Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


Re-Published in
Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Education 5.0: Using the Design Thinking Process – An Interdisciplinary View
Birgit Oberer, Alptekin Erkollar
(pages: 1-17)

Impact of Artificial Intelligence on Smart Cities
Mohammad Ilyas
(pages: 18-39)

A Multi-Disciplinary Cybernetic Approach to Pedagogic Excellence
Russell Jay Hendel
(pages: 40-63)

Data Management Sharing Plan: Fostering Effective Trans-Disciplinary Communication in Collaborative Research
Cristo Ernesto Yáñez León, James Lipuma
(pages: 64-79)

From Disunity to Synergy: Transdisciplinarity in HR Trends
Olga Bernikova, Daria Frolova
(pages: 80-92)

The Impact of Artificial Intelligence on the Future Business World
Hebah Y. AlQato
(pages: 93-104)

Wi-Fi and the Wisdom Exchange: The Role of Lived Experience in the Age of AI
Teresa H. Langness
(pages: 105-113)

Older Adult Online Learning during COVID-19 in Taiwan: Based on Teachers' Perspective
Ya-Hui Lee, Yi-Fen Wang, Hsien-Ta Cha
(pages: 114-129)

Data Visualization of Budgeting Assumptions: An Illustrative Case of Trans-disciplinary Applied Knowledge
Carol E. Cuthbert, Noel J. Pears, Karen Bradshaw
(pages: 130-149)

The Importance of Defining Cybersecurity from a Transdisciplinary Approach
Bilquis Ferdousi
(pages: 150-164)

ChatGPT, Metaverses and the Future of Transdisciplinary Communication
Jasmin (Bey) Cowin
(pages: 165-178)

Trans-Disciplinary Communication for Policy Making: A Reflective Activity Study
Cristo Leon
(pages: 179-192)

Trans-Disciplinary Communication in Collaborative Co-Design for Knowledge Sharing
James Lipuma, Cristo Leon
(pages: 193-210)

Digital Games in Education: An Interdisciplinary View
Birgit Oberer, Alptekin Erkollar
(pages: 211-230)

Disciplinary Inbreeding or Disciplinary Integration?
Nagib Callaos
(pages: 231-281)


 

Abstracts

 


ABSTRACT


System Response Analysis and Model Order Reduction, Using Conventional Method, Bond Graph Technique and Genetic Programming

Lubna Moin, Vali Uddin, Shahid Ali


This research paper basically explores and compares the different modeling and analysis techniques and than it also explores the model order reduction approach and significance. The traditional modeling and simulation techniques for dynamic systems are generally adequate for single-domain systems only, but the Bond Graph technique provides new strategies for reliable solutions of multi-domain system. They are also used for analyzing linear and non linear dynamic production system, artificial intelligence, image processing, robotics and industrial automation. This paper describes a unique technique of generating the Genetic design from the tree structured transfer function obtained from Bond Graph. This research work combines bond graphs for model representation with Genetic programming for exploring different ideas on design space tree structured transfer function result from replacing typical bond graph element with their impedance equivalent specifying impedance lows for Bond Graph multiport. This tree structured form thus obtained from Bond Graph is applied for generating the Genetic Tree. Application studies will identify key issues and importance for advancing this approach towards becoming on effective and efficient design tool for synthesizing design for Electrical system. In the first phase, the system is modeled using Bond Graph technique. Its system response and transfer function with conventional and Bond Graph method is analyzed and then a approach towards model order reduction is observed. The suggested algorithm and other known modern model order reduction techniques are applied to a 11th order high pass filter [1], with different approach. The model order reduction technique developed in this paper has least reduction errors and secondly the final model retains structural information. The system response and the stability analysis of the system transfer function taken by conventional and by Bond Graph method is compared and analyzed. The approach towards the Genetic Tree formation from the Bond Graph is also developed. The model order reduction using Genetic Tree is in progress.

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