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JCR Impact Factor: 0.699
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Issues per year: 4
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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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.

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.

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.

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  4/2018 - 7

Redesign of Morphing UAV for Simultaneous Improvement of Directional Stability and Maximum Lift/Drag Ratio

ARIK, S. See more information about ARIK, S. on SCOPUS See more information about ARIK, S. on IEEExplore See more information about ARIK, S. on Web of Science, TURKMEN, I. See more information about  TURKMEN, I. on SCOPUS See more information about  TURKMEN, I. on SCOPUS See more information about TURKMEN, I. on Web of Science, OKTAY, T. See more information about OKTAY, T. on SCOPUS See more information about OKTAY, T. on SCOPUS See more information about OKTAY, T. on Web of Science
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Download PDF pdficon (1,475 KB) | Citation | Downloads: 141 | Views: 333

Author keywords
unmanned aerial vehicles, stability, artificial intelligence, neural networks, optimization

References keywords
neural(9), optimization(8), design(7), technology(5), networks(5), aerospace(5), performance(4), oktay(4), morphing(4), lift(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 57 - 62
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04007
Web of Science Accession Number: 000451843400007
SCOPUS ID: 85058778064

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This paper presents a novel method based on the artificial intelligence to redesign of morphing Unmanned Aerial Vehicle (UAV) for improvement of index consisting of directional stability and maximum lift/drag (L/D) ratio. In this study, Artificial Neural Network (ANN) based objective function is optimized with Artificial Bee Colony (ABC) algorithm. Firstly, the sweep angle is selected as input parameter and directional stability and maximum L/D ratio are selected as output parameters for ANN. ANN is trained with a small number of data obtained by the computational fluid dynamics method and the trained ANN is used for multiplying these data. Two ABC optimization algorithms with different objective functions are used to improve the index consisting of directional stability and maximum L/D ratio: While the first is used the adjustment of the ANN weights, the second is used the optimization of the ANN based objective function. Simulation results realized with limited data show that although directional stability and maximum L/D ratio have inverse relation, they are optimized equally and simultaneously. Thus, the artificial intelligence techniques provide fast and accurate determination of the optimal aerodynamic shape of UAV without time consuming and complexity caused by theoretical calculations.

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

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[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 6]

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[10] N. R. Secco, B. S. de Mattos, "Artificial neural networks to predict aerodynamic coefficients of transport airplanes," Aircraft Engineering and Aerospace Technology, vol. 89, no. 2, pp. 211-230, 2017.
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[19] T. Oktay, S. Coban, "Simultaneous Longitudinal and Lateral Flight Control Systems Design for Both Passive and Active Morphing TUAVs," Elektronika ir Elektrotechnika, vol. 23, no. 5, pp. 15-20, 2017.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]

[20] T. Oktay, M. Konar, M. A. Mohamed, M. Aydin, F. Sal, M. Onay, M. Soylak, "Autonomous flight performance improvement of load-carrying unmanned aerial vehicles by active morphing," International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, vol. 10, no. 1, pp. 123-132, 2016.

[21] T. Oktay, S. Arik, I. Turkmen, M. Uzun, H. Çelik, "Neural network based redesign of morphing UAV for simultaneous improvement of roll stability and maximum lift/drag ratio," Aircraft Engineering and Aerospace Technology, 2018.
[CrossRef] [Web of Science Times Cited 1]

[22] T. Oktay, M. Uzun, "Aerodynamic Tailcone Shape Optimization for Autonomous Navigation Performance Maximization of Morphing Aerial Robot," presented at the International Conference on Engineering and Natural Science, Sarajevo, 2016.

References Weight

Web of Science® Citations for all references: 2,904 TCR
SCOPUS® Citations for all references: 4,056 TCR

Web of Science® Average Citations per reference: 126 ACR
SCOPUS® Average Citations per reference: 176 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 2019-05-20 02:24 in 75 seconds.

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Faculty of Electrical Engineering and Computer Science
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