141,872 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Fault detection in operating helicopter drive train components based on support vector data description
The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of
mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed
3D and 4D Simulations for Landscape Reconstruction and Damage Scenarios. GIS Pilot Applications
The project 3D and 4D Simulations for Landscape Reconstruction and Damage Scenarios: GIS Pilot
Applications has been devised with the intention to deal with the demand for research, innovation and
applicative methodology on the part of the international programme, requiring concrete results to
increase the capacity to know, anticipate and respond to a natural disaster. This project therefore sets
out to develop an experimental methodology, a wide geodatabase, a connected performant GIS
platform and multifunctional scenarios able to profitably relate the added values deriving from
different geotechnologies, aimed at a series of crucial steps regarding landscape reconstruction, event
simulation, damage evaluation, emergency management, multi-temporal analysis. The Vesuvius area
has been chosen for the pilot application owing to such an impressive number of people and buildings subject to volcanic risk that one could speak in terms of a possible national disaster. The steps of the
project move around the following core elements: creation of models that reproduce the territorial and
anthropic structure of the past periods, and reconstruction of the urbanized area, with temporal
distinctions; three-dimensional representation of the Vesuvius area in terms of infrastructuralresidential
aspects; GIS simulation of the expected event; first examination of the healthcareepidemiological
consequences; educational proposals. This paper represents a proactive contribution
which describes the aims of the project, the steps which constitute a set of specific procedures for the
methodology which we are experimenting, and some thoughts regarding the geodatabase useful to
“package” illustrative elaborations. Since the involvement of the population and adequate hazard
preparedness are very important aspects, some educational and communicational considerations are
presented in connection with the use of geotechnologies to promote the knowledge of risk
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