Manolis Vavalis

Design and Early Simulations of Next Generation Intelligent Energy Systems

The aim of this paper is twofold. Firstly, to briefly present the overall objectives and the expected outcome of an on-going effort concerning the design the implementation and the analysis of next generation intelligent energy systems based on anticipatory control and a set of ICT emerging technologies and innovations. Secondly, to describe an early proof-of-concept implementation and the preliminary experimentation of a simulation platform focused on holistic detailed studies of electric energy markets. The proposed platform allows us to elucidate issues related to the open and smart participation of producers and consumers on large-scale energy e-markets. Based on an existing simulation system we present the required theoretical studies, the enabling technologies, and the practical tools that contribute to the development of such a platform capable of truly large scale simulations that cover real life scenarios and stress most components and modules of next generation smart energy markets. Elements of game theory are utilized to solve the optimization problem related to the maximization of the social welfare of producers and consumers. Selected simulation results associated with the basic required characteristics of our platform are presented.

Personalised news and scientific literature aggregation

Content-based filtering can be deployed for personalised information dissemination on the web, but this is a possibility that has been largely ignored. Nowadays, there are no successful content-based filtering applications available online. Nootropia is an immune-inspired user profiling model for content-based filtering. It has the advantageous property to be able to represent a users multiple interests and adapt to a variety of changes in them. In this paper we describe our early efforts to develop real world personalisation services based on Nootropia. We present, the architecture, implementation, usage and evaluation of the personalised news and paper aggregator, which aggregates news and papers that are relevant to an individuals interests. Our user study shows that Nootropia can effectively learn a users interests and identify relevant information. It also indicates that information filtering is a complicated task with many factors affecting its successful application in a real situation.

Immune Learning in a Dynamic Information Environment

In Adaptive Information Filtering, the user profile has to be able to define and maintain an accurate representation of the users interests over time. According to Autopoietic Theory, the immune system faces a similar continuous learning problem. It is an organisationally closed network that reacts autonomously to define and preserve the organisms identity. Nootropia is a user profiling model, which has been inspired by this view of the immune system. In this paper, we introduce new improvements to the model and propose a methodology for testing the ability of a user profile to continuously learn a users changing interests in a dynamic information environment. Comparative experiments show that Nootropia outperforms a popular learning algorithm, especially when more than one topic of interest has to be represented.

A Bag or a Window of Words for Information Filtering

Treating documents as bag of words is the norm in Information Filtering. Syntactic and semantic correlations between terms are ignored, or in other words, term independence is assumed. In this paper we challenge this common assumption. We use Nootropia, a user profiling model that uses a sliding window approach to capture term dependencies in a network and a spreading activation process to take them into account for document evaluation. Experiments performed based on TRECs routing guidelines demonstrate that given an adequate window size the additional information that term dependencies encode, results in improved filtering performance over a traditional bag of words approach.

Knowledge-Based Representation Of 3D Media

In recent years, 3D media have become more and more widespread and have been made available in numerous online repositories. A systematic and formal approach for representing and organizing shape-related information is needed to share 3D media, to communicate the knowledge associated to shape modeling processes and to facilitate its reuse in useful cross-domain usage scenarios. In this paper we present an initial attempt to formalize an ontology for digital shapes, called the Common Shape Ontology (CSO). We discuss about the rationale, the requirements and the scope of this ontology, we present in detail its structure and describe the most relevant choices related to its development. Finally, we show how the CSO conceptualization is used in domain-specific application scenarios.

Intelligent Bidding in Smart Electricity Markets

This paper has two aims. Firstly, to briefly present overall objectives and expected outcome of an on-going effort concerning design, implementation and the analysis of next generation energy systems based on anticipatory control and a set of ICT emerging technologies and innovations. Secondly, to describe an early proof-of-concept implementation and the associated experimentation of a simulation platform focused on holistic detailed studies of electric energy markets. The proposed platform allows us to elucidate issues related to the open and smart participation of producers and consumers on large-scale e-markets. Based on an existing simulation system, the authors present the required theoretical studies, the enabling technologies, and the practical tools that contribute to the development of such a platform capable of truly large scale simulations. Elements of game theory are utilized to solve the optimization problem related to the maximization of the social welfare of producers and consumers. Selected simulation results associated with the basic required characteristics are presented.

Large scale simulations for electric energy markets

The aim of this paper is to present the design, an early proof-of-concept implementation and the preliminary experimentation of a simulation platform focused on holistic detailed studies of electric energy markets. The proposed platform allows us to elucidate issues related to the open and smart participation of producers and consumers on large scale e-markets. Based on an existing simulation system we present the theoretical studies, the enabling technologies, and the practical tools that contribute to the development of such a platform capable of truly large scale simulations that cover real life scenarios and stress most components and modules of next generation smart energy markets.

Revisiting Evolutionary Information Filtering

Adaptive Information Filtering seeks a solution to the problem of information overload through a tailored representation of the users interests, called user profile, which constantly adapts to changes in them. Evolutionary Algorithms have been proposed as a solution to the problem of profile adaptation, but the relevant attempts have not produced successful real world applications. In this paper, we argue that Adaptive Information Filtering is a complex and dynamic problem not easily addressed with Genetic Algorithms and Memetic Algorithms that adopt weighted keyword vector for profile representation. We discuss the theoretical issues and provide experimental evidence showing that such an approach suffers due to the large number of dimensions in the underlying vector space. The genetic operators cannot randomly produce the right combinations of keyword weights, given the very large number of possible combinations. With the current work, we wish to reanimate the interest in Evolutionary Information Filtering. Profile adaptation is a challenging problem with no established solution and biologically inspired solutions have still an important role to play in solving it. This however requires experimental methodologies that reflect the complexity and dynamics of the problem. This paper is part of ongoing research work in this direction.

A knowledge management architecture for 3d shapes and applications

In this paper, we present a knowledge-based approach to 3D shape management and service composition, exploiting and extending Web Services and Semantic Web technologies. Semantic Web technology fosters semantic interoperability, while Web Services technology is utilized to construct loosely coupled components, which, combined with workflow technology enrich the processing capabilities needed for e-Science and e-Engineering. We propose an open system architecture for the formalization, processing and sharing of shape knowledge in order to efficiently support key activities: from shape creation, retrieval, post-processing and composition, to the categorization and transparent invocation of algorithms, to the automated construction and execution of complex process flows, to capturing metadata for accurate searching of shape and algorithmic resources. Two representative scenarios have been chosen, 3D shape geometry processing and 3D product development, which will demonstrate the potential of the platform in terms of establishing novel, knowledge-based and semantically enriched solutions dealing with the automation of the knowledge lifecycle.

Smart energy for smart irrigation

A large scale and highly accurate simulation system elucidates crucial issues.Smart grid logic effects the efficiency and flexibility of irrigation systems.Market models increase the flexibility of power consumption of irrigation systems.Effective energy microgrid, energy autarky seem to be possible in selected cases. This paper investigates the possibility of reducing the cost of irrigation by utilizing techniques, methods and practices that are common to the smart energy systems. A software platform that couples the smartness of the irrigation systems with the smartness of the energy systems is designed, implemented and evaluated. The resulting simulation engine allows large scale and very detailed experimentation where irrigation experts specify energy effective configurations that lead to the reduction of the irrigation cost through smart utilization of Renewable Energy Sources (RES).