The Line Spectral Frequency Model of a Finite-Length Sequence

The line spectral frequency (LSF) of a causal finite length sequence is a frequency at which the spectrum of the sequence annihilates or the magnitude spectrum has a spectral null. A causal finite-length sequencewith (L + 1) samples having exactly L-LSFs, is referred as an Annihilating (AH) sequence. Using some spectral properties of finite-length sequences, and some model parameters, we develop spectral decomposition structures, which are used to translate any finite-length sequence to an equivalent set of AH-sequences defined by LSFs and some complex constants. This alternate representation format of any finite-length sequence is referred as its LSF-Model. For a finite-length sequence, one can obtain multiple LSF-Models by varying the model parameters. The LSF-Model, in time domain can be used to synthesize any arbitrary causal finite-length sequence in terms of its characteristic AH-sequences. In the frequency domain, the LSF-Model can be used to obtain the spectral samples of the sequence as a linear combination of spectra of its characteristic AH-sequences. We also summarize the utility of the LSF-Model in practical discrete signal processing systems.

Flow Generated By Pitched Blade Turbines Ii: Simulation Using Κ-Ε Model

Experimental data on average velocity and turbulence intensity generated by pitched blade downflow turbines (PTD) were presented in Part I of this paper. Part II presents the results of the simulation of flow generated by PTD The standard κ-ε model along with the boundary conditions developed in the Part 1 have been employed to predict the flow generated by PTD in cylindrical baffled vessel. This part describes the new software FIAT (Flow In Agitated Tanks) for the prediction of three dimensional flow in stirred tanks. The basis of this software has been described adequately. The influence of grid size, impeller boundary conditions and values of model parameters on the predicted flow have been analysed. The model predictions successfully reproduce the three dimensionality and the other essential characteristics of the flow. The model can be used to improve the overall understanding about the relative distribution of turbulence by PTD in the agitated tank

An elastic-viscoplastic constitutive model for the hot-forming of aluminum alloys

Under hot-forming conditions characterized by high homologous temperatures and strain-rates, metals usually exhibit rate-dependent inelastic behavior. An elastic-viscoplastic constitutive model is presented here to describe metal behavior during hot-forming. The model uses an isotropic internal variable to represent the resistance offered to plastic deformation by the microstructure. Evolution equations are developed for the inelastic strain and the deformation resistance based on experimental results. A methodology is presented for extracting model parameters from constant true strain-rate compression tests performed at different temperatures. Model parameters are determined for an Al-1Mn alloy and an Al-Mg-Si alloy, and the predictions of the model are shown to be in good agreement with the experimental data. (C) 2000 Kluwer Academic Publishers.

Parametric study of MSW landfill settlement model

A newly developed and validated constitutive model that accounts for primary compression and time-dependent mechanical creep and biodegradation is used for parametric study to investigate the effects of model parameters on the predicted settlement of municipal solid waste (MSW) with time. The model enables the prediction of stress strain response and yield surfaces for three components of settlement: primary compression, mechanical creep, and biodegradation. The MSW parameters investigated include compression index, coefficient of earth pressure at-rest, overconsolidation ratio, and biodegradation parameters of MSW. A comparison of the predicted settlements for typical MSW landfill conditions showed significant differences in time-settlement response depending on the selected model input parameters. The effect of lift thickness of MSW on predicted settlement is also investigated. Overall, the study shows that the variation in the model parameters can lead to significantly different results; therefore, the model parameter values should be carefully selected to predict landfill settlements accurately. It is shown that the proposed model captures the time settlement response which is in general agreement with the results obtained from the other two reported models having similar features. (C) 2011 Elsevier Ltd. All rights reserved.

