Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy

Zequn Zhu; Zhuye Zhang; Sansan Xiao; Jiangxia Zou; Xinyu Hou; Zhengying Cai

doi:doi:10.11648/j.ajnc.20160506.12

| Peer-Reviewed

Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy

Zequn Zhu, Zhuye Zhang, Sansan Xiao, Jiangxia Zou, Xinyu Hou, Zhengying Cai

Published in American Journal of Networks and Communications (Volume 5, Issue 6)

Received: 24 October 2016 Accepted: 7 November 2016 Published: 26 December 2016

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Fluid sensor network is very difficult to make context-awareness and learning fusion because there is a variety of complex dynamic uncertainties involved ranging from information redundancy, information complementary, to information instability. This paper introduces a fuzzy entropy method into context-awareness and learning fusion method of fluid property sensor networks. First, the architecture of fluid property sensor network is analyzed, and based on it the context characteristics are described. Second, by the introduction of fuzzy entropy, the learning fusion method of fluid property sensor networks is proposed, where the fusion hierarchy of context information is discussed and the fusion algorithm is also illustrated. Third, an example is presented for verification of the proposed model, where the multiple sensor information fusion based on fuzzy logic analysis method can effectively tackle uncertain information. At last, some interesting conclusions are carried out and future researching directions are also indicated at the end of the paper.

Published in	American Journal of Networks and Communications (Volume 5, Issue 6)
DOI	10.11648/j.ajnc.20160506.12
Page(s)	128-138
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Fluid Properties Sensor, Context Awareness, Learning Fusion, Fuzzy Entropy, Uncertain Information

References

[1]	Pajares Martinsanz, Gonzalo, Sensors for Fluid Leak Detection, Sensors, 15 (2015) 3830-3833.
[2]	Dey, Kajal Kumar; Bhatnagar, Divyanshu; Srivastava, Avanish Kumar, VO2 nanorods for efficient performance in thermal fluids and sensors, Nanoscale, 7 (2015) 6159-6172.
[3]	He, Yonglin; Liao, Shenglong; Jia, Hanyu, A Self-Healing Electronic Sensor Based on Thermal-Sensitive Fluids, Advanced Materials,27 (2015) 4622-4627.
[4]	Matzeu, Giusy; Florea, Larisa; Diamond, Dermot, Advances in wearable chemical sensor design for monitoring biological fluids, SENSORS And Actuators B-Chemical,2 11 (2015) 403-418.
[5]	Amrehn, Sabrina; Wu, Xia; Schumacher, Christian, Photonic crystal-based fluid sensors: Toward practical application, Physica Status Solidi A-Applications And Materials Science, 212 (2015) 1266-1272.
[6]	Schultz, Joshua A.; Heinrich, Stephen M.; Josse, Fabien, Lateral-Mode Vibration of Microcantilever-Based Sensors in Viscous Fluids Using Timoshenko Beam Theory, Journal Of Microelectromechanical Systems, 24 (2015) 848-860.
[7]	Kulapina, O. I.; Makarova, N. M.; Kulapina, E. G, Potentiometric sensors for the determination of some cephalosporin antibiotics in biological fluids and medicinal preparations, Journal Of Analytical Chemistry, 70 (2015) 477-484.
[8]	Kamel, Ayman H.; Galal, Hoda R, MIP-Based Biomimetic Sensors for Static and Hydrodynamic Potentiometric Transduction of Sitagliptin in Biological Fluids, International Journal Of Electrochemical Science, 9 (2014) 4361-4373.
[9]	Ali, Mohammed; Barman, Koushik; Jasimuddin, Sk, Fluid interface-mediated nanoparticle membrane as an electrochemical sensor, Rsc Advances, 4 (2014) 61404-61408.
[10]	Xie, Jun; Li, Decai; Xing, Yansi, Parameters optimization of magnetic fluid micro-pressure sensor, Sensors And Actuators A-Physical, 235 (2015) 194-202.
[11]	Varma, Sarvesh; Voldman, Joel, A cell-based sensor of fluid shear stress for microfluidics, Lab On A Chip, 15 (2015) 1563-1573.
[12]	Li, Jianhua; Wang, Rong; Wang, Jingyuan, Novel magnetic field sensor based on magnetic fluids infiltrated dual-core photonic crystal fibers, Optical Fiber Technology, 20 (2014) 100-105.
[13]	Rosy; Yadav, Saurabh K.; Agrawal, Bharati, Graphene modified Palladium sensor for electrochemical analysis of norepinephrine in pharmaceuticals and biological fluids, Electrochimica Acta, 125 (2014) 622-629.
[14]	Fernandes, Luis Andre L.; Azadmehr, Mehdi; Johannessen, Erik A, An Osmotic Pressure Sensor for Monitoring the Level of Hydration in Biological Fluids, Ieee Sensors Journal, 16 (2016) 4331-4337.
[15]	Bley, Torsten; Steffensky, Joerg; Mannebach, Horst, Degradation monitoring of aviation hydraulic fluids using non-dispersive infrared sensor systems, Sensors And Actuators B-Chemical, 224 (2016) 539-546.
[16]	Tadi, Kiran Kumar; Motghare, Ramani V, Voltammetric Determination of Pindolol in Biological Fluids Using Molecularly Imprinted Polymer Based Biomimetic Sensor, Journal Of The Electrochemical Society, 163 (2016) B286-B292.
[17]	Yadav, Saurabh K.; Rosy; Oyama, Munetaka, A Biocompatible Nano Gold Modified Palladium Sensor for Determination of Dopamine in Biological Fluids, Journal Of The Electrochemical Society,161 (2014) H41-H46.
[18]	Pattar, Vijay P.; Nandibewoor, Sharanappa T, Polybenzoin Based Sensor for Determination of 2thiouracil in Biological Fluids and Pharmaceutical Formulations, Journal Of The Chinese Chemical Society, 62 (2015) 287-295.
[19]	Gupta, Pankaj; Yadav, Saurabh K.; Goyal, Rajendra N, A Sensitive Polymelamine Modified Sensor for the Determination of Lomefloxacin in Biological Fluids, Journal Of The Electrochemical Society, 162 (2015) H86-H92.
[20]	Stefan-van Staden, Raluca-Ioana; Moldoveanu, Iuliana, Multimode Sensors Based on Nanostructured Materials for Simultaneous Screening of Biological Fluids for Specific Breast Cancer and Hepatitis B Biomarkers, Journal Of The Electrochemical Society, 161 (2014) B45-B48.
[21]	Alarfaj, Nawal A.; El-Tohamy, Maha F, Construction and Validation of New Electrochemical Carbon Nanotubes Sensors for Determination of Acebutolol Hydrochloride in Pharmaceuticals and Biological Fluids, Journal Of The Chinese Chemical Society, 61 (2014) 910-920.
[22]	Schmitt, B.; Kiefer, C.; Schuetze, A, Microthermal sensors for determining fluid composition and flow rate in fluidic systems, Microsystem Technologies-Micro-And Nanosystems-Information Storage And Processing Systems, 20 (2014) 641-652.
[23]	Couto, Rosa A. S.; Lima, Jose L. F. C.; Quinaz, M. Beatriz, Screen-printed Electrode Based Electrochemical Sensor for the Detection of Isoniazid in Pharmaceutical Formulations and Biological Fluids, International Journal Of Electrochemical Science, 10 (2015) 8738-8749.
[24]	Raj, Mamta; Gupta, Pankaj; Goyal, Rajendra N, Poly-Melamine Film Modified Sensor for the Sensitive and Selective Determination of Propranolol, a beta-blocker in Biological Fluids, Journal Of The Electrochemical Society, 163 (2016) H388-H394.
[25]	Afkhami, Abbas; Soltani-Felehgari, Farzaneh; Madrakian, Tayyebeh, A sensitive electrochemical sensor for rapid determination of methadone in biological fluids using carbon paste electrode modified with gold nanofilm, Talanta, 128 (2014) 203-210.

