive the order of magnitude of the
prediction error over time, and thus the domain of validity of the trajectory
prediction. A first validation experiment of the proposed model is based on the
errors along time for a one-time trajectory prediction at the take off of the
flight with respect to the default values of the theoretical BADA model. This
experiment, assuming complete information, also shows the limit of the model. A
second experiment part presents an on-line trajectory prediction, in which the
prediction is continuously updated based on the current aircraft position. This
approach raises several issues, for which improvements of the basic model are
proposed, and the resulting trajectory prediction tool shows statistically
significantly more accurate results than those of the default model.Comment: SESAR 2nd Innovation Days (2012
Verifying Real-Time Systems using Explicit-time Description Methods
Timed model checking has been extensively researched in recent years. Many
new formalisms with time extensions and tools based on them have been
presented. On the other hand, Explicit-Time Description Methods aim to verify
real-time systems with general untimed model checkers. Lamport presented an
explicit-time description method using a clock-ticking process (Tick) to
simulate the passage of time together with a group of global variables for time
requirements. This paper proposes a new explicit-time description method with
no reliance on global variables. Instead, it uses rendezvous synchronization
steps between the Tick process and each system process to simulate time. This
new method achieves better modularity and facilitates usage of more complex
timing constraints. The two explicit-time description methods are implemented
in DIVINE, a well-known distributed-memory model checker. Preliminary
experiment results show that our new method, with better modularity, is
comparable to Lamport's method with respect to time and memory efficiency
Evaluating search and matching models using experimental data
This paper introduces an innovative test of search and matching models using the exogenous variation available in experimental data. We take an off-the-shelf Pissarides matching model and calibrate it to data on the control group from a randomized social experiment. We then simulate a program group from a randomized experiment within the model. As a measure of the performance of the model, we compare the outcomes of the program groups from the model and from the randomized experiment. We illustrate our methodology using the Canadian Self-Sufficiency Project (SSP), a social experiment providing a time limited earnings supplement for Income Assistance recipients who obtain full time employment within a 12 month period. We find two features of the model are consistent with the experimental results: endogenous search intensity and exogenous job destruction. We find mixed evidence in support of the assumption of fixed hours of labor supply. Finally, we find a constant job destruction rate is not consistent with the experimental data in this context
Using nonparametrics to specify a model to measure the value of travel time
Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time-cost trading experiment. The analysis favours a model with random WTP as the only source of randomness over a model with fixed WTP which is linear in time and cost and has an additive random error term. Results further indicate that the distribution of log WTP can be described as a sum of a linear index fixing the location of the log WTP distribution and an independent random variable representing unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory.Willingness-to-pay; WTP; value of time; nonparametric; semiparametric; local logit
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction
Data analytics helps basketball teams to create tactics. However, manual data
collection and analytics are costly and ineffective. Therefore, we applied a
deep bidirectional long short-term memory (BLSTM) and mixture density network
(MDN) approach. This model is not only capable of predicting a basketball
trajectory based on real data, but it also can generate new trajectory samples.
It is an excellent application to help coaches and players decide when and
where to shoot. Its structure is particularly suitable for dealing with time
series problems. BLSTM receives forward and backward information at the same
time, while stacking multiple BLSTMs further increases the learning ability of
the model. Combined with BLSTMs, MDN is used to generate a multi-modal
distribution of outputs. Thus, the proposed model can, in principle, represent
arbitrary conditional probability distributions of output variables. We tested
our model with two experiments on three-pointer datasets from NBA SportVu data.
In the hit-or-miss classification experiment, the proposed model outperformed
other models in terms of the convergence speed and accuracy. In the trajectory
generation experiment, eight model-generated trajectories at a given time
closely matched real trajectories
Trend-resistant and cost-efficient cross-over designs for mixed models.
A mixed model approach is used to construct optimal cross-over designs. In a cross-over experiment the same subject is tested at different points in time. Consider as an example an experiment to investigate the influence of physical attributes of the work environment such as luminance, ambient temperature and relative humidity on human performance of acceptance inspection in quality assurance. In a mixed model context, the subject effects are assumed to be independent and normally distributed. Besides the induction of correlated observations within the same inspector, the mixed model approach also enables one to specify the covariance structure of the inspection data. Here, several covariance structures are considered either depending on the time variable or not. Unfortunately, a serious drawback of the inspection experiment is that the results may be influenced by an unknown time trend because of inspector fatigue due to monotony of the inspection task. In other circumstances, time trend effects can be caused by learning effects of the test subjects in behavioural and life sciences, heating or aging of material in prototype experiments, etc. An algorithm is presented to construct cross-over designs that are optimally balanced for time trend effects. The costs for using the subjects and for altering the factor levels between consecutive observations can also be taken into account. A number of examples illustrate utility of the outlined design methodology.Optimal; Models; Model;
Shear-driven size segregation of granular materials: modeling and experiment
Granular materials segregate by size under shear, and the ability to
quantitatively predict the time required to achieve complete segregation is a
key test of our understanding of the segregation process. In this paper, we
apply the Gray-Thornton model of segregation (developed for linear shear
profiles) to a granular flow with an exponential profile, and evaluate its
ability to describe the observed segregation dynamics. Our experiment is
conducted in an annular Couette cell with a moving lower boundary. The granular
material is initially prepared in an unstable configuration with a layer of
small particles above a layer of large particles. Under shear, the sample mixes
and then re-segregates so that the large particles are located in the top half
of the system in the final state. During this segregation process, we measure
the velocity profile and use the resulting exponential fit as input parameters
to the model. To make a direct comparison between the continuum model and the
observed segregation dynamics, we locally map the measured height of the
experimental sample (which indicates the degree of segregation) to the local
packing density. We observe that the model successfully captures the presence
of a fast mixing process and relatively slower re-segregation process, but the
model predicts a finite re-segregation time, while in the experiment
re-segregation occurs only exponentially in time
D'yakonov-Perel' spin relaxation in InSb/AlInSb quantum wells
We investigate theoretically the D'yakonov-Perel' spin relaxation time by
solving the eight-band Kane model and Poisson equation self-consistently. Our
results show distinct behavior with the single-band model due to the anomalous
spin-orbit interactions in narrow band-gap semiconductors, and agree well with
the experiment values reported in recent experiment (K. L. Litvinenko, et al.,
New J. Phys. \textbf{8}, 49 (2006)). We find a strong resonant enhancement of
the spin relaxation time appears for spin align along [] at a
certain electron density at 4 K. This resonant peak is smeared out with
increasing the temperature.Comment: 4 pages, 4 figure
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