A numerical model of a liquid-feed solid polymer electrolyte DMFC and its experimental validation

A one-dimensional, biphasic, multicomponent steady-state model based on phenomenological transport equations for the catalyst layer, diffusion layer, and polymeric electrolyte membrane has been developed for a liquid-feed solid polymer electrolyte direct methanol fuel cell (SPE- DMFC). The model employs three important requisites: (i) implementation of analytical treatment of nonlinear terms to obtain a faster numerical solution as also to render the iterative scheme easier to converge, (ii) an appropriate description of two-phase transport phenomena in the diffusive region of the cell to account for flooding and water condensation/evaporation effects, and (iii) treatment of polarization effects due to methanol crossover. An improved numerical solution has been achieved by coupling analytical integration of kinetics and transport equations in the reaction layer, which explicitly include the effect of concentration and pressure gradient on cell polarization within the bulk catalyst layer. In particular, the integrated kinetic treatment explicitly accounts for the nonhomogeneous porous structure of the catalyst layer and the diffusion of reactants within and between the pores in the cathode. At the anode, the analytical integration of electrode kinetics has been obtained within the assumption of macrohomogeneous electrode porous structure, because methanol transport in a liquid-feed SPE- DMFC is essentially a single-phase process because of the high miscibility of methanol with water and its higher concentration in relation to gaseous reactants. A simple empirical model accounts for the effect of capillary forces on liquid-phase saturation in the diffusion layer. Consequently, diffusive and convective flow equations, comprising Nernst-Plank relation for solutes, Darcy law for liquid water, and Stefan-Maxwell equation for gaseous species, have been modified to include the capillary flow contribution to transport. To understand fully the role of model parameters in simulating the performance of the DMCF, we have carried out its parametric study. An experimental validation of model has also been carried out. (C) 2003 The Electrochemical Society.

A rational approach to model the behavior of bonded soils

The paper presents a rational approach to model the behavior of bonded soils within the frame work of hardening plasticity. The approach is based on the premise that the resistance of bonded materials is a superposition of the two components of cement bond strength and soil frictional strength and that the deformation of the soil is associated with the frictional component of stresses just as in the case of a remoulded soil, the bonds offering additional resistance at any given strain level. This concept is similar to two stiffnesses acting in parallel for the same strain response. The proposed model considers the constitutive laws separately for the two components (bond and frictional) and adds the two to get the overall response. The unbonded soil component is described by the well known 'modified Cam clay' model. The response of the bond component is also described by a strain softening elasto-plastic model, considering the behavior to be elastic up to the yield surface and elasto-plastic beyond yield surface. To illustrate the capability of the proposed, model some laboratory test results of both compression and-extension shear tests are predicted. Despite the model being simple, several typical features of the behavior of bonded materials are well reproduced. The model parameters are well defined and easily determinable.

A spring-mass-lever model, stiffness and inertia maps for single-input, single-output compliant mechanisms

A spring-mass-lever (SML) model is introduced in this paper for a single-input-single-output compliant mechanism to capture its static and dynamic behavior. The SML model is a reduced-order model, and its five parameters provide physical insight and quantify the stiffness and inertia(1) at the input and output ports as well as the transformation of force and displacement between the input and output. The model parameters can be determined with reasonable accuracy without performing dynamic or modal analysis. The paper describes two uses of the SML model: computationally efficient analysis of a system of which the compliant mechanism is a part; and design of compliant mechanisms for the given user-specifications. During design, the SML model enables determining the feasible parameter space of user-specified requirements, assessing the suitability of a compliant mechanism to meet the user-specifications and also selecting and/or re-designing compliant mechanisms from an existing database. Manufacturing constraints, material choice, and other practical considerations are incorporated into this methodology. A micromachined accelerometer and a valve mechanism are used as examples to show the effectiveness of the SML model in analysis and design. (C) 2012 Published by Elsevier Ltd.

Estimation of Parameters for Battery Storage Models

This paper presents a comprehensive and robust strategy for the estimation of battery model parameters from noise corrupted data. The deficiencies of the existing methods for parameter estimation are studied and the proposed parameter estimation strategy improves on earlier methods by working optimally for low as well as high discharge currents, providing accurate estimates even under high levels of noise, and with a wide range of initial values. Testing on different data sets confirms the performance of the proposed parameter estimation strategy.

An approximate likelihood method for estimating static parameters in multi-target tracking models

This paper considers a class of dynamic Spatial Point Processes (PP) that evolves over time in a Markovian fashion. This Markov in time PP is hidden and observed indirectly through another PP via thinning, displacement and noise. This statistical model is important for Multi object Tracking applications and we present an approximate likelihood based method for estimating the model parameters. The work is supported by an extensive numerical study.

