Steady-state analytical model for the trench insulated gate bipolar transistor

A steady-state, physically-based analytical model for the Trench Insulated Gate Bipolar Transistor which accounts for a combined PIN diode - PNP transistor carrier dynamics is proposed. Previous models (i.e. PIN model and PNP transistor model) cannot account properly for the carrier dynamics in Trench IGBT since neither the PNP transistor nor the PIN diode effect can be neglected. An optimized Trench IGBT with a large ratio between the accumulation layer and the cell size leads to substantially improved on-state characteristics, which makes the Trench IGBT potentially the most attractive device in the area of high voltage fast switching devices.

Interplay between the Dzyaloshinskii-Moriya anisotropic antisymmetric interaction and the SWAP operation in a two-qubit Heisenberg model

The SWAP operation in a two-qubit Heisenberg model in the presence of Dzyaloshinskii-Moriya (DM) anisotropic antisymmetric interaction is investigated. 1t is shown that the SWAP operation can be implemented for some kinds of DM coupling and the influence of DM couplings is divided into different cases. The conditions of the DM coupling under which the SWAP operation is feasible are established. (C) 2007 Elsevier B.V. All rights reserved.

Motor Control and Learning by the State Space Model

A model is presented that deals with problems of motor control, motor learning, and sensorimotor integration. The equations of motion for a limb are parameterized and used in conjunction with a quantized, multi-dimensional memory organized by state variables. Descriptions of desired trajectories are translated into motor commands which will replicate the specified motions. The initial specification of a movement is free of information regarding the mechanics of the effector system. Learning occurs without the use of error correction when practice data are collected and analyzed.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Three-phase, three-limb, steady-state transformer model

This paper presents the development and implementation of a digital simulation model of a threephase, three-leg, three-winding power transformer. The proposed model, implemented in MATLAB environment, is based on the simultaneous analysis of both magnetic and electric lumped-parameters equivalents circuits, and it is intended to study its adequacy to incorporate, at a later stage, the influences of the occurrence of windings interturn short-circuit faults. Both simulation and laboratory tests results, obtained so far, for a three-phase, 6 kVA transformer, demonstrate the adequacy of the model under normal operating conditions.