By Alessandra De Paola (auth.), Salvatore Gaglio, Giuseppe Lo Re (eds.)
The identify of this ebook is a pun at the use of the preposition “onto” with the purpose of recalling “Ontology”, the time period regularly followed within the desktop technological know-how group to point the examine of the formal specification for organizing knowledge.
In the sphere of data engineering, Ontologies are used for modeling recommendations and relationships on a few domain.
The 12 months 2013 celebrates the 20th anniversary of the realm broad internet. the straightforward community of hypermedia has remodeled the realm of communications with huge, immense implications at the social relationships. in spite of the fact that, conventional world-wide-web is at the moment experiencing a not easy evolution towards the web of items (IoT), this present day possible because of the combination of pervasive applied sciences able to sensing the environment.
The most crucial contribution of IoT regards the potential for permitting extra effective machine-to-machine cooperation. To such objective, ontologies characterize the main compatible software to permit move and comprehension of data between desktop purposes, even these designed and constructed by means of unrelated humans in numerous places.
This booklet proposes a suite of contributions illustrating varied functions following those instructions and which are the results of genuine stories constructed within the context of analysis projects.
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As described in , rules can be learned through a reinforcement algorithm such as Q-Learning. This technique creates a table where each configuration is rewarded or penalized according to the approach of the target system. The configurations that achieve the highest scores will be selected as the best set of rules for the system. The authors of [14, 16], adopt a hierarchical approach to learn fuzzy rules in medical applications, for reducing the memory consumption. In  a genetic algorithm, a heuristic search methodology, is adopted in order to determine to best membership functions that optimize the behavior of a fuzzy controller designed to manage packet queues on routers of the Internet [8, 9].
In a home scenario, an AmI system controls the environment according to a user’s lifestyle and daily routine. To achieve this goal, one fundamental task is to recognize the user’s activities in order to generate his daily activities profile. In this chapter, we present a simple AMI system for a home scenario to recognize and predict users’ activities. With this predictive capability, it is possible to anticipate their actions and improve their quality of life. Our approach uses a Hidden Markov Model (HMM) to recognize activities and deal with the intrinsic uncertainty of sensory information.
For example the perceived temperature in the summer is more pleasant if there is low rather than high humidity. Bioclimatic indices, such as the PET (Physiological Equivalent Temperature) and HI (Heat Index), evaluate many physiological conditions related to human perception of the environmental situation on the basis of the correlation of various parameters . These weather parameters determine the apparent temperature. HI as a combination of temperature and humidity, whilst PET considers different values, such as the heat balance of the human body, skin temperatures, activity, basic metabolism, the heat resistance of clothing, air temperature and humidity.