Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: May 2018
Next issue: Aug 2018
Avg review time: 104 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


TRAFFIC STATS

1,991,957 unique visits
539,069 downloads
Since November 1, 2009



No robots online now


SJR SCImago RANK

SCImago Journal & Country Rank


SEARCH ENGINES

aece.ro - Google Pagerank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 18 (2018)
 
     »   Issue 2 / 2018
 
     »   Issue 1 / 2018
 
 
 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
 Volume 16 (2016)
 
     »   Issue 4 / 2016
 
     »   Issue 3 / 2016
 
     »   Issue 2 / 2016
 
     »   Issue 1 / 2016
 
 
 Volume 15 (2015)
 
     »   Issue 4 / 2015
 
     »   Issue 3 / 2015
 
     »   Issue 2 / 2015
 
     »   Issue 1 / 2015
 
 
  View all issues  








LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  4/2017 - 11

Developing Automatic Multi-Objective Optimization Methods for Complex Actuators

CHIS, R. See more information about CHIS, R. on SCOPUS See more information about CHIS, R. on IEEExplore See more information about CHIS, R. on Web of Science, VINTAN, L. See more information about VINTAN, L. on SCOPUS See more information about VINTAN, L. on SCOPUS See more information about VINTAN, L. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,410 KB) | Citation | Downloads: 141 | Views: 297

Author keywords
actuators, computer aided engineering, machine learning, pareto optimization, response surface methodology

References keywords
optimization(11), design(9), systems(7), multi(7), vintan(5), computing(5), objective(4), multiobjective(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 89 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04011
Web of Science Accession Number: 000417674300011
SCOPUS ID: 85035746256

Abstract
Quick view
Full text preview
This paper presents the analysis and multiobjective optimization of a magnetic actuator. By varying just 8 parameters of the magnetic actuators model the design space grows to more than 6 million configurations. Much more, the 8 objectives that must be optimized are conflicting and generate a huge objectives space, too. To cope with this complexity, we use advanced heuristic methods for Automatic Design Space Exploration. FADSE tool is one Automatic Design Space Exploration framework including different state of the art multi-objective meta-heuristics for solving NP-hard problems, which we used for the analysis and optimization of the COMSOL and MATLAB model of the magnetic actuator. We show that using a state of the art genetic multi-objective algorithm, response surface modelling methods and some machine learning techniques, the timing complexity of the design space exploration can be reduced, while still taking into consideration objective constraints so that various Pareto optimal configurations can be found. Using our developed approach, we were able to decrease the simulation time by at least a factor of 10, compared to a run that does all the simulations, while keeping prediction errors to around 1%.


References | Cited By  «-- Click to see who has cited this paper

[1] H. A. Calborean, "Multi-Objective Optimization of Advanced Computer Architectures using Domain- Knowledge," Ph.D. Thesis, "Lucian Blaga" University of Sibiu, Sibiu, 2011 (Ph.D. Supervisor: Prof. L. Vintan)

[2] L. Vintan, R. Chis, M. A. Ismail, C. Cotofana, "Improving Computing Systems Automatic Multiobjective Optimization Through Meta-Optimization, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems", Volume: 35, Issue: 7, July 2016,
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 1]


[3] R. Chis, L. Vintan, "Multi-Objective Hardware-Software Co-Optimization for the SNIPER Multi-Core Simulator", Proceedings of 10th International Conference on Intelligent Computer Communication and Processing, pp. 3-9, Cluj-Napoca, September 2014

[4] G. Palermo, C. Silvano, V. Zaccaria, "ReSPIR: A Response Surface-Based Pareto Iterative Refinement for Application-Specific Design Space Exploration", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Volume: 28, Issue: 12, Dec. 2009,
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 70]


[5] A. J. Keane, "Wing Optimization Using Design of Experiment, Response Surface, and Data Fusion Methods", Journal of Aircraft, Vol. 40, No. 4 (2003), pp. 741-750,
[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 87]


