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Intelligent Systems, IEEE

Issue 2 • Date March-April 2011

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Displaying Results 1 - 16 of 16
  • [Front cover]

    Page(s): c1
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  • [Table of contents - Front cover]

    Page(s): c2
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  • Contents

    Page(s): 1
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  • Social Media and the Jasmine Revolution

    Page(s): 2 - 4
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  • AI in Power Systems and Energy Markets

    Page(s): 5 - 8
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  • MASCEM: Electricity Markets Simulation with Strategic Agents

    Page(s): 9 - 17
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    To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short and medium term simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly. View full abstract»

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  • Grid Monitoring and Market Risk Management

    Page(s): 18 - 21
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    The advent of electricity market deregulation has placed great emphasis on information availability and analysis and the subsequent decision making to optimize system operation in a competitive environment. This creates a need for better ways to correlate market activity with the physical grid-operating states in real time and share such information among market participants. Command and control choices might result in different financial consequences for market participants and severely impact their profits. The effects are both short term, as in day-ahead and real-time markets for energy, reserves, and congestion relief, and long term, as in investments in transmission and generation capacity. Be cause of this, new solutions are necessary to integrate grid control and market operations while accounting for both good engineering practices and appropriate economic incentives. View full abstract»

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  • A Data-Mining-Based Methodology for Transmission Expansion Planning

    Page(s): 28 - 37
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    Analyzing electrical and economical locational marginal pricing (LMP) values can help operators in the electric power sector plan future investments and network expansion. View full abstract»

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  • Embedded Intelligence for Electrical Network Operation and Control

    Page(s): 38 - 45
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    The paper states that integrating multiple types of intelligent, mulit-agent data analysis within a smart grid can pave the way for flexible, extensible, and robust solutions to power network management. View full abstract»

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  • Using Genetic Algorithms and Fuzzy Programming to Monitor Voltage Sags and Swells

    Page(s): 46 - 53
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    A genetic algorithms and fuzzy programming methodology can help determine the minimum number of meters (and their location) needed to monitor voltage sags and swells in power networks. This paper proposes a methodology that determines the minimal number of power-quality meters needed to monitor a power network and identifies the buses on which those meters should be installed. View full abstract»

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  • Automatically Analyzing Facial-Feature Movements to Identify Human Errors

    Page(s): 54 - 63
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    Everyday countless human errors occur around the globe. Although many of these errors are harmless, disastrous errors-such as Bhopal, Chernobyl, and Three Mile Island-demonstrate that developing ways to improve human performance is not only desirable but crucial. Considerable research exists in human-error identification (HEI), a field devoted to developing systems to predict human errors. However, these systems typically predict only instantaneous errors, not overall human performance. Furthermore, they often rely on predefined hierarchies of errors and manual minute by-minute analyses of users by trained analysts, making them costly and time consuming to implement. Using facial feature points automatically extracted from short video segments of participants' faces during laboratory experiments, our work applies a bottom-up approach to predict human performance. View full abstract»

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  • Multicriteria User Modeling in Recommender Systems

    Page(s): 64 - 76
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    The paper mentions that a hybrid recommender systems framework creates user-profile groups before applying a collaborative-filtering algorithm by incorporating techniques from the multiple-criteria decision-analysis (MCDA) field. View full abstract»

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  • Improving Users' Mental Models of Intelligent Software Tools

    Page(s): 77 - 83
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    Intelligent software algorithm are increasingly becoming a tool in consumers daily lives. Users understand the basic mechanics of the intelligent software systems they rely on, but often novices have no direct knowledge of their intelligent devices algorithm, data requirements, limitations, and representations. Problems can go beyond those caused by a poor user interface design and a user's ability to under stand a tool's simple components, which could be alleviated with proper instruction. This article describes the Experiential User Guide (EUG), a concept designed to address these challenges. View full abstract»

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  • ACP-Based Control and Management of Urban Rail Transportation Systems

    Page(s): 84 - 88
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    Urban rail transportation (URT) has long become the preferred public transportation choice for major metropolitan areas such as New York, London, Paris, Moscow, Tokyo, and Beijing. The highest daily record for Beijing's URT reached 5.71 million passenger trips in 2010, which makes the network extremely crowded in rush hours. To accommodate the increasing demand for URT, the service frequencies have been increased tremendously. To address these safety, efficiency, and reliability issues, the paper presents a novel parallel system for URT operations that uses the concept of parallel system and computational experiments based on artificial systems (ACP). The parallel URT system can analyze and facilitate passenger-flow management, vehicle scheduling, and other operational issues while considering human-related, environmental, and other social and economical factors. View full abstract»

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  • [Advertisement - Back cover]

    Page(s): c3
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  • [Advertisement - Back cover]

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Aims & Scope

IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications.

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Meet Our Editors

Editor-in-Chief
Daniel Zeng
University of Arizona