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    The simulation of transients in thermal plant. Part II: Applications

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    This paper deals with the simulation of the transients of thermal plant with control systems. In the companion paper forming part I of this article [G.L. Morini, S. Piva, The simulation of transients in thermal plant. Part I: Mathematical model, Applied Thermal Engineering 27 (2007) 2138-2144] it has been described how a ‘‘thermal-library’’ of customised blocks can be built and used, in an intuitive way, to study the transients of any kind of thermal plant. Each component of plant such as valves, boilers, and pumps, is represented by a single block. In this paper, the ‘‘thermal-library’’ approach is demonstrated by the analysis of the dynamic behaviour of a central heating plant of a typical apartment house during a sinusoidal variation of the external temperature. A comparison of the behaviour of such a plant with three way valve working either in flow rate or in temperature control, is presented and discussed. Finally, the results show the delaying effect of the thermal capacity of the building on the performance of the control system

    "Inferring Cause/Effect Relationships in Multi-sensor Ambient Intelligence Systems"

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    In this work a learning technique to provide an Ambient Intelligence (smart space) system with the capacity of predicting variation events in its own internal state is presented. The system and the interacting users are modeled through the instantaneous state vectors obtained as output of two trained Self Organizing Map-based classifiers. The information processed by the system is collected by two sensors sets monitoring several internal and external system variables. Starting from the hypothesis that the user actions have a direct influence on internal system state variables (e.g. work load on personal computers computation or storage devices in a University laboratory, in our current test implementation) we developed a statistical voting algorithm for inferring cause/effect relationships in these instantaneous variations. Logical connections are obtained in unsupervised mode with no a priori information and leads to the definition of a knowledge base the system can exploit to predict its own near future internal state variations, given the observation of the lab user

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The 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
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