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Full-Text Articles in Engineering
Deep Learning Based Power System Stability Assessment For Reduced Wecc System, Yinfeng Zhao
Deep Learning Based Power System Stability Assessment For Reduced Wecc System, Yinfeng Zhao
Doctoral Dissertations
Power system stability is the ability of power system, for a giving initial operating condition, to reach a new operation condition with most of the system variables bounded in normal range after subjecting to a short or long disturbance. Traditional power system stability mainly uses time-domain simulation which is very time consuming and only appropriate for offline assessment.
Nowadays, with increasing penetration of inverter based renewable, large-scale distributed energy storage integration and operation uncertainty brought by weather and electricity market, system dynamic and operating condition is more dramatic, and traditional power system stability assessment based on scheduling may not be …
Automatic Classification And Segmentation Of Patterned Martian Ground Using Deep Learning Techniques, Ruthy Brito
Automatic Classification And Segmentation Of Patterned Martian Ground Using Deep Learning Techniques, Ruthy Brito
Electronic Thesis and Dissertation Repository
Science autonomy onboard spacecraft can optimize image return by prioritizing downlink of meaningful data. Martian polygonally cracked ground is actively studied by planetary geologists and may be indicative of subsurface water. Filtering images containing these polygonal features can be used as a case study for science autonomy and to reduce the overhead associated with parsing through Martian surface images. This thesis demonstrates the use of deep learning techniques in the classification of Martian polygonally patterned ground from HiRISE images. Three tasks are considered, a binary classification to identify images containing polygons, multiclass classification distinguishing different polygon types and semantic segmentation …
Detection Of Crypto-Ransomware Attack Using Deep Learning, Muna Jemal
Detection Of Crypto-Ransomware Attack Using Deep Learning, Muna Jemal
Master of Science in Computer Science Theses
The number one threat to the digital world is the exponential increase in ransomware attacks. Ransomware is malware that prevents victims from accessing their resources by locking or encrypting the data until a ransom is paid. With individuals and businesses growing dependencies on technology and the Internet, researchers in the cyber security field are looking for different measures to prevent malicious attackers from having a successful campaign. A new ransomware variant is being introduced daily, thus behavior-based analysis of detecting ransomware attacks is more effective than the traditional static analysis. This paper proposes a multi-variant classification to detect ransomware I/O …
A Long-Term Funds Predictor Based On Deep Learning, Shuiyi Kuang
A Long-Term Funds Predictor Based On Deep Learning, Shuiyi Kuang
Electronic Theses, Projects, and Dissertations
Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, we try to find a suitable long-term predictor for the funds market by testing different kinds of neural network models, including the Long Short-Term Memory(LSTM) model with different layers, the Gated Recurrent Units(GRU) model with different layers, and the combination …
Machine Learning Models To Automate Radiotherapy Structure Name Standardization, Priyankar Bose
Machine Learning Models To Automate Radiotherapy Structure Name Standardization, Priyankar Bose
Theses and Dissertations
Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and `Other' organs for monitoring present and future medications. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and `Other' organs is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. We compare both traditional methods and deep neural network-based approaches on the multimodal vision-language prostate cancer …