1,720,956 research outputs found
Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach
Get PDFIn this diploma thesis, the combined problem of power company selection and Demand Response Management in a Smart Grid Network consisting of multiple power companies and multiple customers is studied via adopting a distributed learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers who act as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a distributed learning based mechanism. Given customers\u27 power company selection, the Demand Response Management problem is formulated as a two-stage game theoretic optimization framework, where at the first stage the optimal customers\u27 electricity consumption is determined and at the second stage the optimal power companies’ pricing is calculated. The output of the Demand Response Management problem feeds the learning system in order to build knowledge and conclude to the optimal power company selection. A two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is proposed in order to realize the distributed learning power company selection and the two-stage distributed Demand Response Management framework. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated
Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach
Get PDFIn this paper, the combined problem of power company selection and demand response management (DRM) in a smart grid network consisting of multiple power companies and multiple customers is studied via adopting a reinforcement learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers, acting as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a reinforcement learning based mechanism. Given customers’ power company selection, the DRM problem is formulated as a two-stage game theoretic optimization framework. At the first stage the optimal customers’ electricity consumption is determined and at the second stage the optimal power companies’ pricing is obtained. The output of the DRM problem feeds the learning system to build knowledge and to conclude to the optimal power company selection. To realize the aforementioned framework a two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is introduced. The performance evaluation of the proposed approach is achieved via modeling and simulation and its superiority against other approaches is illustrated
Artificial Intelligent Risk-aware Autonomous Decision-Making in Resource-Constrained Computing Systems
No full textArtificial Intelligent autonomous systems are becoming increasingly ubiquitous in daily life. Mobile devices for example provide mechanical-generated intelligent support to humans, with various degrees of autonomy, and are a key part of the recent autonomous revolution. Autonomous intelligent systems aim to understand and interact with their users in a timely manner, while many of them are characterized by constrained resources. Despite that, the average person does not act in a formulaic and risk-neutral manner but instead exhibits risk-aware attitudes when performing a task that includes sources of uncertainties. When humans make decisions, they explore their surroundings, understand the emerging risks, perform actions, and evaluate their perceived outcomes. What a person characterizes as a satisfactory outcome is subjective to her own reasoning, behavior, and risk capacity. Therefore, an autonomous intelligent system should be enriched with human awareness, thus it should account for and sometimes mimic its owner\u27s cognitive behavior and behavioral patterns, such that the latter\u27s subjective satisfaction is optimized, and personalized service is provided. Furthermore, the proliferation of autonomous systems, e.g., mobile or wearable devices, boosts the data volume and service demand. Each autonomous system aims to optimize its owner\u27s experience in a self-centric manner, and in several application domains, its actions impact the others\u27 experience and decision-making process generally. To this end, the users\u27 subjective goals generate conflicts, and the autonomous intelligent systems are expected to make decisions in non-cooperative environments. In this thesis, we investigate and introduce distributed autonomous decision-making frameworks by focusing on motivating application domains with the aforementioned challenges. We utilize Game Theory for studying the strategic interaction of the autonomous intelligent systems in non-cooperative environments and tackling the necessity of non-centralized and scalable solutions. We build autonomous intelligent decision-making agents through Reinforcement Learning, which is a popular statistical Artificial Intelligence (AI) technique for controlling unknown environments with partial, and incomplete information. Reinforcement Learning (RL) introduces the concept of an agent that learns to interact with an unknown environment by performing actions that are mainly driven by particular observations, and by evaluating the resulted feedback. We extend the regular RL setting through reward reshaping for considering the user\u27s risk-aware characteristics that are exhibited in real life. We incorporate Prospect Theory, which belongs to the behavioral economic subgroup, and describes how individuals make decisions between probabilistic alternatives, where risk is involved, and the probability of different outcomes is unknown. In the considered non-cooperative environments, we seek distributed solutions, thus Equilibrium points, where each autonomous intelligent agent does not have the incentive to change its own decision unilaterally. Our investigation leads to autonomous intelligent decision-making frameworks that could serve as a step towards Artificial General Intelligence (AGI), where the computing systems learn to perform a task in a human-centric manner, thus in a similar way that the task would be completed by a person in real life
Βέλτιστη επιλογή εταιρείας ηλεκτρικής ενέργειας & διαχείριση της απόκρισης της ζήτησης βασισμένη στη μηχανική μάθηση και στη θεωρία παιγνίων
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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