By Wenjun Liao, Guohong Gao, Ying Wang (auth.), David Jin, Sally Lin (eds.)

This quantity provides the most result of 2011 overseas convention on digital Engineering, communique and administration (EECM2011) held December 24-25, 2011, Beijing China. The EECM2011 is an built-in convention offering a helpful chance for researchers, students and scientists to replace their rules head to head jointly. the main target of the EECM 2011 and the current 2 volumes “Advances in digital Engineering, verbal exchange and administration” is on strength Engineering, electric engineering functions, electric machines, in addition to verbal exchange and data structures Engineering.

This quantity offers the most result of 2011 overseas convention on digital Engineering, communique and administration (EECM2011) held December 24-25, 2011, Beijing China. The EECM2011 is an built-in convention supplying a helpful chance for researchers, students and scientists to interchange their principles nose to nose jointly. the main target of the EECM 2011 and the current 2 volumes “Advances in digital Engineering, communique and administration” is on energy Engineering, electric engineering functions, electric machines, in addition to communique and knowledge platforms Engineering.

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Advances in Electronic Engineering, Communication and Management Vol.2: Proceedings of 2011 International Conference on Electronic Engineering, Communication and Management (EECM 2011), held on December 24–25, 2011, Beijing, China

This quantity provides the most result of 2011 overseas convention on digital Engineering, verbal exchange and administration (EECM2011) held December 24-25, 2011, Beijing China. The EECM2011 is an built-in convention delivering a worthwhile chance for researchers, students and scientists to replace their rules head to head jointly.

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Additional info for Advances in Electronic Engineering, Communication and Management Vol.2: Proceedings of 2011 International Conference on Electronic Engineering, Communication and Management (EECM 2011), held on December 24–25, 2011, Beijing, China

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Firstly, determine the prior distribution of the success rate R. Usually, Beta distribution is used as the prior distribution for binomial case[2], so we use the Beta distribution of R: π ( R) = R a −1 (1 − R) b −1 , β ( a, b) 0 ≤ R ≤1 (2) Where a and b are the prior hyper-parameters, and: β ( a, b) = Γ(a)Γ(b) Γ ( a + b) (3) Usually, we use empirical Bayes method proposed by Martz & Waller[5] , or the method of prior quantile [3] to determine the prior hyper-parameter a, b . (2), the posterior distribution of R is: π ( R | D) = L( R)π ( R) ∫ L( R)π ( R)dR = R n − f + a −1 (1 − R ) b + f −1 β ( n − f + a, b + f ) (4) ΘR Assume that qualification scheme is (n, c), if the system reliability is R, then the acceptance probability of the qualification test is: c P( Ac | R) = ⎛n ⎞ n− f ⎛n ⎞ n− f ∑ ⎜⎜⎝ f ⎟⎟⎠R (1 − R) f (5) (1 − R) f (6) f =0 The rejection probability is: n P(Re | R) = ∑ ⎜⎜⎝ f ⎟⎟⎠R f = c +1 Suppose that we use the primary sampling scheme (n, c ) , n is the number of tests, c is the maximum acceptable fault numbers, the acceptance quality is R0 , the Analysis of Test Scheme for a Bayesian Plan of Qualification Test in Binomial Case 41 、 limiting quality is R0' , the producer’s risk and user’s risk are α β , respectively.

1 So this judgment matrix consistency is acceptable. 1 So this judgment matrix consistency is acceptable. 243 this judgment matrix consistency is acceptable. 1, So this judgment matrix consistency is acceptable. 1, that the consistency of each comparison matrix is acceptable. 4 The Total Level of Sorting and Consistency Check The above is a set of elements to get a layer on an element in its weight vector. To get the lowest element, especially in the sort of programs for the target weight, the total order is needed.

1 (N C) (N C) (N C) (14, 2) (31, 5) (64, 11) Step 3. Using the environmental factors to convert the system reliability[7]. (8) is the converting equation. Assume that environment II represents the real environment (rigor), the environmental factor K is known, and the good environment is converted to the rigor one. K is divided with the sample size of good environment, if the fault number f is invariable, that means the test information is turning worse. Assume that the unreliability is P1 and P2 under environment I and II, respectively.

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