By Lingxia Liu (auth.), Gang Shen, Xiong Huang (eds.)
This two-volume set (CCIS 152 and CCIS 153) constitutes the refereed lawsuits of the foreign convention on computing device technological know-how and knowledge Engineering, CSIE 2011, held in Zhengzhou, China, in may well 2011. The 159 revised complete papers provided in either volumes have been rigorously reviewed and chosen from a great number of submissions. The papers current unique examine effects which are generally appropriate to the speculation and functions of computing device technology and knowledge Engineering and tackle a large choice of subject matters reminiscent of algorithms, automation, synthetic intelligence, bioinformatics, machine networks, desktop safeguard, desktop imaginative and prescient, modeling and simulation, databases, facts mining, e-learning, e-commerce, e-business, picture processing, wisdom administration, multimedia, cellular computing, typical computing, open and cutting edge schooling, trend acceptance, parallel computing, robotics, instant networks, and internet applications.
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Extra info for Advanced Research on Computer Science and Information Engineering: International Conference, CSIE 2011, Zhengzhou, China, May 21-22, 2011. Proceedings, Part I
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2. Topology of three-phase MegaDySC 3 Compensation Strategy for Dynamic Sag Corrector DySC operates to maintain the load supply voltage at its rated value and exchanges active/reactive power with the surrounding system. Hence, it is necessary to provide proper compensation strategies in order to reduce the active power exchange. Widely used in present DySC control are pre-sag compensation strategy and in-phase compensation strategy. The pre-sag compensation strategy tracks supply voltage continuously and restores load voltage to the pre-sag condition.
If F (t ) ∈ L2 ( R 2 , C v ) , defined by (5), is an orthogonal vector-valued scaling function, then for a = 2 , ∀ u ∈ Z 2 , we have the following equalities, ∑ Pσ ( Pσ σ ∈Z 2 + 4u )* = 4δ 0,u I v . 3 ∑ P (ξ + σ ι π )P (ξ + σ ι π ) * (16) = Iv , ξ ∈ R 2 , σι ∈ Z . 2 (17) ι =0 Proof. By substituting (5) into the relation (12), for ∀ k ∈ Z 2 , we obtain that δ 0, k I v = F (⋅ − k ), F (⋅) = ∑ ∑ ∫ Pl F (2t − 2k − l ) F (2t − u )* ( Pu )* dt l ∈ Z u ∈Z 2 = 1 4 ⋅ ∑∑P l l ∈Z 2 u ∈Z 2 R 2 F (⋅ − 2 k − l ), F (⋅ − u ) ( Pu ) 2 * = 1 ∑ P (P 4 u ∈Z u u+4k * ) .
9) a 2 B ( s ) (ξ ) = ∑ u∈Z 2 Bu( s ) exp{−iuξ }. (10) Then, the refinement equation (10) becomes the following equation Gˆ s (aξ ) = B ( s ) (ξ ) (ξ ), s = 1, 2,3, , a 2 − 1, ξ ∈ R 2 . (11) If F (t ) ∈ L2 ( R 2 , C v ) is an orthogonal vector one, then it follows from (3) that F (⋅), F (⋅ − u ) = δ 0,u I v , u∈Z . 2 (12) We say that Gs (t ) ∈ L2 ( R 2 , C v ), s ∈ A = {1, 2, , a 2 − 1} are orthogonal vectorvalued wavelet functions associated with the vector-valued scaling function F (t ) , if F (⋅ − k ), Gs (⋅ − u ) = O, s = 1,2, , a 2 − 1, k , u ∈ Z , 2 (13) and { Gs (t − u ), s ∈ A, u ∈ Z 2 } is an orthonomal basis of W0 .