42,027 research outputs found
Information Nonanticipative Rate Distortion Function and Its Applications
This paper investigates applications of nonanticipative Rate Distortion
Function (RDF) in a) zero-delay Joint Source-Channel Coding (JSCC) design based
on average and excess distortion probability, b) in bounding the Optimal
Performance Theoretically Attainable (OPTA) by noncausal and causal codes, and
computing the Rate Loss (RL) of zero-delay and causal codes with respect to
noncausal codes. These applications are described using two running examples,
the Binary Symmetric Markov Source with parameter p, (BSMS(p)) and the
multidimensional partially observed Gaussian-Markov source. For the
multidimensional Gaussian-Markov source with square error distortion, the
solution of the nonanticipative RDF is derived, its operational meaning using
JSCC design via a noisy coding theorem is shown by providing the optimal
encoding-decoding scheme over a vector Gaussian channel, and the RL of causal
and zero-delay codes with respect to noncausal codes is computed.
For the BSMS(p) with Hamming distortion, the solution of the nonanticipative
RDF is derived, the RL of causal codes with respect to noncausal codes is
computed, and an uncoded noisy coding theorem based on excess distortion
probability is shown. The information nonanticipative RDF is shown to be
equivalent to the nonanticipatory epsilon-entropy, which corresponds to the
classical RDF with an additional causality or nonanticipative condition imposed
on the optimal reproduction conditional distribution.Comment: 34 pages, 12 figures, part of this paper was accepted for publication
in IEEE International Symposium on Information Theory (ISIT), 2014 and in
book Coordination Control of Distributed Systems of series Lecture Notes in
Control and Information Sciences, 201
Bayesian Gait Optimization for Bipedal Locomotion
One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance criterion, such as speed, robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. We propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, we use Bayesian optimization to efficiently find gait parameters that optimize the desired performance metric. As a proof of concept we demonstrate that Bayesian optimization is near-optimal in a classical stochastic optimal control framework. Moreover, we validate our approach to Bayesian gait optimization on a low-cost and fragile real bipedal walker and show that good walking gaits can be efficiently found by Bayesian optimization. © 2014 Springer International Publishing
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A non-separable stochastic model for pulse-like ground motions
A phenomenological non-separable non-stationary stochastic model is proposed to represent near-fault pulse-like ground motions (PLGMs) by means of a parametrically defined evolutionary power spectrum (EPSD). Numerical data pertaining to ensembles of EPSD compatible realizations and considering statistical analysis of peak elastic and inelastic spectral ordinates demonstrate the applicability of the model to capture the salient effects of PLGMs to structural responses. To this aim, the model parameters are calibrated against a field recorded PLGM. Further numerical data considering stochastic processes compatible with the response spectrum of the European aseismic code (EC8) are furnished to demonstrate the potential of the proposed model for including near-fault effects to spectrum compatible representations of the seismic action. It is foreseen that this model can be a useful tool in accounting for the low-frequency content of PLGMs in both Monte Carlo simulation-based analyses and in statistical linearization based studies
Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a
posteriori estimation of probabilistic program variables. By using a series of
code transformations, the evidence of any probabilistic program, and therefore
of any graphical model, can be optimized with respect to an arbitrary subset of
its sampled variables. To carry out this optimization, we develop the first
Bayesian optimization package to directly exploit the source code of its
target, leading to innovations in problem-independent hyperpriors, unbounded
optimization, and implicit constraint satisfaction; delivering significant
performance improvements over prominent existing packages. We present
applications of our method to a number of tasks including engineering design
and parameter optimization
Dynamic Motion Planning for Aerial Surveillance on a Fixed-Wing UAV
We present an efficient path planning algorithm for an Unmanned Aerial
Vehicle surveying a cluttered urban landscape. A special emphasis is on
maximizing area surveyed while adhering to constraints of the UAV and partially
known and updating environment. A Voronoi bias is introduced in the
probabilistic roadmap building phase to identify certain critical milestones
for maximal surveillance of the search space. A kinematically feasible but
coarse tour connecting these milestones is generated by the global path
planner. A local path planner then generates smooth motion primitives between
consecutive nodes of the global path based on UAV as a Dubins vehicle and
taking into account any impending obstacles. A Markov Decision Process (MDP)
models the control policy for the UAV and determines the optimal action to be
undertaken for evading the obstacles in the vicinity with minimal deviation
from current path. The efficacy of the proposed algorithm is evaluated in an
updating simulation environment with dynamic and static obstacles.Comment: Accepted at International Conference on Unmanned Aircraft Systems
201
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