I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
LightNILM : lightweight neural network methods for non-i..:
, In:
Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
,
Lu, Zhenyu
;
Cheng, Yurong
;
Zhong, Mingjun
... - p. 383-387 , 2022
Link:
https://dl.acm.org/doi/10.1145/3563357.3566152
RT T1 <cTitle>Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation</cTitle>: T1 <Title>LightNILM : lightweight neural network methods for non-intrusive load monitoring</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3566152&Exemplar=1&LAN=DE A1 Lu, Zhenyu A1 Cheng, Yurong A1 Zhong, Mingjun A1 Luan, Wenpeng A1 Ye, Yuan A1 Wang, Guoren PB ACM YR 2022 K1 deep learning K1 edge NILM K1 energy disaggregation K1 lightweight NILM K1 sequence-to-point learning K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks SP 383 OP 387 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3563357.3566152 DO https://dl.acm.org/doi/10.1145/3563357.3566152 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