[6] P. V. Huong, N. N. Binh, "An approach to design embedded systems by multi-objective optimization", 2012 International Conference on Advanced Technologies for Communications (ATC), 10-12 Oct. 2012,
[CrossRef] [SCOPUS Times Cited 3]


[7] H. Shim, H. Moon, S. Wang, K. Hameyer, "Topology Optimization for Compliance Reduction of Magnetomechanical Systems", IEEE Transactions on Magnetics 44(3): 346 - 351, April 2008,
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]


[8] C. A. Borghi, P. Di Barba, M. Fabbri, A. Savini, "Loney’s Solenoid: A Multi-Ojective Optimization Problem" IEEE Trans. on Magnetics, vol. 35, no. 3, pp. 1706-1709, 1999

[9] H. Calborean, L. Vintan, "An Automatic Design Space Exploration Framework for Multicore Architecture Optimizations", Proceedings of the 9th IEEE RoEduNet International Conference, pp. 202-207, Sibiu, June 24-26, 2010

[10] J. J. Durillo, A. J. Nebro, "jMetal: A Java framework for multi-objective optimization," Adv. Eng. Softw., vol. 42, pp. 760-771, 2011

[11] R. Jahr, H. Calborean, L. Vintan, and T. Ungerer, "Boosting Design Space Explorations with Existing or Automatically Learned Knowledge," in Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance, 2012, pp. 221-235.

[12] E. Zitzler, L. Thiele, "Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach," Evol. Comput. IEEE Trans. On, vol. 3, no. 4, pp. 257-271, Nov. 1999

[13] E. Zitzler, "Evolutionary algorithms for multiobjective optimization: Methods and applications", vol. 63, Shaker Ithaca, 1999

[14] T. E. Carlson, W. Heirman, and L. Eeckhout, "Sniper: Exploring the level of abstraction for scalable and accurate parallel multi-core simulations," in International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Nov. 2011

[15] S. Uhrig, B. Shehan, R. Jahr, and T. Ungerer, "A Two-Dimensional Superscalar Processor Architecture," in Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATIONWORLD '09. Computation World: 2009, pp. 608-611

[16] "M-Sim: The Multithreaded Simulator." [Online] Available: Temporary on-line reference link removed - see the PDF document

[17] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," Evol. Comput. IEEE Trans. On, vol. 6, no. 2, pp. 182-197, Apr. 2002

[18] M. Abadi, P. Barham, J. Chen, Z. Chen et al., "TensorFlow: a system for large-scale machine learning", Proceeding OSDI'16 Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, Pages 265-283, Savannah, GA, USA - November 02 - 04, 2016

[19] C. Francois, "Keras", [Online] Available: Temporary on-line reference link removed - see the PDF document

[20] T. J. Santner, B. Williams, and W. Notz, "The Design and Analysis of Computer Experiments.", New York: Springer, 2003

[21] Stefan van der Walt, S. Chris Colbert and Gael Varoquaux, "The NumPy Array: A Structure for Efficient Numerical Computation", Computing in Science & Engineering, 13, 22-30 (2011),
[CrossRef] [Web of Science Times Cited 1205] [SCOPUS Times Cited 1362]


[22] D. P. Kingma, J. Ba, "Adam: A Method for Stochastic Optimization", 3rd International Conference for Learning Representations, San Diego, 2015, arXiv:1412.6980

[23] Deeplearning4j Development Team. Deeplearning4j: Open-source distributed deep learning for the JVM, Apache Software Foundation License 2.0. [Online] Available: Temporary on-line reference link removed - see the PDF document



References Weight

Web of Science® Citations for all references: 1,319 TCR
SCOPUS® Citations for all references: 1,531 TCR

Web of Science® Average Citations per reference: 55 ACR
SCOPUS® Average Citations per reference: 64 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-07-16 13:20 in 46 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2018
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: