Bayesian learning of noisy Markov decision processes

This work addresses the problem of estimating the optimal value function in a Markov Decision Process from observed state-action pairs. We adopt a Bayesian approach to inference, which allows both the model to be estimated and predictions about actions to be made in a unified framework, providing a principled approach to mimicry of a controller on the basis of observed data. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from theposterior distribution over the optimal value function. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.

Analytical model for the 3D-RESURF effect

This paper presents an analytical model for the determination of the basic breakdown properties of three-dimensional (3D)-RESURF/CoolMOS/super junction type structures. To account for the two-dimensional (2D) effect of the 3D-RESURF action, 2D models of the electric field distribution are developed. Based on these, expressions are derived for the breakdown voltage as a function of doping concentration and physical dimensions. In addition to cases where the drift regions are fully depleted, the model developed is also applicable to situations involving drift regions which are almost depleted. Accuracy of the analytical approach is verified by comparison with numerical results obtained from the MEDICI device simulator.

Thin film shape memory alloys and microactuators

For Micro-electro-mechanical System (MEMS) applications, TiNi-based thin film Shape Memory Alloys (SMAs) possess many desirable properties, such as high power density, large transformation stress and strain upon heating and cooling, superelasticity and biocompatibility. In this paper, recent development in TiNi-based thin film SMA and microactuator applications is discussed. The topics related to film deposition and characterisation is mainly focused on crystal nucleation and growth during annealing, film thickness effect, film texture, stress induced surface relief, wrinkling and trenches as well as Temperature Memory Effect (TME). The microactuator applications are mainly focused on microvalve and microcage for biological applications, micromirror for optical applications and data storage using nanoindentation method. Copyright © 2009, Inderscience Publishers.

Industrial ecology at factory level: A prototype methodology

The concept of sustainable manufacturing is a form of pollution prevention that integrates environmental considerations in the production of goods while focusing on efficient resource use. Taking the industrial ecology perspective, this efficiency comes from improved resource flow management. The assessment of material, energy and waste resource flows, therefore, offers a route to viewing and analysing a manufacturing system as an ecosystem using industrial ecology biological analogy and can, in turn, support the identification of improvement opportunities in the material, energy and waste flows. This application of industrial ecology at factory level is absent from the literature. This article provides a prototype methodology to apply the concepts of industrial ecology using material, energy and waste process flows to address this gap in the literature. Various modelling techniques were reviewed and candidates selected to test the prototype methodology in an industrial case. The application of the prototype methodology showed the possibility of using the material, energy and waste resource flows through the factory to link manufacturing operations and supporting facilities, and to identify potential improvements in resource use. The outcomes of the work provide a basis to build the specifications for a modelling tool that can support those analysing their manufacturing system to improve their environmental performance and move towards sustainable manufacturing. © IMechE 2012.

Large scale heterogeneous networks, the Davis-Wielandt shell, and graph separation

We consider a large scale network of interconnected heterogeneous dynamical components. Scalable stability conditions are derived that involve the input/output properties of individual subsystems and the interconnection matrix. The analysis is based on the Davis-Wielandt shell, a higher dimensional version of the numerical range with important convexity properties. This can be used to allow heterogeneity in the agent dynamics while relaxing normality and symmetry assumptions on the interconnection matrix. The results include small gain and passivity approaches as special cases, with the three dimensional shell shown to be inherently connected with corresponding graph separation arguments. © 2012 Society for Industrial and Applied Mathematics.

Manopt, a matlab toolbox for optimization on manifolds

Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on problems where the smooth geometry of the search space can be leveraged to design effcient numerical algorithms. In particular, optimization on manifolds is well-suited to deal with rank and orthogonality constraints. Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network registration, independent component analysis, metric learning, dimensionality reduction and so on. The Manopt toolbox, available at www.manopt.org, is a user-friendly, documented piece of software dedicated to simplify experimenting with state of the art Riemannian optimization algorithms. By dealing internally with most of the differential geometry, the package aims particularly at lowering the entrance barrier. © 2014 Nicolas Boumal.