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elated weighted vector with ∑ n i= 1 ω = 1 weighted average operator of linguistic 2-tuples ξ ω = ∇( (( s , α ),( s 1 n ∑ i= 1 1 ∇ −1 2 , α ), L,( s 2 ( s , α ) ω ) i C. WEIGHT OF EVALUATION INDICATORS i i n i , α )) = (ˆ, s ˆ) α n , then the ω ξ is[15] (6) The questionnaires were analyzed using linguistic 2- tuple representation model. And the weight of each indicator was obtained. The weight of each sub-indicator was also analyzed. Thus, evaluation system for blended learning and its weights of each indicator and its subindicator could be demonstrated as table 1. indicators (weight) Quality of BB resource (0.35) Mutual exchange quality of BB (0.37) Quality of blended discussion (0.28) Table 1 evaluation system and its weight Sub-indicators (weight) Abundance of resources in BB (0.37) Relativity of resource provided in BB (0.34) Distribution reasonability of resources in BB (0.29) Punctuality of teacher’s answer(0.33) Accuracy of teacher’s answer(0.38) Energetic discussion inspired by teachers(0.29) The abundance of the materials and its relationship to the topic(0.22) The logic of the team representation report (0.20) Frequency of the team discussion(0.14) The accuracy and depth of the problem solving.(0.19) Question posted and frequency of answers (0.09) Attitude of the group team(0.16) Ⅴ. CONCLUSION The paper introduced the architecture of blended learning system. It got the indicators to evaluation the blended learning. Then it used linguistic 2-tuple representation model to handle fuzzy term in questionnaire and obtained the weight of each indicators and its sub-indictors. The education management department can use the evaluation system proposed in the paper to assess the network resource in BB. ACKNOWLEDGMENT This work is supported by Education Planning Research foundation of Zhejiang Province Grant by scg85. It is also supported by Jiaxing University Education Research Foundation Grant by 85150932 and Economic Commence Market Application Technology Foundation Grant by 2007gdecof004. REFERENCES [1] Prahalad. Case study: University of Michigan Business School Using PRAJA. Technology to Enhance Learning,2002. [2] K.K.He, “E-Learning and the revolution of university/college“ ,http://www.edu.Cn / 20011225/3015160. shtml”, 05-10-2009 [3] K.D. Li, J.H.Zhao, “Theory and its application of blended learning”, Electrical education research, Vol.4, pp.78-81, July 2004 [4] M.Driscoll·, “Blended learning : Let’s get beyond the hype”, Learning and Training Innovations[R],2002. [5] H.Singh, C.Reed, “Achieving Success with Blended Learning”, http://www.centra.com/downloa whitepapers/ blended learning. Pdf, 30-01-2002. [6] D.Clark(2003), “Whitepaper: Blended Learning”, http://www.epic.co.uk/content/resources/white_papers/blen ded. Htm, 21-05-2005 [7] W.L.Chen andC.K.Looi, “Incorporating online discussion in face to face classroom learning: A new blended learning approach”, Australasian Journal of Educational Technology, Vol.23, pp. 307-326, March 2007:. [8] N.H.Yuan, “Blended instructional schema based on virtual technology”, Journal of Guangdong technology normal college,Vol.5, pp. 135-137, May 2006 [9] W.S. Pan, “Research on blended instructionalschema based on network resource”, Electrical education research, Vol. 8, pp. 49-51, August 2006.8 [10] X.Li, “Exploring effective learning schema based on blended learning and self-studying”, Modern education techniques, Vol.4, pp.32-35, April 2007 [11] Y.P.Jiang, Y.N.Xing,“Consistency Analysis of Two-Tuple Linguistic Judgement Matrix,” Journal of Northeastern University(Natural Science), Vol.28, pp.129-132, January 2007 [12] Z.S.Xu, ” A Multi-attribute group decision making method based on fuzzy linguistic evaluationa and GIOWA operator”, J.Sys.Sci.& Math. Sci., Vol.24, pp.:218-224, April 2004 (in Chinese). [13] F. Herrera, L.Martinez. A model based on linguistic 2- tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision making. IEEE Transactions on Systems, Man, and Cybernetics-part b: Cybernetics, Vol.31, pp. 227-234, April 2001 [14] F.Herrera , L.Martinez, “The 2-tuple linguistic computational model—advantages of its linguistic description, accuracy and consistency”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol.9, pp. 33-48, September 2003 [15] X.W.Liao, H.Li,G.M.Dong., “A Multi-attribute Group Decision-making Approach Dealing with Linguistic Assessment Information,” Theory and Practice of System Engineering, Vol.9, pp.90-98, September 2006 (in Chinese) 84
ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 085-088 Swarm Dynamics Behavior Analysis and Coordinated Control of Limited-Range Perceived Agents Zhibin Xue 1*,3 , and Jianchao Zeng 2 1 Department of chemical machinery, Chemical engineering college, Qinghai University, Xining, China Email: zbxue_jack@163.com 2 Complex System & Computer Intelligence Laboratory; Taiyuan University of Science & Technology, Taiyuan, China Email: zengjianchao@263.net 3 College of Electric & Information Engineering, Lanzhou University of Technology, Lanzhou, China Abstract—Swarming behavior is ubiquitous in nature and society. It is of both theoretical and practical importance to investigate the underlying principles and mechanisms of coordination and cooperation emerging in swarms. In this paper we propose a simple isotropic range limited-perceive agents dynamic model to study collective and formation behavior of a group of mobile autonomous agents interacting through a long range attraction and short range repulsion function. It is shown that the individuals (agents) in the swarm during the course of coordinative motion can realize the local collision-free stabilization of constituting a particular predefined geometrical configuration. Numerical simulations are also worked out to illustrate the analytical results. Index Terms—swarm dynamics, coordinated control, isotropic, limited-range perceive, multi-agent, collision-free, formation control, numerical simulations I. INTRODUCTION Swarming behavior has been observed in nature. For example, bees, ants and birds often work together in groups for viability[1]. It is known that such cooperative behavior has certain advantages, for example, predator avoidance, foraging success and so on. The general understanding in biology is that the swarming behavior is a result of an interplay between a long range attraction and a short range repulsion between the individuals [1]. Understanding the cooperative and operational principles of such systems may provide useful ideas for modeling and exploring the collective dynamics of