Investigation of Rheological and Geometric Properties Effect on Nonlinear Behaviour of Fluid Viscous Dampers

Global approval of the use of fluid viscous dampers to control the buildings response against dynamic loadings is growing. The idea behind incorporating additional dampers is that they will reduce most of the energy that is transmitted to the building during shaking event. The objective of this work is to identify and enhance the design parameters that control the nonlinear behaviour of fluid viscous damper subjected to sinusoidal excitation. For this, a numerical model of the flow inside the dissipater has been carried out based on finite volume method. A novel approach has been adopted to simulate elastic behaviour of the fluid, taking into account its compressibility by using the Murnaghan equation of state. A comparison between the calculations of the proposed model and the experimental tests was carried out. The model proved to be sufficiently accurate. A fluid flow analysis was then conducted to fully understand the internal mechanism of the damper. A parametric study was then performed by varying aspects such as dimensions, geometric relationships between components and fluid properties in order to better understand their effect on the non-linear behaviour of the device.  The results highlight the relationship between the parameters governing the shear thinning behaviour of the fluid and the non-linearity exponent of the damper. This makes it possible to better control the non-linear behaviour of the device by selecting the appropriate silicone oil and the appropriate geometric dimensions of its components

An Ontological Framework for Decision Support

In the last few years, ontologies have been successfully exploited by Decision Support Systems (DSSs) to support some phases of the decisionmaking process. In this paper, we propose to employ an ontological representation for all the content both processed and produced by a DSS in answering requests. This semantic representation supports the DSS in the whole decisionmaking process, and it is capable of encoding (i) the request, (ii) the data relevant for it, and (iii) the conclusions/suggestions/decisions produced by the DSS. The advantages of using an ontology-based representation of the main data structure of a DSS are many: (i) it enables the integration of heterogeneous sources of data available in the web, and to be processed by the DSS, (ii) it allows to track, and to expose in a structured form to additional services (e.g., explanation or case reuse services), all the content processed and produced by the DSS for each request, and (iii) it enables to exploit logical reasoning for some of the inference steps of the DSS decision-making process. The proposed approach have been successfully implemented and exploited in a DSS for personalized environmental information, developed in the context of the PESCaDO EU project

Generating texts in different styles

Natural Language Generation (nlg) systems generate texts in English and other human languages from non-linguistic input data. Usually there are a large number of possible texts that can communicate the input data, and nlg systems must choose one of these. This decision can partially be based on style (interpreted broadly). We explore three mechanisms for incorporating style into nlg choice-making: (1) explicit stylistic parameters, (2) imitating a genre style, and (3) imitating an individual's style

Personalized environmental service configuration and delivery orchestration: The PESCaDO demonstrator

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this demonstration, we present an environmental information system that addresses this demand in its full complexity in the context of the PESCaDO EU project. Specifically, we will show a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-based knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference

Personalized environmental service orchestration for quality life improvement

Environmental and meteorological conditions are of utmost importance for the population, as they are strongly related to the quality of life. Citizens are increasingly aware of this importance. This awareness results in an increasing demand for environmental information tailored to their specific needs and background. We present an environmental information platform that supports submission of user queries related to environmental conditions and orchestrates results from complementary services to generate personalized suggestions. From the technical viewpoint, the system discovers and processes reliable data in the web in order to convert them into knowledge. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference. The platform is demonstrated with real world use cases in the south area of Finland showing the impact it can have on the quality of everyday life