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- Autonomic Networking (1)
- Convolutional Autoencoder (1)
- Convolutional Neural Network (1)
- Data-driven Networks (1)
- Deep Learning (1)
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- Haptic feedback (1)
- High-Impedance Fault (1)
- Intelligent Networking Automation (1)
- Intent-based Networking (1)
- Machine Learning (1)
- Power System Protection (1)
- Proprioception (1)
- Prosthetics (1)
- Sensory augmentation (1)
- Sensory substitution (1)
- Transformer Network (1)
- Zero-touch Network Management (1)
Articles 1 - 3 of 3
Full-Text Articles in Computer Engineering
Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez
Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez
Electronic Thesis and Dissertation Repository
In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …
Deep Learning For High-Impedance Fault Detection And Classification, Khushwant Rai
Deep Learning For High-Impedance Fault Detection And Classification, Khushwant Rai
Electronic Thesis and Dissertation Repository
High-Impedance Faults (HIFs) are a hazard to public safety but are difficult to detect because of their low current amplitude and diverse characteristics. Supervised machine learning techniques have shown great success in HIF detection; however, these approaches rely on resource-intensive signal processing techniques and fail in presence of non-HIF disturbances and even for scenarios not included in training data. This thesis leverages unsupervised learning and proposes a Convolutional Autoencoder framework for HIF Detection (CAE-HIFD). In CAE-HIFD, Convolutional Autoencoder learns only from HIF signals by employing cross-correlation; consequently, eliminating the need for diverse non-HIF scenarios in training. Furthermore, this thesis proposes …
Development Of A Wearable Haptic Feedback Device For Upper Limb Prosthetics Through Sensory Substitution, Marco B.S. Gallone
Development Of A Wearable Haptic Feedback Device For Upper Limb Prosthetics Through Sensory Substitution, Marco B.S. Gallone
Electronic Thesis and Dissertation Repository
Haptics can enable a direct communication pipeline between the artificial limb and the brain; adding haptic sensory feedback for prosthesis wearers is believed to improve operation without drawing too much of the user's attention. Through neuroplasticity, the brain can become more cognizant of the information delivered through the skin and may eventually interpret it as inherently as other natural senses. In this thesis, a wearable haptic feedback device (WHFD) is developed to communicate prosthesis sensory information. A 14-week, 6-stage, between subjects study was created to investigate the learning trajectory as participants were stimulated with haptic patterns conveying joint proprioception. 37 …