| 1. |
- Degas, A., et al.
(författare)
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A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management : Current Trends and Development with Future Research Trajectory
- 2022
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:3
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Forskningsöversikt (refereegranskat)abstract
- Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 2. |
- Förster, A, et al.
(författare)
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Baseline characterization of the CO2SINK geological storage site at Ketzin, Germany
- 2006
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Ingår i: Environmental Geosciences. - : American Association of Petroleum Geologists AAPG/Datapages. - 1075-9565 .- 1526-0984. ; 13:3, s. 145-161
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Tidskriftsartikel (refereegranskat)abstract
- Since April 2004, preparatory work prior to CO2injection hasbeen conducted in the CO2SINK Project, the European Union’sfirst research and development activity on the in-situ testing ofgeological storage of CO2near the town of Ketzin, Germany.Carbon dioxide will be injected into a saline aquifer of the TriassicStuttgart Formation in an anticlinal structure of the northeastGerman Basin. The drilling of one injection and two observationwells will commence at the end of 2006. The predrilling phasefocuses on the baseline geological parameters of the anticline. TheStuttgart Formation is lithologically heterogeneous; it consists ofsandy channel-(string)-facies rocks, with good reservoir propertiesalternating with muddy flood-plain-facies rocks of poor reservoirquality. Playa-type rocks form the immediate cap rock above theCO2SINK reservoir. A geostatistical approach has been applied todescribe the reservoir architecture between and beyond well con-trol. This model forms the basis for the generation of reservoir-dynamic models of CO2injection that assist in the planning ofinjection operations and in the understanding of CO2plume evo-lution. A verification of the geometry of the reservoir and thestructural situation of its overburden is expected from a three-dimensional baseline seismic survey that was conducted in theautumn of 2005. Laboratory experiments under simulated in-situconditions were performed to evaluate the geophysical signatureof rocks saturated with CO2. The chemical composition of thegroundwater and the CO2flux in the soil were analyzed across theKetzin anticline, providing the baseline for a monitoring programduring and after injection of CO2, targeted at the detection ofpotential CO2leakage from the storage reservoir.
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| 3. |
- Hurter, C., et al.
(författare)
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Usage of more transparent and explainable conflict resolution algorithm : Air traffic controller feedback
- 2022
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Ingår i: Transportation Research Procedia. - : Elsevier B.V.. - 2352-1457 .- 2352-1465. ; 66:C, s. 270-278
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Tidskriftsartikel (refereegranskat)abstract
- Recently, Artificial intelligence (AI) algorithms have received increasable interest in various application domains including in Air Transportation Management (ATM). Different AI in particular Machine Learning (ML) algorithms are used to provide decision support in autonomous decision-making tasks in the ATM domain e.g., predicting air transportation traffic and optimizing traffic flows. However, most of the time these automated systems are not accepted or trusted by the intended users as the decisions provided by AI are often opaque, non-intuitive and not understandable by human operators. Safety is the major pillar to air traffic management, and no black box process can be inserted in a decision-making process when human life is involved. To address this challenge related to transparency of the automated system in the ATM domain, we investigated AI methods in predicting air transportation traffic conflict and optimizing traffic flows based on the domain of Explainable Artificial Intelligence (XAI). Here, AI models’ explainability in terms of understanding a decision i.e., post hoc interpretability and understanding how the model works i.e., transparency can be provided for air traffic controllers. In this paper, we report our research directions and our findings to support better decision making with AI algorithms with extended transparency.
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