Instability in lean premixed combustors

A generalized acoustic equation is used to identify the mechanisms driving combustion instability. The relationship between the unsteady rate of heat release and the flow is found to influence significantly the frequency of oscillation. A kinematic flame model is reviewed and used to describe the unsteady combustion in a premixed ducted flame and in a typical lean premixed industrial gas turbine. Comparison is made between theory and experiment. | A generalized acoustic equation is used to identify the mechanisms driving combustion instability. The relationship between the unsteady rate of heat release and the flow is found to influence significantly the frequency of oscillation. A kinematic flame model is reviewed and used to describe the unsteady combustion in a premixed ducted flame and in a typical lean premixed industrial gas turbine. Comparison is made between theory and experiment.

Negligible penalty all-optical routing using a 12 × 12 passive InP wavelength-selective router

This work demonstrates transmission at 2.5 Gbit/s across two wavelength-division multiplexing (WDM) network nodes, constructed using counter-propagating semiconductor optical amplifier (SOA) wavelength converters and an integrated wavelength-selective router separated by 45 km of fiber, with an overall penalty of 0.6 dB. Minimal degradation of the eye diagram is evident across the whole system. Full utilization of the capacity of the router would allow an aggregate 360-Gbit/s node capacity for a WDM channel of 2.5 Gb/s.

On-line policy optimisation of spoken dialogue systems via live interaction with human subjects

Statistical dialogue models have required a large number of dialogues to optimise the dialogue policy, relying on the use of a simulated user. This results in a mismatch between training and live conditions, and significant development costs for the simulator thereby mitigating many of the claimed benefits of such models. Recent work on Gaussian process reinforcement learning, has shown that learning can be substantially accelerated. This paper reports on an experiment to learn a policy for a real-world task directly from human interaction using rewards provided by users. It shows that a usable policy can be learnt in just a few hundred dialogues without needing a user simulator and, using a learning strategy that reduces the risk of taking bad actions. The paper also investigates adaptation behaviour when the system continues learning for several thousand dialogues and highlights the need for robustness to noisy rewards. © 2011 IEEE.

Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.

The price of pain and the value of suffering.

Estimating the financial value of pain informs issues as diverse as the market price of analgesics, the cost-effectiveness of clinical treatments, compensation for injury, and the response to public hazards. Such valuations are assumed to reflect a stable trade-off between relief of discomfort and money. Here, using an auction-based health-market experiment, we show that the price people pay for relief of pain is strongly determined by the local context of the market, that is, by recent intensities of pain or immediately disposable income (but not overall wealth). The absence of a stable valuation metric suggests that the dynamic behavior of health markets is not predictable from the static behavior of individuals. We conclude that the results follow the dynamics of habit-formation models of economic theory, and thus, this study provides the first scientific basis for this type of preference modeling.

When fear is near: threat imminence elicits prefrontal-periaqueductal gray shifts in humans.

Humans, like other animals, alter their behavior depending on whether a threat is close or distant. We investigated spatial imminence of threat by developing an active avoidance paradigm in which volunteers were pursued through a maze by a virtual predator endowed with an ability to chase, capture, and inflict pain. Using functional magnetic resonance imaging, we found that as the virtual predator grew closer, brain activity shifted from the ventromedial prefrontal cortex to the periaqueductal gray. This shift showed maximal expression when a high degree of pain was anticipated. Moreover, imminence-driven periaqueductal gray activity correlated with increased subjective degree of dread and decreased confidence of escape. Our findings cast light on the neural dynamics of threat anticipation and have implications for the neurobiology of human anxiety-related disorders.