Alise Danielle Midtfjord

• Midtfjord, Alise Danielle & Huseby, Arne (2020). Estimating Runway Friction Using Flight Data, In Piero Baraldi; Francesco P. Di Maio & Enrico Zio (ed.),  e-proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15).  Research Publishing Services.  ISBN 9789811485930.  Artikkel. Show summary

  During the winter season, contamination of runway surfaces with snow, ice, or slush causes potential economic and safety threats for the aviation industry. The presence of these materials reduces the available tire-pavement friction needed for retardation and directional control. Therefore, pilots operating on contaminated runways need accurate and timely information on the actual runway surface conditions. Avinor, the company that operates most civil airports in Norway, have developed an integrated runway information system, called IRIS, currently used on 16 Norwegian airports. The system uses a scenario approach to identify slippery conditions. In order to validate the scenario model, it is necessary to estimate runway friction. The present paper outlines how this can be done using flight data from the Quick Access Recorder (QAR) of Boeing 737-600/700/800 NG airplanes. Data such as longitudinal acceleration, airspeed, ground speed, flap settings, engine speed, brake pressures are sampled at least each second during landings. The paper discusses some of the challenges with this. In particular, issues related to calibration of data are considered, and two different regression methods are compared.

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• Midtfjord, Alise Danielle (2021). The Fundamentals of AI. Show summary

  What's Artificial Intelligence (AI)? What's the difference between AI and machine learning (ML), as they're sometimes used interchangeably? What's the current state of AI? What is AI capable of these days and what not? What are the societal impact and implications of AI? These are questions we will discuss in the upcoming event co-organized by Pint of Science Norway (http://pintofscience.no/) and Oslo AI (http://oslo.city.ai/). We've gathered a panel of experts coming from academia and industry. They will demystify AI for you and answers all your questions.

• Midtfjord, Alise Danielle (2020). Estimating Runway Friction Using Flight Data. Show summary

  During the winter season, contamination of runway surfaces with snow, ice, or slush causes potential economic and safety threats for the aviation industry. The presence of these materials reduces the available tire-pavement friction needed for retardation and directional control. Therefore, pilots operating on contaminated runways need accurate and timely information on the actual runway surface conditions. Avinor, the company that operates most civil airports in Norway, have developed an integrated runway information system, called IRIS, currently used on 16 Norwegian airports. The system uses a scenario approach to identify slippery conditions. In order to validate the scenario model, it is necessary to estimate runway friction. The present paper outlines how this can be done using flight data from the Quick Access Recorder (QAR) of Boeing 737-600/700/800 NG airplanes. Data such as longitudinal acceleration, airspeed, ground speed, flap settings, engine speed, brake pressures are sampled at least each second during landings. The paper discusses some of the challenges with this. In particular, issues related to calibration of data are considered, and two different regression methods are compared.

• Midtfjord, Alise Danielle (2020). Explainable Artificial Intelligence (XAI). Show summary

  As the use of “black box” algorithms such as deep learning increases rapidly, several challenges related to ethics and law arise. Explainable Artificial Intelligence (XAI) addresses these issues by creating understandable explanations of how and why AI systems arrive at their decisions, and we will try out some of these methods during this workshop.

• Midtfjord, Alise Danielle (2020). Explainable Artificial Intelligence: How to make AI responsible. Show summary

  As the use of “black box” algorithms such as deep learning increases rapidly, several challenges related to ethics and law arise. These systems are not capable of explaining their decisions and actions to human users, making it difficult to make sure they follow the laws are morals of our society. Explainable Artificial Intelligence (XAI) addresses these issues by creating understandable explanations of how and why AI systems arrive at their decisions. In this presentation you will learn about some of the moral challenges rising with the use of artificial intelligence, what explainable artificial intelligence is and how it can address a lot of these challenges and contribute to creating trustworthy and responsible AI.

• Grøndahl, Aurora Rosvoll; Midtfjord, Alise Danielle; Langberg, Geir Severin Rakh Elvatun; Tomic, Oliver; Indahl, Ulf Geir; Knudtsen, Ingerid Skjei; Malinen, Eirik; Dale, Einar & Futsæther, Cecilia Marie (2019). Prediction of treatment outcome for head and neck cancers using radiomics of PET/CT images. Radiotherapy and Oncology.  ISSN 0167-8140.  133, s 526- 526
• Langberg, Geir Severin; Grøndahl, Aurora Rosvoll; Midtfjord, Alise Danielle; Tomic, Oliver; Liland, Kristian Hovde; Knudtsen, Ingerid Skjei; Dale, Einar; Malinen, Eirik & Futsæther, Cecilia Marie (2019). Establishing a complete radiomics framework for biomarker identification and outcome prediction using PET/CT images of head & neck cancers.
• Midtfjord, Alise Danielle & Futsæther, Cecilia Marie (2018). Prediksjon av behandlingsutfall for hode-og halskreft ved bruk av radiomics av PET/CT-bilder.

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