Browsing by Author "Neves-Silva, Rui"
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Item Ambient intelligence based system for life-cycle management of complex manufacturing and assembly lines(Institute of Electrical and Electronics Engineers Inc., 2007) Stokic, Dragan; Neves-Silva, Rui; Marques, Maria; Reimer, Philip; Ibarbia, Jon Agirre; GENERALThe key idea of the work presented is to explore how a combination of advanced Ambient Intelligence (AmI) and Knowledge Management (KM) technologies can be used to assure a sustainable and safe use of Manufacturing and Assembly Lines (MAL) and their infrastructure over their life-cycle. The specific intention is to reduce the whole service-life operational costs and impact of MAL, providing new ways to monitor on-line Life-Cycle Parameters (LCP) of MAL and improved services to support MAL throughout its whole life-cycle. The paper presents a new system for effective life cycle management of complex MAL, including computation of LCP based on information obtained from AmI systems within MAL, and delivery of improved services around MAL.Item Collaborative environment for intelligent monitoring in manufacturing industry(Institute of Electrical and Electronics Engineers Inc., 2016-04-27) Correia, Ana; Stokic, Dragan; Neves-Silva, Rui; Campos, Ana Rita; Agirre, Jon; GENERALThis paper presents an innovative approach to support collaboration in manufacturing industry, especially related to intelligent monitoring of complex assembly and manufacturing systems in global settings. The new collaborative platform built based on service-oriented architectures principles is proposed. The innovative collaborative services to be combined with the existing tools for monitoring of assembly and manufacturing systems and information obtained via ambient intelligence systems installed within assembly and manufacturing systems are described. The application of such services in real manufacturing environment is presented.Item Energy consumption prediction from usage data for decision support on investments: The EnPROVE approach(IFAC Secretariat, 2010) Neves-Silva, Rui; Ruzzelli, Antonio; Fuhrmann, Peter; Bourdeau, Marc; Pérez, Juan; Michaelis, Eberhard; SGWhen intending to renovate an existing building, with energy efficiency and greenhouse gas emissions in mind, a building owner is always questioning himself if the available investment resources are being directed to an effective return and if there are ways to improve this return? This paper presents the innovative approach from EnPROVE project that responds the previous question in a positive way. The approach is based on predicting the energy consumption of a specific building, with different scenarios implementing energy-efficient technologies and control solutions, based on actual measured performance and usage data of the building itself. The key hypothesis of EnPROVE is that it is possible, from adequate gathering and assessing data on how a structure performs and is being used by its occupants from an energy viewpoint, to build highly accurate and specific energy consumption models relevant for prediction of alternative scenarios. The EnPROVE software tools assess the energyefficiency impact of alternative technologies for which available investment resources can be directed and, thus, support the decision maker finding the optimized set of energy-efficient solutions to be implemented. These results are tailored to the actual building itself, through automated measurements of building usage and energy consumption.