National Science Library of Georgia

Machine Learning for Cyber Physical Systems (Record no. 524399)

MARC details
000 -LEADER
fixed length control field 05490nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-662-58485-9
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200127152619.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 181217s2019 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783662584859
-- 978-3-662-58485-9
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-662-58485-9
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q342
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code TEC009000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
245 10 - TITLE STATEMENT
Title Machine Learning for Cyber Physical Systems
Medium [electronic resource] :
Remainder of title Selected papers from the International Conference ML4CPS 2018 /
Statement of responsibility, etc edited by Jürgen Beyerer, Christian Kühnert, Oliver Niggemann.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2019.
264 #1 - Production, Publication, Distribution, Manufacture, and Copyright Notice (R)
Place of production, publication, distribution, manufacture (R) Berlin, Heidelberg :
Name of producer, publisher, distributor, manufacturer (R) Springer Berlin Heidelberg :
-- Imprint: Springer Vieweg,
Date of production, publication, distribution, manufacture, or copyright notice 2019.
300 ## - PHYSICAL DESCRIPTION
Extent VII, 136 p.
Other physical details online resource.
336 ## - Content Type (R)
Content type term (R) text
Content type code (R) txt
Source (NR) rdacontent
337 ## - Media Type (R)
Media type term (R) computer
Media type code (R) c
Source (NR) rdamedia
338 ## - Carrier Type (R)
Carrier type term (R) online resource
Carrier type code (R) cr
Source (NR) rdacarrier
347 ## -
-- text file
-- PDF
-- rda
490 1# - SERIES STATEMENT
სერიის ცნობა Technologien für die intelligente Automation, Technologies for Intelligent Automation,
International Standard Serial Number 2522-8579 ;
Volume number/sequential designation 9
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project -- Deduction of time-dependent machine tool characteristics by fuzzy-clustering -- Unsupervised Anomaly Detection in Production Lines -- A Random Forest Based Classifer for Error Prediction of Highly Individualized Products -- Web-based Machine Learning Platform for Condition-Monitoring -- Selection and Application of Machine Learning-Algorithms in Production Quality -- Which deep artifificial neural network architecture to use for anomaly detection in Mobile Robots kinematic data -- GPU GEMM-Kernel Autotuning for scalable machine learners -- Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria -- A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance -- Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality -- Enabling Self-Diagnosis of Automation Devices through Industrial Analytics -- Making Industrial Analytics work for Factory Automation Applications -- Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems -- LoRaWan for Smarter Management of Water Network: From metering to data analysis.
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open Access
520 ## - SUMMARY, ETC.
Summary, etc This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Dr. Christian Kühnert is a senior researcher at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data-driven condition monitoring. Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer organization.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Electrical engineering.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational Intelligence.
-- http://scigraph.springernature.com/things/product-market-codes/T11014
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Systems Organization and Communication Networks.
-- http://scigraph.springernature.com/things/product-market-codes/I13006
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Communications Engineering, Networks.
-- http://scigraph.springernature.com/things/product-market-codes/T24035
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining and Knowledge Discovery.
-- http://scigraph.springernature.com/things/product-market-codes/I18030
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Beyerer, Jürgen.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Kühnert, Christian.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Niggemann, Oliver.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783662584842
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783662584866
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Technologien für die intelligente Automation, Technologies for Intelligent Automation,
-- 2522-8579 ;
Volume number/sequential designation 9
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-662-58485-9">https://doi.org/10.1007/978-3-662-58485-9</a>
912 ## -
-- ZDB-2-INR
912 ## -
-- ZDB-2-SOB

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