Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.825
JCR 5-Year IF: 0.752
SCOPUS CiteScore: 2.5
Issues per year: 4
Current issue: Aug 2022
Next issue: Nov 2022
Avg review time: 77 days
Avg accept to publ: 48 days
APC: 300 EUR


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

2,005,337 unique visits
805,754 downloads
Since November 1, 2009



Robots online now
Googlebot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 22 (2022)
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
 Volume 20 (2020)
 
     »   Issue 4 / 2020
 
     »   Issue 3 / 2020
 
     »   Issue 2 / 2020
 
     »   Issue 1 / 2020
 
 
 Volume 19 (2019)
 
     »   Issue 4 / 2019
 
     »   Issue 3 / 2019
 
     »   Issue 2 / 2019
 
     »   Issue 1 / 2019
 
 
  View all issues  




SAMPLE ARTICLES

HPOFS: A High Performance and Secured OpenFlow Switch Architecture for FPGA, PHAM-QUOC, C., NGO, D.-M., THINH, T. N.
Issue 3/2019

AbstractPlus

Top-Down Approach to the Automatic Extraction of Individual Trees from Scanned Scene Point Cloud Data, NING, X., TIAN, G., WANG, Y.
Issue 3/2019

AbstractPlus

Quantum Steganography Using Two Hidden Thresholds, TUDORACHE, A.-G., MANTA, V., CARAIMAN, S.
Issue 4/2021

AbstractPlus

A Novel Approach to Speech Enhancement Based on Deep Neural Networks, SALEHI, M., MIRZAKUCHAKI, S.
Issue 2/2022

AbstractPlus

Peak Points Detection Using Spline Interpolation Based on FPGA Implementation, COLAK, A. M., MANABE, T., KAMASAKA, R., SHIBATA, Y., KUROKAWA, F.
Issue 4/2019

AbstractPlus

Structural Wall Facade Reconstruction of Scanned Scene in Point Clouds, NING, X., WANG, M., TANG, J., ZHANG, H., WANG, Y.
Issue 4/2021

AbstractPlus




LATEST NEWS

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

2021-Apr-15
Release of the v3 version of AECE Journal website. We moved to a new server and implemented the latest cryptographic protocols to assure better compatibility with the most recent browsers. Our website accepts now only TLS 1.2 and TLS 1.3 secure connections.

Read More »


    
 

  3/2022 - 1
View TOC | « Previous Article | Next Article »

On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing

RIOS, Y. See more information about RIOS, Y. on SCOPUS See more information about RIOS, Y. on IEEExplore See more information about RIOS, Y. on Web of Science, GARCIA-RODRIGUEZ, J. See more information about  GARCIA-RODRIGUEZ, J. on SCOPUS See more information about  GARCIA-RODRIGUEZ, J. on SCOPUS See more information about GARCIA-RODRIGUEZ, J. on Web of Science, SANCHEZ, E. See more information about  SANCHEZ, E. on SCOPUS See more information about  SANCHEZ, E. on SCOPUS See more information about SANCHEZ, E. on Web of Science, ALANIS, A. See more information about  ALANIS, A. on SCOPUS See more information about  ALANIS, A. on SCOPUS See more information about ALANIS, A. on Web of Science, RUIZ-VELAZQUEZ, E. See more information about  RUIZ-VELAZQUEZ, E. on SCOPUS See more information about  RUIZ-VELAZQUEZ, E. on SCOPUS See more information about RUIZ-VELAZQUEZ, E. on Web of Science, PARDO-GARCIA, A. See more information about PARDO-GARCIA, A. on SCOPUS See more information about PARDO-GARCIA, A. on SCOPUS See more information about PARDO-GARCIA, A. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
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 (3,680 KB) | Citation | Downloads: 535 | Views: 305

Author keywords
biomedical electronics, biomedical monitoring, fuzzy neural networks, neurocontrollers, virtual prototyping

References keywords
control(39), diabetes(29), systems(16), optimal(15), sanchez(13), neural(13), inverse(12), type(10), fuzzy(9), networks(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-08-31
Volume 22, Issue 3, Year 2022, On page(s): 3 - 14
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.03001
Web of Science Accession Number: 000861021000001
SCOPUS ID: 85137729315

Abstract
Quick view
Full text preview
Type 1 Diabetes Mellitus (T1DM) is one of the most adverse diseases in the modern era; its treatment is mainly based on exogenous insulin injections. The scientific community has formulated strategies to improve insulin supply using state-of-the-art technology. Therefore, this article develops a multi-age glycemic control scheme, which can be implemented in an Artificial Pancreas (AP) device to enhance diabetics treatment. The procedure is based on the implementation of a neuro-fuzzy inverse optimal control (NF-IOC) algorithm on the Texas Instrument LAUNCHXL-F28069M development board; this controller communicates with the Uva/Padova simulator for diabetics' patients of different ages under predefined meal protocols running on a Personal Computer (PC). The novelty lies in the proposed NF-IOC capability to regulate glucose within safe levels for virtual populations of 10 adults, 10 adolescents and, 10 children.


