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欢迎大家一起研究S变换 7 G" b2 n% g- H3 g% r7 o) h5 ~: d8 u1 S' c% x' S& W! ^. l( w4 H
A new approach to voltage sag detection based on wavelet transform.pdf3 O1 Z6 n2 Z# O8 @$ \1 L* p
7 t, v. y, p( v9 |9 hAn expert system based on S-transform and neural network for automatic classification of power quality disturbances.pdf0 d9 }5 n' v4 u3 @
/ S5 {8 V' m+ w+ m; V# T2 T- ~Detection and classification of power quality disturbances using S-transform and modular neural network.pdf5 z9 s8 q: N. r9 c! a4 F' _# o Q
7 j7 e0 C7 z. K! C0 C: hIEEE Recommended Practice for Monitoring Electric Power Quality.pdf , i" w! D2 @* r6 {' J+ l6 F9 d$ A% l* H8 D
Localization of the Complex SpectrumThe S Transform.pdf * o9 J S) o7 ?& i. R% t9 I4 r- H' x% O; d9 G0 ?
Pattern recognition of power signal disturbances using S Transform and TT Transfom.pdf " b2 p7 [' H2 j3 U( \$ ?% u3 | " x4 n4 L: |/ H6 tPower quality disturbance classification utilizing S-transform and binary feature matrix method.pdf " @4 r% @% S+ ]9 t' b0 u ) K1 ^/ K( s) ?+ c3 hRule based system for power quality disturbance classification incorporating S-transform features.pdf 8 ^* Y/ h5 Y# A0 O: \7 R 9 ]6 Q) T) T9 r+ J4 w6 BShort duration disturbance classifying based on S-transform maximum similarity.pdf