구속 포텐셜에서 반도체 물질(Ge)의 자기장 의존성

Abstract

This paper proposes an application of artificial neural networks for analyzing electricity market that has insufficient information for calculating equilibrium. Neural networks are constructed and trained on two representative cases in the electricity market. One is for calculating equilibrium price in perfect competition market and the other is for determining whether the transmission congestion occurs. The neural network uses a multilayer structure and learns with backpropagation algorithms for training. The neural networks trained in the case studies calculate the market price with a high probability and also determines an occurrence of the transmission congestion accurately. We study optical quantum transition line shapes (QTLS) showing the absorption power and quantum transition line widths (QTLW) of an electron deformation potential phonon interaction system. In order to analyze quantum transfer, the QTLW and QTLS with the magnetic field dependence was compared in two transfer processes, I.e., intra-Land level transfer process. In order to apply quantum transport theory (QTR) to a system that confines electrons by a square well confining potential, the Liouville equation projected with the Equilibrium Average Projection Scheme (EAPS) was used.