Comparison of pH data measured with a pH sensor array using different data fusion methods

Sensors (Switzerland)

Volumn 12, Issue 9, 2012, Pages 12098-12109

  Liao, Yi Hung ^a   Chou, Jung Chuan ^b  

^a TRANSWORLD UNIVERSITY   (Taiwan)

^b NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Data fusion; Electrochemical; Ruthenium dioxide; Sensor array

Indexed keywords

DATA FUSION METHODS; ELECTROCHEMICAL; ELECTROCHEMICAL MEASUREMENTS; GRAPE WINE; MEASURED RESULTS; PH ELECTRODE; RUTHENIUM DIOXIDE; SENSING MEMBRANES;

PH EFFECTS; PH SENSORS; RUTHENIUM ALLOYS; SENSOR ARRAYS; SENSORS;

DATA FUSION;

EID: 84866990026     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s120912098     Document Type: Article

References (15)

1. Klein, L.A. Sensor Technologies and Data Requirements for ITS; Artech House: Norwood, MA, USA, 2001; pp. 399-497.
2. Sharma, V.S.; Raju, S. Multisensor data fusion and decision support for airborne target identification. IEEE Trans. Syst. Man Cybern. 1991, 21, 1224-1230.
3. Zhou, Y.; Li, H.S.; Ding, Y.Z. Self-adaptive sensor weighted data fusion in strain detection. In Proceedings of the 8th International Conference on Electronic Measurement and Instruments, Hefei, Anhui, China, 16-18 August 2007; pp. 4:55-4:58.
4. Zhang, Q.Y.; Sun, L.; Jin, Y.F. Description of key performance index states based on cloud theory and weighted mean method for multi-sensor. In Proceedings of the 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, Qingdao, Shandong, China, 30 July-1 August 2007; pp. 350-354.
5. Gao, S.; Feng, Z.; Li, H. The research of data fusion method for sample mean random weighting estimation. In Proceedings of the 2006 IEEE International Conference on Information Acquisition, Weihai, Shandong, China, 20-23 August 2006; pp. 584-588.
6. Guo, L.; Chen, X.; Hu, S. Fusion tracking algorithm based on stochastic approximation. In Proceedings of the 2006 IEEE International Conference on Information Acquisition, Weihai, Shandong, China, 20-23 August 2006; pp. 802-807.
7. Zhang, J.W.; Wang, K.Q.; Yue, Q. Data fusion algorithm based on functional link artificial neural networks. In Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, 21-23 June 2006; pp. 2806-2810.
8. Wan, S.P. Method based on the ratio of signal to noise for multi-sensor data fusion. Chin. J. Sens. Actuators 2008, 21, 178-181.
9. Wan, S.P. Fusion method for fuzzy sensor data based on coefficient of variance. Transducer Microsyst. Technol. 2008, 27, 63-65.
10. Li, J.Z.; Li, J.B.; Shi, S.F. Concepts, issues and advance of sensor networks and data management of sensor networks. J. Softw. 2003, 14, 1717-1727.
11. Wang, T.J.; Yang, Z.; Liu, G.Q. A mobile-agent-based adaptive data fusion algorithm for multiple signal ensembles in wireless sensor networks. Wirel. Sens. Netw. 2009, 1, 458-466.
12. Xia, Y.S.; Leung, H.; Bossé, E. Neural data fusion algorithms based on a linearly constrained least square method. IEEE Trans. Neural Netw. 2002, 13, 320-329.
13. Wei, X.M. Design and implementation of multi-sensor data fusion simulation platform. Adv. Intell. Soft Comput. 2012, 112, 11-16.
14. Zakaria, A.; Shakaff, A.Y.M.; Adom, A.H.; Ahmad, M.; Masnan, M.J.; Aziz, A.H.A.; Fikri, N.A.; Abdullah, A.H.; Kamarudin, L.M. Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors. Sensors 2010, 10, 8782-8796.
15. Liao, Y.H.; Chou, J.C. Weighted data fusion use for ruthenium dioxide thin film pH array electrodes. IEEE Sens. J. 2009, 9, 842-848.