67,679 research outputs found
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
Metamodel-based importance sampling for the simulation of rare events
In the field of structural reliability, the Monte-Carlo estimator is
considered as the reference probability estimator. However, it is still
untractable for real engineering cases since it requires a high number of runs
of the model. In order to reduce the number of computer experiments, many other
approaches known as reliability methods have been proposed. A certain approach
consists in replacing the original experiment by a surrogate which is much
faster to evaluate. Nevertheless, it is often difficult (or even impossible) to
quantify the error made by this substitution. In this paper an alternative
approach is developed. It takes advantage of the kriging meta-modeling and
importance sampling techniques. The proposed alternative estimator is finally
applied to a finite element based structural reliability analysis.Comment: 8 pages, 3 figures, 1 table. Preprint submitted to ICASP11
Mini-symposia entitled "Meta-models/surrogate models for uncertainty
propagation, sensitivity and reliability analysis
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning
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