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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Coventry University

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2021Generation of Pedestrian Crossing Scenarios Using Ped-Cross Generative Adversarial Network16citations
  • 2018A Combined CNN and LSTM Model for Arabic Sentiment Analysis164citations
  • 2010Combined deep and shallow knowledge in a unified model for diagnosis by abductioncitations

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Chart of shared publication
Daneshkhah, Alireza
1 / 1 shared
Spooner, James Patrick
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Cheah, Madeline
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Kanarachos, Stratis
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Alayba, Abdulaziz
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England, Matthew
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Iqbal, Rahat
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Ariton, Viorel
1 / 4 shared
Postolache, Florin
1 / 4 shared
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2021
2018
2010

Co-Authors (by relevance)

  • Daneshkhah, Alireza
  • Spooner, James Patrick
  • Cheah, Madeline
  • Kanarachos, Stratis
  • Alayba, Abdulaziz
  • England, Matthew
  • Iqbal, Rahat
  • Ariton, Viorel
  • Postolache, Florin
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article

Combined deep and shallow knowledge in a unified model for diagnosis by abduction

  • Ariton, Viorel
  • Palade, Vasile
  • Postolache, Florin
Abstract

Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant.

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