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 20 of 71 for “"Parameter Tuning"”.
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Automatic Game Parameter Tuning using General Video Game Agents
… applying an evolutionary algorithm to video game parameterization. The task we are interested in is player experience. N-Tuple Bandit Evolutionary Algorithm (NTBEA) is an evolutionary algorithm that was recently proposed and successfully applied in game parameterization in a simple domain, which …
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Channel parameter tuning in a hybrid Wi-Fi-Dynamic Spectrum Access Wireless Mesh Network
… activities required to improve the channel parameter settings of a multi-radio multi-channel DSA-WMN. The work facilitates the extension of Internet connectivity to the unconnected or unreliably connected in rural or peri-urban areas in a more cost-effective way, enabling more meaningful and …
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Reinforcement learning approach for optimizing load shedding and controller parameter tuning in active islanded power systems
… issue addressed in this context is the tuning of controllers for active, decentralized systems. These controllers need to be adjusted to function optimally across a wide range of operating conditions, both in grid-connected and islanded modes. This poses numerous challenges due to the …
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Techniques to account for and reduce model inadequacy in ensemble-based filters
… is the sensitivity of the local attractor to the parameter. The Multimodel Method (MMM) and Parametric Vector Method (PVM) to estimate this state-dependent sensitivity are introduced and investigated in the low-dimensional Ikeda and L63 systems. The MMM involves assimilating data independently …
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Numerical simulations of the forward problem and compressive digital holographic reconstruction of weak scatterers on a planar substrate
… system noise with a sparsity prior and auto-parameter tuning based on signal characteristics. Strong denoising parameter reduces false alarms and increases miss detection at the same time. The compressive framework is followed by a defect candidate selection process which helps to eliminate …
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Baldwinian-based meta-heuristic for robust engineering optimisation
… problems. The first is the problem of parameter tuning and the second is the problem of finding robust solutions. A wellknown engineering design problem, the pressure vessel problem, was selected as a case study. A problem of engineering optimisation is that the theoretical solutions …
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Particle selection and parameterisation in virtual reality
… (VR) technology in enhancing the selection and parameterisation of astronomical data compared to traditional desktop environments. Through the development and evaluation of the Immersive Data Visualisation Interactive Explorer for Particle Rendering (iDaVIE-p), a novel software application …
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Image Miner : an architecture to support deep mining of images
… a cloud based system, called ImageMiner, to tune parameters of feature extraction process in a machine learning pipeline for images. Feature extraction is a key component of the machine learning pipeline, and tune its parameters to extract the best features can have significant effect on the …
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Investigating the use of ensemble techniques in predicting object-oriented software maintainability
… base models and analysing the impact of parameter tuning.;Method: In the first part of this thesis, a systematic review of studies related to the prediction of the maintainability of object-oriented software systems using machine learning techniques is presented. In the remaining parts of …
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Improved 2D Camera-Based Multi-Object Tracking for Autonomous Vehicles
… Multi-Object Tracking, require extensive parameter tuning to match features between consecutive frames accurately. 3) Simple intersection over union (IoU) metric is insufficient for reliable identification in such environments. This thesis proposes a novel methodology for 2D multi-object …
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Deep transfer-learning based lithium-ion battery fault diagnosis
… regimes. This framework removes the need for parameter tuning and is also adaptable to varying battery chemistries and performs well with a small amount of available data. The framework leverages voltage residuals generated via a randomly initialized or pre-trained LSTM (Long Short Term …
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Improving Tree Crown Mapping using Airborne LiDAR with Genetic Algorithms
… to LiDAR point clouds, which have complex parameter sets. Genetic algorithms (GA) have been demonstrated to be excellent function optimizers for very complex search spaces and perform well for parameter tuning. Here, we use GAs to identify the best of a set of published ITC models and their …
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ScPEFT : a parameter-efficient fine-tuning framework for enhancing single-cell large language models in out-of-context
… To address this, we introduce a single-cell parameter-efficient fine-tuning (scPEFT) framework that integrates learnable, low-dimensional adapters into scLLMs. By freezing the backbone model and updating only the adapter parameters, scPEFT efficiently adapts to specific tasks using limited …
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Hyperparameter Optimization of Opaque Models for Autonomous Vehicle Algorithms
Algorithms usually consist of many hyperparameters that need to be tuned to perform efficiently. It may be possible to tune a handful of parameters manually for simple algorithms however as the algorithm becomes more complex the number of hyper- parameters also increases which makes finding the …
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Smart Process Design with Machine Learning for Quality Assurance in Metal Additive Manufacturing
… methodologies: (1) layer-wise printing parameter optimization, (2) reinforcement learning-enabled scan path planning, and (3) a multi-fidelity Bayesian optimization framework for efficient process parameter tuning. Collectively, these approaches enhance process control and reduce …
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Combinatorial optimization using quantum computing
… size, reflecting sensitivity to variational parameter tuning, penalty scaling, and hardware noise. Among classical optimizers for QAOA, COBYLA offers the most effective tradeoff between efficiency and noise tolerance. Overall, this work establishes a reproducible quantum-classical workflow, …
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A Transfer Learning Approach for Automatic Mapping of Retrogressive Thaw Slumps (RTSs) in the Western Canadian Arctic
… backbone trainable layers, and performed hyper-parameter tuning and determined the optimal learning rate, momentum, and decay rate for each of the model settings. Our final model successfully mapped most of the RTSs in our test sites, with F1 scores ranging from 0.61 to 0.79. Our study …
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Learning to fly : computational controller design for hybrid UAVs with reinforcement learning
… works for real models without any additional parameter tuning process, closing the gap between virtual simulation and real fabrication. We demonstrate the efficacy of the proposed controller both in simulation and in our custom-built hybrid UAVs. The experiments show that the controller is …
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CRAFT : ClusteR-specific Assorted Feature selecTion
… to implement and scales nicely, requires minimal parameter tuning, obviates the need to specify the number of clusters a priori, and compares favorably with other state-of-the-art methods on several datasets. We provide empirical evidence on carefully designed synthetic data sets to highlight the …
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Regulating Traffic Flow and Speed on Large Networks: Control and Geographical Self Organizing Map (Geo-SOM) Clustering
… problem, but suffers from sensitivity to parameter tuning and the need for model linearization. A weather-tuned perimeter control (WTPC) and a jam density-tuned perimeter controller (JTPC) were developed to cope with parameter sensitivity for different weather conditions and jam densities, …
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