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A new technique combining semi-supervised and active learning for non-intrusive load monitoring / Ahmed Mohamed Fatouh Ahmed ; Supervised Omar Ahmed Ali Nasr

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Ahmed Mohamed Fatouh Ahmed , 2019Description: 49 P. : charts ; 30cmOther title:
  • دمج التعلم الشبه إشرافي والتعلم النشط كأسلوب جديد في المتابعة الغير متداخلة للأحمال [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics And Communications Summary: The current work introduces a new technique that leverages both the semi-supervised and active learning together to the benefit of non-intrusive load monitoring, that is the procedure used to disaggregate the contributions of different appliances in a building. The main idea is that semi-supervised learning improves the results of active learning aiming to decrease the need to the user. Two different approaches were utilized, one used active and reactive power features and the other used current waveform harmonics to use them later in the machine learning model
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Item type Current library Home library Call number Copy number Status Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2019.Ah.N (Browse shelf(Opens below)) Not for loan 01010110079270000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2019.Ah.N (Browse shelf(Opens below)) 79270.CD Not for loan 01020110079270000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics And Communications

The current work introduces a new technique that leverages both the semi-supervised and active learning together to the benefit of non-intrusive load monitoring, that is the procedure used to disaggregate the contributions of different appliances in a building. The main idea is that semi-supervised learning improves the results of active learning aiming to decrease the need to the user. Two different approaches were utilized, one used active and reactive power features and the other used current waveform harmonics to use them later in the machine learning model

Issued also as CD

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