10 resultados para Data Generation

em Cambridge University Engineering Department Publications Database


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Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing.

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This paper proposes an HMM-based approach to generating emotional intonation patterns. A set of models were built to represent syllable-length intonation units. In a classification framework, the models were able to detect a sequence of intonation units from raw fundamental frequency values. Using the models in a generative framework, we were able to synthesize smooth and natural sounding pitch contours. As a case study for emotional intonation generation, Maximum Likelihood Linear Regression (MLLR) adaptation was used to transform the neutral model parameters with a small amount of happy and sad speech data. Perceptual tests showed that listeners could identify the speech with the sad intonation 80% of the time. On the other hand, listeners formed a bimodal distribution in their ability to detect the system generated happy intontation and on average listeners were able to detect happy intonation only 46% of the time. © Springer-Verlag Berlin Heidelberg 2005.

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Abrasive wear is likely to occur whenever a hard asperity or a trapped hard particle is dragged across a softer surface, and it has been estimated that this form of wear contributes to as many as half of the wear problems that are met in industry. Such damaging hard particles may be external contaminants, products of corrosion or even the debris from previous wear events. During the life of a component, damage caused by individual asperity or particle interactions builds up and, at each stage of its life, the worn surface is the result of many such superimposed wear events. The practical, quantitative prediction of wear rates depends on having both a satisfactory understanding of individual interactions and a suitable procedure for combining these when subsequent contacts are made on a surface whose topography and material properties may have been much changed Irom their initial states. The paper includes some details of an analytical model for the interaction of a representative asperity and the worn surface which can both predict the frictional force and the balance between ploughing, when material is displaced but not lost from the surface, and micromachining or cutting, when actual detachment occurs. Experiments tö !rvvéSuQ8Î8 the validity of the model have been carried out on a novel wear rig which provides very precise control over the position of the asperity and the counterface. This facility, together with that of on-board profilometry, means that it is possible to carry out wear experiments on areas of the surface whose previous deformation history is well known; in this way it is possible to follow the development of a worn surface in a controlled manner as the damage from individual wear events accumulates. Experimental data on the development of such a surface, produced by repeated parallel abrasion, are compared with the predictions of the model. © 1992 IOP Publishing Ltd.

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Theoretical and experimental AC loss data on a superconducting pancake coil wound using second generation (2 G) conductors are presented. An anisotropic critical state model is used to calculate critical current and the AC losses of a superconducting pancake coil. In the coil there are two regions, the critical state region and the subcritical region. The model assumes that in the subcritical region the flux lines are parallel to the tape wide face. AC losses of the superconducting pancake coil are calculated using this model. Both calorimetric and electrical techniques were used to measure AC losses in the coil. The calorimetric method is based on measuring the boil-off rate of liquid nitrogen. The electric method used a compensation circuit to eliminate the inductive component to measure the loss voltage of the coil. The experimental results are consistent with the theoretical calculations thus validating the anisotropic critical state model for loss estimations in the superconducting pancake coil. © 2011 American Institute of Physics.

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Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.

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The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

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This paper extends n-gram graphone model pronunciation generation to use a mixture of such models. This technique is useful when pronunciation data is for a specific variant (or set of variants) of a language, such as for a dialect, and only a small amount of pronunciation dictionary training data for that specific variant is available. The performance of the interpolated n-gram graphone model is evaluated on Arabic phonetic pronunciation generation for words that can't be handled by the Buckwalter Morphological Analyser. The pronunciations produced are also used to train an Arabic broadcast audio speech recognition system. In both cases the interpolated graphone model leads to improved performance. Copyright © 2011 ISCA.

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The RF locking of a self-Q-switching diode laser is shown to reduce the jitter of a 2.48 GHz train of 1 W peak power picosecond pulses to less than 300 fs. By using direct modulation of the loss in the Q-switched laser, direct encoding of data has been achieved at rates in excess of 2 Gbit/s.