223,716 research outputs found
Sequential Bayesian inference for static parameters in dynamic state space models
A method for sequential Bayesian inference of the static parameters of a
dynamic state space model is proposed. The method is based on the observation
that many dynamic state space models have a relatively small number of static
parameters (or hyper-parameters), so that in principle the posterior can be
computed and stored on a discrete grid of practical size which can be tracked
dynamically. Further to this, this approach is able to use any existing
methodology which computes the filtering and prediction distributions of the
state process. Kalman filter and its extensions to non-linear/non-Gaussian
situations have been used in this paper. This is illustrated using several
applications: linear Gaussian model, Binomial model, stochastic volatility
model and the extremely non-linear univariate non-stationary growth model.
Performance has been compared to both existing on-line method and off-line
methods
Enhanced Deadbeat Control Approach for Grid-Tied Multilevel Flying Capacitors Inverter
This paper proposes an enhanced Deadbeat Controller (DBC) for a grid-tied Flying Capacitors Inverter (FCI). The proposed DBC guarantees the balancing of the capacitors' voltages while injecting current to the grid with lower Total Harmonics Distortion (THD). The proposed controller has the following advantages: 1) Improved current tracking quality even at zero crossing instants by using a weighted state-space model, 2) Superior steady-state performance (lower current THD) compared to other prediction-based control techniques such as Finite-Control-Set Model Predictive Control, 3) The generated duty cycles are normalized to the common base when the desired state is out of reach within the sampling time, 4) Voltage Ride-Through (VRT) capability, and 5) Robustness to parameters variation. Theoretical analysis, simulation, and experimental results are presented to show the effectiveness of the proposed control technique in ensuring uninterruptible and smooth transfer of energy to the grid during normal/abnormal operating conditions (severe voltage sags, parameters variation, etc.)
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
This paper reports on a data-driven, interaction-aware motion prediction
approach for pedestrians in environments cluttered with static obstacles. When
navigating in such workspaces shared with humans, robots need accurate motion
predictions of the surrounding pedestrians. Human navigation behavior is mostly
influenced by their surrounding pedestrians and by the static obstacles in
their vicinity. In this paper we introduce a new model based on Long-Short Term
Memory (LSTM) neural networks, which is able to learn human motion behavior
from demonstrated data. To the best of our knowledge, this is the first
approach using LSTMs, that incorporates both static obstacles and surrounding
pedestrians for trajectory forecasting. As part of the model, we introduce a
new way of encoding surrounding pedestrians based on a 1d-grid in polar angle
space. We evaluate the benefit of interaction-aware motion prediction and the
added value of incorporating static obstacles on both simulation and real-world
datasets by comparing with state-of-the-art approaches. The results show, that
our new approach outperforms the other approaches while being very
computationally efficient and that taking into account static obstacles for
motion predictions significantly improves the prediction accuracy, especially
in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International
Conference on Robotics and Automation (ICRA) 201
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
This paper reports on a data-driven, interaction-aware motion prediction
approach for pedestrians in environments cluttered with static obstacles. When
navigating in such workspaces shared with humans, robots need accurate motion
predictions of the surrounding pedestrians. Human navigation behavior is mostly
influenced by their surrounding pedestrians and by the static obstacles in
their vicinity. In this paper we introduce a new model based on Long-Short Term
Memory (LSTM) neural networks, which is able to learn human motion behavior
from demonstrated data. To the best of our knowledge, this is the first
approach using LSTMs, that incorporates both static obstacles and surrounding
pedestrians for trajectory forecasting. As part of the model, we introduce a
new way of encoding surrounding pedestrians based on a 1d-grid in polar angle
space. We evaluate the benefit of interaction-aware motion prediction and the
added value of incorporating static obstacles on both simulation and real-world
datasets by comparing with state-of-the-art approaches. The results show, that
our new approach outperforms the other approaches while being very
computationally efficient and that taking into account static obstacles for
motion predictions significantly improves the prediction accuracy, especially
in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International
Conference on Robotics and Automation (ICRA) 201
Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions
Machine learning-based models to predict product state distributions from a
distribution of reactant conditions for atom-diatom collisions are presented
and quantitatively tested. The models are based on function-, kernel- and
grid-based representations of the reactant and product state distributions.
While all three methods predict final state distributions from explicit
quasi-classical trajectory simulations with R > 0.998, the grid-based
approach performs best. Although a function-based approach is found to be more
than two times better in computational performance, the kernel- and grid-based
approaches are preferred in terms of prediction accuracy, practicability and
generality. The function-based approach also suffers from lacking a general set
of model functions. Applications of the grid-based approach to nonequilibrium,
multi-temperature initial state distributions are presented, a situation common
to energy distributions in hypersonic flows. The role of such models in Direct
Simulation Monte Carlo and computational fluid dynamics simulations is also
discussed
CAR-Net: Clairvoyant Attentive Recurrent Network
We present an interpretable framework for path prediction that leverages
dependencies between agents' behaviors and their spatial navigation
environment. We exploit two sources of information: the past motion trajectory
of the agent of interest and a wide top-view image of the navigation scene. We
propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where
to look in a large image of the scene when solving the path prediction task.
Our method can attend to any area, or combination of areas, within the raw
image (e.g., road intersections) when predicting the trajectory of the agent.
This allows us to visualize fine-grained semantic elements of navigation scenes
that influence the prediction of trajectories. To study the impact of space on
agents' trajectories, we build a new dataset made of top-view images of
hundreds of scenes (Formula One racing tracks) where agents' behaviors are
heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net
successfully attends to these salient regions. Additionally, CAR-Net reaches
state-of-the-art accuracy on the standard trajectory forecasting benchmark,
Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize
to unseen scenes.Comment: The 2nd and 3rd authors contributed equall
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