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Full-Text Articles in Computer Engineering
Risk-Based Machine Learning Approaches For Probabilistic Transient Stability, Umair Shahzad
Risk-Based Machine Learning Approaches For Probabilistic Transient Stability, Umair Shahzad
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive …
Distributed Neural Network Based Architecture For Ddos Detection In Vehicular Communication Systems, Nicholas Jaton
Distributed Neural Network Based Architecture For Ddos Detection In Vehicular Communication Systems, Nicholas Jaton
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
With the continued development of modern vehicular communication systems, there is an ever growing need for cutting edge security in these systems. A misbehavior detection systems (MDS) is a tool developed to determine if a vehicle is being attacked so that the vehicle can take steps to mitigate harm from the attacker. Some attacks such as distributed denial of service (DDoS) attacks are a concern for vehicular communication systems. During a DDoS attack, multiple nodes are used to flood the target with an overwhelming amount of communication packets. In this thesis, we investigated the current MDS literature and how it …
Classification Of Primary Versus Metastatic Pancreatic Tumor Cells Using Multiple Biomarkers And Whole Slide Imaging, Poupack Pooshang Baghery
Classification Of Primary Versus Metastatic Pancreatic Tumor Cells Using Multiple Biomarkers And Whole Slide Imaging, Poupack Pooshang Baghery
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Pancreatic cancer is a challenging cancer with a high mortality rate and a 5-year survival rate between 2% to 9%. The role of biomarkers is crucial in cancer prognosis, diagnosis, and predicting the possible responses to a specific therapy. The Discovery and development of various types of biomarkers have been studied intensively in the hope of determining the best treatment approaches, better management, and possibly cure of this deadly cancer. However, metastasis, responsible for about 90% of the deaths from cancer, is still poorly understood. A few research that have investigated the expression of a particular biomarker or a panel …
Learning Discriminative And Efficient Attention For Person Re-Identification Using Agglomerative Clustering Frameworks, Kshitij Nikhal
Learning Discriminative And Efficient Attention For Person Re-Identification Using Agglomerative Clustering Frameworks, Kshitij Nikhal
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Recent advancements like multiple contextual analysis, attention mechanisms, distance-aware optimization, and multi-task guidance have been widely used for supervised person re-identification (ReID), but the implementation and effects of such methods in unsupervised person ReID frameworks are non-trivial and unclear, respectively. Moreover, with increasing size and complexity of image- and video-based ReID datasets, manual or semi-automated annotation procedures for supervised ReID are becoming labor intensive and cost prohibitive, which is undesirable especially considering the likelihood of annotation errors increase with scale/complexity of data collections. Therefore, this thesis proposes a new iterative clustering framework that incorporates (a) two attention architectures that learn …