Abstract
The task-oriented proactive seamless migration is one of difficult problems to be solved in pervasive computing paradigm. Apparently, this function of seamless mobility is suitable for mobile services, such as mobile Web-based learning. But when seamless migration for computing task of learning is realized among PC, laptop, or PDA, there are several difficult problems to be solved, such as how to supply the proactive/attentive service with uncertainty for aware context. In order to realize E-learning based on proactive seamless migration, we design and improve relative fuzzy-neural approach (of course, besides it, there are other approaches). Generally, the network can be classified into two. One is that fuzzy logic reasoning is completed by fuzzy weight in neural system. The other is that the input data must be fuzzified in the first or second level, but not weight. We discuss and study the second in this paper. For proactive decision, fusion method based on fuzzy-neural can make Web-based learning system keep advantage of fuzzy logic system and remain adaptive optimum in proactive/attentive service. The correctness and validity of our new approach have been tested.
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References
Garlan, D., Siewiorek, D.P.: Project aura: toward distraction-free pervasive computing. IEEE Pervasive Computing 1, 22–31 (2002)
Bagrodia, R., Chu, W., Kleinrock, L., Popek, G.: Vision, Issues, and Architecture for Nomadic Computing. In: IEEE Personal Communications, pp. 14–27 ( December 1995)
Satyanarayanan, M.: Pervasive computing: vision and challenge. In: IEEE Personal Communications, August 10-17, vol. 8 (2001)
Paul, C.: Managing context data for smart spaces. IEEE Personal Communications 10, 44–46 (2000)
Guangyou, X., Yuanchun, S., Weikai, X.: Pervasive computing. Chinese Journal of Computer 26(9), 1042–1050 (2003)
Shi, Y., Xie, W., Xu, G.: Smart Classroom: Merging Technologies for Seamless Teleducation. In: IEEE Pervasive Computing Magazine, vol. 2(2) (April-June 2003)
Zhang, D., Xu, G., Shi, Y.: Moblie agents with intrusion detection during sealess transfer. In: The 2nd Internation Conference of Pervasive Computing, April 18 (2004)
Zhang, D., Shi, Y., Xu, G.: A Kind of Smart Space for Remote Interactive Access Based on Pervasive Computing. In: Zhou, W., Nicholson, P., Corbitt, B., Fong, J. (eds.) ICWL 2003. LNCS, vol. 2783, pp. 297–307. Springer, Heidelberg (2003)
Zhang, D., Shi, Y., Xu, G.: Learning by Seamless Migration—A Kind of Mobile Working Paradigm. In: Liu, W., Shi, Y., Li, Q. (eds.) ICWL 2004. LNCS, vol. 3143, pp. 128–135. Springer, Heidelberg (2004)
Zhang, D., Shi, Y., Xu, G.: Context-aware Computing during Seamless Transfer Based on Random Set Theory for Active Space. In: Yang, L.T., Guo, M., Gao, G.R., Jha, N.K. (eds.) EUC 2004. LNCS, vol. 3207, pp. 692–701. Springer, Heidelberg (2004)
Yao-hong, K.: Data Fusion Theory and Applicaiton. Electrical Technology University Press, Xi’an (1998)
Harney, R.C.: Practical Issues Multisensors Target Recongnition. SPIE,Sensor Fusion 1306 (1990)
Ling-yu, X., Hai, Z.: Applicaion of Neural Fusion to Accident Forecast in Hydropower station. In: Proceedings of The Second International Conference on Information Fusion, vol. 2 (1999)
Qing-dong, D., Hai, Z.: D-S Evidence Theory Applied to Fault Diagnosis of Generator Based on Embedded Sensors. In: Proceedings of The Third International Conference on Information Fusion, vol. 1 (2000)
Zhu-xun, T.: Usual Process of Information Fusion and Application in Fault Diagnosis. Detection Technology (3), 15–17 (1995)
Yan-duo, Z., Xingwei, J.: Multisensor Information Fusion and Application in Intelligent Fault Diagnosis. Sensor Technology 18(2), 18–22 (1999)
Hall, D.: Mathmatical Techniques in Multisensor Data Fusion. Artech House Inc. (1992)
Ishibuchi, H., Tanaka, H.: Fuzzy neural networks with fuzzy weights and fuzzy bias. In: Proc. ICNN 1993, pp. 1650–1655 (1993)
Lin, Y.H., George, A.: A new approach to fuzzy-neural system modeling. IEEE Trans. Fuzzy System 3(2), 190–197 (1995)
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Zhang, D., Zeng, G., Ban, X., Yin, Y. (2005). A Kind of Context-Aware Approach Based on Fuzzy-Neural for Proactive Service of Pervasive Computing. In: Yang, L.T., Zhou, X., Zhao, W., Wu, Z., Zhu, Y., Lin, M. (eds) Embedded Software and Systems. ICESS 2005. Lecture Notes in Computer Science, vol 3820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11599555_53
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DOI: https://doi.org/10.1007/11599555_53
Publisher Name: Springer, Berlin, Heidelberg
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