↓ Skip to main content
What is this page? Embed badge

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

Overview of attention for book
Cover of 'IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems
  3. Altmetric Badge
    Chapter 2 Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory
  4. Altmetric Badge
    Chapter 3 Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps
  5. Altmetric Badge
    Chapter 4 Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps
  6. Altmetric Badge
    Chapter 5 A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes
  7. Altmetric Badge
    Chapter 6 Validation of similarity measures for industrial alarm flood analysis?
  8. Altmetric Badge
    Chapter 7 Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause
Attention for Chapter 3: Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps
Altmetric Badge

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
6 Mendeley
Summary Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps
Chapter number 3
Book title
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency
Published by
Springer Vieweg, Berlin, Heidelberg, January 2018
DOI 10.1007/978-3-662-57805-6_3
Book ISBNs
978-3-66-257804-9, 978-3-66-257805-6
Authors

Alexander von Birgelen, Oliver Niggemann, von Birgelen, Alexander, Niggemann, Oliver

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Student > Master 2 33%
Other 1 17%
Unknown 1 17%
Readers by discipline Count As %
Computer Science 5 83%
Unknown 1 17%

This page is provided by Altmetric.