Browsing by Author "Lu, Y"

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Characteristics of transmission of electrons in a quantum wire tangentially attached to a superconductor ring threaded by magnetic flux
(2002-04-01)
Gu, BY
;
Lu, Y
;
Yang, TJ
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電子物理學系
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Department of Electrophysics
We present numerical investigations of the transmission properties of electrons in a normal quantum wire tangentially attached to a superconductor ring threaded by magnetic flux. A point scatterer with a delta-function potential is placed at node to model scattering effect. We find that the transmission characteristics of electrons in this structure strongly depend on the normal or superconducting state of the ring. The transmission probability as a function of the energy of incident electrons, in the case of a superconductor ring threaded by one quantum magnetic flux, emerges one deep dip, imposed upon the first broad bump in spectrum. This intrinsic conductance dip originates from the superconductor state of the ring. When increasing the magnetic flux from one quantum magnetic flux to two, the spectrum shifts toward higher energy region in the whole. This conductance dip accordingly shifts and appears in the second bump. In the presence of a point-scatterer at the node, the spectrum is substantially modified. Based on the condition of I he formation of the standing wave functions in the ring and the broken of the time-reserve symmetry of Schrodinger equation after switching magnetic flux, the characteristics of transmission of electrons in this structure can be well understood.
Forecasting electricity market prices: A neural network based approach
(2004)
Xu, YY
;
Hsieh, R
;
Lu, Y
;
Shen, YC
;
Chuang, SC
This paper presents a neural network approach to forecast the Phelix Base (PB) electricity market prices for European Energy Exchange (EEX). Up to now there has been little scientific work on forecasting the price development on the electricity markets. In this study, the Phelix Base moving average (PBMA), the moving difference (PBMD), and multilayer feedforward neural networks (MLNN) are used to predict various period for 7, 14, 21, 28, 63, 91, 182, and 273 days ahead of electric prices. The experimental results of forecasting by MLNNs and linear methods (autoregressive error model) are compared and discussed. The MLNNs outperform from 11.4% to 64.6% superior to the traditional linear regression method. It seems that the proposed MLNN can be very useful in predicting the electricity market prices of EEX.