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Corrosione sotto sforzo e tempo vita nel vetro : influenza delle condizioni ambientali
No full textThe problem of glass strength arises from the increasingly spread use of
the material in building. Due to its high transparency, glass has been
always used as closure element, but in recent years a tendency of using
it for structural purpose is growing rapidly. In addition, glass provides
challenging solutions for conservation of archaeological heritage. These
reasons require a deep investigation on failure mechanisms of glass and
above all on its resistance to tensile stress.
Since 1920, linear elastic fracture mechanics allowed understanding
the reasons of the low tensile strength values registered in glass.
It seems that this is due to the propagation, even very slow, of surface
microdefects induced by manufacturing processes. Later, physical
theories empirically founded further investigated on the processes of
rupture in glass, observing that microdefects may evolve up to structural
failure not only for a stress increase but also as consequence of
chemical-physical interaction with surrounding environment.
Most of prediction models which aim at evaluating a glass element
lifetime are based on empirical laws. The parameters of those theories
are related to the influence of environment but their values are provided
by the literature only within limited ranges. However, the influence of
environmental variables is complex and not secondary. In addition,
in recent years, glass is being used in building even in non favorable
climatic conditions. This latter is a challenging aim, considering the
high aesthetic qualities of glass.
For these reasons, the present thesis considered necessary to investigate
on the microphysics and chemistry which determine the phenomenon
of environmental corrosion, and this allowed identifying the
physical variables which play the most influent role. Secondarily, an expression
of failure time as explicit function of environmental variables
is provided, focusing on temperature and relative humidity. Through
parameter calibration, on the basis of four-point beam-bending tests,
a lifetime curve is obtained for a given environment.
This work arises on the one hand from a physical interest and it
shows a scientific coherence, considering the strong influence of environmental
variables, and on the other hand it is strictly connected to design purposes, since glass is being used in increasingly disparate climatic
conditions. For this latter reason, to be able to express failure
time as function of environment becomes a non negligible need
Agile knowledge management for situation awareness
No full textIn the information age, access to data is easily achieved thanks to the development
of new advanced sensors and information sources, able to measure
all kind of features, to acquire several kind of information and to transfer
those data fast and effectively all over the world.
Taking into account newly developed sensors and networks, the access
and communication of information are not unyielding and crucial as their
analysis, aggregation and elaboration.
It is within this context, that of Data Fusion finds its applicability. At
the end of the 1990, Data Fusion doctrine is formalized as the ensemble
of scientific techniques and algorithms, properly implemented in a single
framework that is able to: (a) support human operators to gather huge
quantities of heterogeneous data (some of which may not be synchronized)
from sensors observing the scenario of interest; (b) detect and classify objects
acting in the scenario; (c) understand the relationships among them, and the
intent, and threats that they could cause; (d) foresee future evolutions of the
scenario; and (e) take the best decisions in order to maximize human operator
utility.
The above mentioned process has been formalized in the Joint Directors
of Laboratories (JDL) model, and progressively revised mainly to give greater
emphasis to the human operator as an active sensor within the process, and
to evaluate the quality of the whole inference process.
Within the Data Fusion process, the goal of Situation Awareness (JDL
Level 2) is to recognize relationships existing among objects observed in
the scenario, in order to recognize situations of interest, and evaluate their
threats. Hence, Situation Awareness should help human operators to be aware of the context they are observing, especially when the scenario is
wide, or phenomena observed are complex and evolve fast.
The focus of this PhD thesis is exactly Situation Awareness, and in particular
knowledge management: in order to recognize situations and infer them
from observations, knowledge models describing situations of interest must
be effective, correct, and should be able to catch relevant and discriminant
aspects.
The definition of a good knowledge model is crucial for effective Situation
Awareness, and it is usually hard because it requires experience in the
domain, or the availability of huge quantities of data to be input to learning
algorithms (that generates usually models difficult to interpretate). Moreover,
once the model has been defined, the evaluation of its quality is difficult,
especially in real-time, because the truth about the observed situation is not
known.
The goal of this thesis has been the investigation about effective knowledge
management for correct inferences, and in particular the following aspects
of knowledge management have been considered:
• real-time knowledge model construction with regard to specific situations
or events of interest, adopting Data Mining techniques;
• real-time knowledge model refinement, according to metrics expressing
the adequacy of the model to the observations gathered.
Knowledge models employed in Situation Awareness usually differ from
each depending on the mathematical approach adopted (Bayesian approach
refers to Bayesian Networks, Hidden Markov Models requires a probabilistic
inference algorithm, Evidence theory refers to cause-effects models). In this
work, real-time model construction has been apply to Hidden Markov Models;
while real-time knowledge refinement has been investigated with regard
to Evidence Theory.
Moreover, considerations derived from the implementation of Situation
Awareness frameworks within the military context and critical infrastructure
protection domain have been reported.
Majour results of this research can be summarized in the characterization
of the agility measure, able to quantify the capability of a model to revise
itself by evaluating inconsistencies, contradictions and errors, and taking into
account uncertainty of information employed.
Model agility has be identified as a powerful feature in JDL Level 4 Process
Refinement, because it can guide and improve the overall data collection
process, eventually cueing the user or the system to search for lacking information.
Main features identified for an agile model are the following:
• an agile model does not require to be perfect since its construction: it
can be obtained with imperfect knowledge of the whole system, because
it is able to learn from its experience;
• agility extends the model lifetime: agile models are able to manage a
greater number of scenarios that maybe were not even included when
the model was created;
• an agile model is more resilient, more robust, and able to perform
better and wider range of real life scenarios.
Investigations about agility measure within Evidence Theory, have highlighted
the inability of knowledge models and algorithms to recognize timedependent
situations. In this regard, the trend of the empty set mass has
been identified as an agility measure, able to identify the fitness of the model
to the observed situations, and in particular model inadequacy to describe,
and hence recognize, time-dependent patterns. It has been shown how to
employ the measure for model review and correction, in order to allow in
Evidence Theory dynamic pattern recognition, besides to static classification.
Finally, research conducted for this PhD thesis have lead to the definition
of a system architecture combining Data Mining and Data Fusion techniques
in order to allow the construction of knowledge models able to recognize
effectively situations of interest, that can be specified by the user in realtime.
In the proposed framework Data Mining approach is employed to
define correlations among data stored in databases, and events or objects of
interest for the user; mined correlations are employed to build in real-time
knowledge models to be adopted in the Situation Awareness process
Filosofia e pratiche filosofiche
No full textLa consulenza filosofica è proprio uno di questi ambiti che, nonostante la sua relativamente giovane età, con baldanza interroga la filosofia, ma non solo. La fecondità di questo dibattito infatti risiede nell’allargare i confini tradizionali della riflessione filosofica (intesa in senso genericamente teoretico-astratto) e consentirle sia una possibilità di incontro con i saperi umani tutti, anche quelli ritenuti a torto più lontani (come la biologia molecolare e la neurobiologia, solo per citarne due all’avanguardia), sia anche una sua presenza, trasformata e rinnovata, novello Proteo, nei territori più consueti (morale, filosofia politica, antropologia, filosofia della mente, etc.).
La consulenza interroga la filosofia in uno dei nodi non ancora risolti: chi è il soggetto e quale il suo rapporto con il mondo? [dall'editoriale di Francesca Brezzi
