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
Deep Learning Techniques for Fault Detection in Industrial ..:
, In:
2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST)
,
Panigrahi, Bhawani Sankar
;
T, Thiyagarajan
;
Tamilselvi, M.
... - p. 221-226 , 2024
Link:
https://doi.org/10.1109/ICRTCST61793.2024.10578499
RT T1 <cTitle>2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST)</cTitle>: T1 <Title>Deep Learning Techniques for Fault Detection in Industrial Machinery</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10578499&Exemplar=1&LAN=DE A1 Panigrahi, Bhawani Sankar A1 T, Thiyagarajan A1 Tamilselvi, M. A1 Tilak Babu, S. B G A1 G, Pavithra A1 Shaik, Bazani YR 2024 K1 Deep learning K1 Recurrent neural networks K1 Limiting K1 Fault detection K1 Noise K1 Market research K1 Reliability K1 Industrial machinery K1 Machine learning K1 Predictive maintenance K1 Anomaly detection K1 Condition monitoring SP 221 OP 226 LK http://dx.doi.org/https://doi.org/10.1109/ICRTCST61793.2024.10578499 DO https://doi.org/10.1109/ICRTCST61793.2024.10578499 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
