Numerical analysis of the current and voltage sharing issues for resistive fault current limiter using YBCO coated conductors

YBaCuO-coated conductors offer great potential in terms of performance and cost-saving for superconducting fault current limiter (SFCL). A resistive SFCL based on coated conductors can be made from several tapes connected in parallel or in series. Ideally, the current and voltage are shared uniformly by the tapes when quench occurs. However, due to the non-uniformity of property of the tapes and the relative positions of the tapes, the currents and the voltages of the tapes are different. In this paper, a numerical model is developed to investigate the current and voltage sharing problem for the resistive SFCL. This model is able to simulate the dynamic response of YBCO tapes in normal and quench conditions. Firstly, four tapes with different Jc 's and n values in E-J power law are connected in parallel to carry the fault current. The model demonstrates how the currents are distributed among the four tapes. These four tapes are then connected in series to withstand the line voltage. In this case, the model investigates the voltage sharing between the tapes. Several factors that would affect the process of quenches are discussed including the field dependency of Jc, the magnetic coupling between the tapes and the relative positions of the tapes. © 2010 IEEE.

Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the Active Appearance Model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases. © 2009 IEEE.