183 resultados para Discrete Choice Experiment


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The DDES method is employed to investigate the complex physics involved in supersonic combustion and in particular in the SCHOLAR scramjet test case. The influence on computational results of prescribing turbulent fluctuations at the entrance to the combustion chamber is investigated. The interaction of shock waves, vortices, turbulence and combustion is studied and the existence of secondary vortices on the upper will of the combustor is proposed. © 2012 by Peter Cocks.

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Diagrammatic representations, such as process mapping and care pathways, have been often used for service evaluation and improvement in healthcare. While a broad range of diagrammatic representations exist, their application in healthcare has been very limited. There is a lack of understanding about how and which diagrams could be usable and useful to health workers. In this study, ten mental health workers were asked to discuss positive and negative issues around their service delivery using one or two diagrams of their choice out of seven different diagrams representing their service: care pathway diagram; organisation diagram; communication diagram; service blueprint; patient state transition diagram; free form diagram; geographic map. Their interactions with diagrams were video-taped for analysis. The patient state transition diagram was the most popular choice in spite of relatively low previous familiarity. The overall findings provided insight into a better use of diagrams in healthcare. © 2012 Springer-Verlag.

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While a large amount of research over the past two decades has focused on discrete abstractions of infinite-state dynamical systems, many structural and algorithmic details of these abstractions remain unknown. To clarify the computational resources needed to perform discrete abstractions, this paper examines the algorithmic properties of an existing method for deriving finite-state systems that are bisimilar to linear discrete-time control systems. We explicitly find the structure of the finite-state system, show that it can be enormous compared to the original linear system, and give conditions to guarantee that the finite-state system is reasonably sized and efficiently computable. Though constructing the finite-state system is generally impractical, we see that special cases could be amenable to satisfiability based verification techniques. ©2009 IEEE.

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In this paper, a novel MPC strategy is proposed, and referred to as asso MPC. The new paradigm features an 1-regularised least squares loss function, in which the control error variance competes with the sum of input channels magnitude (or slew rate) over the whole horizon length. This cost choice is motivated by the successful development of LASSO theory in signal processing and machine learning. In the latter fields, sum-of-norms regularisation have shown a strong capability to provide robust and sparse solutions for system identification and feature selection. In this paper, a discrete-time dual-mode asso MPC is formulated, and its stability is proven by application of standard MPC arguments. The controller is then tested for the problem of ship course keeping and roll reduction with rudder and fins, in a directional stochastic sea. Simulations show the asso MPC to inherit positive features from its corresponding regressor: extreme reduction of decision variables' magnitude, namely, actuators' magnitude (or variations), with a finite energy error, being particularly promising for over-actuated systems. © 2012 AACC American Automatic Control Council).

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Analyses of crack growth under cyclic loading conditions are discussed where plastic flow arises from the motion of large numbers of discrete dislocations and the fracture properties are embedded in a cohesive surface constitutive relation. The formulation is the same as used to analyse crack growth under monotonic loading conditions, differing only in the remote loading being a cyclic function of time. Fatigue, i.e. crack growth in cyclic loading at a driving force for which the crack would have arrested under monotonic loading, emerges in the simulations as a consequence of the evolution of internal stresses associated with the irreversibility of the dislocation motion. A fatigue threshold, Paris law behaviour, striations, the accelerated growth of short cracks and the scaling with material properties are outcomes of the calculations. Results for single crystals and polycrystals will be discussed.

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This paper presents the design of an AC loss experiment using nitrogen boil-off method. This experiment is aimed at exploring the AC loss of HTS double race-track coils which will be installed on the rotor of a wind turbine generator. The operating environment is simulated by designing a cryostat with rotating magnetic field windings. Apart from the fact that the alternating magnetic field causes most of AC loss on the HTS coils, we also believe that the DC background field would be another important factor causing AC loss if the HTS coil is experiencing by both alternating magnetic field in the perpendicular direction and DC background field in the parallel direction. In order to perform the boil-off measurement, we present the method to estimate the heat leakage in the cryostat which might cause errors to the measurement. © 2011 IEEE.

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This paper deals with the magnetic properties of bulk high temperature superconducting cylinders used as magnetic shields. We investigate, both numerically and experimentally, the magnetic properties of a hollow cylinder with two axial slits which cut the cylinder in equal halves. Finite element method modelling has been used with a three-dimensional geometry to help us in understanding how the superconducting currents flow in such a cut cylinder and therefore how the magnetic shielding properties are affected, depending on the magnetic field orientation. Modelling results show that the slits block the shielding current flow and act as an entrance channel for the magnetic flux lines. The contribution of the slits to the total flux density that enters the cylinder is studied through the angle formed between the applied field and the internal field. The modelled data agree nicely with magnetic shielding properties measured on a bulk Bi-2212 hollow cylinder at 77K. The results demonstrate that the magnetic flux penetration in such a geometry can be modelled successfully using only two parameters of the superconductor (constant J c and n value), which were determined from magnetic measurements on the plain cylinder. © 2012 IOP Publishing Ltd.

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The desire to seek new and unfamiliar experiences is a fundamental behavioral tendency in humans and other species. In economic decision making, novelty seeking is often rational, insofar as uncertain options may prove valuable and advantageous in the long run. Here, we show that, even when the degree of perceptual familiarity of an option is unrelated to choice outcome, novelty nevertheless drives choice behavior. Using functional magnetic resonance imaging (fMRI), we show that this behavior is specifically associated with striatal activity, in a manner consistent with computational accounts of decision making under uncertainty. Furthermore, this activity predicts interindividual differences in susceptibility to novelty. These data indicate that the brain uses perceptual novelty to approximate choice uncertainty in decision making, which in certain contexts gives rise to a newly identified and quantifiable source of human irrationality.

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Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

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A technique has been developed to measure the desorption and subsequent oxidation of fuel in the oil layer by spiking the oil with liquid fuel and firing the engine on gaseous fuel or motoring with air. Experiments suggest that fuel desorption is not diffusion limited above 50°C and indicated that approximately two to four percent of the cylinder oil layer is fresh oil from the sump. The increase in hydrocarbon emissions is of the order of 100 ppmC1 per 1% liquid fuel introduced into the fresh oil in a methane fired engine at mid-speed and light load conditions. Calculations indicate that fuel desorbing from oil is much more likely to produce hydrocarbon emissions than fuel emerging from crevices. © Copyright 1994 Society of Automotive Engineers, Inc.

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We study the problem of finding a local minimum of a multilinear function E over the discrete set {0,1}n. The search is achieved by a gradient-like system in [0,1]n with cost function E. Under mild restrictions on the metric, the stable attractors of the gradient-like system are shown to produce solutions of the problem, even when they are not in the vicinity of the discrete set {0,1}n. Moreover, the gradient-like system connects with interior point methods for linear programming and with the analog neural network studied by Vidyasagar (IEEE Trans. Automat. Control 40 (8) (1995) 1359), in the same context. © 2004 Elsevier B.V. All rights reserved.