14 resultados para Equation of prediction

em Cambridge University Engineering Department Publications Database


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We present a method to experimentally characterize the gain filter and calculate a corresponding parabolic gain bandwidth of lasers that are described by "class A" dynamics by solving the master equation of spectral condensation for Gaussian spectra. We experimentally determine the gain filter, with an equivalent parabolic gain bandwidth of up to 51 nm, for broad-band InGaAs/GaAs quantum well gain surface-emitting semiconductor laser structures capable of producing pulses down to 60 fs width when mode-locked with an optical Stark saturable absorber mirror. © 2010 Optical Society of America.

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In this paper, we develop a novel moving mesh method suitable for solving axisymmetric free-boundary problems, including the Marangoni effect induced by surfactant or temperature variation. This method employs a body-fitted grid system where the gas-liquid interface is one line of the grid system. We model the surfactant equation of state with a non-linear Langmuir law, and, for simplicity, we limit ourselves to the situation of an insoluble surfactant. We solve complicated dynamic boundary conditions accurately on the gas-liquid interface in the framework of finite-volume methods. Our method is used to study the effect of a surfactant on the skin friction of a bubble in a uniaxial flow. For the limiting case where the surface diffusivity is zero, the effect of a tangential stress generated by the surface tension gradient, allows us to explain a new phenomenon in high concentration regimes: larger surface tension, but also larger deformation. Furthermore, this condition leads to the formation of boundary layers and flow separation at high Reynolds numbers. The influence of these complex flow patterns is examined. © 2005 Elsevier SAS. All rights reserved.

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A theoretical approach for calculating the movement of liquid water following deposition onto a turbomachine rotor blade is described. Such a situation can occur during operation of an aero-engine in rain. The equation of motion of the deposited water is developed on an arbitrarily oriented plane triangular surface facet. By dividing the blade surface into a large number of facets and calculating the water trajectory over each one crossed in turn, the overall trajectory can be constructed. Apart from the centrifugal and Coriolis inertia effects, the forces acting on the water arise from the blade surface friction, and the aerodynamic shear and pressure gradient. Non- dimensionalisation of the equations of motion provides considerable insight and a detailed study of water flow on a flat rotating plate set at different stagger angles demonstrates the paramount importance of blade surface friction. The extreme cases of low and high blade friction are examined and it is concluded that the latter (which allows considerable mathematical generalisation) is the most likely in practice. It is also shown that the aerodynamic shear force, but not the pressure force, may influence the water motion. Calculations of water movement on a low-speed compressor blade and the fan blade of a high bypass ratio aero-engine suggest that in low rotational speed situations most of the deposited water is centrifuged rapidly to the blade tip region. Copyright © 2006 by ASME.

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One of the main claims of the nonparametric model of random uncertainty introduced by Soize (2000) [3] is its ability to account for model uncertainty. The present paper investigates this claim by examining the statistics of natural frequencies, total energy and underlying dispersion equation yielded by the nonparametric approach for two simple systems: a thin plate in bending and a one-dimensional finite periodic massspring chain. Results for the plate show that the average modal density and the underlying dispersion equation of the structure are gradually and systematically altered with increasing uncertainty. The findings for the massspring chain corroborate the findings for the plate and show that the remote coupling of nonadjacent degrees of freedom induced by the approach suppresses the phenomenon of mode localization. This remote coupling also leads to an instantaneous response of all points in the chain when one mass is excited. In the light of these results, it is argued that the nonparametric approach can deal with a certain type of model uncertainty, in this case the presence of unknown terms of higher or lower order in the governing differential equation, but that certain expectations about the system such as the average modal density may conflict with these results. © 2012 Elsevier Ltd.

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Predictions about sensory input exert a dominant effect on what we perceive, and this is particularly true for the experience of pain. However, it remains unclear what component of prediction, from an information-theoretic perspective, controls this effect. We used a vicarious pain observation paradigm to study how the underlying statistics of predictive information modulate experience. Subjects observed judgments that a group of people made to a painful thermal stimulus, before receiving the same stimulus themselves. We show that the mean observed rating exerted a strong assimilative effect on subjective pain. In addition, we show that observed uncertainty had a specific and potent hyperalgesic effect. Using computational functional magnetic resonance imaging, we found that this effect correlated with activity in the periaqueductal gray. Our results provide evidence for a novel form of cognitive hyperalgesia relating to perceptual uncertainty, induced here by vicarious observation, with control mediated by the brainstem pain modulatory system.

