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.

An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods. © 2009 IFAC.

Parameter measurement for heavy-vehicle fuel consumption modelling

A mathematical model is developed to predict the energy consumption of a heavy vehicle. It includes the important factors of heavy-vehicle energy consumption, namely engine and drivetrain performances, losses due to accessories, aerodynamic drag, rolling resistance, road gradients, and driver behaviour. Novel low-cost testing methods were developed to determine engine and drivetrain characteristics. A simple drive cycle was used to validate the model. The model is able to predict the fuel use for a 371 tractor-semitrailer vehicle over a 4 km drive cycle within 1 per cent. This paper demonstrates that accurate and reliable vehicle benchmarking and model parameter measurement can be achieved without expensive equipment overheads, e.g. engine and chassis dynamometers.

Seismic model parameter estimation using least-squares migration

We describe a method for verifying seismic modelling parameters. It is equivalent to performing several iterations of unconstrained least-squares migration (LSM). The approach allows the comparison of modelling/imaging parameter configurations with greater confidence than simply viewing the migrated images. The method is best suited to determining discrete parameters but can be used for continuous parameters albeit with greater computational expense.

Parameter extraction and calorimetric validation for a silicon carbide jfet pspice model

This paper focuses on the PSpice model of SiC-JFET element inside a SiCED cascode device. The device model parameters are extracted from the I-V and C-V characterization curves. In order to validate the model, an inductive test rig circuit is designed and tested. The switching loss is estimated both using oscilloscope and calorimeter. These results are found to be in good agreement with the simulated results.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.

Parameter estimation for agenda-based user simulation

This paper presents an agenda-based user simulator which has been extended to be trainable on real data with the aim of more closely modelling the complex rational behaviour exhibited by real users. The train-able part is formed by a set of random decision points that may be encountered during the process of receiving a system act and responding with a user act. A sample-based method is presented for using real user data to estimate the parameters that control these decisions. Evaluation results are given both in terms of statistics of generated user behaviour and the quality of policies trained with different simulators. Compared to a handcrafted simulator, the trained system provides a much better fit to corpus data and evaluations suggest that this better fit should result in improved dialogue performance. © 2010 Association for Computational Linguistics.