Optical designs for improved solar cell performance

The solar resource is the most abundant renewable resource on earth, yet it is currently exploited with relatively low efficiencies. To make solar energy more affordable, we can either reduce the cost of the cell or increase the efficiency with a similar cost cell. In this thesis, we consider several different optical approaches to achieve these goals. First, we consider a ray optical model for light trapping in silicon microwires. With this approach, much less material can be used, allowing for a cost savings. We next focus on reducing the escape of radiatively emitted and scattered light from the solar cell. With this angle restriction approach, light can only enter and escape the cell near normal incidence, allowing for thinner cells and higher efficiencies. In Auger-limited GaAs, we find that efficiencies greater than 38% may be achievable, a significant improvement over the current world record. To experimentally validate these results, we use a Bragg stack to restrict the angles of emitted light. Our measurements show an increase in voltage and a decrease in dark current, as less radiatively emitted light escapes. While the results in GaAs are interesting as a proof of concept, GaAs solar cells are not currently made on the production scale for terrestrial photovoltaic applications. We therefore explore the application of angle restriction to silicon solar cells. While our calculations show that Auger-limited cells give efficiency increases of up to 3% absolute, we also find that current amorphous silicion-crystalline silicon heterojunction with intrinsic thin layer (HIT) cells give significant efficiency gains with angle restriction of up to 1% absolute. Thus, angle restriction has the potential for unprecedented one sun efficiencies in GaAs, but also may be applicable to current silicon solar cell technology. Finally, we consider spectrum splitting, where optics direct light in different wavelength bands to solar cells with band gaps tuned to those wavelengths. This approach has the potential for very high efficiencies, and excellent annual power production. Using a light-trapping filtered concentrator approach, we design filter elements and find an optimal design. Thus, this thesis explores silicon microwires, angle restriction, and spectral splitting as different optical approaches for improving the cost and efficiency of solar cells.

Natural actor and belief critic: Reinforcement algorithm for learning parameters of dialogue systems modelled as POMDPs

This article presents a novel algorithm for learning parameters in statistical dialogue systems which are modeled as Partially Observable Markov Decision Processes (POMDPs). The three main components of a POMDP dialogue manager are a dialogue model representing dialogue state information; a policy that selects the system's responses based on the inferred state; and a reward function that specifies the desired behavior of the system. Ideally both the model parameters and the policy would be designed to maximize the cumulative reward. However, while there are many techniques available for learning the optimal policy, no good ways of learning the optimal model parameters that scale to real-world dialogue systems have been found yet. The presented algorithm, called the Natural Actor and Belief Critic (NABC), is a policy gradient method that offers a solution to this problem. Based on observed rewards, the algorithm estimates the natural gradient of the expected cumulative reward. The resulting gradient is then used to adapt both the prior distribution of the dialogue model parameters and the policy parameters. In addition, the article presents a variant of the NABC algorithm, called the Natural Belief Critic (NBC), which assumes that the policy is fixed and only the model parameters need to be estimated. The algorithms are evaluated on a spoken dialogue system in the tourist information domain. The experiments show that model parameters estimated to maximize the expected cumulative reward result in significantly improved performance compared to the baseline hand-crafted model parameters. The algorithms are also compared to optimization techniques using plain gradients and state-of-the-art random search algorithms. In all cases, the algorithms based on the natural gradient work significantly better. © 2011 ACM.

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.

Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment

In this paper, we consider Bayesian interpolation and parameter estimation in a dynamic sinusoidal model. This model is more flexible than the static sinusoidal model since it enables the amplitudes and phases of the sinusoids to be time-varying. For the dynamic sinusoidal model, we derive a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment of lost audio and speech packets. © EURASIP, 2010.

A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method. © 2012 Elsevier B.V. All rights reserved.

A Hierarchical Model for Ordinal Matrix Factorization

This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based on Gibbs sampling and one based on variational Bayes. Importantly, these algorithms may be implemented in the factorization of very large matrices with missing entries. The model is evaluated on a collaborative filtering task, where users have rated a collection of movies and the system is asked to predict their ratings for other movies. The Netflix data set is used for evaluation, which consists of around 100 million ratings. Using root mean-squared error (RMSE) as an evaluation metric, results show that the suggested model outperforms alternative factorization techniques. Results also show how Gibbs sampling outperforms variational Bayes on this task, despite the large number of ratings and model parameters. Matlab implementations of the proposed algorithms are available from cogsys.imm.dtu.dk/ordinalmatrixfactorization.