swarming behavior in bionics for using in engineering applications, such as the coordinated control of multiagent systems. However, most available results in the literature are on the isotropic global perceive swarming model, convincing results on the isotropic local perceive swarming model are relatively few. Based on the analysis of various biological swarms of dynamic aggregation mechanism, an isotropic limited-range perceived swarming dynamic model is proposed in this paper. Meanwhile, the chief research objective of the paper is to use the limited range perceive model to solve the formation control question of multi-agent systems to constitute various predefined geometrical configuration formations pattern on multiagent systems in an n-dimensional Euclidean space by using artificial potential functions and Newton’s iteration method. Dynamic change of the environment, local observation and nonlinear characteristics are ubiquitous phenomena in nature, but the study is very difficult and it has profound engineering significance. So this article is able to provide some results on this topic. II. SWARM DYNAMICAL MODELS Gazi and Passino[2] proposed an isotropic global perceive swarm model with a simple attraction-repulsion function specifying the inter-individual interactions and showed that the model can exhibit the basic features of aggregation, cohesion and complete stability. However, the attraction-repulsion functions considered in this study could not avoid collisions since they are not unbounded for infinitesimally small arguments. g(y) 5 4 3 2 1 0 -1 -2 -3 -4 Manuscript received January 28, 2010; revised February 28, 2010; accepted March 16, 2010. *Corresponding author (email: zbxue_jack@163.com). This work was supported by the National Natural Science Foundation of China (Grant No. 60975074). -5 -5 -4 -3 -2 -1 0 1 2 3 4 5 y Figure 1. The attraction/repulsion function g(⋅ ) in Ref. [2] © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 85
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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that studying being going to be to
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Reference[10] analyzed the evolutio
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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The QoE i (T i ) is the ith user’
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nodes will be formed one cluster, t
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Where n is the number of data point
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Keyboard event Application User mod
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SQL Azure will eventually include a
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The nodes in the suffix tree are dr
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[3] Y. Li, S. M. Chung, and J. D. H
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some drilling fluid produces hydrog
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"normalization", whose membership b
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and minimum structural elements in
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esults of the spatial transform par
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B. Research on protocol actions Com
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ACKNOWLEDGMENT This work is funded
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A. Problem Description In a small b
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[4] Huang, Hung, and J. Y. jen Hsu.
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Further by calculating, the followi
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TABLE II. THE CONCENTRATION BETWEEN
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private key that obtained by using
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ontology, and the domain dictionary
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Eq.4 is NP-hard and can be solved b
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Where: G i is the selection field o
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Theorem 2.4([10]). Let L 1 and L 2
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Figure 2. Example of single-step de
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evocation and the fourth group stor
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focused mainly on rule-based forms
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Ⅵ. CONCLUSION Figure 6. The Flask
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EDCF in comparison with DCF, has so
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B. Simulation results and analysis
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in routing table. When search resou
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others through the selective emotio
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such as weather information, techno
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the opening window, a related page
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1) Process context storage areas. I
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V. CONCLUSION In this thesis, sCPU-
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main types of horizontal search env
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Enterprise Portal security provides
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() t = [ S () t S () t ] T N S ,...
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[2] Kumaravel, N., and Kavitha, V.,
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output, so BP network has been wide
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input to the artificial neural netw
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(2) In a process (tokens from outsi
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The reduction process consists of t
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all eight normal vectors are identi
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is B object , and the number of emp
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xi, j y' = εα ( (1 + tanh( )) −
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Error 8000 7000 6000 5000 4000 3000
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Architecture (AMBA) a new bus archi
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four layers.The far right of the gr
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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