46,719 research outputs found
An intelligent framework and prototype for autonomous maintenance planning in the rail industry
This paper details the development of the AUTONOM project, a project that aims to provide an enterprise system tailored to the planning needs of the rail industry. AUTONOM extends research in novel sensing, scheduling, and decision-making strategies customised for the automated planning of maintenance activities within the rail industry. This paper sets out a framework and software prototype and details the current progress of the project. In the continuation of the AUTONOM project it is anticipated that the combination of techniques brought together in this work will be capable of addressing a wider range of problem types, offered by Network rail and organisations in different industries
Building Information Modeling as Tool for Enhancing Disaster Resilience of the Construction Industry
As frequencies of the disasters are increasing, new technologies can be used to enhance
disaster resilience performance of the construction industry. This paper investigates
the usage of BIM (Building Information Modeling) in enhancing disaster resilience of
the construction industry and in the establishment of the resilient built environment.
In-depth literature review findings reveal BIM’s contribution to the disaster resilience in the
pre-disaster and post-disaster phases especially through influencing the performance of
the supply chain, construction process, and rescue operations. This paper emphasises
the need for BIM’s integration to the education and training curriculums of the built
environment professionals. Policy makers, construction professionals, professional
bodies, academics can benefit from this research
The Application of the Montage Image Mosaic Engine To The Visualization Of Astronomical Images
The Montage Image Mosaic Engine was designed as a scalable toolkit, written
in C for performance and portability across *nix platforms, that assembles FITS
images into mosaics. The code is freely available and has been widely used in
the astronomy and IT communities for research, product generation and for
developing next-generation cyber-infrastructure. Recently, it has begun to
finding applicability in the field of visualization. This has come about
because the toolkit design allows easy integration into scalable systems that
process data for subsequent visualization in a browser or client. And it
includes a visualization tool suitable for automation and for integration into
Python: mViewer creates, with a single command, complex multi-color images
overlaid with coordinate displays, labels, and observation footprints, and
includes an adaptive image histogram equalization method that preserves the
structure of a stretched image over its dynamic range. The Montage toolkit
contains functionality originally developed to support the creation and
management of mosaics but which also offers value to visualization: a
background rectification algorithm that reveals the faint structure in an
image; and tools for creating cutout and down-sampled versions of large images.
Version 5 of Montage offers support for visualizing data written in HEALPix
sky-tessellation scheme, and functionality for processing and organizing images
to comply with the TOAST sky-tessellation scheme required for consumption by
the World Wide Telescope (WWT). Four online tutorials enable readers to
reproduce and extend all the visualizations presented in this paper.Comment: 16 pages, 9 figures; accepted for publication in the PASP Special
Focus Issue: Techniques and Methods for Astrophysical Data Visualizatio
PRIMA — Privacy research through the perspective of a multidisciplinary mash up
Based on a summary description of privacy protection research within three fields of inquiry, viz. social sciences, legal science, and computer and systems sciences, we discuss multidisciplinary approaches with regard to the difficulties and the risks that they entail as well as their possible advantages. The latter include the identification of relevant perspectives of privacy, increased expressiveness in the formulation of research goals, opportunities for improved research methods, and a boost in the utility of invested research efforts
Who is the director of this movie? Automatic style recognition based on shot features
We show how low-level formal features, such as shot duration, meant as length
of camera takes, and shot scale, i.e. the distance between the camera and the
subject, are distinctive of a director's style in art movies. So far such
features were thought of not having enough varieties to become distinctive of
an author. However our investigation on the full filmographies of six different
authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total
number of 120 movies analysed second by second, confirms that these
shot-related features do not appear as random patterns in movies from the same
director. For feature extraction we adopt methods based on both conventional
and deep learning techniques. Our findings suggest that feature sequential
patterns, i.e. how features evolve in time, are at least as important as the
related feature distributions. To the best of our knowledge this is the first
study dealing with automatic attribution of movie authorship, which opens up
interesting lines of cross-disciplinary research on the impact of style on the
aesthetic and emotional effects on the viewers
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