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Application of Machine Learning to E...

ProQuest Information and Learning Co.

Application of Machine Learning to Electrical Data Analysis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Application of Machine Learning to Electrical Data Analysis./
作者:	Bao, Zhen.
面頁冊數:	1 online resource (132 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369852752

Application of Machine Learning to Electrical Data Analysis.
Bao, Zhen.

Application of Machine Learning to Electrical Data Analysis. - 1 online resource (132 pages)

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--Illinois Institute of Technology, 2017.

Includes bibliographical references

The dissertation is composed of four parts: modeling demand response capability by internet data centers processing batch computing jobs, cloud storage based power consumption management in internet data center, identifying hot socket problem in smart meters, and online event detection for non-intrusive load monitoring without knowing label. Mathematical models are constructed to fulfill the research of the four targets, and numerical examples are used to test the effectiveness of the models.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369852752Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Application of Machine Learning to Electrical Data Analysis.
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100 1 $a Bao, Zhen. $3 1194176
245 1 0 $a Application of Machine Learning to Electrical Data Analysis.
264 0 $c 2017
300 $a 1 online resource (132 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Adviser: Zuyi Li.
502 $a Thesis (Ph.D.)--Illinois Institute of Technology, 2017.
504 $a Includes bibliographical references
520 $a The dissertation is composed of four parts: modeling demand response capability by internet data centers processing batch computing jobs, cloud storage based power consumption management in internet data center, identifying hot socket problem in smart meters, and online event detection for non-intrusive load monitoring without knowing label. Mathematical models are constructed to fulfill the research of the four targets, and numerical examples are used to test the effectiveness of the models.
520 $a The first two parts optimize jobs in Data Center in order to find the best way of utilizing the existing computing resources and storage. Mixed-integer programming (MIP) is used in the formulation. The purpose of the third part is to identify the hot socket problem in smart meter. Machine learning method has been used to locate the bad installation of smart meters by analyzing historical data from smart meters. The fourth part is non-intrusive load monitoring for residential load in houses. Signal processing and deep learning methods are used to identify the specific loads from high frequency signals.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Illinois Institute of Technology. $b Electrical and Computer Engineering. $3 1179314
773 0 $t Dissertation Abstracts International $g 78-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10271021 $z click for full text (PQDT)

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