Engineering Commons^™

Data-Driven Protection Of Transformers, Phase Angle Regulators, And Transmission Lines In Interconnected Power Systems, Pallav Kumar Bera

Dissertations - ALL

This dissertation highlights the growing interest in and adoption of machine learning approaches for fault detection in modern electric power grids. Once a fault has occurred, it must be identified quickly and a variety of preventative steps must be taken to remove or insulate it. As a result, detecting, locating, and classifying faults early and accurately can improve safety and dependability while reducing downtime and hardware damage. Machine learning-based solutions and tools to carry out effective data processing and analysis to aid power system operations and decision-making are becoming preeminent with better system condition awareness and data availability.

Power transformers, …

Mitigating Insider Threat Risks In Cyber-Physical Manufacturing Systems, Jinwoo Song

Dissertations - ALL

Cyber-Physical Manufacturing System (CPMS)—a next generation manufacturing system—seamlessly integrates digital and physical domains via the internet or computer networks. It will enable drastic improvements in production flexibility, capacity, and cost-efficiency. However, enlarged connectivity and accessibility from the integration can yield unintended security concerns. The major concern arises from cyber-physical attacks, which can cause damages to the physical domain while attacks originate in the digital domain. Especially, such attacks can be performed by insiders easily but in a more critical manner: Insider Threats.

Insiders can be defined as anyone who is or has been affiliated with a system. Insiders have knowledge …

Machine Learning Methods For Functional Near Infrared Spectroscopy, Danushka Sandaruwan Bandara

Dissertations - ALL

Identification of user state is of interest in a wide range of disciplines that fall under the umbrella of human machine interaction. Functional Near Infra-Red Spectroscopy (fNIRS) device is a relatively new device that enables inference of brain activity through non-invasively pulsing infra-red light into the brain. The fNIRS device is particularly useful as it has a better spatial resolution than the Electroencephalograph (EEG) device that is most commonly used in Human Computer Interaction studies under ecologically valid settings. But this key advantage of fNIRS device is underutilized in current literature in the fNIRS domain.

We propose machine learning methods …

The Scalable And Accountable Binary Code Search And Its Applications, Qian Feng

Dissertations - ALL

The past decade has been witnessing an explosion of various applications and devices.

This big-data era challenges the existing security technologies: new analysis techniques

should be scalable to handle “big data” scale codebase; They should be become smart

and proactive by using the data to understand what the vulnerable points are and where

they locate; effective protection will be provided for dissemination and analysis of the data

involving sensitive information on an unprecedented scale.

In this dissertation, I argue that the code search techniques can boost existing security

analysis techniques (vulnerability identification and memory analysis) in terms of scalability and …

Link Prediction In Dynamic Weighted And Directed Social Network Using Supervised Learning, Ricky Laishram

Dissertations - ALL

Link Prediction is an area of great interest in social network analy- sis. Previous works in the area of link prediction have only focused on networks where the links once created cannot be removed. In many real world social networks, the links should be assigned strengths; for example, the strength of a link should decrease over time, if there are no interactions between the two nodes for a long time and increase if the two nodes interact often. In this thesis we modify existing meth- ods of link prediction to apply to weighted and directed networks. The features, developed in …