High speed electro-thermal models for inverter simulations

This paper presents the application of advanced compact models of the IGBT and PIN diode to the full electrothermal system simulation of a hybrid electric vehicle converter using a look-up table of device losses. The Fourier-based solution model is used, which takes account of features such as local lifetime control and field-stop technology. Device and circuit parameters are extracted from experimental waveforms and device structural data. Matching of the switching waveforms and the resulting generation of the look-up table is presented. An example of the use of the look-up tables in simulation of inverter device temperatures is also given, for a hypothetical electric vehicle subjected to an urban driving cycle. © 2006 IEEE.

Ultra-structural defects cause low bone matrix stiffness despite high mineralization in osteogenesis imperfecta mice.

Bone is a complex material with a hierarchical multi-scale organization from the molecule to the organ scale. The genetic bone disease, osteogenesis imperfecta, is primarily caused by mutations in the collagen type I genes, resulting in bone fragility. Because the basis of the disease is molecular with ramifications at the whole bone level, it provides a platform for investigating the relationship between structure, composition, and mechanics throughout the hierarchy. Prior studies have individually shown that OI leads to: 1. increased bone mineralization, 2. decreased elastic modulus, and 3. smaller apatite crystal size. However, these have not been studied together and the mechanism for how mineral structure influences tissue mechanics has not been identified. This lack of understanding inhibits the development of more accurate models and therapies. To address this research gap, we used a mouse model of the disease (oim) to measure these outcomes together in order to propose an underlying mechanism for the changes in properties. Our main finding was that despite increased mineralization, oim bones have lower stiffness that may result from the poorly organized mineral matrix with significantly smaller, highly packed and disoriented apatite crystals. Using a composite framework, we interpret the lower oim bone matrix elasticity observed as the result of a change in the aspect ratio of apatite crystals and a disruption of the crystal connectivity.

Construction of nonparametric Bayesian models from parametric Bayes equations

We consider the general problem of constructing nonparametric Bayesian models on infinite-dimensional random objects, such as functions, infinite graphs or infinite permutations. The problem has generated much interest in machine learning, where it is treated heuristically, but has not been studied in full generality in non-parametric Bayesian statistics, which tends to focus on models over probability distributions. Our approach applies a standard tool of stochastic process theory, the construction of stochastic processes from their finite-dimensional marginal distributions. The main contribution of the paper is a generalization of the classic Kolmogorov extension theorem to conditional probabilities. This extension allows a rigorous construction of nonparametric Bayesian models from systems of finite-dimensional, parametric Bayes equations. Using this approach, we show (i) how existence of a conjugate posterior for the nonparametric model can be guaranteed by choosing conjugate finite-dimensional models in the construction, (ii) how the mapping to the posterior parameters of the nonparametric model can be explicitly determined, and (iii) that the construction of conjugate models in essence requires the finite-dimensional models to be in the exponential family. As an application of our constructive framework, we derive a model on infinite permutations, the nonparametric Bayesian analogue of a model recently proposed for the analysis of rank data.

Improving data quality in DSM modelling: A structural comparison approach

The Dependency Structure Matrix (DSM) has proved to be a useful tool for system structure elicitation and analysis. However, as with any modelling approach, the insights gained from analysis are limited by the quality and correctness of input information. This paper explores how the quality of data in a DSM can be enhanced by elicitation methods which include comparison of information acquired from different perspectives and levels of abstraction. The approach is based on comparison of dependencies according to their structural importance. It is illustrated through two case studies: creation of a DSM showing the spatial connections between elements in a product, and a DSM capturing information flows in an organisation. We conclude that considering structural criteria can lead to improved data quality in DSM models, although further research is required to fully explore the benefits and limitations of our proposed approach.

A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models

In this article, we develop a new Rao-Blackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forward-filtering backward-simulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the non-Gaussian state component while treating the linear part analytically. Unlike the previously proposed backward-simulation based Rao-Blackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of two-filter smoother based approaches. The performance of the algorithm is illustrated in a simulated application. © 2012 IFAC.

A comparison of two models for the vibration response of piled foundations to inertial and underground-railway-induced loadings

The vibration response of piled foundations due to ground-borne vibration produced by an underground railway is a largely-neglected area in the field of structural dynamics. However, this continues to be an important aspect of research as it is expected that the presence of piled foundations can have a significant influence on the propagation and transmission of the wavefield produced by the underground railway. This paper presents a comparison of two methods that can be employed in calculating the vibration response of a piled foundation: an efficient semi-analytical model, and a Boundary Element model. The semi-analytical model uses a column or an Euler beam to model the pile, and the soil is modelled as a linear, elastic continuum that has the geometry of a thick-walled cylinder with an infinite outer radius and an inner radius equal to the radius of the pile. The boundary element model uses a constant-element BEM formulation for the halfspace, and a rectangular discretisation of the circular pile-soil interface. The piles are modelled as Timoshenko beams. Pile-soil-pile interactions are inherently accounted for in the BEM equations, whereas in the semi-analytical model these are quantified using the superposition of interaction factors. Both models use the method of joining subsystems to incorporate the incident wavefield generated by the underground railway into the pile model. Results are computed for a single pile subject to an inertial loading, pile-soil-pile interactions, and a pile group subjected to excitation from an underground railway. The two models are compared in terms of accuracy, computation time, versatility and applicability, and guidelines for future vibration prediction models involving piled foundations are proposed.

Paraphrastic language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, this paper presents a novel form of language model, the paraphrastic LM. A phrase level transduction model that is statistically learned from standard text data is used to generate paraphrase variants. LM probabilities are then estimated by maximizing their marginal probability. Significant error rate reductions of 0.5%-0.6% absolute were obtained on a state-ofthe-art conversational telephone speech recognition task using a paraphrastic multi-level LM modelling both word and phrase sequences.