Intent-based automation networks: Toward a common reference model for the self-orchestration of industrial intranets

2016 ◽  
Author(s):  
Dirk Schulz
Keyword(s):  
Reference Model ◽  
The Self ◽  
Common Reference ◽  
Common Reference Model

scholarly journals A Data Science Enhanced Framework for Applied and Computational Math

10.28945/3961 ◽  
2018 ◽  
Keyword(s):  
Information Technology ◽  
Data Science ◽  
Reference Model ◽  
Primary Objective ◽  
Common Reference ◽  
Real World Applications ◽  
Applied Math ◽  
Common Reference Model ◽  
Computational Math ◽  
Math Concepts

[This Proceedings paper was revised and published in the 2018 issue of the journal Issues in Informing Science and Information Technology, Volume 15] The primary objective of this research was to build an enhanced framework for Applied and Computational Math. This framework allows a variety of applied math concepts to be organized into a meaningful whole. The framework can help students grasp new mathematical applications by comparing them to a common reference model. In this research, we measured the most frequent words used in a sample of Math and Computer Science books. We integrated these words with those obtained in an earlier study, from which we had constructed the original Computational Math scale. The enhanced framework improves our Computational Math scale by integrating selected concepts from the field of Data Science. The resulting enhanced framework better explains how abstract mathematical models and algorithms are tied to real world applications and computer implementations.

A Combined Reference Model- and View-Based Approach to System Specification

1997 ◽  
Vol 07 (04) ◽  
pp. 457-477 ◽  
Author(s):  
Gregor Engels ◽  
Reiko Heckel ◽  
Gabriele Taentzer ◽  
Hartmut Ehrig
Keyword(s):  
Software Engineering ◽  
Reference Model ◽  
Banking System ◽  
Not Given ◽  
System Specification ◽  
Common Reference ◽  
View Integration ◽  
Graph Transformations ◽  
Development Approach ◽  
Common Reference Model

The idea of a combined reference model- and view-based specification approach has been proposed recently in the software engineering community. In this paper we present a specification technique based on graph transformations which supports such a development approach. The use of graphs and graph transformations supports an intuitive understanding and an integration of static and dynamic aspects on a well-defined semantical base. On this background, formal notions of view and view relation are developed and the behaviour of views is described by a loose semantics. The integration of two views derived from a common reference model is done in two steps. First, dependencies between the views which are not given by the reference model are determined, and the reference model is extended appropriately. This is the task of a model manager. If the two views and the reference model are consistent, the actual view integration can be performed automatically. For the case of more than two views more general scenarios are developed and discussed. All concepts and results are illustrated at the well-known example of a banking system.

scholarly journals A Data Science Enhanced Framework for Applied and Computational Math

10.28945/4032 ◽  
2018 ◽  
Vol 15 ◽  
pp. 191-206
Author(s):  
Kirby McMaster ◽  
Samuel Sambasivam ◽  
Brian Rague ◽  
Stuart L Wolthuis
Keyword(s):  
Data Science ◽  
Reference Model ◽  
Primary Objective ◽  
Future Research ◽  
Common Reference ◽  
Real World Applications ◽  
Applied Math ◽  
Common Reference Model ◽  
Computational Math ◽  
Math Concepts

Aim/Purpose: The primary objective of this research is to build an enhanced framework for Applied and Computational Math. This framework allows a variety of applied math concepts to be organized into a meaningful whole. Background: The framework can help students grasp new mathematical applications by comparing them to a common reference model. Methodology: In this research, we measure the most frequent words used in a sample of Math and Computer Science books. We combine these words with those obtained in an earlier study, from which we constructed our original Computational Math scale. Contribution: The enhanced framework improves the Computational Math scale by integrating selected concepts from the field of Data Science. Findings: The resulting enhanced framework better explains how abstract mathematical models and algorithms are tied to real world applications and computer implementations. Future Research: We want to empirically test our enhanced Applied and Computational Math framework in a classroom setting. Our goal is to measure how effective the use of this framework is in improving students’ understanding of newly introduced Math concepts.

A NEW METHOD FOR FACIAL FEATURES QUANTIFICATION OF CARICATURE BASED ON SELF-REFERENCE MODEL

2008 ◽  
Vol 22 (08) ◽  
pp. 1647-1668 ◽  
Author(s):  
FEI NI ◽  
ZHUANG FU ◽  
QIXIN CAO ◽  
YANZHENG ZHAO
Keyword(s):  
Reference Model ◽  
Face Image ◽  
The Self ◽  
New Method ◽  
Front Face ◽  
Facial Features ◽  
Face Model ◽  
Input Face ◽  
Facial Images ◽  
Self Reference

Some facial features that differ from an ordinary face should be identified by a computer when generating a facial caricature. These distinctive facial features are called self-features. Compared with traditional Mean Face Model (MFM) that is unable to quantify these self-features well, a Self-Reference Model (SRM) is presented in this paper. Firstly, based on the physiology structure of a front face, a self-reference is found, and this reference is used to measure the self-features. According to the self-reference, some standard facial parameters are worked out by collecting statistic data of many facial images. Then, in an input face image, by evaluating some differences between the input face and the standard facial parameters, the self-features are properly estimated and quantified. Finally, by analyzing some caricatures produced by caricaturists, the SRM can prove the validity of the proposed Algorithm.

Standardizing a reference model and autonomic network architectures for the self-managing future internet

IEEE Network ◽  
2011 ◽  
Vol 25 (6) ◽  
pp. 50-56 ◽  
Author(s):  
Michal Wodczak ◽  
Tayeb Ben Meriem ◽  
Benoit Radier ◽  
Ranganai Chaparadza ◽  
Kevin Quinn ◽  
...  
Keyword(s):  
Reference Model ◽  
Future Internet ◽  
The Self ◽  
Network Architectures
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