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ISA SOFTWARE LTD

Country: United Kingdom

ISA SOFTWARE LTD

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5 Projects, page 1 of 1
  • Funder: European Commission Project Code: 892517
    Overall Budget: 1,771,360 EURFunder Contribution: 1,771,360 EUR

    The ATM system is composed of a myriad of elements that interact with each other generating a number of properties characteristic of complex adaptive systems, which make the ATM system intrinsically difficult to model. One of the most challenging modelling problems is the assessment of the performance impact of new solutions at a system-wide level, which has been a long-time objective of the ATM research community. NOSTROMO project aims to develop new approaches to ATM performance modelling able to reconcile model transparency, computational tractability and ease of use with the necessary sophistication required for a realistic representation of the ATM system. The main objectives of NOSTROMO are: 1. Develop a methodology for the construction of ATM performance metamodels that approximate the behaviour of computationally expensive simulation models to allow a systematic and efficient exploration of the model input-output space and a robust handling of the associated uncertainty, by exploiting the recent advances in the field of active learning; 2. Implement the proposed metamodelling methodology by developing Open-Source metamodels of different state-of-the-art microsimulation tools able to reproduce ATM performance at ECAC level; 3. Develop a set of visualisation and visual analytics tools that facilitate the analysis, interpretation and communication of the results of the new metamodels; 4. Demonstrate and evaluate the maturity of the NOSTROMO approach and the capabilities of the newly developed toolset through a set of case studies addressing the performance assessment of SESAR Solutions at ECAC level. They will cover a variety of ATM phases, solutions and KPAs/KPIs sufficiently heterogeneous to allow a comprehensive benchmarking against the performance modelling methodologies currently in use, to analyse the added value and the limitations of the NOSTROMO approach and evaluate the appropriateness of its transition to SESAR IR and improvement of the E-OCVM.

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  • Funder: UK Research and Innovation Project Code: EP/G069557/1
    Funder Contribution: 610,071 GBP

    Wireless sensor networks are more and more seen as a solution to large-scale tracking and monitoring applications. The deployment and management of these networks, however, is handled by a central controlling entity and the sensor network is often dedicated to a single application. We argue that this is due to the fact that we do not yet have the means to deal with a secure multi-purpose federated sensor network, running different applications in parallel and able to reconfigure dynamically to run others.The communication paradigms which have been usually devised for small and single owner sensor networks simply do not have the right scalability, security, reconfigurability characteristics required for this environment.With FRESNEL we aim to build a large scale federated sensor network framework with multiple applications sharing the same resources. We want to guarantee a reliable intra-application communication as well as a scalable and distributed management infrastructure. Orthogonally, privacy and application security should also be maintained.We evaluate our proposal though a large scale federation of sensor networks over the Cambridge campus. The sensors monitor different aspects (temperature, pollution, movement, etc) and the network will be running various applications belonging to different authorities in the city.

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  • Funder: European Commission Project Code: 893864
    Overall Budget: 1,910,730 EURFunder Contribution: 1,739,620 EUR

    DACUS aims at the development of a service-oriented Demand and Capacity Balancing (DCB) process for drone traffic management. This overall objective responds to an operational and technical need in European drone operations for a tangible solution integrating the functionalities of the SESAR U-space services for Drone Traffic Management (DTM) to produce timely, efficient and safe decisions. The project intends to integrate in a consistent DCB solution the relevant demand and capacity influence factors (such as CNS performances availability), definitions (such as airspace structure), processes (such as separation management), and services (such as Strategic and Tactical Conflict Resolution). Therefore, to achieve the overall DACUS objective, five specific objectives are set: 1. Develop a drone DCB process, from strategic to tactical phase, integrating uncertainty of planned operations and guided by the definition of a U-space performance scheme that include the development of metrics for airspace capacity appropriate for an environment with no human controller. 2. Develop innovative services algorithms and enabling models and technologies as functional blocks of DCB process, able to support large number of simultaneous operations and to design and manage efficient and safe drone trajectories. 3. Define a structure for Very Low Level (VLL) airspace and a set of airspace rules that optimises the trade-off between capacity and safety, including the definition of separation management process. 4. Find the optimal balance between on-board separation intelligence and U-space separation service intelligence in tactical separation depending. 5. Refine Communication, Navigation and Surveillance (CNS) requirements in support of tactical and procedural separation, with a focus on urban environment.

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  • Funder: UK Research and Innovation Project Code: EP/G070687/1
    Funder Contribution: 446,934 GBP

    Wireless sensor networks are more and more seen as a solution to large-scale tracking and monitoring applications. The deployment and management of these networks, however, is handled by a central controlling entity and the sensor network is often dedicated to a single application. We argue that this is due to the fact that we do not yet have the means to deal with a secure multi-purpose federated sensor network, running different applications in parallel and able to reconfigure dynamically to run others.The communication paradigms which have been usually devised for small and single owner sensor networks simply do not have the right scalability, security, reconfigurability characteristics required for this environment.With FRESNEL we aim to build a large scale federated sensor network framework with multiple applications sharing the same resources. We want to guarantee a reliable intra-application communication as well as a scalable and distributed management infrastructure. Orthogonally, privacy and application security should also be maintained.We evaluate our proposal though a large scale federation of sensor networks over the Cambridge campus. The sensors monitor different aspects (temperature, pollution, movement, etc) and the network will be running various applications belonging to different authorities in the city.

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  • Funder: European Commission Project Code: 892358
    Overall Budget: 997,410 EURFunder Contribution: 997,410 EUR

    As Artificial Intelligence (AI) becomes an increasing part of our lives in general, individuals are finding that the need to trust these AI based systems is paramount. Air Traffic Management (ATM) is not an stranger to this: with a system close to, or already at, a saturation level, AI applications are considered a main enabler to reach higher levels of automation. This would mean a fundamental shift in the automation approach when moving from the classical human-machine interaction to a potentially much richer solution enabled by these AI systems, in which trust in the operations needs to be generated. As humans, operators must be able to fully understand how decisions are being made so that they can trust the decisions of AI systems. The lack of explainability and trust hampers the ability (both individual and global) to fully trust AI systems. TAPAS aims at exploring highly automated AI-based scenarios through analysis and experimental activities applying eXplainable Artificial Intelligence (XAI) and Visual Analytics, in order to derive general principles of transparency which pave the way for the application of these AI technologies in ATM environments, enabling higher levels of automation. Specifically, TAPAS will: • Analyse two operational environments: ATC (Air Traffir Control)Conflict Detection & Resolution (tactical), and Air Traffic Flow Management (pre-tactical). For them, levels of automation 1 to 3 according to SESAR Model will be considered. • Develop eXplainable Artificial Intelligence (XAI) prototypes addressing the requirements and acceptability criteria of the scenarios. • Run experiments that assess the applicability of these XAI modules in the higher levels of automation considered, exploring different ways of interaction and information exchange. • Apply Visual Analytics techniques to contribute to explainability of decissions. • Extract conclusions, principles and recommendations related to transparency of AI in ATM.

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