Hans Jonas Fossum Moen

• Stensrud, Mathias; Moen, Hans Jonas Fossum & Bjerknes, Jan Dyre (2021). Information sharing in multi-agent search and task allocation problems. In Haddow, Pauline (Eds.), IEEE Symposium Series on Computational Intelligence 2021. IEEE Press. ISSN 978-1-7281-9048-8. doi: 10.1109/SSCI50451.2021.9660121.
• (2021). Modelling the impact of UAV navigation errors on infrared PV inspection data quality and efficiency. Conference record of the Photovoltaic Specialists Conference. ISSN 0160-8371. p. 991–996. doi: 10.1109/PVSC43889.2021.9518810.
• Engebråten, Sondre Andreas; Moen, Hans Jonas Fossum; Yakimenko, Oleg A. & Glette, Kyrre (2020). A framework for automatic behavior generation in multi-function swarms. Frontiers in Robotics and AI. ISSN 2296-9144. 7:579403, p. 1–19. doi: 10.3389/frobt.2020.579403. Full text in Research Archive
• Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre (2018). Towards a Multi-Function Swarm That Adapts to User Preferences, Swarms: From Biology to Robotics and Back. ICRA 2018 Workshop. Harvard University. Full text in Research Archive
• Rauniyar, Ashish; Engelstad, Paal E. & Moen, Hans Jonas Fossum (2018). A New Distributed Localization Algorithm Using Social Learning Based Particle Swarm Optimization for Internet of Things. IEEE Vehicular Technology Conference (VTC) Proceedings. ISSN 1550-2252. 2018-June, p. 1–7. doi: 10.1109/VTCSpring.2018.8417665.
• Engebråten, Sondre Andreas; Moen, Hans Jonas Fossum; Yakimenko, Oleg & Glette, Kyrre (2018). Evolving a Repertoire of Controllers for a Multi-function Swarm. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10784, p. 734–749. doi: 10.1007/978-3-319-77538-8_49.
• (2017). Meta-heuristics for improved RF emitter localization. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10200 LNCS(Part II), p. 207–223. doi: 10.1007/978-3-319-55792-2_14.
• Hovland, Harald; Tørresen, Jim; Moen, Hans Jonas Fossum & Hansen, Nikolai Bjørnestøl (2013). Many Objective Optimization Using Taxi-Cab Surface Evolutionary Algorithm. In Purshouse, R.; Fleming, P.; Fonseca, C.M; Greco, S. & Shaw, J. (Ed.), Proceedings of the 7th Evolutionary Multi-Criterion Optimization (EMO 2013). Springer. ISSN 978-3642371394. p. 656–668. doi: 10.1007/978-3-642-37140-0_13.
• (2012). Improved radar detection using evolutionary optimised filter. IET Radar, Sonar & Navigation. ISSN 1751-8784. 6(9), p. 803–812. doi: 10.1049/iet-rsn.2012.0099.
• Nyhavn, Ragnhild; Moen, Hans Jonas Fossum; Farsund, Øystein & Rustad, Gunnar (2011). Optimal classification of standoff bioaerosol measurements using evolutionary algorithms. Proceedings of SPIE, the International Society for Optical Engineering. ISSN 0277-786X. 8018. doi: 10.1117/12.883919.

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• (2022). Positioning a UAV for Infrared Inspection of Several Rows of Solar Panels Simultaneously. Proceedings of the European Photovoltaic Solar Energy Conference. ISSN 2196-100X. Show summary

  When photovoltaic (PV) solar panels in a PV power plant deteriorate after some time of operation, a thermographic inspection with unmanned aerial vehicles (UAVs) can be necessary. Often, faulty panels, or cells within panels, radiate heat that is visible with infrared (IR) cameras. When performing a thermographic inspection of solar panel faults from a UAV, the correct choice of position for the UAV depends on several factors. The camera's pixel resolution and field of view (FOV) set a distance threshold between the panel and the UAV according to a minimum resolution requirement. This threshold is also dependent on the angle between the panel and the UAV. Because of the vast size of some PV power plants, inspecting several rows of panels simultaneously is often appropriate. Latitude-dependent row-to-row distances limit the number of rows which fit inside the FOV which leads to site-dependent strategies. This paper analyzes several key factors, concerning the choice of position for a thermographic inspection of a solar PV power plant with a UAV.

• (2022). Positioning a UAV for Infrared Inspection of Several Rows of Solar Panels Simultaneously.
• Stensrud, Mathias; Moen, Hans Jonas Fossum & Bjerknes, Jan Dyre (2021). Information sharing in multi-agent search and task allocation problems.
• Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre (2018). Evolving a Repertoire of Controllers for a Multi-Function Swarm.
• Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre (2018). Towards a Multi-Function Swarm That Adapts to User Preferences .
• Moen, Hans Jonas Fossum; Glette, Kyrre; Nygaard, Tønnes Frostad & Johnsrud, Mette (2018). Fem felt der vi får en førerløs fremtid. [Internet]. Titan.uio.no.
• Moen, Hans Jonas Fossum; Hansen, Nikolai Bjørnestøl; Hovland, Harald & Tørresen, Jim (2013). Many Objective Optimization Using Taxi-Cab Surface Evolutionary Algorithm.
• (2011). Norwegian approach and vision for new EW technologies and techniques.
• (2011). Spanning the Pareto Front of a Counter Radar Detection Problem.
• Nyhavn, Ragnhild; Moen, Hans Jonas Fossum; Farsund, Øystein & Rustad, Gunnar (2011). Optimal classification of standoff bioaerosol measurements using evolutionary algorithms.
• Moen, Hans Jonas Fossum; Ulversøy, Tore & Nyhavn, Ragnhild (2010). Evolutionary Computation at the Norwegian Defence Research Establishment.
• (2010). The Complexity of Optimal DRFM Jamming Techniques Developed Using Multiobjective Evolutionary Algorithms. Show summary

  The EKKO II experimental radar jammer has recently been used to demonstrate denial of detection jamming techniques against experimental radars employing the constant false alarm rate detection algorithm. Effective denial of detection was then demonstrated using 10 to 30 dB less jamming power than what would be required of conventional noise jammers to achieve the same performance. EKKO II is an experimental radar jammer based on digital RF memory (DRFM) developed at the Norwegian Defence Research Establishment (FFI). In these studies it has been shown how multiobjective evolutionary algorithms are capable of finding the Pareto optimal solutions in the 2 dimensional objective space of jammer transmitting power versus detectability by manipulating the constant false alarm rate decision domain of the radar. However, in the energy domain of the radar the jamming produces structures of high complexity, potentially exposing the jamming to radar systems operating man-in-the-loop. Therefore, in this paper, an extended 3 dimensional multiobjective optimization is performed in order to analyze the trade-offs between jamming technique complexity and the two other objectives of jammer transmitting power and detectability. A suitable complexity metric characterizing how natural or noise-like the optimal jammer matrices look is proposed and compared to more traditional entropy-based metrics. It will be shown how this complexity metric is more in line with human image characterization

• (2008). Denial Jamming Technique Development Against Pulse-Doppler Radars Using Genetic Algorithms.
• (2008). Multi-Resistant Radar Jamming Using Genetic Algorithms.
• (2013). Optimizing Software-Defined Radar and Radar Electronic Warfare Systems using Evolutionary Algorithms. Akademika forlag.

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