Ai-enhanced methods in autonomous systems: large language models, dl techniques, and optimization algorithms
- Autores: Irene de Zarzà i Cubero
- Directores de la Tesis: Carlos Miguel Tavares Calafate (dir. tes.)
- Lectura: En la Universitat Politècnica de València ( España ) en 2023
- Idioma: inglés
- Tribunal Calificador de la Tesis: Piedad Garrido Picazo (presid.), Andrés Bueno Crespo (secret.), Silvia Mirri (voc.)
- Programa de doctorado: Programa de Doctorado en Informática por la Universitat Politècnica de València
- Materias:
- Enlaces
- Tesis en acceso abierto en: RiuNet
- Resumen
The proliferation of autonomous systems, and their increasing integration with day-to-day human life, have opened new frontiers of research and development. Within this scope, the current thesis dives into the multifaceted applications of Large Language Models (LLMs), Deep Learning (DL) techniques, and Optimization Algorithms within the realm of these autonomous systems. Drawing from the principles of AI-enhanced methods, the studies encapsulated within this work converge on the exploration and enhancement of different autonomous systems ranging from B5G Truck Platooning Systems, Multi-Agent Systems (MASs), Unmanned Aerial Vehicles, Forest Fire Area Estimation, to the early detection of diseases like Glaucoma.
A key research focus, pursued in this work, revolves around the innovative deployment of adaptive PID controllers in vehicle platooning, facilitated through the integration of LLMs. These PID controllers, when infused with AI capabilities, offer new possibilities in terms of efficiency, reliability, and security of platooning systems. We developed a DL model that emulates an adaptive PID controller, thereby showcasing its potential in AI-enabled radio and networks. Simultaneously, our exploration extends to multi-agent systems, proposing an Extended Coevolutionary (EC) Theory that amalgamates elements of coevolutionary dynamics, adaptive learning, and LLM-based strategy recommendations. This allows for a more nuanced and dynamic understanding of the strategic interactions among heterogeneous agents in MASs.
Moreover, we delve into the realm of Unmanned Aerial Vehicles (UAVs), proposing a system for video understanding that employs a language-based world-state history of events and objects present in a scene captured by a UAV. The use of LLMs here enables open-ended reasoning such as event forecasting with minimal human intervention. Furthermore, an alternative DL methodology is applied for the estimation of the affected area during forest fires. This approach leverages a novel architecture called TabNet, integrated with Transformers, thus providing accurate and efficient area estimation.
In the field of healthcare, our research outlines a successful early detection methodology for glaucoma. Using a three-stage training approach with EfficientNet on retinal images, we achieved high accuracy in detecting early signs of this disease.
Across these diverse applications, the core focus remains: the exploration of advanced AI methodologies within autonomous systems. The studies within this thesis seek to demonstrate the power and potential of AI-enhanced techniques in tackling complex problems within these systems. These in-depth investigations, experimental analyses, and developed solutions shed light on the transformative potential of AI methodologies in improving the efficiency, reliability, and security of autonomous systems, ultimately contributing to future research and development in this expansive field.
