Context Aware Data Acquisition Framework for Dynamic Data Driven Applications Systems (DDDAS)

2013 ◽  
Author(s):  
Nhan Nguyen ◽  
Mohammad Maifi Hasan Khan
Keyword(s):  
Data Acquisition ◽  
Data Driven ◽  
Context Aware ◽  
Dynamic Data

scholarly journals Context-aware Dynamic Data-driven Pattern Classification

2014 ◽  
Vol 29 ◽  
pp. 1324-1333 ◽  
Author(s):  
Shashi Phoha ◽  
Nurali Virani ◽  
Pritthi Chattopadhyay ◽  
Soumalya Sarkar ◽  
Brian Smith ◽  
...  
Keyword(s):  
Pattern Classification ◽  
Data Driven ◽  
Context Aware ◽  
Dynamic Data

scholarly journals Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements

2017 ◽  
Vol 137 ◽  
pp. 571-578 ◽  
Author(s):  
L. Colone ◽  
M. Reder ◽  
J. Tautz-Weinert ◽  
J.J. Melero ◽  
A. Natarajan ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Wind Turbines ◽  
Data Driven

Kinetics of postoperative drain fluid amylase values after pancreatoduodenectomy: New insights to dynamic, data-driven drain management

Surgery ◽  
2021 ◽  
Author(s):  
Amer H. Zureikat ◽  
Fabio Casciani ◽  
Sarwat Ahmad ◽  
Claudio Bassi ◽  
Charles M. Vollmer
Keyword(s):  
Data Driven ◽  
Dynamic Data ◽  
Drain Fluid ◽  
Kinetics Of ◽  
Drain Management

scholarly journals Dynamic Data Driven Methodologies for Multiphysics System Modeling and Simulation

2005 ◽  
pp. 616-623 ◽  
Author(s):  
J. Michopoulos ◽  
C. Farhat ◽  
E. Houstis ◽  
P. Tsompanopoulou ◽  
H. Zhang ◽  
...  
Keyword(s):  
Modeling And Simulation ◽  
System Modeling ◽  
Data Driven ◽  
Dynamic Data

Introduction to Dynamic Data Driven Applications Systems

2018 ◽  
pp. 1-25 ◽  
Author(s):  
Erik Blasch ◽  
Dennis Bernstein ◽  
Murali Rangaswamy
Keyword(s):  
Data Driven ◽  
Dynamic Data

Stamping Plant 4.0 – Basics for the Application of Data Mining Methods in Manufacturing Car Body Parts

2015 ◽  
Vol 639 ◽  
pp. 21-30 ◽  
Author(s):  
Stephan Purr ◽  
Josef Meinhardt ◽  
Arnulf Lipp ◽  
Axel Werner ◽  
Martin Ostermair ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Acquisition ◽  
Data Driven ◽  
Quality Analysis ◽  
Process Conditions ◽  
Data Sets ◽  
Body Parts ◽  
Car Body ◽  
Sample Data ◽  
Mining Methods

Data-driven quality evaluation in the stamping process of car body parts is quite promising because dependencies in the process have not yet been sufficiently researched. However, the application of data mining methods for the process in stamping plants would require a large number of sample data sets. Today, acquiring these data represents a major challenge, because the necessary data are inadequately measured, recorded or stored. Thus, the preconditions for the sample data acquisition must first be created before being able to investigate any correlations. In addition, the process conditions change over time due to wear mechanisms. Therefore, the results do not remain valid and a constant data acquisition is required. In this publication, the current situation in stamping plants regarding the process robustness will be first discussed and the need for data-driven methods will be shown. Subsequently, the state of technology regarding the possibility of collecting the sample data sets for quality analysis in producing car body parts will be researched. At the end of this work, an overview will be provided concerning how this data collection was implemented at BMW as well as what kind of potential can be expected.

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