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Termination of a painful or unpleasant event can be rewarding. However, whether the brain treats relief in a similar way as it treats natural reward is unclear, and the neural processes that underlie its representation as a motivational goal remain poorly understood. We used fMRI (functional magnetic resonance imaging) to investigate how humans learn to generate expectations of pain relief. Using a pavlovian conditioning procedure, we show that subjects experiencing prolonged experimentally induced pain can be conditioned to predict pain relief. This proceeds in a manner consistent with contemporary reward-learning theory (average reward/loss reinforcement learning), reflected by neural activity in the amygdala and midbrain. Furthermore, these reward-like learning signals are mirrored by opposite aversion-like signals in lateral orbitofrontal cortex and anterior cingulate cortex. This dual coding has parallels to 'opponent process' theories in psychology and promotes a formal account of prediction and expectation during pain.

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In this paper, we survey some recent results on stabilization and disturbance attenuation for nonlinear systems using a dissipativity approach. After reviewing the basic dissipativity concept, we stress the connections between Lyapunov designs and the problem of achieving passivity by feedback. Focusing on physical models, we then illustrate how the design of stabilizing feedback can take advantage of the natural energy balance equation of the system. Here stabilization is viewed as the task of shaping the energy of the system to enforce a minimum at the desired equilibrium. Finally, we show the implications of dissipativity theory as an appropriate framework to study the nonlinear H∞ control problem. © 2002 EUCA.

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Over recent years academia and industry have engaged with the challenge of model testing deepwater structures at conventional scales. One approach to the limited depth problem has been to truncate the lines. This concept will be introduced, highlighting the need to better understand line dynamic processes. The type of line truncation developed here models the upper sections of each line in detail, capturing wave action and all coupling effects with the vessel, terminating to an approximate analytical model that aims to simulate the remainder of the line. A rationale for this is that in deep water transverse elastic waves of a line are likely to decay before they are reflected at the seabed because of nonlinear hydrodynamic drag forces. The first part of this paper is centered on verification of this rationale. A simplified model of a mooring line that describes the transverse dynamics in wave frequency is used, adopting the equation of motion of an inextensible taut string. The line is submerged in still water, one end fixed at the bottom the other assumed to follow the vessel response, which can be harmonic or random. A dimensional analysis, supported by exact benchmark numerical solutions, has shown that it is possible to produce a universal curve for the decay of transverse vibrations along the line, which is suitable for any kind of line with any top motion. This has a significant engineering benefit, allowing for a rapid assessment of line dynamics - it can be useful in deciding whether a truncated line model is appropriate, and if so, at which point truncation might be applied. This is followed by developing a truncation mechanism, formulating an end approximation that can reproduce the correct impedance, had the line been continuous to full depth. It has been found that below a certain length criterion, which is also universal, the transverse vibrational characteristics for each line are inertia driven. As such the truncated model can assume a linear damper whose coefficient depends on the line properties and frequency of vibration. Copyright © 2011 by the International Society of Offshore and Polar Engineers (ISOPE).

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Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirrors the scientific discovery process. The learned structures can often decompose functions into interpretable components and enable long-range extrapolation on time-series datasets. Our structure search method outperforms many widely used kernels and kernel combination methods on a variety of prediction tasks.

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This paper discusses user target intention recognition algorithms for pointing - clicking tasks to reduce users' pointing time and difficulty. Predicting targets by comparing the bearing angles to targets proposed as one of the first algorithms [1] is compared with a Kalman Filter prediction algorithm. Accuracy and sensitivity of prediction are used as performance criteria. The outcomes of a standard point and click experiment are used for performance comparison, collected from both able-bodied and impaired users. © 2013 Springer-Verlag Berlin Heidelberg.