Potential tuning of porous silicon luminescence

Electrochemical investigations were conducted of the effect of potential on the luminescence of porous silico (PS). The use of liquid contacts allows the potential to be controlled during studies of the photoluminescence (PL) and electroluminescence (EI). The PL and EL of PS samples prepared from n-type substrates is considered. To obtain luminescence from such PS it is necessary to generate holes in the valence band. This is achieved by either photoexcitation or an electrochemical process involving the reduction of persulfate. This paper describes the investigations of the effect of potential on the PL and EL of PS. A mechanism of 'potential tuning' based on electron occupancy and Auger quenching is then proposed.

Destruction-free parameter extraction for a physics-based circuit simulator IGCT model

This paper presents a practical destruction-free parameter extraction methodology for a new physics-based circuit simulator buffer-layer Integrated Gate Commutated Thyristor (IGCT) model. Most key parameters needed for this model can be extracted by one simple clamped inductive-load switching experiment. To validate this extraction method, a clamped inductive load switching experiment was performed, and corresponding simulations were carried out by employing the IGCT model with parameters extracted through the presented methodology. Good agreement has been obtained between the experimental data and simulation results.

Study of the electric loading aspects of the BDFM using a lumped parameter thermal model

Details of a lumped parameter thermal model for studying thermal aspects of the frame size 180 nested loop rotor BDFM at the University of Cambridge are presented. Predictions of the model are verified against measured end winding and rotor bar temperatures that were measured with the machine excited from a DC source. The model is used to assess the thermal coupling between the stator windings and rotor heating. The thermal coupling between the stator windings is assessed by studying the difference of the steady state temperatures of the two stator end windings for different excitations. The rotor heating is assessed by studying the temperatures of regions of interest for different excitations.

Parameter learning for POMDP spoken dialogue models

The partially observable Markov decision process (POMDP) provides a popular framework for modelling spoken dialogue. This paper describes how the expectation propagation algorithm (EP) can be used to learn the parameters of the POMDP user model. Various special probability factors applicable to this task are presented, which allow the parameters be to learned when the structure of the dialogue is complex. No annotations, neither the true dialogue state nor the true semantics of user utterances, are required. Parameters optimised using the proposed techniques are shown to improve the performance of both offline transcription experiments as well as simulated dialogue management performance. ©2010 IEEE.