5,664 research outputs found
Modeling the hall-petch effect with a gradient crystal plasticity theory including a grain boundary yield criterion
Abstract. A strain gradient crystal plasticity theory including the gradient of the equiv-
alent plastic strain ∇γeq is discussed. A grain boundary yield condition is proposed
in order to account for the influence of the grain boundaries. Periodic tensile test simulations
show the mechanical predictions of the numerical model
A Residual Bootstrap for Conditional Value-at-Risk
This paper proposes a fixed-design residual bootstrap method for the two-step
estimator of Francq and Zako\"ian (2015) associated with the conditional
Value-at-Risk. The bootstrap's consistency is proven for a general class of
volatility models and intervals are constructed for the conditional
Value-at-Risk. A simulation study reveals that the equal-tailed percentile
bootstrap interval tends to fall short of its nominal value. In contrast, the
reversed-tails bootstrap interval yields accurate coverage. We also compare the
theoretically analyzed fixed-design bootstrap with the recursive-design
bootstrap. It turns out that the fixed-design bootstrap performs equally well
in terms of average coverage, yet leads on average to shorter intervals in
smaller samples. An empirical application illustrates the interval estimation
Ranking Median Regression: Learning to Order through Local Consensus
This article is devoted to the problem of predicting the value taken by a
random permutation , describing the preferences of an individual over a
set of numbered items say, based on the observation of
an input/explanatory r.v. e.g. characteristics of the individual), when
error is measured by the Kendall distance. In the probabilistic
formulation of the 'Learning to Order' problem we propose, which extends the
framework for statistical Kemeny ranking aggregation developped in
\citet{CKS17}, this boils down to recovering conditional Kemeny medians of
given from i.i.d. training examples . For this reason, this statistical learning problem is
referred to as \textit{ranking median regression} here. Our contribution is
twofold. We first propose a probabilistic theory of ranking median regression:
the set of optimal elements is characterized, the performance of empirical risk
minimizers is investigated in this context and situations where fast learning
rates can be achieved are also exhibited. Next we introduce the concept of
local consensus/median, in order to derive efficient methods for ranking median
regression. The major advantage of this local learning approach lies in its
close connection with the widely studied Kemeny aggregation problem. From an
algorithmic perspective, this permits to build predictive rules for ranking
median regression by implementing efficient techniques for (approximate) Kemeny
median computations at a local level in a tractable manner. In particular,
versions of -nearest neighbor and tree-based methods, tailored to ranking
median regression, are investigated. Accuracy of piecewise constant ranking
median regression rules is studied under a specific smoothness assumption for
's conditional distribution given
Autonomous take-off and landing of a tethered aircraft: a simulation study
The problem of autonomous launch and landing of a tethered rigid aircraft for
airborne wind energy generation is addressed. The system operates with
ground-based power conversion and pumping cycles, where the tether is
repeatedly reeled in and out of a winch installed on the ground and linked to
an electric motor/generator. In order to accelerate the aircraft to take-off
speed, the ground station is augmented with a linear motion system composed by
a slide translating on rails and controlled by a second motor. An onboard
propeller is used to sustain the forward velocity during the ascend of the
aircraft. During landing, a slight tension on the line is kept, while the
onboard control surfaces are used to align the aircraft with the rails and to
land again on them. A model-based, decentralized control approach is proposed,
capable to carry out a full cycle of launch, low-tension flight, and landing
again on the rails. The derived controller is tested via numerical simulations
with a realistic dynamical model of the system, in presence of different wind
speeds and turbulence, and its performance in terms of landing accuracy is
assessed. This study is part of a project aimed to experimentally verify the
launch and landing approach on a small-scale prototype.Comment: This is the longer version of a paper submitted to the 2016 American
Control Conference 2016, with more details on the simulation parameter
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