語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Smart Agents for the Industry 4.0 = ...
~
SpringerLink (Online service)
Smart Agents for the Industry 4.0 = Enabling Machine Learning in Industrial Production /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Smart Agents for the Industry 4.0/ by Max Hoffmann.
其他題名:
Enabling Machine Learning in Industrial Production /
作者:
Hoffmann, Max.
面頁冊數:
XXXIV, 318 p. 111 illus.online resource. :
Contained By:
Springer Nature eBook
標題:
Artificial intelligence. -
電子資源:
https://doi.org/10.1007/978-3-658-27742-0
ISBN:
9783658277420
Smart Agents for the Industry 4.0 = Enabling Machine Learning in Industrial Production /
Hoffmann, Max.
Smart Agents for the Industry 4.0
Enabling Machine Learning in Industrial Production /[electronic resource] :by Max Hoffmann. - 1st ed. 2019. - XXXIV, 318 p. 111 illus.online resource.
Agent OPC UA – Semantic Scalability and Interoperability Architecture for MAS through OPC UA -- Management System Integration of OPC UA Based MAS -- Flexible Manufacturing Based on Autonomous, Decentralized Systems -- Use Cases for Industrial Automation.
Max Hoffmann describes the realization of a framework that enables autonomous decision-making in industrial manufacturing processes by means of multi-agent systems and the OPC UA meta-modeling standard. The integration of communication patterns and SOA with grown manufacturing systems enables an upgrade of legacy environments in terms of Industry 4.0 related technologies. The added value of the derived solutions are validated through an industrial use case and verified by the development of a demonstrator that includes elements of self-optimization through Machine Learning and communication with high-level planning systems such as ERP. Contents Agent OPC UA – Semantic Scalability and Interoperability Architecture for MAS through OPC UA Management System Integration of OPC UA Based MAS Flexible Manufacturing Based on Autonomous, Decentralized Systems Use Cases for Industrial Automation Target Groups Scientists and students in automation technology, production technology, mechanical engineering, process control, factory planning Practitioners in these fields About the Author Dr.-Ing. Max Hoffmann is a scientific researcher at the Institute of Information Management in Mechanical Engineering, RWTH Aachen University, Germany, and leads the group “Industrial Big Data”. His research emphasizes on production optimization by means of data integration through interoperability and communication standards for industrial manufacturing and integrated analysis by using Machine Learning and stream-based information processing.
ISBN: 9783658277420
Standard No.: 10.1007/978-3-658-27742-0doiSubjects--Topical Terms:
559380
Artificial intelligence.
LC Class. No.: Q334-342
Dewey Class. No.: 006.3
Smart Agents for the Industry 4.0 = Enabling Machine Learning in Industrial Production /
LDR
:03161nam a22003975i 4500
001
1003955
003
DE-He213
005
20200702011307.0
007
cr nn 008mamaa
008
210106s2019 gw | s |||| 0|eng d
020
$a
9783658277420
$9
978-3-658-27742-0
024
7
$a
10.1007/978-3-658-27742-0
$2
doi
035
$a
978-3-658-27742-0
050
4
$a
Q334-342
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.3
$2
23
100
1
$a
Hoffmann, Max.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1261067
245
1 0
$a
Smart Agents for the Industry 4.0
$h
[electronic resource] :
$b
Enabling Machine Learning in Industrial Production /
$c
by Max Hoffmann.
250
$a
1st ed. 2019.
264
1
$a
Wiesbaden :
$b
Springer Fachmedien Wiesbaden :
$b
Imprint: Springer Vieweg,
$c
2019.
300
$a
XXXIV, 318 p. 111 illus.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
505
0
$a
Agent OPC UA – Semantic Scalability and Interoperability Architecture for MAS through OPC UA -- Management System Integration of OPC UA Based MAS -- Flexible Manufacturing Based on Autonomous, Decentralized Systems -- Use Cases for Industrial Automation.
520
$a
Max Hoffmann describes the realization of a framework that enables autonomous decision-making in industrial manufacturing processes by means of multi-agent systems and the OPC UA meta-modeling standard. The integration of communication patterns and SOA with grown manufacturing systems enables an upgrade of legacy environments in terms of Industry 4.0 related technologies. The added value of the derived solutions are validated through an industrial use case and verified by the development of a demonstrator that includes elements of self-optimization through Machine Learning and communication with high-level planning systems such as ERP. Contents Agent OPC UA – Semantic Scalability and Interoperability Architecture for MAS through OPC UA Management System Integration of OPC UA Based MAS Flexible Manufacturing Based on Autonomous, Decentralized Systems Use Cases for Industrial Automation Target Groups Scientists and students in automation technology, production technology, mechanical engineering, process control, factory planning Practitioners in these fields About the Author Dr.-Ing. Max Hoffmann is a scientific researcher at the Institute of Information Management in Mechanical Engineering, RWTH Aachen University, Germany, and leads the group “Industrial Big Data”. His research emphasizes on production optimization by means of data integration through interoperability and communication standards for industrial manufacturing and integrated analysis by using Machine Learning and stream-based information processing.
650
0
$a
Artificial intelligence.
$3
559380
650
0
$a
Industrial engineering.
$3
679492
650
0
$a
Production engineering.
$3
566269
650
0
$a
Electrical engineering.
$3
596380
650
1 4
$a
Artificial Intelligence.
$3
646849
650
2 4
$a
Industrial and Production Engineering.
$3
593943
650
2 4
$a
Communications Engineering, Networks.
$3
669809
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783658277413
776
0 8
$i
Printed edition:
$z
9783658277437
776
0 8
$i
Printed edition:
$z
9783658277444
856
4 0
$u
https://doi.org/10.1007/978-3-658-27742-0
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入