8,675 research outputs found

    Multi-Layer Cyber-Physical Security and Resilience for Smart Grid

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    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

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    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

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    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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