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navigation and control system of intelligent vehicle. Some typical path such as S-path and different angle curve road are chosen in test. The picture of intelligent vehicle searching path in the S-path test is shown in Fig.7. The maximum speed for this road conditions is 1.6m / s. The picture of intelligent vehicle driving at turning is shown in Fig.8. The maximum speed for this road conditions is 1.6m / s. Figure 5. Straight Road Image Figure 6. Curve Road Image The image is 40 × 71 pixels, and it has been enlarged here for easier looking and recognised. the image of a curve road is shown in Fig.6. It is a map of right-angle corner, that is a right-angle of straight road and then right turn. In this image it’s clear to see that it’s a right turn. B. Real vehicle test results Experimental test is carried out in order to check the VI. CONCLUSION Seen from Fig.5 and Fig.6, that black navigation line at road centre can be recognised and displayed in the picture clearly. It’s also found that picture quality will be affected of the light strength and the projection uniformity of the light on the objects. With enough and uniform light, it will be better in the contrast and color evenness. It is suggested in Fig.7 that intelligent vehicle has good performance of searching line, because it can adjust the front wheel turning angle in time in accordance with S-path’s change. With Fig.8 the same conclusion can be gained: when intelligent vehicle rushs out of path because of too fast speed, the algorithm can find that it is out of control and give orders of returning to the path. REFERENCES Figure 7. intelligent vehicle in S-path test [1] Huang K, Jin H, and Jiang D. , “Overview of Design for Korea Intelligent Model Car Design Contest,” ELECTRONIC ENGINEERING & PRODUCT WORLD, 2006(5), pp. 150-156. [2] Foster I, Kesselman C, and Nick J M, etal, “Grid Services for Distributed System Integration,” Computer, 2002,35(6). [3] Foster I, Kesselman C., “The Grid Blueprint for a New Computing Infrastructure,” Beijing: Mechanical Industry Press, 2005. [4] Hersbkop S, FersterR, and Bui L H, etal, “Host-based Anomaly Detection Using Wrapping File Systems,” CU Tech Report, April 2004. [5] Eskin E., “Probabilistic anomaly detection over discrete records using inconsistency checks,” Technical report, Columbia University Computer Science Technical Report, 2002. [6] DASH P K, W A C., “Anticipatory fuzzy control of power systems” IEE Proc Cener Transm Distrib, 95, 2 (2), pp. 211-218. [7] FRANK KLAWONN, ORG GEBHARDT, and UDOLF KRUSE, “Fuzzy ontrol on the basis of equality relations with an example from idle speed control,” IEEE Transactions on Fuzzy Systems, 1995, 3 (3), pp.336 -350. Figure 8. intelligent vehicle driving at turning. 100
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. 101-104 Prediction of Reservoir-induced Earthquake Based on Fuzzy Theory Ming Zhong, and Qiuwen Zhang College of Hydropower and Information Engineering, Huazhong University of Science and Technology Wuhan, Hubei, China zhongming_youye@sina.com; qwzhang_hust@163.com Abstract—With more and more reservoirs have been and are being built all over the world, reservoir-induced earthquake has received a great deal of attention from geoscientists mainly because of its potential to damage constructions and to cause human losses. Based on the previous researches on the environmental conditions of reservoir-induced earthquake, a criteria hierarchy model has been constructed. We give the environmental condition an uncertain description by applying weight in this theory, which shows that the different factor plays different role in risk assessment. Taking one candidate assessment unit into consideration, the risk class through fuzzy theory is obtained. Based on the original mechanism, a fuzzy mathematical prediction model of three-gorge reservoir in China has been created. It is concluded that compared with other mathematical prediction models, the reservoirinduced earthquake prediction model based on the fuzzy theory can analyze data with fuzzy processing and therefore possesses advantages of high prediction accuracy. Index Terms—reservoir-induced earthquake, fuzzy theory, risk prediction, uncertain analysis, three-gorge reservoir I. INTRODUCTION Reservoir-induced earthquake is a special engineering geological problem resulting from development of hydraulic resources, which occurred in reservoir and its peripheral. Previous researches emphasis on differential features analysis, origin mechanism and formation condition among several earthquake examples. Prof. Gupta [1], a world-famous expert in reservoir-induced earthquake, investigated the formation condition and mechanism of reservoir-induced earthquake by analyzing the seismicity examples of Koyna Reservoir in India, Nuek Reservoir in Tajikistan, Lake Mead in America and Xinfengjiang Reservoir in china in his classic study “Dam and Earthquake”; Denlinger [2] studied geological environmental conditions relating to Reservoir-induced earthquake in Geysers reservoir in northern California of the United states; Simpson [3] classified Reservoirinduced earthquake into two major categories: water permeation and additional strain; Keith [4] analyzed the relationship between reservoir-induced earthquake and crustal deformation in Nuek Reservoir in Tajikistan; Chadha [5] investigated the relationship between reservoir-induced earthquake and water lever change. Edelman [6] presented a rigorously derived analytical Corresponding author: Qiuwen Zhang (qwzhang_hust@163.com) method to describe and interpret the low magnitude earthquakes caused by injection of the borehole fluids into surrounding porous reservoirs. With the accumulation of observation data of reservoir-induced earthquake, base on the preview paper, works in this area have been pursued by different investigators to relate different geological features with the occurrence of the reservoir-induced earthquake or to predict the magnitude of water-induced earthquakes. Many prediction theories with different characteristics are proposed, such as Geology and environmental method, Bayesian probability method, Gray clustering method, Patten recognition method and Logic of information method. Habibagahi [7] assessed the risk of reservoirinduced earthquake by RBF neural network; Fahjan [8] forecasted the risk of reservoir-induced earthquake by the change of water;Ping Li [9] analyzed the capacity of seismogenesis at active faults in the Three-gorge Reservoir of The Yangtze river; Qifa Xia [10] assessed seismic hazard according to engineering geology and earthquake geology; Qinyun Wang [11] used Probability Analytic Method and geological Environment Method to assess seismic hazard of The Three-gorge Reservoir; Houqun Chen [12] established the database of reservoirinduced earthquake and predicted the seismic risk by using GIS technology; According to the record of the LiJiaXia water-induced seismicity in 1998-2002, Xin Sun [13] analyzed seismicity feature after the reservoir impoundment and then predicted seismic hazard in the future; Zongji Ma [14] proposed a reservoir-induced earthquake prediction and evaluation Model based on Digital Watershed; Qiuwen Zhang [15] proposed a model to quantitatively predict and assess the risk of reservoirinduced earthquake, the results are descried with risk probability of reservoir-induced earthquake. More recently the theory of fuzzy sets has been used to evaluate the risk of reservoir-induced earthquake. So far, the cause and mechanism of the reservoir-induced earthquake is a matter of speculation and requires further research. A quantitative and qualitative prediction theory has been presented in this research, which evaluate the risk of reservoir-induced earthquake based on the analytic hierarchy process (AHP) model, and then apply this fuzzy comprehensive evaluation theory in every divided prediction unit. This paper is organized as follows: In section 2, the evaluation criteria hierarchy is constructed; it is the result © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 101
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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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PDF document structure is a tree st
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support 10 Mb / s. But ENC28J60 onl
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condition the first byte output of
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According to maximum membership deg
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Page 81 and 82: The preceding three formulas can be
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Page 133 and 134: TABLE I. DESCRIPTION OF PROPOSITION
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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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If there is X j in a generation, wh
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Theorem 2.4([10]). Let L 1 and L 2
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The relation between the lattice im
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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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In order to ensure the smooth proce
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In this paper, we adopt two Sobel o
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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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