Artificial Intelligence

The research within the Artificial Intelligence group conduct research focused on all aspects of algorithms and methods in respect to Artificial Intelligence. This includes probabilistic and biologically inspired methods, big data, data mining, machine learning, search, information integration, and semantic web as well as applications in bioinformatics, engineering informatics, social informatics, and related areas.

The AI the research group is focusing on reasoning, knowledge, planning, learning, natural language processing, image analysis, prediction, perception and the ability to move and manipulate objects in addition to statistical methods, computational intelligence, and traditional symbolic AI.

Researchers:

  • Bernt Arild Bremdal, Professor
  • Asbjørn Danielsen, Associate Professor
  • Hans Olofsen, Associate Professor
  • Arild Steen, Assistant Professor
  • Børre Bang, Professor
  • Rune Dalmo, Associate Professor
  • Arne Lakså, Professor

Students:

  • Tatiana Kravetc
  • Kristoffer Tangrand
  • Andreas Dyrøy Jansson
  • Shayan Dadman

Recent publications:

  • B. Bremdal, K. Tangrand, A. Danielsen, E. Gramme: The E-Regio project: for a distributed local energy market. The Innovation Platform ISSUE 2, www.innovationnewsnetwork.com, p. 85-86

    Tangrand, Kristoffer and Bremdal, Bernt. (2020). Using Deep Learning Methods to Monitor Non-Observable States in a Building. Proceedings of the Northern Lights Deep Learning Workshop. 1. 6. 10.7557/18.5159.

    Tangrand, Kristoffer; Bremdal, Bernt Arild. The FlexNett Simulator. IOP Conference Series: Earth and Environmental Science (EES) 2019; Volum 352:012005. ISSN 1755-1307.p 1 - 9.

    Bremdal, Bernt Arild; Ilieva, Iliana. Micro Markets in Microgrids. John Wiley & Sons 2019 ISBN 9781119434542.s 97 - 164.

    Ilieva, Iliana; Bremdal, Bernt Arild; de la Nieta Lopez, A.A.S.; Simonsen, S.H.. Local energy markets as a solution for increased energy efficiency and flexibility. IOP Conference Series: Earth and Environmental Science (EES) 2019; Volum 352 (012036). ISSN 1755-1307.s

    Olivella-Rosell, Pol; Lloret-Gallego, Pau; Munne-Collado, Ingrid; Villafafila-Robles, Roberto; Sumper, Andreas; Ottesen, Stig Ødegaard; Rajasekharan, Jayaprakash; Bremdal, Bernt Arild. Local Flexibility Market Design for Aggregators Providing Multiple Flexibility Services at Distribution Network Level. Energies 2018; Volum 11 (4). ISSN 1996-1073.

    Jansson, Andreas Dyrøy; Bremdal, Bernt Arild. Genetic Algorithm for Adaptable Design using Crowdsourced Learning as Fitness Measure. IEEE conference proceedings 2018 ISBN 978-1-5386-7189-4.

    Asbjørn Danielsen: Increasing fall risk awareness using wearables: A fall risk awareness protocol. Journal of Biomedical Informatics, 2016 DOI: 10.1016/j.jbi.2016.08.016

    Danielsen, Asbjørn and Tørresen, Jim. Recognizing Bedside Events Using Thermal and Ultrasonic Readings. Sensors (Basel). 2017;17(6):1342. Published 2017 Jun 9. doi:10.3390/s17061342

    Danielsen, Asbjørn and Bremdal, Bernt. (2017). Predicting Bedside Falls using Current Context. 10.1109/SSCI.2017.8280988.

    Asbjørn Danielsen: Non-intrusive Bedside Event Recognition Using Infrared Array and Ultrasonic Sensor, Published in: Ubiquitous Computing and Ambient Intelligence, 2016

News

Kristoffer Tangrand publiserer ny artikkel rundt anvendelsen av LSTM og Stordata

Januar 2020: Kilmasensorer i bygg gir opphav til estimering av byggets bruk uten at personvernet utfordres.

18.06.2020

The UIT Group in Narvik is R&D partner in BOS and Avinor project at OSL

November 2019: The ALI-T-Pilot project has started. Bertel O. Steen Industries and Avinor with partners have achieved NFR funding for a major R&D project at OSL at Gardermoen.  The idea is to revamp and modernize the baggage handling system.

18.06.2020

The Smart Charge Project has started

October 2019: The Interreg funded project has started. Smart Charge is a cooperation between the Computer Science and Computational Engineering Department of UiT and The University of Lappland in Rovaniemi in Finland.

18.06.2020

Kreativ kunstig intelligens vinner pris

AI Gruppen arbeider med ulike former for kreativ, kunstig intelligens. For sitt arbeide innenfor dette området fikk Andreas Dyrøy Jansson Best Paper Award. Den prisbelønnede artikkelen har tittelen “Genetic Algorithm for Adaptable Design using Crowdsourced Learning as Fitness Measure”, og ble publisert ved International Conference of Smart Systems and Technologies (SST 2018) som arrangeres av IEEE (The Institute of Electrical and Electronics Engineers). Kunst og design appelerer gjerne til følelsesmessige forhold som ikke er lett å definere. Enda verre er det å bestemme hva som kan bli trendy.  Dette har Andreas gjort et forsøk på å løse.  En mer teknisk utfordring knytter seg til den såkalte fitness funksjonen i en genetisk algoritme. En genetisk algoritme benytter en evolusjonsbasert utvikling av potensielle løsninger. I forhold til design ligger mye av utfordringen i å bestemme hvilken løsning som er bedre enn andre og som kan danne grunnlag for det  mest optimale designet.  Her har Andreas benyttet teknikker fra sosiale media og klyngeteknikker som sammen måler ethvert design som den genetiske algoritme utvikler i forhold til en mågruppens "likes" og "dislikes".  På denne måten løses fitness problemet og det blir også mulig å forutsi hva slags type design kan bli mest attraktiv et stykke fremover i tid.  Andreas har anvendt metoden han har utviklet på web design. Arbeidet skal videreføres på andre designrelaterte områder hvor geometrisk utforming vil være sentralt.

18.06.2020

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