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Compressive Sensing Framework For Mass Spectrometry Data Analysis, Khalfalla Ahmad Kh. Awedat
Compressive Sensing Framework For Mass Spectrometry Data Analysis, Khalfalla Ahmad Kh. Awedat
Dissertations
Mass Spectrometry (MS) data is ideal for identifying unique bio-signatures of diseases. However, the high dimensionality of MS data hinders any promising MS-based proteomics development. The goal of this dissertation is to develop an accurate classification tool by employing compressive sensing (CS). Not only can CS significantly reduce MS data dimensionality, but it also will allow for full reconstruction of original data. The framework developed in this work is based on using L2 and a mixed L2-L1 norms, allowing an overdetermined system to be resolved. The results show that the L2- based algorithm with regularization terms has a better performance …
Power Transmission Line Fault Classification Using Support Vector Machines, Zhuokang Jia
Power Transmission Line Fault Classification Using Support Vector Machines, Zhuokang Jia
Masters Theses
Electrical transmission lines are prone to faults and failures. When a fault occurs, it is impossible most of the times to fix it manually. Many methods have been adopted in the past in order to serve the purpose as fault diagnosing application. In this thesis, I discuss the method of Support Vector Machine (SVM) for fault diagnosis. SVM has the edge of good generalization over other fault diagnosing applications because it is based on pattern recognize algorithms. The aim is to classify the type of fault in the lines. Furthermore, in this work, the current and voltage of each phase …