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

[1] Centers for disease control and prevention, National Diabetes Statistics Report, 2020. Estimates of diabetes and its burden in the United States, National Center for Chronic Disease Prevention and Health Promotion. USA. (1) (2020) 1-32

[2] American Diabetes Association, Economic costs of diabetes in the U.S. in 2017, Diabetes Care 41 (5) (2018) 917-928.
[CrossRef] [Web of Science Times Cited 1061] [SCOPUS Times Cited 1147]


[3] S. Kim, "Burden of hospitalizations primarily due to uncontrolled diabetes: Implications of inadequate primary health care in the United States". Diabetes Care 1 May 2007. 30 (5): 1281-1282.
[CrossRef] [Web of Science Times Cited 86] [SCOPUS Times Cited 97]


[4] J. B. Brown, K. L. Pedula, A. W. Bakst, "The progressive cost of complications in Type 2 diabetes Mellitus". Archives of Internal Medicine 1999; 159(16): 1873-1880.
[CrossRef] [Web of Science Times Cited 168] [SCOPUS Times Cited 194]


[5] D. M. Nathan, S. Genuth, J. Lachin, P. Cleary, O. Crofford, M. Davis, L. Rand, C. Siebert, "The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus," The New England Journal of Medicine. The Diabetes Control and Complications Trial Research Group, 1993; 329(14): 977-986.
[CrossRef] [Web of Science Times Cited 17411] [SCOPUS Times Cited 22560]


[6] H.-C. Yeh, T. T. Brown, N. Maruthur, et al. "Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus: A systematic review and meta-analysis," Annals of Internal Medicine. 2012; 157: 336-347.
[CrossRef] [Web of Science Times Cited 273] [SCOPUS Record]


[7] Y. Rios, J. A. Garcia-Rodriguez, O. Sanchez, E. Sanchez, A. Alanis, E. Ruiz-Velazquez, N. Arana-Daniel, "Inverse optimal control using a neural multi-step predictor for T1DM treatment," 2018 International Joint Conference on Neural Networks (IJCNN), 2018, pp. 1-8,
[CrossRef]


[8] A. Y. Alanis, Y. Rios, J. A. Garcia-Rodriguez, E. Sanchez, E. Ruiz-Velazquez, A. P. Garcia, "Neuro-fuzzy inverse optimal control incorporating a multistep predictor as applied to T1DM patients," Control Applications for Biomedical Engineering Systems. Academic Press. 2020, Pages 1-24, ISBN 9780128174616.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[9] Y. Rios, J. A. Garcia-Rodriguez, E. Sanchez, A. Y. Alanis, E. Ruiz-Velazquez, "Rapid prototyping of neuro-fuzzy inverse optimal control as applied to T1DM patients," 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI), IEEE, Guadalajara, November 7-9, 2018, pp. 1-5.
[CrossRef] [SCOPUS Times Cited 4]


[10] P. Pesl et al., "An advanced bolus calculator for type 1 diabetes: System architecture and usability results," in IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 1, pp. 11-17, Jan. 2016.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 34]


[11] K. Turksoy, S. Samadi, J. Feng, E. Littlejohn, L. Quinn, A. Cinar, "Meal detection in patients with type 1 diabetes: A new module for the multivariable adaptive artificial pancreas control system," in IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 1, pp. 47-54, Jan. 2016.
[CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 89]


[12] H. Thabit, R. Hovorka, "Coming of age: the artificial pancreas for type 1 diabetes," Diabetologia 59, 1795-1805 (2016).
[CrossRef] [Web of Science Times Cited 131] [SCOPUS Times Cited 158]


[13] A. C. van Bon, Y. M. Luijf, R. Koebrugge, R. Koops, J. B. L. Hoekstra, J. H. DeVries, "Feasibility of a portable bihormonal closed-loop system to control glucose excursions at home under free-living conditions for 48 hours," Diabetes Technology & Therapeutics. Mar 2014. 131-136.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 67]


