7,905 research outputs found

    Investigation of nonlinear motion simulator washout schemes

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    An overview is presented of some of the promising washout schemes which have been devised. The four schemes presented fall into two basic configurations; crossfeed and crossproduct. Various nonlinear modifications further differentiate the four schemes. One nonlinear scheme is discussed in detail. This washout scheme takes advantage of subliminal motions to speed up simulator cab centering. It exploits so-called perceptual indifference thresholds to center the simulator cab at a faster rate whenever the input to the simulator is below the perceptual indifference level. The effect is to reduce the angular and translational simulation motion by comparison with that for the linear washout case. Finally, the conclusions and implications for further research in the area of nonlinear washout filters are presented

    Inference by Minimizing Size, Divergence, or their Sum

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    We speed up marginal inference by ignoring factors that do not significantly contribute to overall accuracy. In order to pick a suitable subset of factors to ignore, we propose three schemes: minimizing the number of model factors under a bound on the KL divergence between pruned and full models; minimizing the KL divergence under a bound on factor count; and minimizing the weighted sum of KL divergence and factor count. All three problems are solved using an approximation of the KL divergence than can be calculated in terms of marginals computed on a simple seed graph. Applied to synthetic image denoising and to three different types of NLP parsing models, this technique performs marginal inference up to 11 times faster than loopy BP, with graph sizes reduced up to 98%-at comparable error in marginals and parsing accuracy. We also show that minimizing the weighted sum of divergence and size is substantially faster than minimizing either of the other objectives based on the approximation to divergence presented here.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010

    A Portable, Low-Cost Wheelchair Ergometer Design Based on a Mathematical Model of Pediatric Wheelchair Dynamics

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    Evaluation and training of wheelchair propulsion improves efficiency and prevents orthopaedic injury in pediatric manual wheelchair users. Ergometers allow static propulsion and emulate typical conditions. Currently available ergometers have deficiencies that limit their use in motion analysis. A new ergometer is developed and evaluated based on a model of wheelchair inertial dynamics that eliminates these deficiencies. This makes integrated motion analysis of wheelchair propulsion in current community, home, and international outreach efforts possible
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