A general design method for electric machines using magnetic circuit model considering the flux saturation problem

This paper proposes an analytical approach that is generalized for the design of various types of electric machines based on a physical magnetic circuit model. Conventional approaches have been used to predict the behavior of electric machines but have limitations in accurate flux saturation analysis and hence machine dimensioning at the initial design stage. In particular, magnetic saturation is generally ignored or compensated by correction factors in simplified models since it is difficult to determine the flux in each stator tooth for machines with any slot-pole combinations. In this paper, the flux produced by stator winding currents can be calculated accurately and rapidly for each stator tooth using the developed model, taking saturation into account. This aids machine dimensioning without the need for a computationally expensive finite element analysis (FEA). A 48-slot machine operated in induction and doubly-fed modes is used to demonstrate the proposed model. FEA is employed for verification.

Unified circuit model for the polysilicon TFT

This paper proposes two methods to improve the modelling of thin film transistors (TFTs). The first involves integrating Poissons equation numerically, given a density of trap states and other relevant material parameters including a constant mobility. Theresult is conductance as a numerical function of gate voltage. The second method recognizes that the data for areal conductance found by numerical integration, may easily be found by measurement without making assumptions about the density of trap states.

A unified circuit model for the polysilicon thin film transistor

This paper describes a unified approach to modelling the polysilicon thin film transistor (TFT) for the purposes of circuit design. The approach uses accurate methods of predicting the channel conductance and then fitting the resulting data with a polynomial. Two methods are proposed to find the channel conductance: a device model and measurement. The approach is suitable because the TFT does not have a well defined threshold voltage. The polynomial conductance is then integrated generally to find the drain current and channel charge, necessary for a complete circuit model. © 1991 The Japan Society of Applied Physics.

Small-Signal Equivalent-Circuit Model and Characterization of 1.55-mu m Buried Tunnel Junction Vertical-Cavity Surface-Emitting Lasers

The work was supported in part by the National Natural Science Foundation of China under Grant 60536010, Grant 60606019, Grant 60777029, and Grant 60820106004, and in part by the National Basic Research Program of China under Grant 2006CB604902, Grant 2006CB302806, and Grant 2006dfa11880.

Rate-equation-based circuit model of high-speed semiconductor lasers

Based oil rare equations of semiconductor laser, a symbolically-defined model for optical transmission system performance evaluation and network characterization in both time- and frequency domains is presented. The steady-state and small-signal characteristics, such as current-photon density curve, current-voltage curve, and input impedance, call be predicted from this model. Two important dynamic characteristics, second-order harmonic distortion and two-tone third-order intermodulation products, are evaluated under different driving conditions. Experiments show that the simulated results agree well with the published data. (c) 2007 Wiley Periodicals, Inc.

A small signal equivalent circuit model for resonant tunnelling diode

We report a resonant tunneling diode (RTD) small signal equivalent circuit model consisting of quantum capacitance and quantum inductance. The model is verified through the actual InAs/In0.53Ga0.47As/AlAs RTD fabricated on an InP substrate. Model parameters are extracted by fitting the equivalent circuit model with ac measurement data in three different regions of RTD current-voltage (I-V) characteristics. The electron lifetime, representing the average time that the carriers remain in the quasibound states during the tunneling process, is also calculated to be 2.09 ps.

Small Signal Equivalent Circuit Model and Modulation Properties of Vertical Cavity-Surface Emitting Lasers

Small signal equivalent circuit model and modulation properties of vertical cavity-surface emitting lasers (VCSEL's) are presented. The modulation properties both in analytic-equation calculation and in circuit model simulation are studied. The analytic-equation calculation of the modulation properties is calculated by using Mathcad program and the circuit model simulation is simulation is simulated by using Pspice program respectively. The results of calculation and the simulation are in good agreement with each other. Experiment is performed to testify the circuit model.

Small signal equivalent circuit model of vertical cavity surface emitting lasers

Small signal equivalent circuit model of vertical cavity surface emitting lasers (VCSEL's) is given in this paper. The modulation properties of VCSEL are simulated using this model in Pspice program. The simulation results are good agree with experiment data. Experiment is performed to testify the circuit model.

A Local Circuit Model of Learned Straitial and Dopamine Cell Responses under Probabilistic Schedules of Reward

Before choosing, it helps to know both the expected value signaled by a predictive cue and the associated uncertainty that the reward will be forthcoming. Recently, Fiorillo et al. (2003) found the dopamine (DA) neurons of the SNc exhibit sustained responses related to the uncertainty that a cure will be followed by reward, in addition to phasic responses related to reward prediction errors (RPEs). This suggests that cue-dependent anticipations of the timing, magnitude, and uncertainty of rewards are learned and reflected in components of the DA signals broadcast by SNc neurons. What is the minimal local circuit model that can explain such multifaceted reward-related learning? A new computational model shows how learned uncertainty responses emerge robustly on single trial along with phasic RPE responses, such that both types of DA responses exhibit the empirically observed dependence on conditional probability, expected value of reward, and time since onset of the reward-predicting cue. The model includes three major pathways for computing: immediate expected values of cures, timed predictions of reward magnitudes (and RPEs), and the uncertainty associated with these predictions. The first two model pathways refine those previously modeled by Brown et al. (1999). A third, newly modeled, pathway is formed by medium spiny projection neurons (MSPNs) of the matrix compartment of the striatum, whose axons co-release GABA and a neuropeptide, substance P, both at synapses with GABAergic neurons in the SNr and with the dendrites (in SNr) of DA neurons whose somas are in ventral SNc. Co-release enables efficient computation of sustained DA uncertainty responses that are a non-monotonic function of the conditonal probability that a reward will follow the cue. The new model's incorporation of a striatal microcircuit allowed it to reveals that variability in striatal cholinergic transmission can explain observed difference, between monkeys, in the amplitutude of the non-monotonic uncertainty function. Involvement of matriceal MSPNs and striatal cholinergic transmission implpies a relation between uncertainty in the cue-reward contigency and action-selection functions of the basal ganglia. The model synthesizes anatomical, electrophysiological and behavioral data regarding the midbrain DA system in a novel way, by relating the ability to compute uncertainty, in parallel with other aspects of reward contingencies, to the unique distribution of SP inputs in ventral SN.