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Level: masters  Dates: Last 2 Years

You searched for subject:(source identification). Showing records 1 – 2 of 2 total matches.

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University of Manitoba

1. Nasr Esfahani, Ali. Detection and classification of partial discharge sources under variable frequency and air pressure.

Degree: Electrical and Computer Engineering, 2018, University of Manitoba

With the development of electrical apparatus in more-electric aircraft (MEA), demand for higher electric power is increasing rapidly. In turn, this requires a higher voltage level in the power generation system of aircraft which increases the electric stress on the insulation systems. At higher voltages, the insulation systems of more-electric aircraft are prone to partial discharge (PDs) initiation under the operating condition. The objective of this thesis is to perform a comprehensive study on developing diagnostic methods for the insulation condition monitoring and PD source identification. An algorithm is developed based on the combination of wavelet and energy techniques to detect the PD pulses from the measured noisy PD signals. In addition, based on the statistical distributions of PD pulse waveform characteristics, a classification and separation algorithm is developed for the identification of multi-source PDs using kernel support vector machine (KSVM) as the classifier. The experimental results show that the proposed algorithms show a high performance and accuracy for PD source detection and recognition. Advisors/Committee Members: Kordi, Behzad (Electrical and Computer Engineering) (supervisor), Major, Arkady (Electrical and Computer Engineering).

Subjects/Keywords: Partial discharge source identification

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Nasr Esfahani, A. (2018). Detection and classification of partial discharge sources under variable frequency and air pressure. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/33747

Chicago Manual of Style (16th Edition):

Nasr Esfahani, Ali. “Detection and classification of partial discharge sources under variable frequency and air pressure.” 2018. Masters Thesis, University of Manitoba. Accessed January 19, 2020. http://hdl.handle.net/1993/33747.

MLA Handbook (7th Edition):

Nasr Esfahani, Ali. “Detection and classification of partial discharge sources under variable frequency and air pressure.” 2018. Web. 19 Jan 2020.

Vancouver:

Nasr Esfahani A. Detection and classification of partial discharge sources under variable frequency and air pressure. [Internet] [Masters thesis]. University of Manitoba; 2018. [cited 2020 Jan 19]. Available from: http://hdl.handle.net/1993/33747.

Council of Science Editors:

Nasr Esfahani A. Detection and classification of partial discharge sources under variable frequency and air pressure. [Masters Thesis]. University of Manitoba; 2018. Available from: http://hdl.handle.net/1993/33747

2. White, Parker Douglas. Constrained Clustering for Frequency Hopping Spread Spectrum Signal Separation.

Degree: MS, Electrical Engineering, 2019, Virginia Tech

The expansion of technology in areas such as smart homes and appliances, personal devices, smart vehicles, and many others, leads to more and more devices using common wireless communication techniques such as WiFi and Bluetooth. While the number of wirelessly connected users expands, the range of frequencies that support wireless communications does not. It is therefore essential that each of these devices unselfishly share the available wireless resources. If a device is using more resources than the required limits, or causing interference with other’s communications, this device will impact many others negatively and therefore preventative action must be taken to prevent further disruption in the wireless environment. Before action can be taken however, the device must first be identified in a mixture of other wireless activity. To identify a specific device, first, a wireless receiver must be in close enough proximity to detect the power that the malicious device is emitting through its wireless communication. This thesis provides a method that can be used to identify a problem user based only off of its wireless transmission behavior. The performance of this identification is shown with respect to the received signal power which represents the necessary range that a listening device must be to identify and separate a problem user from other cooperative users that are communicating wirelessly. Advisors/Committee Members: Headley, William C. (committeechair), Buehrer, Richard M. (committeechair), Williams, Ryan K. (committee member), Reed, Jeffrey H. (committee member).

Subjects/Keywords: FHSS; signal separation; constrained clustering; source identification; signal detection

…47 4.2 The steps used in this work to generate complex data for every hopping source. 48… …Specific Emitter Identification SNR Signal-to-Noise Ratio STFT Short Time Fourier Transform TF… …lead to the identification of users in an environment which can aid in friend or foe… …determination. Also, if there is interest in a specific type of user, the identification of this user… …commercial or military applications, the identification of a user of interest first involves the… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

White, P. D. (2019). Constrained Clustering for Frequency Hopping Spread Spectrum Signal Separation. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/93726

Chicago Manual of Style (16th Edition):

White, Parker Douglas. “Constrained Clustering for Frequency Hopping Spread Spectrum Signal Separation.” 2019. Masters Thesis, Virginia Tech. Accessed January 19, 2020. http://hdl.handle.net/10919/93726.

MLA Handbook (7th Edition):

White, Parker Douglas. “Constrained Clustering for Frequency Hopping Spread Spectrum Signal Separation.” 2019. Web. 19 Jan 2020.

Vancouver:

White PD. Constrained Clustering for Frequency Hopping Spread Spectrum Signal Separation. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2020 Jan 19]. Available from: http://hdl.handle.net/10919/93726.

Council of Science Editors:

White PD. Constrained Clustering for Frequency Hopping Spread Spectrum Signal Separation. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/93726

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