Ultra-high-voltage (UHV) transmission lines have a higher requirement for selecting faulted phases fast and reliably. The coupling relationship between the three phases is analysed in detail. On the basis of the coupl...
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Ultra-high-voltage (UHV) transmission lines have a higher requirement for selecting faulted phases fast and reliably. The coupling relationship between the three phases is analysed in detail. On the basis of the coupling relationship, the principle of faulted phase selection (fps) is proposed, which utilises the time-domain energy of three-phase transient currents. To remove high-frequency noise and preserve the transient current waveform features invariant, a morphological filter with flat structure element is applied in fps algorithm. The simulation model of Jindongnan-Nanyang-Jingmen UHV transmission project is established in EMTP. The testing results show that the proposed algorithm is feasible and fast (<2ms) in case of various fault conditions.
The Bieberbach conjecture about the coefficients of univalent functions of the unit disk was formulated by Ludwig Bieberbach in 1916 [4]. The conjecture states that the coefficients of univalent functions are majorize...
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The Bieberbach conjecture about the coefficients of univalent functions of the unit disk was formulated by Ludwig Bieberbach in 1916 [4]. The conjecture states that the coefficients of univalent functions are majorized by those of the Koebe function which maps the unit disk onto a radially slit plane. The Bieberbach conjecture was quite a difficult problem, and it was surprisingly proved by Louis de Branges in 1984 [5] when some experts were rather trying to disprove it. It turned out that an inequality of Askey and Gasper [2] about certain hypergeometric functions played a crucial role in de Branges' proof. In this article I describe the historical development of the conjecture and the main ideas that led to the proof. The proof of Lenard Weinstein (1991) [72] follows, and it is shown how the two proofs are interrelated. Both proofs depend on polynomial systems that are directly related with the Koebe function. At this point algorithms of computer algebra come into the play, and computer demonstrations are given that show how important parts of the proofs can be automated.
Motivation: Statistical models of protein families, such as position-specific scoring matrices, profiles and hidden Markov models, have been used effectively to find remote homologs when given a set of known protein f...
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Motivation: Statistical models of protein families, such as position-specific scoring matrices, profiles and hidden Markov models, have been used effectively to find remote homologs when given a set of known protein family members. Unfortunately training these models typically requires a relatively large set of training sequences. Recent work (Grundy, J. Comput. Biol., 5, 479-492, 1998) has shown that, when only a few family members are known, several theoretically justified statistical modeling techniques fail to provide homology detection performance on a par with Family Pairwise Search (fps), an algorithm that combines scores from a pairwise sequence similarity algorithm such as BLAST. Results: The present paper provides a model-based algorithm that improves fps by incorporating hybrid motif-based models of the form generated by Cobbler (Henikoff and Henikoff, Protein Sci., 6, 698-705, 1997). For the 73 protein families investigated here, this cobbled fps algorithm provides better homology detection performance than either Cobbler or fps alone. This improvement is maintained when BLAST is replaced with the fill Smith-Waterman algorithm.
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