Difference between revisions of "PRIME"

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This project relates broadly to ocular biometrics in unconstrained sensing environments. Particularly, to methods for reliable detection/segmentation of ocular regions, and reconstruction of low-resolution images. The specific research objectives are:
 
 
*'''Detection of ocular region in unconstrained sensing environments''' under variations in scale, illumination, pose, low resolution, noise, etc. This is novel, since the vast majority of ocular recognition works have relied on manual annotation.
 
 
*'''Super-resolution  reconstruction of ocular images'''. It may be used to iteratively improve detection (which may improve reconstruction further too), and ultimately to get better recognition accuracy thanks to enhanced image quality. Despite low resolution is frequent in relaxed environments, few ocular reconstruction works exist.
 
 
*'''Ocular recognition by case studies using data at a distance and on the move'''. Fundamental research contributions can be greatly benefited with practical applications in mind, since they enable to assess merits of the developments. We will concentrate on two cases: cooperative scenario with personal devices (smartphone), and uncooperative with surveillance cameras.
 
 
A primary consequence will be facilitated user interaction by enabling the use of data acquired in a wide range of operational conditions. More comfort and convenience can be achieved thanks to the use of own devices and natural interaction patterns with digital systems.
 
  
 
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Revision as of 11:54, 16 November 2017


PRedictive Intelligent Maintenance Enabler

PRIME
Project start:
1 September 2016
Project end:
1 March 2021
More info (PDF):
[[media: | pdf]]
Contact:
Pablo del Moral
Application Area:
Intelligent Vehicles

Involved internal personnel
Involved external personnel
Involved partners
 - Test

Abstract

The goal of this project is predicting failures in a fleet of sterilizers deployed in hospitals all over the world. The characteristics of this problem are general to the field of predictive maintenance for different application fields. Companies are interested in predictive maintenance to reduce the down time of their machines. In general the list of critical components, whose unexpected breakdowns would result in stopping the machine, is long. Therefore, the scope of a predictive maintenance system should be predicting failures in a big number of different components. For several years, systems such as cars, sterilizers or industrial equipment have been equipped with a significant amount of sensors. Which signals to record is in general not decided based on the predictive maintenance needs, but on the requirements of security or controllers among other reasons. The sensors mounted usually don’t describe the particular behavior of the components of interest, but measure physical quantities that can be influenced by the different behavior of several components. Predicting what component will fail when requires historic data about the operation of the machines, but also needs to be linked to the occurrence of failures, so that we can label the recorded data. In general, companies have access and store data coming from their machines, but don’t necessary have access to the whole history of repairs. The owner of the machines can decide whether to perform maintenance and repairs with the official service or any other unofficial service. The main research goal of this project is to build a framework that allows predicting all type of failures that can happen in a machine.

Swedish Research Council, Research Project No: 2016-03497

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