83,267 research outputs found
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
The discriminative functional mixture model for a comparative analysis of bike sharing systems
Bike sharing systems (BSSs) have become a means of sustainable intermodal
transport and are now proposed in many cities worldwide. Most BSSs also provide
open access to their data, particularly to real-time status reports on their
bike stations. The analysis of the mass of data generated by such systems is of
particular interest to BSS providers to update system structures and policies.
This work was motivated by interest in analyzing and comparing several European
BSSs to identify common operating patterns in BSSs and to propose practical
solutions to avoid potential issues. Our approach relies on the identification
of common patterns between and within systems. To this end, a model-based
clustering method, called FunFEM, for time series (or more generally functional
data) is developed. It is based on a functional mixture model that allows the
clustering of the data in a discriminative functional subspace. This model
presents the advantage in this context to be parsimonious and to allow the
visualization of the clustered systems. Numerical experiments confirm the good
behavior of FunFEM, particularly compared to state-of-the-art methods. The
application of FunFEM to BSS data from JCDecaux and the Transport for London
Initiative allows us to identify 10 general patterns, including pathological
ones, and to propose practical improvement strategies based on the system
comparison. The visualization of the clustered data within the discriminative
subspace turns out to be particularly informative regarding the system
efficiency. The proposed methodology is implemented in a package for the R
software, named funFEM, which is available on the CRAN. The package also
provides a subset of the data analyzed in this work.Comment: Published at http://dx.doi.org/10.1214/15-AOAS861 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Highly comparative feature-based time-series classification
A highly comparative, feature-based approach to time series classification is
introduced that uses an extensive database of algorithms to extract thousands
of interpretable features from time series. These features are derived from
across the scientific time-series analysis literature, and include summaries of
time series in terms of their correlation structure, distribution, entropy,
stationarity, scaling properties, and fits to a range of time-series models.
After computing thousands of features for each time series in a training set,
those that are most informative of the class structure are selected using
greedy forward feature selection with a linear classifier. The resulting
feature-based classifiers automatically learn the differences between classes
using a reduced number of time-series properties, and circumvent the need to
calculate distances between time series. Representing time series in this way
results in orders of magnitude of dimensionality reduction, allowing the method
to perform well on very large datasets containing long time series or time
series of different lengths. For many of the datasets studied, classification
performance exceeded that of conventional instance-based classifiers, including
one nearest neighbor classifiers using Euclidean distances and dynamic time
warping and, most importantly, the features selected provide an understanding
of the properties of the dataset, insight that can guide further scientific
investigation
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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