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Formally Designing And Implementing Cyber Security Mechanisms In Industrial Control Networks., Mehdi Sabraoui
Formally Designing And Implementing Cyber Security Mechanisms In Industrial Control Networks., Mehdi Sabraoui
Electronic Theses and Dissertations
This dissertation describes progress in the state-of-the-art for developing and deploying formally verified cyber security devices in industrial control networks. It begins by detailing the unique struggles that are faced in industrial control networks and why concepts and technologies developed for securing traditional networks might not be appropriate. It uses these unique struggles and examples of contemporary cyber-attacks targeting control systems to argue that progress in securing control systems is best met with formal verification of systems, their specifications, and their security properties. This dissertation then presents a development process and identifies two technologies, TLA+ and seL4, that can be …
Lithium Molybdate-Sulfur Battery., Ruchira Ravinath Dharmasena
Lithium Molybdate-Sulfur Battery., Ruchira Ravinath Dharmasena
Electronic Theses and Dissertations
Rechargeable energy storage systems play a vital role in today’s automobile industry with the emergence of electric vehicles (EVs). In order to meet the targets set by the department of energy (DOE), there is an immediate need of new battery chemistries with higher energy density than the current Li- ion technology. Lithium–sulfur (Li–S) batteries have attracted enormous attention in the energy-storage, due to their high specific energy density of 2600 Wh kg-1 and operational voltage of 2.0 V. Despite the promising electrochemical characteristics, Li-S batteries suffer from serious technical challenges such as dissolution of polysulfides Li2Sx …
An Explainable Sequence-Based Deep Learning Predictor With Applications To Song Recommendation And Text Classification., Khalil Damak
An Explainable Sequence-Based Deep Learning Predictor With Applications To Song Recommendation And Text Classification., Khalil Damak
Electronic Theses and Dissertations
Streaming applications are now the predominant tools for listening to music. What makes the success of such software is the availability of songs and especially their ability to provide users with relevant personalized recommendations. State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction (listening to a song) using a memory-based deep learning structure that learns from temporal sequences of user actions. Despite advances in deep learning models for song recommendation systems, none has taken …