Cite This Article

Plain Text BibTeX RIS

APA Style

Zequn Zhu, Zhuye Zhang, Sansan Xiao, Jiangxia Zou, Xinyu Hou, et al. (2016). Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy. American Journal of Networks and Communications, 5(6), 128-138. https://doi.org/10.11648/j.ajnc.20160506.12

Copy | Download

ACS Style

Zequn Zhu; Zhuye Zhang; Sansan Xiao; Jiangxia Zou; Xinyu Hou, et al. Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy. Am. J. Netw. Commun. 2016, 5(6), 128-138. doi: 10.11648/j.ajnc.20160506.12

Copy | Download

AMA Style

Zequn Zhu, Zhuye Zhang, Sansan Xiao, Jiangxia Zou, Xinyu Hou, et al. Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy. Am J Netw Commun. 2016;5(6):128-138. doi: 10.11648/j.ajnc.20160506.12

Copy | Download

@article{10.11648/j.ajnc.20160506.12,
  author = {Zequn Zhu and Zhuye Zhang and Sansan Xiao and Jiangxia Zou and Xinyu Hou and Zhengying Cai},
  title = {Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy},
  journal = {American Journal of Networks and Communications},
  volume = {5},
  number = {6},
  pages = {128-138},
  doi = {10.11648/j.ajnc.20160506.12},
  url = {https://doi.org/10.11648/j.ajnc.20160506.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20160506.12},
  abstract = {Fluid sensor network is very difficult to make context-awareness and learning fusion because there is a variety of complex dynamic uncertainties involved ranging from information redundancy, information complementary, to information instability. This paper introduces a fuzzy entropy method into context-awareness and learning fusion method of fluid property sensor networks. First, the architecture of fluid property sensor network is analyzed, and based on it the context characteristics are described. Second, by the introduction of fuzzy entropy, the learning fusion method of fluid property sensor networks is proposed, where the fusion hierarchy of context information is discussed and the fusion algorithm is also illustrated. Third, an example is presented for verification of the proposed model, where the multiple sensor information fusion based on fuzzy logic analysis method can effectively tackle uncertain information. At last, some interesting conclusions are carried out and future researching directions are also indicated at the end of the paper.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy
AU  - Zequn Zhu
AU  - Zhuye Zhang
AU  - Sansan Xiao
AU  - Jiangxia Zou
AU  - Xinyu Hou
AU  - Zhengying Cai
Y1  - 2016/12/26
PY  - 2016
N1  - https://doi.org/10.11648/j.ajnc.20160506.12
DO  - 10.11648/j.ajnc.20160506.12
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 128
EP  - 138
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20160506.12
AB  - Fluid sensor network is very difficult to make context-awareness and learning fusion because there is a variety of complex dynamic uncertainties involved ranging from information redundancy, information complementary, to information instability. This paper introduces a fuzzy entropy method into context-awareness and learning fusion method of fluid property sensor networks. First, the architecture of fluid property sensor network is analyzed, and based on it the context characteristics are described. Second, by the introduction of fuzzy entropy, the learning fusion method of fluid property sensor networks is proposed, where the fusion hierarchy of context information is discussed and the fusion algorithm is also illustrated. Third, an example is presented for verification of the proposed model, where the multiple sensor information fusion based on fuzzy logic analysis method can effectively tackle uncertain information. At last, some interesting conclusions are carried out and future researching directions are also indicated at the end of the paper.
VL  - 5
IS  - 6
ER  -

