In this talk, 3D Network Analyses and Interactive Navigation System for indoor which consists of three components will be presented. The first component is used to extract the geometrical and 3D topological vector data automatically from architectural raster floor plans. The second component is used for network analysis and simulations. It generates and presents the optimum path in a 3D modeled building, and provides 3D visualization and simulation. And the third component is used to carry out the generation of the guiding expressions and it also provides that information for the mobile devices. In addition, an Intelligent Multi Storey Car Parking Model for Smart Buildings will be introduced in this presentation.
Maintenance costs are a significant part of the total operating costs of all manufacturing or production plants. Depending on the specific industry, they can represent between 15 and 60 percent of the cost of goods produced. Recent surveys of maintenance management effectiveness indicate that one-third of all maintenance costs is wasted as the result of unnecessary or improperly carried out maintenance. Is then clear the enormous impact the maintenance operation plays in productivity.
Modern manufacturing systems use thousands of sensors retrieving information at hundreds to thousands of samples per second. Predictive maintenance makes use of this massive amount of data to predict malfunctioning or failures in the system and recommend a maintenance operation just before the failure happens. The objective of this presentation is showing the role that plays machine learning and big data mining algorithms in the development of predictive maintenance strategies, illustrating with several successful examples described in the literature.