Browsing by Author "Arana-Catania, Miguel"

Now showing 1 - 5 of 5
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    A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers
    (International Astronautical Federation (IAF), 2024-10-18) Platanitis, Konstantinos; Arana-Catania, Miguel; Upadhyay, Saurabh; Felicetti, Leonard
    This paper discusses an approach to inertial parameter estimation for the case of cargo carrying spacecraft that is based on causal learning, i.e. learning from the responses of the spacecraft, under actuation. Different spacecraft configurations (inertial parameter sets) are simulated under different actuation profiles, in order to produce an optimised time-series clustering classifier that can be used to distinguish between them. The actuation is comprised of finite sequences of constant inputs that are applied in order, based on typical actuators available. By learning from the system’s responses across multiple input sequences, and then applying measures of time-series similarity and F1-score, an optimal actuation sequence can be chosen either for one specific system configuration or for the overall set of possible configurations. This allows for both estimation of the inertial parameter set without any prior knowledge of state, as well as validation of transitions between different configurations after a deployment event. The optimisation of the actuation sequence is handled by a reinforcement learning model that uses the proximal policy optimisation (PPO) algorithm, by repeatedly trying different sequences and evaluating the impact on classifier performance according to a multi-objective metric.
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    Autonomous robotic arm manipulation for planetary missions using causal machine learning
    (European Space Agency (ESA), 2023-10-20) McDonnell, Cian; Arana-Catania, Miguel; Upadhyay, Saurabh
    Autonomous robotic arm manipulators have the potential to make planetary exploration and in-situ resource utilization missions more time efficient and productive, as the manipulator can handle the objects itself and perform goal-specific actions. We train a manipulator to autonomously study objects of which it has no prior knowledge, such as planetary rocks. This is achieved using causal machine learning in a simulated planetary environment. Here, the manipulator interacts with objects, and classifies them based on differing causal factors. These are parameters, such as mass or friction coefficient, that causally determine the outcomes of its interactions. Through reinforcement learning, the manipulator learns to interact in ways that reveal the underlying causal factors. We show that this method works even without any prior knowledge of the objects, or any previously collected training data. We carry out the training in planetary exploration conditions, with realistic manipulator models.
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    Causal discovery to understand hot corrosion
    (Wiley, 2024-12) Varghese, Akhil; Arana-Catania, Miguel; Mori, Stefano; Encinas-Oropesa, Adriana; Sumner, Joy
    Gas turbine superalloys experience hot corrosion, driven by factors including corrosive deposit flux, temperature, gas composition, and component material. The full mechanism still needs clarification and research often focuses on laboratory work. As such, there is interest in causal discovery to confirm the significance of factors and identify potential missing causal relationships or codependencies between these factors. The causal discovery algorithm fast causal inference (FCI) has been trialled on a small set of laboratory data, with the outputs evaluated for their significance to corrosion propagation, and compared to existing mechanistic understanding. FCI identified salt deposition flux as the most influential corrosion variable for this limited data set. However, HCl was the second most influential for pitting regions, compared to temperature for more uniformly corroding regions. Thus, FCI generated causal links aligned with literature from a randomised corrosion data set, while also identifying the presence of two different degradation modes in operation.
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    Embedding digital participatory budgeting within local government: motivations, strategies and barriers faced
    (Association for Computing Machinery (ACM), 2022-11-18) Davies, Jonathan; Arana-Catania, Miguel; Procter, Rob
    The challenging task of embedding innovative participatory processes and technologies within local government often falls upon local council officers. Using qualitative data collection and analysis, we investigate the ongoing work of Scottish local councils seeking to run the process of participatory budgeting (PB) within their institution, the use of digital platforms to support this and the challenges faced. In doing so this paper draws on empirical material to support the growing discussion on the dynamics or forces behind embedding. Our analysis shows that formal agreement alone does not make the process a certainty. Local council officers must work as mediators in the transitional space between representative structures and new, innovative ways of working, unsettling the entrenched power dynamics. To do so they must be well trained and well resourced, including the ability to use digital platforms effectively as part of the process. This provides the necessary, accessible, transparent and deliberative space for participation.
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    PANACEA: an automated misinformation detection system on COVID-19
    (Association for Computational Linguistics, 2023-05-04) Zhao, Runcong; Arana-Catania, Miguel; Zhu, Lixing; Kochkina, Elena; Gui, Lin; Zubiaga, Arkaitz; Procter, Rob; Liakata, Maria; He, Yulan
    In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-ofthe-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.