Effects of the user model on simulation-based learning of dialogue strategies

Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model. © 2005 IEEE.

Optimisation of the chirp performance of electroabsorption modulators using a numerical system model

A model to simulate an electroabsorption modulator in a dispersive communications system is described and confirmed experimentally for a 5Gbit/s 100km system. Optimisation of the device shows that transmission of 10Gbit/s over 100km is possible.

Tsunami wave loading on coastal houses: A model approach

The Asian tsunami of 26 December 2004 killed over 220 000 people and devastated coastal structures, including many thousands of traditional brick-built homes. This paper presents the results of model tests that compare the impact of a tsunami wave on a typical coastal house with that on a new tsunami resistant design developed in the USA and now built in Sri Lanka Digital images recorded during the test reveal how the tsunami wave passed through the new house design without damaging it but severely damaged the typical coastal house. Pressure sensor results also provided further insight into tsunami wave loading, indicating that the established Japanese method significantly underestimates maximum impact load.

The elastic-plastic indentation response of a columnar thermal barrier coating

Thermal barrier coatings with a columnar microstructure are prone to erosion damage by a mechanism of surface cracking upon impact by small foreign particles. In order to explore this erosion mechanism, the elastic indentation and the elastic-plastic indentation responses of a columnar thermal barrier coating to a spherical indenter were determined by the finite element method and by analytical models. It was shown that the indentation response is intermediate between that of a homogeneous half-space and that given by an elastic-plastic mattress model (with the columns behaving as independent non-linear springs). The sensitivity of the indentation behaviour to geometry and to the material parameters was explored: the diameter of the columns, the gap width between columns, the coefficient of Coulomb friction between columns and the layer height of the thermal barrier coating. The calculations revealed that the level of induced tensile stress is sufficient to lead to cracking of the columns at a depth of about the column radius. It was also demonstrated that the underlying soft bond coat can undergo plastic indentation when the coating comprises parallel columns, but this is less likely for the more realistic case of a random arrangement of tapered columns. © 2009 Elsevier B.V.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. ©2010 IEEE.

Surface excavation with model predictive control

This paper describes the application of variable-horizon model predictive control to trajectory generation in surface excavation. A nonlinear dynamic model of a surface mining machine digging in oil sand is developed as a test platform. This model is then stabilised with an inner-loop controller before being linearised to generate a prediction model. The linear model is used to design a predictive controller for trajectory generation. A variable horizon formulation is augmented with extra terms in the cost function to allow more control over digging, whilst still preserving the guarantee of finite-time completion. Simulations show the generation of realistic trajectories, motivating new applications of variable horizon MPC for autonomy that go beyond the realm of vehicle path planning. ©2010 IEEE.

Flow of wet powder in a conical centrifugal filter-an analytical model

A one-dimensional analytical model is developed for the steady state, axisymmetric, slender flow of saturated powder in a rotating perforated cone. Both the powder and the fluid spin with the cone with negligible slip in the hoop direction. They migrate up the wall of the cone along a generator under centrifugal force, which also forces the fluid out of the cone through the powder layer and the porous wall. The flow thus evolves from an over-saturated paste at inlet into a nearly dry powder at outlet. The powder is treated as a Mohr-Coulomb granular solid of constant void fraction and permeability. The shear traction at the wall is assumed to be velocity and pressure dependent. The fluid is treated as Newtonian viscous. The model provides the position of the colour line (the transition from over- to under-saturation) and the flow velocity and thickness profiles over the cone. Surface tension effects are assumed negligible compared to the centrifugal acceleration. Two alternative conditions are considered for the flow structure at inlet: fully settled powder at inlet, and progressive settling of an initially homogeneous slurry. The position of the colour line is found to be similar for these two cases over a wide range of operating conditions. Dominant dimensionless groups are identified which control the position of the colour line in a continuous conical centrifuge. Experimental observations of centrifuges used in the sugar industry provide preliminary validation of the model. © 2011 Elsevier Ltd.

