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
Comparison of motor fault diagnosis performance using RNN a..:
, In:
2020 20th International Conference on Control, Automation and Systems (ICCAS)
,
Choi, Dong-Jin
;
Han, Ji-Hoon
;
Park, Sang-Uk
. - p. 443-446 , 2020
Link:
https://doi.org/10.23919/ICCAS50221.2020.9268271
RT T1
2020 20th International Conference on Control, Automation and Systems (ICCAS)
: T1
Comparison of motor fault diagnosis performance using RNN and K-means for data with disturbance
UL https://suche.suub.uni-bremen.de/peid=ieee-9268271&Exemplar=1&LAN=DE A1 Choi, Dong-Jin A1 Han, Ji-Hoon A1 Park, Sang-Uk A1 Hong, Sun-Ki YR 2020 SN 2642-3901 K1 Classification algorithms K1 Fault diagnosis K1 Clustering algorithms K1 Recurrent neural networks K1 Gears K1 Vibrations K1 Feature extraction K1 Deep Learning K1 Motor Fault Diagnosis K1 RNN K1 K-means SP 443 OP 446 LK http://dx.doi.org/https://doi.org/10.23919/ICCAS50221.2020.9268271 DO https://doi.org/10.23919/ICCAS50221.2020.9268271 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)