Copy | Download

Author Information

Zequn Zhu

College of Computer and Information Technology, China Three Gorg
Zhuye Zhang

School of Law and Public Administration, China Three Gorges Univ
Sansan Xiao

College of Computer and Information Technology, China Three Gorg
Jiangxia Zou

School of Foreign Languages, China Three Gorges University, Yich
Xinyu Hou

School of Foreign Languages, China Three Gorges University, Yich
Zhengying Cai

College of Computer and Information Technology, China Three Gorg

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Zequn Zhu, Zhuye Zhang, Sansan Xiao, Jiangxia Zou, Xinyu Hou, et al. (2016). Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy. American Journal of Networks and Communications, 5(6), 128-138. https://doi.org/10.11648/j.ajnc.20160506.12

Copy | Download

ACS Style

Zequn Zhu; Zhuye Zhang; Sansan Xiao; Jiangxia Zou; Xinyu Hou, et al. Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy. Am. J. Netw. Commun. 2016, 5(6), 128-138. doi: 10.11648/j.ajnc.20160506.12

Copy | Download

AMA Style

Zequn Zhu, Zhuye Zhang, Sansan Xiao, Jiangxia Zou, Xinyu Hou, et al. Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy. Am J Netw Commun. 2016;5(6):128-138. doi: 10.11648/j.ajnc.20160506.12

Copy | Download

@article{10.11648/j.ajnc.20160506.12,
  author = {Zequn Zhu and Zhuye Zhang and Sansan Xiao and Jiangxia Zou and Xinyu Hou and Zhengying Cai},
  title = {Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy},
  journal = {American Journal of Networks and Communications},
  volume = {5},
  number = {6},
  pages = {128-138},
  doi = {10.11648/j.ajnc.20160506.12},
  url = {https://doi.org/10.11648/j.ajnc.20160506.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20160506.12},
  abstract = {Fluid sensor network is very difficult to make context-awareness and learning fusion because there is a variety of complex dynamic uncertainties involved ranging from information redundancy, information complementary, to information instability. This paper introduces a fuzzy entropy method into context-awareness and learning fusion method of fluid property sensor networks. First, the architecture of fluid property sensor network is analyzed, and based on it the context characteristics are described. Second, by the introduction of fuzzy entropy, the learning fusion method of fluid property sensor networks is proposed, where the fusion hierarchy of context information is discussed and the fusion algorithm is also illustrated. Third, an example is presented for verification of the proposed model, where the multiple sensor information fusion based on fuzzy logic analysis method can effectively tackle uncertain information. At last, some interesting conclusions are carried out and future researching directions are also indicated at the end of the paper.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy
AU  - Zequn Zhu
AU  - Zhuye Zhang
AU  - Sansan Xiao
AU  - Jiangxia Zou
AU  - Xinyu Hou
AU  - Zhengying Cai
Y1  - 2016/12/26
PY  - 2016
N1  - https://doi.org/10.11648/j.ajnc.20160506.12
DO  - 10.11648/j.ajnc.20160506.12
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 128
EP  - 138
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20160506.12
AB  - Fluid sensor network is very difficult to make context-awareness and learning fusion because there is a variety of complex dynamic uncertainties involved ranging from information redundancy, information complementary, to information instability. This paper introduces a fuzzy entropy method into context-awareness and learning fusion method of fluid property sensor networks. First, the architecture of fluid property sensor network is analyzed, and based on it the context characteristics are described. Second, by the introduction of fuzzy entropy, the learning fusion method of fluid property sensor networks is proposed, where the fusion hierarchy of context information is discussed and the fusion algorithm is also illustrated. Third, an example is presented for verification of the proposed model, where the multiple sensor information fusion based on fuzzy logic analysis method can effectively tackle uncertain information. At last, some interesting conclusions are carried out and future researching directions are also indicated at the end of the paper.
VL  - 5
IS  - 6
ER  -

Copy | Download