94 resultados para Feed training


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The use of hidden Markov models is placed in a connectionist framework, and an alternative approach to improving their ability to discriminate between classes is described. Using a network style of training, a measure of discrimination based on the a posteriori probability of state occupation is proposed, and the theory for its optimization using error back-propagation and gradient ascent is presented. The method is shown to be numerically well behaved, and results are presented which demonstrate that when using a simple threshold test on the probability of state occupation, the proposed optimization scheme leads to improved recognition performance.

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This paper reports our experiences with a phoneme recognition system for the TIMIT database which uses multiple mixture continuous density monophone HMMs trained using MMI. A comprehensive set of results are presented comparing the ML and MMI training criteria for both diagonal and full covariance models. These results using simple monophone HMMs show clear performance gains achieved by MMI training, and are comparable to the best reported by others including those which use context-dependent models. In addition, the paper discusses a number of performance and implementation issues which are crucial to successful MMI training.

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Common-rail fuel injection systems on modern light duty diesel engines are effectively able to respond instantaneously to changes in the demanded injection quantity. In contrast, the air-system is subject to significantly slower dynamics, primarily due to filling/emptying effects in the manifolds and turbocharger inertia. The behaviour of the air-path in a diesel engine is therefore the main limiting factor in terms of engine-out emissions during transient operation. This paper presents a simple mean-value model for the air-path during throttled operation, which is used to design a feed-forward controller that delivers very rapid changes in the in-cylinder charge properties. The feed-forward control action is validated using a state-of-the-art sampling system that allows true cycle-by-cycle measurement of the in-cylinder CO2 concentration. © 2011 SAE International.

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We introduce a new algorithm to automatically identify the time and pixel location of foot contact events in high speed video of sprinters. We use this information to autonomously synchronise and overlay multiple recorded performances to provide feedback to athletes and coaches during their training sessions. The algorithm exploits the variation in speed of different parts of the body during sprinting. We use an array of foreground accumulators to identify short-term static pixels and a temporal analysis of the associated static regions to identify foot contacts. We evaluated the technique using 13 videos of three sprinters. It successfully identifed 55 of the 56 contacts, with a mean localisation error of 1.39±1.05 pixels. Some videos were also seen to produce additional, spurious contacts. We present heuristics to help identify the true contacts. © 2011 Springer-Verlag Berlin Heidelberg.