Engineering Commons^™

Deep Learning System Identification, Linearization And Control Of Dynamical Systems Utilizing Koopman Theory With Applications In Orbital Systems, George Mario Nehma

Theses and Dissertations

The computational and complexity burden of current linearization techniques is one that is a hinderance in the application of real world guidance, navigation and control systems. With the advancements in Deep Neural Networks, large data handling and Koopman Theory, the possibility of global linearizations of nonlinear systems is more prominent. This work demonstrates the capability of a Deep Neural Network learned Koopman operator to transform a nonlinear system into a Linear Time-Invariant system. The method presented is applied to both two purely dynamical systems and one controlled system to emphasize the ability for the technique to be applied in all …

Underwater Image Enhancement: A Pipeline For Underwater Computer Visions, Humberto Lebron Rivera

Theses and Dissertations

Ocean exploration has surged in popularity and significance in recent years, including diverse areas like maritime archeology, underwater resources, and submerged structure inspection. The activities mentioned above heavily depend on vision and imagery, a challenge in the unpredictable marine world. This thesis presents a conditional generative adversarial network model for image-to-image translation problems. We designed and trained the model with the end goal of enhancing underwater images. Five metrics were employed for validation to quantify our model’s resulting enhanced images. By doing so, we aim to establish a pipeline that can leverage aerial computer vision algorithms for marine applications.

Our …

Deep Learning And Generative Ai Approaches For Automated Diagnosis And Personalized Treatment: Bridging Machine Learning, Medicine, And Biomechanics In Predicting Tissue Mechanics And Biomaterial Properties., Yasin Shokrollahi

Theses and Dissertations

Machine learning, particularly deep neural networks, has demonstrated significant potential in predicting high-dimensional tasks across various domains. This work encompasses a detailed review of Generative AI in healthcare and three studies integrating machine learning with finite element analysis for predicting biomechanical behaviors and properties. Initially, we provide a comprehensive overview of Generative AI applications in healthcare, focusing on Transformers and Denoising Diffusion models and suggesting potential research avenues to address existing challenges.

Subsequently, we addressed soccer-related ocular injuries by combining finite element analysis and machine learning to predict retinal mechanics following a soccer ball hit rapidly. The prediction errors are …