Theoretical model for dicke superradiance in a semiconductor laser device

A theoretical model for Dicke superradiance (SR) in diode lasers is proposed using the travelling wave method with a spatially resolved absorber and spectrally resolved gain. The role of electrode configuration and optical bandwidth are compared and contrasted as a route to enhance femtosecond pulse power. While pulse duration can be significantly reduced through careful absorber length specification, stability is degraded. However an increased spectral gain bandwidth of up to 150 nm is predicted to allow pulsewidth reductions of down to 10 fs and over 500-W peak power without further degradation in pulse stability. © 2011 IEEE.

Modelling of liquefaction-induced instability in pile

Centrifuge testing has been undertaken to investigate instability failure of pile groups during seismic liquefaction, with specific reference to the 'top-down' propagation of liquefaction during the earthquake and to account for initial imperfections in pile geometry. The results of these tests were used to validate numerical models within the finite element program ABAQUS, based on the popular p-y analysis method. Pseudostatic classical and post-buckling analyses were conducted to examine the collapse behaviour of the pile groups and were found to give reasonable predictions of collapse load and conservative predictions of the associated deflection conditions. This numerical model was compared to currently published methods which were found to over-predict collapse loads. The resulting insights into the collapse of axially loaded pile groups revealed that the failure load is strongly dependent on both the depth of liquefaction propagation and initial imperfections, which reduce the collapse load.

Multiplexed model predictive control

Most academic control schemes for MIMO systems assume all the control variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The multiplexed MPC scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, and hence improved performance, despite finding sub-optimal solutions to the original problem. The multiplexed MPC scheme is also closer to industrial practice in many cases. This paper presents initial stability results for two variants of multiplexed MPC, and illustrates the performance benefit by an example. Copyright copy; 2005 IFAC. Copyright © 2005 IFAC.

A whole-life value based assessment and optimisation model for high-performance glazed facades'

Façade design is a complex and multi-disciplinary process. One major barrier to devising optimal façade solutions is the lack of a systematic way of evaluating the true social, economic and environmental impacts of a design. Another barrier is the lack of automated design aids to assist decision-making. In this paper, we present our on-going study in developing a whole-life value based multi-objective optimisation model for high-performance façades. The principal outcome of this paper is a multi-objective optimisation model for early-stage façade design. The optimisation technique coupled with other 3rd party software and/or specially developed scripts provide façade designers with an integrated design tool of wide applicability.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for sigma point placement, potentially causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. © 2011 Elsevier B.V.

Multiplexed model predictive control

This paper proposes a form of MPC in which the control variables are moved asynchronously. This contrasts with most MIMO control schemes, which assume that all variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The Multiplexed MPC (MMPC) scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, which may result in improved performance, despite finding sub-optimal solutions to the original problem. This paper describes nominal and robust MMPC, states some stability results, and demonstrates the effectiveness of MMPC through two examples. © 2011 Elsevier Ltd. All rights reserved.

Optimal design of Functionally Graded Materials using a procedural model and Particle Swarm Optimization

A new method for the optimal design of Functionally Graded Materials (FGM) is proposed in this paper. Instead of using the widely used explicit functional models, a feature tree based procedural model is proposed to represent generic material heterogeneities. A procedural model of this sort allows more than one explicit function to be incorporated to describe versatile material gradations and the material composition at a given location is no longer computed by simple evaluation of an analytic function, but obtained by execution of customizable procedures. This enables generic and diverse types of material variations to be represented, and most importantly, by a reasonably small number of design variables. The descriptive flexibility in the material heterogeneity formulation as well as the low dimensionality of the design vectors help facilitate the optimal design of functionally graded materials. Using the nature-inspired Particle Swarm Optimization (PSO) method, functionally graded materials with generic distributions can be efficiently optimized. We demonstrate, for the first time, that a PSO based optimizer outperforms classical mathematical programming based methods, such as active set and trust region algorithms, in the optimal design of functionally graded materials. The underlying reason for this performance boost is also elucidated with the help of benchmarked examples. © 2011 Elsevier Ltd. All rights reserved.

An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.