[14] J. Kropff, et al., "2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: a randomised crossover trial," The Lancet Diabetes & Endocrinology 3 (2) (2015) 939-947.
[CrossRef] [Web of Science Times Cited 145] [SCOPUS Times Cited 172]


[15] E. Quintero-Manriquez, E. N. Sanchez, R. G. Harley, S. Li, R. A. Felix, "Neural inverse optimal control implementation for induction motors via rapid control prototyping," in IEEE Transactions on Power Electronics, vol. 34, no. 6, pp. 5981-5992, June 2019.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 20]


[16] R. Ruiz-Cruz, E. N. Sanchez, A. Loukianov, J. A. Ruz-Hernandez, "Real-time neural inverse optimal control for a wind generator," in IEEE Transactions on Sustainable Energy, vol. 10, no. 3, pp. 1172-1183, July 2019.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 22]


[17] K. Kelly, E. N. Sanchez, A. Coronado, G. V. Zuniga, B. Sulbaran, L. Breton-Deval, "Inverse optimal neural control via passivity approach for nonlinear anaerobic bioprocesses with biofuels production," Optimal Control Applications and Methods.

[18] V. M. Chan, E. A. Hernandez-Vargas, E. N. Sanchez, "Neural inverse optimal control applied to design therapeutic options for patients with COVID-19," 2021 International Joint Conference on Neural Networks (IJCNN). 2021, pp. 1-7.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]


[19] R. E. Precup, M. Tomescu, S. Preitl, "Lorenz system stabilization using fuzzy controllers," International Journal of Computers Communications and Control Vol. II. 2007. 279-287.
[CrossRef] [Web of Science Times Cited 52]


[20] T. Chen, A. Babanin, A. Muhammad, B. Chapron, C. Chen, "Modified evolved bat algorithm of fuzzy optimal control for complex nonlinear systems," Romanian Journal of Information Science and Technology (ROMJIST). Volume 23, No. T, 2020, pp. T28-T40, Paper no. 672/2020

[21] A. Karahoca, D. Karahoca, A. Kara, "Diagnosis of diabetes by using adaptive neuro fuzzy inference systems," 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009, pp. 1-4.
[CrossRef] [SCOPUS Times Cited 11]


[22] O. Geman, I. Chiuchisan, R. Toderean, "Application of adaptive neuro-fuzzy inference system for diabetes classification and prediction," 2017 E-Health and Bioengineering Conference (EHB), 2017, pp. 639-642.
[CrossRef] [SCOPUS Times Cited 25]


[23] L. Stavros, M. Ludmil, "Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases," Artificial Intelligence in Medicine. Volume 50, Issue 2, 2010, Pages 117-126.
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 72]


[24] A. Nath, R. Dey, V. E. Balas, "Closed loop blood glucose regulation of type 1 diabetic patient using Takagi-Sugeno fuzzy logic control," In Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 634. Springer, Cham.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 10]


[25] Guidance for Industry and Food and Drug Administration Staff. "The content of Investigational Device Exemption (IDE) and Premarket Approval (PMA) Applications for artificial pancreas device systems". Center for Devices and Radiological Health. 2012. Docket Number: FDA-2011-D-0464. Pages 1-63

[26] S. Trevitt, S. Simpson, A. Wood, "Artificial pancreas device systems for the closed-loop control of type 1 diabetes: What systems are in development?," Journal of Diabetes Science and Technology 10, no. 3. May 2016: 714-23.
[CrossRef] [SCOPUS Times Cited 114]


[27] A. Cinar, "Artificial pancreas systems: An introduction to the special issue," in IEEE Control Systems Magazine, vol. 38, no. 1, pp. 26-29, Feb. 2018.
[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 27]


[28] C. Dalla Man, F. Micheletto, D. Lv, M. Breton, B. Kovatchev, C. Cobelli, "The UVA/PADOVA type 1 diabetes simulator: New features". Journal of Diabetes Science and Technology 8, no. 1 January 2014: 26-34.
[CrossRef] [SCOPUS Times Cited 438]


[29] K. Boris, R. Davide, B. Marc, S. Patek, C. Cobelli, "In silico testing and in vivo experiments with closed-loop control of blood glucose in diabetes," IFAC Proceedings Volumes, Volume 41, Issue 2, 2008, pp 4234-4239.
[CrossRef]


[30] B. Kovatchev, M. D. Breton, C. Dalla Man, C. Cobelli, "In silico preclinical trials: A proof of concept in closed-loop control of type 1 diabetes," Journal of Diabetes Science and Technology 3, no. 1 January 2009: 44-55.
[CrossRef] [SCOPUS Times Cited 569]


