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.

Parameter optimization for automated concrete detection in image data

Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.

The parameter space of graphene chemical vapor deposition on polycrystalline Cu

A systematic study of the parameter space of graphene chemical vapor deposition (CVD) on polycrystalline Cu foils is presented, aiming at a more fundamental process rationale in particular regarding the choice of carbon precursor and mitigation of Cu sublimation. CH 4 as precursor requires H 2 dilution and temperatures ≥1000 °C to keep the Cu surface reduced and yield a high-quality, complete monolayer graphene coverage. The H 2 atmosphere etches as-grown graphene; hence, maintaining a balanced CH 4/H 2 ratio is critical. Such balance is more easily achieved at low-pressure conditions, at which however Cu sublimation reaches deleterious levels. In contrast, C 6H 6 as precursor requires no reactive diluent and consistently gives similar graphene quality at 100-150 °C lower temperatures. The lower process temperature and more robust processing conditions allow the problem of Cu sublimation to be effectively addressed. Graphene formation is not inherently self-limited to a monolayer for any of the precursors. Rather, the higher the supplied carbon chemical potential, the higher the likelihood of film inhomogeneity and primary and secondary multilayer graphene nucleation. For the latter, domain boundaries of the inherently polycrystalline CVD graphene offer pathways for a continued carbon supply to the catalyst. Graphene formation is significantly affected by the Cu crystallography; i.e., the evolution of microstructure and texture of the catalyst template form an integral part of the CVD process. © 2012 American Chemical Society.

Stable multivariate rational interpolation for parameter-dependent aerospace models

A multivariate, robust, rational interpolation method for propagating uncertainties in several dimensions is presented. The algorithm for selecting numerator and denominator polynomial orders is based on recent work that uses a singular value decomposition approach. In this paper we extend this algorithm to higher dimensions and demonstrate its efficacy in terms of convergence and accuracy, both as a method for response suface generation and interpolation. To obtain stable approximants for continuous functions, we use an L2 error norm indicator to rank optimal numerator and denominator solutions. For discontinous functions, a second criterion setting an upper limit on the approximant value is employed. Analytical examples demonstrate that, for the same stencil, rational methods can yield more rapid convergence compared to pseudospectral or collocation approaches for certain problems. © 2012 AIAA.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.

Growth parameter dependence of magnetic property of CrAs thin film

This paper has systematically investigated the substrate temperature and thickness dependence of surface morphology and magnetic property of CrAs compound films grown on GaAs by molecular-beam epitaxy. It finds that the substrate temperature affects the surface morphology and magnetic property of CrAs thin film more potently than the thickness.

Growth-Parameter Spaces and Optical Properties of Cubic Boron Nitride Films on Si(001)

Cubic boron nitride (c-BN) films were deposited on Si(001) substrates in an ion beam assisted deposition (IBAD) system under various conditions, and the growth parameter spaces and optical properties of c-BN films have been investigated systematically. The results indicate that suitable ion bombardment is necessary for the growth of c-BN films, and a well defined parameter space can be established by using the P/a-parameter. The refractive index of BN films keeps a constant of 1.8 for the c-BN content lower than 50%, while for c-BN films with higher cubic phase the refractive index increases with the c-BN content from 1.8 at chi(c) = 50% to 2.1 at chi(c) = 90%. Furthermore, the relationship between n and rho for BN films can be described by the Anderson-Schreiber equation, and the overlap field parameter gamma is determined to be 2.05.

Experimental Demonstration on Structure-Parameter Dependence of Photonic Crystal Optical Spectrum

We present fabrication and experimental measurement of a series of photonic crystal waveguides and coupled structure of PC waveguide and PC micro-cavity. The complete devices consist of an injector taper down from 3 mu m into a triangular-lattice air-holes single-line-defect waveguide. We fabricated these devices on a silicon-on-insulator substrate and characterized them using tunable laser source. We've obtained high-efficiency light propagation and broad flat spectrum response of photonic-crystal waveguides. A sharp attenuation at photonic crystal waveguide mode edge was observed for most structures. The edge of guided band is shifted about 31 nm with the 10 nm increase of lattice constant. Mode resonance was observed in coupled structure. Our experimental results indicate that the optical spectra of photonic crystal are very sensitive to structure parameters.

Growth Parameter Dependence of Structural Characterizations of Diluted Magnetic Semiconductor (Ga, Cr)As

We report the influence of growth parameters and post-growth annealing on the structural characterizations and magnetic properties of (Ga, Cr)As films. The crystalline quality and magnetic properties are sensitive to the growth conditions. The single-phase (Ga, Cr)As film with the Curie temperature of 10 K is synthesized at growth temperature T-s = 250 degrees C and with nominal Cr content x = 0.016. However, for the films with x > 0.02, the aggregation of Cr atoms is strongly enhanced as both T. and x increase, which not only brings strong compressive strain in the epilayer, but also roughens the surface. The origin of room-temperature ferromagnetism in (Ga, Cr)As films with nanoclusters is also discussed.

Measurement of chirp parameter and modulation index of a semiconductor laser based on optical spectrum analysis

A novel and simple method for measuring the chirp parameter, frequency, and intensity modulation indexes of directly modulated lasers is proposed in a small-signal modulation scheme. A graphical approach is presented. An analytical solution to the measurement of low chirp parameters is also given. The measured results agree well with those obtained using the conventional methods.

Geometry parameter optimization of large cross-sectional S-shaped bent rib waveguides for silicon based optical switches

地址: Chinese Acad Sci, Inst Semicond, State Key Lab Integrated Optoelect, Beijing 100083, Peoples R China

Method for measurement of lattice parameter of cubic GaN layers on GaAs (001)

An extended technique derived from triple-axis diffraction setup was proposed to measure lattice parameters of cubic GaN(c-GaN) films. The fully relaxed lattice parameters of c-GaN are determined to be 4.5036+0.0004 Angstrom, which is closer to the values of a hypothetical perfect crystal. The speculated zero setting correction (Deltatheta) is very slight and within the range of the accuracy of measurement. Additionally, we applied this method to analyze strain of four different kinds of c-GaN samples. It is found that in-plane strain caused by large lattice mismatch and thermal expansion coefficients mismatch directly influence the epilayer growth at high temperatures, indicating that the relaxation of tensile strain after thermal annealing helps to improve the crystalline quality of c-GaN films and optical properties. (C) 2003 Elsevier Science B.V. All rights reserved.