A Fuzzy Logic-Based Approach for Multi-Places and Multi-Sensors Data Fusion

Abstract:

According to the features and needs of collecting the data of temperature and humidity of current industrial environment, and the multi-places and multi-sensors data fusion as well, this paper proposes a fuzzy logic-based approach for the multi-places and multi-sensors data fusion. Firstly, this paper introduces the hardware construction of monitor system of temperature and humidity. Then, based on fuzzy set theory, the paper describes the model of fuzzy synthetic evaluation, and then proposes a novel algorithm for choosing the weights assignment proposals. Finally, a multi-places and multi-sensors data fusion approach, which is based on fuzzy synthetic evaluation, is presented. An example is also used for demonstrating the proposed approach. The results of system implementation identify that the approach can remove the influence of incorrect data on evaluating the temperature and humidity of current environment, and the data fusion result is objective and correct as well.

• Periodical: Applied Mechanics and Materials (Volumes 241-244)
• Pages: 2524-2527
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.241-244.2524
• Online since: December 2012
• Authors: Yi Lv
• Keywords: Data Fusion, Fuzzy Logic, Sensor, Synthetic Evaluation

References

[1] Q. Zhui, S. Yu. Ds evidence theory based data fusion algorithm and its application in circuit fault diagnosis,. E-Journal, 2002, 30(2): 221-223.

[2] M. Qiu, X. Liao. Theory of evidence in the vehicle identification of the applied research, Computer Engineering and Applications, 2007, 43(2): 241-244.

[3] J. L. Wang, J. Y. Zhang. Based on statistical evidence of the mass function and DS evidence theory of multi-sensor target recognition, Journal of Transducer Technology, 2006, 19(3): 862-864.

[4] J. Chanussot, G. Mauris, P. Lambert. Fuzzy fusion techniques for linear features detection in multi -temporal SAR images, IEEE Trans. on Geosci and Remote Sensing, 1999, 37(3): 1292-1305.

DOI: 10.1109/36.763290

[5] J. Y. Shen, X, Wu. A fuzzy-based remote sensing image fusion method of target recognition, Computer Engineering and Applications, 2005, 41(16): 83-85.

[6] X. J. Wu, Q. Cao, B. Chen. Bayes estimates based on the multi-sensor data fusion method, " Systems Engineering Theory and Practice, 2000, 7: 45-48.

[7] Z. Luo, M. Ye. Bayes method based on multi-sensory information fusion algorithm and its application,. Journal of Transducer Technology, 2001, 3 : 210-215.

[8] L. F. Che, X. J. Zhou, Z. Xu. Extension Methods in Multi-Sensor Information Fusion Workpiece Recognition, Systems Engineering Theory and Practice，2000, 8: 91-94.

[9] J. C. Gower. Properties of euclidean and non-euclidean distance matrices, Linear Algebra Appl, 67, 81-97, (1985).

DOI: 10.1016/0024-3795(85)90187-9

[10] L. A. Zadeh. Fuzzy sets, Information and Control, 1965, 8-3: 338-353.