[31] G. Rovithakis, C. Manolis, "Adaptive control with recurrent high-order neural networks," Advances in Industrial Control. Springer-Verlag London 2000. pp 1-28.
[CrossRef]


[32] A. Alanis, E. Sanchez, A. Loukianov, "Discrete-time adaptive backstepping nonlinear control via high-order neural networks," in IEEE Transactions on Neural Networks, vol. 18, no. 4, pp. 1185-1195, July 2007.
[CrossRef] [Web of Science Times Cited 121] [SCOPUS Times Cited 147]


[33] F.-J. Chang, Y.-M. Chiang, L.-C. Chang, "Multi-step-ahead neural networks for flood forecasting," Hydrological Sciences Journal, 52:1, 114-130, 2007.
[CrossRef] [Web of Science Times Cited 97] [SCOPUS Times Cited 112]


[34] P.-A. Chen, L.-C. Chang, F.-J. Chang, "Reinforced recurrent neural networks for multi-step-ahead flood forecasts," Journal of Hydrology, Volume 497, 2013, pp 71-79, ISSN 0022-1694,
[CrossRef] [Web of Science Times Cited 87] [SCOPUS Times Cited 102]


[35] F. Randy, K. Petar, "Robust nonlinear control design," Modern Birkhauser Classics Series. Birkhauser. Boston 1996. pp 65-100.
[CrossRef]


[36] D. E. Kirk, "Optimal control theory: An introduction," Dover publications, Inc. Mineola New York, 2004

[37] T. Basar, G. J. Olsder, "Dynamic noncooperative game theory," 1999, Second edition. Society for Industrial and Applied Mathematics, New Brunswick, NJ, ISBN: 9780898714296, p. 526.
[CrossRef]


[38] T. Ohsawa, A. M. Bloch, M. Leok, "Discrete Hamilton-Jacobi theory and discrete optimal control," 49th IEEE Conference on Decision and Control (CDC), 2010, pp. 5438-5443,
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 38]


[39] A. Al-Tamimi, F. L. Lewis, M. Abu-Khalaf, "Discrete-time nonlinear HJB solution using approximate dynamic programming: convergence proof," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 38, no. 4, pp. 943-949, Aug. 2008.
[CrossRef] [Web of Science Times Cited 685] [SCOPUS Times Cited 766]


[40] E. Sanchez, F. Ornelas-Tellez, "Discrete-time inverse optimal control for nonlinear systems," 1st edition. Taylor & Francis Edition. CRC Press. Boca Raton. 2013.
[CrossRef] [SCOPUS Times Cited 34]


[41] T. Takagi, M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-15, no. 1, pp. 116-132, Jan.-Feb. 1985.
[CrossRef] [Web of Science Times Cited 12934] [SCOPUS Times Cited 16180]


[42] B. Leon, A. Alanis, E. Sanchez, F. Ornelas-Tellez, E. Ruiz-Velazquez, "Neural inverse optimal control applied to type 1 diabetes mellitus patients," Analog Integrated Circuits and Signal Processing. 2013. 76,343-352.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]


[43] W. Li, E. Todorov, D. Liu, "Inverse optimality design for biological movement systems," IFAC Proceedings Volumes. Volume 44, Issue 1, 2011, pp 9662-9667.
[CrossRef] [SCOPUS Times Cited 23]


[44] F. Ornelas, E. Sanchez, A. Loukianov, "Discrete-time nonlinear systems inverse optimal control: A control Lyapunov function approach," 2011 IEEE International Conference on Control Applications (CCA), 2011, pp. 1431-1436.
[CrossRef] [SCOPUS Times Cited 32]


[45] F. Ornelas-Tellez, E. Sanchez, A. Loukianov, E. Navarro-López, "Speed-gradient inverse optimal control for discrete-time nonlinear systems," 2011 50th IEEE Conference on Decision and Control and European Control Conference, 2011, pp. 290-295.
[CrossRef] [SCOPUS Times Cited 27]


[46] Institute of Medicine. "Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids," 2005. Washington, DC: The National Academies Press.
[CrossRef] [SCOPUS Times Cited 2997]




References Weight

Web of Science® Citations for all references: 33,567 TCR
SCOPUS® Citations for all references: 46,299 TCR

Web of Science® Average Citations per reference: 714 ACR
SCOPUS® Average Citations per reference: 985 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 2022-11-24 22:26 in 278 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 ©2001-2022
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: