Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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693.932 PEOPLE
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University of Bath

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (7/7 displayed)

  • 2017Chassis Dynamometer Testingcitations
  • 2017Distance Estimation by Fusing Radar and Monocular Camera with Kalman Filtercitations
  • 2012The effect of advance combustion control features on the performance of a highly downsized gasoline enginecitations
  • 2011Spatially resolved heat flux measurements from a HSDI engine over NEDCcitations
  • 2007Automated data processing and metric generation for driveability analysis7citations
  • 2006A Study of Natural Neighbour Interpolation and its application to automotive engine test datacitations
  • 2002Investigation of 'sweep' mapping approach on engine testbed15citations

Places of action

Chart of shared publication
Burke, Richard D.
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Galindo, Eduardo
1 / 1 shared
Chappell, Edward
2 / 2 shared
Blanco, David
1 / 2 shared
Iravani, Pejman
1 / 2 shared
Feng, Yuxiang
1 / 1 shared
Pickering, Simon G.
1 / 11 shared
Lewis, Andrew
2 / 5 shared
Giles, Karl
1 / 2 shared
Akehurst, Sam
2 / 7 shared
Luard, Nick
1 / 1 shared
Robinson, Kevin
1 / 1 shared
Pegg, I.
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Pickering, S. G.
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Tily, R.
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Kennedy, G.
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Hale, T.
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Ceen, R.
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Vaughan, N. D.
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Ward, M. C.
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Chart of publication period
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Co-Authors (by relevance)

  • Burke, Richard D.
  • Galindo, Eduardo
  • Chappell, Edward
  • Blanco, David
  • Iravani, Pejman
  • Feng, Yuxiang
  • Pickering, Simon G.
  • Lewis, Andrew
  • Giles, Karl
  • Akehurst, Sam
  • Luard, Nick
  • Robinson, Kevin
  • Pegg, I.
  • Pickering, S. G.
  • Tily, R.
  • Kennedy, G.
  • Hale, T.
  • Ceen, R.
  • Vaughan, N. D.
  • Ward, M. C.
OrganizationsLocationPeople

article

Automated data processing and metric generation for driveability analysis

  • Pickering, S. G.
  • Brace, Christian
Abstract

This paper describes automated data processing and manoeuvre detection techniques developed as partof a suite of tools used for the prediction of longitudinal vehicle driveability. The core task is toidentify events of interest in recorded objective driveability time-series data and generate metricsdescribing these manoeuvres, which can then be correlated with subjective evaluations provided by thevehicle test drivers. The objective events of particular interest are the start of transients and gear-shiftevents. As a necessary precursor to the generation of the objective metrics, procedures were designedto check data integrity and to automatically replace data that were found to be faulty, ensuring that aslittle data and testing time were wasted as is possible.Of 741 tests analysed (average of 12 drivers for each of 5 vehicles), only 11\% of tests needed furthermanual attention following the automated processing. Of these, 64\% proved to be irrecoverable due todata problems and were rejected. The processing and generation of metrics takes approximately onesecond per set of test data, producing a time saving of approximately 95\%. This makes it possible toperform real-time processing and metric generation as part of a continuous testing scheme or real-timeevaluation.

Topics
  • impedance spectroscopy