8,675 research outputs found
Multi-Layer Cyber-Physical Security and Resilience for Smart Grid
The smart grid is a large-scale complex system that integrates communication
technologies with the physical layer operation of the energy systems. Security
and resilience mechanisms by design are important to provide guarantee
operations for the system. This chapter provides a layered perspective of the
smart grid security and discusses game and decision theory as a tool to model
the interactions among system components and the interaction between attackers
and the system. We discuss game-theoretic applications and challenges in the
design of cross-layer robust and resilient controller, secure network routing
protocol at the data communication and networking layers, and the challenges of
the information security at the management layer of the grid. The chapter will
discuss the future directions of using game-theoretic tools in addressing
multi-layer security issues in the smart grid.Comment: 16 page
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
Footballonomics: The Anatomy of American Football; Evidence from 7 years of NFL game data
Do NFL teams make rational decisions? What factors potentially affect the
probability of wining a game in NFL? How can a team come back from a
demoralizing interception? In this study we begin by examining the hypothesis
of rational coaching, that is, coaching decisions are always rational with
respect to the maximization of the expected points scored. We reject this
hypothesis by analyzing the decisions made in the past 7 NFL seasons for two
particular plays; (i) the Point(s) After Touchdown (PAT) and (ii) the fourth
down decisions. Having rejected the rational coaching hypothesis we move on to
examine how the detailed game data collected can potentially inform game-day
decisions. While NFL teams personnel definitely have an intuition on which
factors are crucial for winning a game, in this work we take a data-driven
approach and provide quantifiable evidence using a large dataset of NFL games
for the 7-year period between 2009 and 2015. In particular, we use a logistic
regression model to identify the impact and the corresponding statistical
significance of factors such as possession time, number of penalty yards,
balance between passing and rushing offense etc. Our results clearly imply that
avoiding turnovers is the best strategy for winning a game but turnovers can be
overcome with letting the offense on the field for more time. Finally we
combine our descriptive model with statistical bootstrap in order to provide a
prediction engine for upcoming NFL games. Our evaluations indicate that even by
only considering a small number of (straightforward) factors, we can achieve a
very good prediction accuracy. In particular, the average accuracy during
seasons 2014 and 2015 is approximately 63%. This performance is comparable to
the more complicated state-of-the-art prediction systems, while it outperforms
expert analysts 60% of the time.Comment: Working study - Papers has been presented at the Machine Learning and
Data Mining for Sports Analytics 2016 workshop and accepted at PLOS ON
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