Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"Model optimisation"”.
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Surrogate Model Optimisation for PWR Fuel Management
… In this thesis, the method of surrogate model optimisation is adapted to PWR loading pattern generation. Surrogate models are developed based around three approaches: deep learning methods (convolutional neural networks and multi-layer perceptrons), the fission matrix and simulated …
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Exergy Based SI Engine Model Optimisation. Exergy Based Simulation and Modelling of Bi-fuel SI Engine for Optimisation of Equivalence Ratio and Ignition Time Using Artificial Neural Network (ANN) Emulation and Particle Swarm Optimisation (PSO).
In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the …
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Cloud-based machine learning architecture for big data analysis
… In addition, for specific applications, special optimisations are often developed based on the requirements of the particular application. This thesis also addresses the challenge of efficiency of machine learning over big data but does so in a way that is complementary to specialised hardware …
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Towards Characterisation and Classification of Canadian Macrotidal Salt Marshes
… tidal marsh extent, indicating that a regional model may be required. This thesis provides a review of freely available remote sensing data relevant to tidal marshes in the Bay of Fundy and uses freely available medium-resolution imagery to classify high and low tidal marsh extent in the …
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Performance Augmentation: Immersive Technology for Workplace Training
… Social Systems Theory. It takes structure from models of technology acceptance (UTAUT2) and Anderson and Krathwohl's taxonomy of cognitive processes, using these to shed light on the attitudes and expectations that surround the use of this technology. An `experience capture system' is described, …
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Reconfigurable modelling of physically based systems: Dynamic modelling and optimisation for product design and development applied to the automotive drivetrain system.
… with the aggregation and advancement of modelling practise as used within modern day product development and optimisation environments making use of Model Based Design (¿MBD¿) and similar procedures. A review of model development and use forms the foundation of the work, with the findings …
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Application of Optimisation Techniques to Planning and Estimating Decisions in the Building Process
An integrated computer model for time and cost optimisation has been developed for multi-storey reinforced concrete office buildings.<br/><br/>The development of the model has been based on interviews completed with Planners, Estimators and Researchers within 2 of the top 20 (in terms of turnover) …
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Determination of a robust metabolic barcoding model for chemotaxonomy in Aizoaceae species : expanding morphological and genetic understanding
… store of species-specific information to use in model optimisation across 5 Aizoaceae species (Galenia africana, Aridaria noctiora, Carpobrotus edulis, Ruschia robusta, and Tetragonia fruticosa) using two Crassulaceae species as CAM controls (Cotyledon orbiculata and Tylecodon wallichii ). …
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Natural Language Understanding and Generation for Task-Oriented Dialogue
… research areas. Still, task-oriented dialogue modelling remains challenging due to both the inherent complexity of human language and task difficulty. Moreover, building such systems usually relies on large amounts of data with fine-grained annotations, and in many situations, it is difficult …
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Uncertainty-aware learning from sparse, unlabelled, and out-of-distribution time series
… Despite the potential that machine learning models offer for healthcare time series, they still face notable challenges. Sensor-based datasets are frequently sparse (with missing values) if acquired outside controlled environments, while a substantial proportion remain unlabelled or only …
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Predicting radioactive waste glass properties with machine learning and modelling
… data using machine learning or apply mechanistic modelling on a large scale both to evaluate the impact of glass within the wider geological disposal setting and understand glass precipitate formation. Machine learning was first applied to predict static leaching, specifically boron releases, …