Hybrid model predictive control applied to switching control of burner load for a compact marine boi

This paper discusses the application of hybrid model predictive control to control switching between different burner modes in a novel compact marine boiler design. A further purpose of the present work is to point out problems with finite horizon model predictive control applied to systems for which the optimal solution is a limit cycle. Regarding the marine boiler control the aim is to find an optimal control strategy which minimizes a trade-off between deviations in boiler pressure and water level from their respective setpoints while limiting burner switches.The approach taken is based on the Mixed Logic Dynamical framework. The whole boiler systems is modelled in this framework and a model predictive controller is designed. However to facilitate on-line implementation only a small part of the search tree in the mixed integer optimization is evaluated to find out whether a switch should occur or not. The strategy is verified on a simulation model of the compact marine boiler for control of low/high burner load switches. It is shown that even though performance is adequate for some disturbance levels it becomes deteriorated when the optimal solution is a limit cycle. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.

Parallel implementation of Multilevel BDDC

In application of the Balancing Domain Decomposition by Constraints (BDDC) to a case with many substructures, solving the coarse problem exactly becomes the bottleneck which spoils scalability of the solver. However, it is straightforward for BDDC to substitute the exact solution of the coarse problem by another step of BDDC method with subdomains playing the role of elements. In this way, the algorithm of three-level BDDC method is obtained. If this approach is applied recursively, multilevel BDDC method is derived. We present a detailed description of a recently developed parallel implementation of this algorithm. The implementation is applied to an engineering problem of linear elasticity and a benchmark problem of Stokes flow in a cavity. Results by the multilevel approach are compared to those by the standard (two-level) BDDC method.

The influence of a grain boundary on the thermal transport properties of bulk, melt-processed Y-Ba-Cu-O

We report the dependence of thermal conductivity, thermoelectric power and electrical resistivity on temperature for a bulk, large grain melt-processed Y-Ba-Cu-O (YBCO) high temperature superconductor (HTS) containing two grains separated by a well-defined grain boundary. Transport measurements at temperatures between 10 and 300 K were carried out both within one single grain (intra-granular properties) and across the grain boundary (inter-granular properties). The influence of an applied external magnetic field of up to 8 T on the measured sample properties was also investigated. The presence of the grain boundary is found to affect strongly the electrical resistivity of the melt-processed bulk sample, but has almost no effect on its thermoelectric power and thermal conductivity, within experimental error. The results of this study provide direct evidence that the heat flow in multi-granular melt-processed YBCO bulk samples should be virtually unaffected by the presence of grain boundaries in the material. © 2013 IOP Publishing Ltd.

High-resolution γ-ray spectroscopy: A versatile tool for nuclear β-decay studies at TRIUMF-ISAC

High-resolution γ-ray spectroscopy is essential to fully exploit the unique, high-quality beams available at the next generation of radioactive ion beam facilities such as the TRIUMF isotope separator and accelerator (ISAC). The 8π spectrometer, which consists of 20 Compton-suppressed HPGe detectors, has recently been reconfigured for a vigorous research programme in weak interaction and nuclear structure physics. With the addition of a variety of ancillary detectors it has become the world's most powerful device dedicated to β-decay studies. This paper provides a brief overview of the apparatus and highlights from recent experiments. © 2005 IOP Publishing Ltd.

Discriminative spoken language understanding using word confusion networks

Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output. © 2012 IEEE.

Policy optimisation of POMDP-based dialogue systems without state space compression

The partially observable Markov decision process (POMDP) has been proposed as a dialogue model that enables automatic improvement of the dialogue policy and robustness to speech understanding errors. It requires, however, a large number of dialogues to train the dialogue policy. Gaussian processes (GP) have recently been applied to POMDP dialogue management optimisation showing an ability to substantially increase the speed of learning. Here, we investigate this further using the Bayesian Update of Dialogue State dialogue manager. We show that it is possible to apply Gaussian processes directly to the belief state, removing the need for a parametric policy representation. In addition, the resulting policy learns significantly faster while maintaining operational performance. © 2012 IEEE.