We describe a software platform for the rapid development of general purpose GPU (GPGPU) computing applications within the MATLAB computing environment, C, and C++: Jacket. Jacket provides thousands of GPU-tuned funct...
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Artificial intelligence techniques such as neural networks are modelling tools that can be applied to analyse urban runoff water quality issues. Artificial neural networks are frequently used to model various highly v...
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ISBN:
(纸本)0953914089
Artificial intelligence techniques such as neural networks are modelling tools that can be applied to analyse urban runoff water quality issues. Artificial neural networks are frequently used to model various highly variable and non-linear physical phenomena in the water and environmental engineering fields. the application of neural networks for analysing the performance of permeable pavements is timely and novel. Artificial neural networks are a promising tool for environmental process assessment and modelling. Feed-forward neural networks are the most widely adopted methodology for the prediction and forecasting of water quality and quantity variables. this paper presents the application of back-propagation neural networks and the testing of the Levenberg-Marquardt, Quasi-Newton and Bayesian Regularization algorithms. the back-propagation neural network models incorporating these algorithms performed classification and regression tasks without knowledge of the underlying physical processes occurring throughout the pavement system. the neural networks were statistically assessed for their goodness of prediction of outflow water quality with respect to ammonia-nitrogen, nitrate-nitrogen, and ortho-phosphate-phosphorus by numerical computation of the mean absolute error, root square mean error, mean absolute relative error and the coefficient of correlation for the prediction versus measured dataset. these performance indices compared the measured and estimated water quality parameters. the neural network models were functions of the readily available water quality parameters. three neural network models were assessed for their efficiency in accurately simulating effluent water quality parameters from various experimental pavement systems. the models predicted all key parameters with high correlation coefficients and low minimum statistical errors. the back-propagation and feed-forward neural network models performed optimally as pollutant removal predictors with re
Energy efficient systems are highly demanded as the power consumption in HPC region increase. the use of GPUs has attracted attention as a possible solution to these problems because of their parallel performance and ...
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the proceedings contain 74 papers. the topics discussed include: speeding up dense disparity estimation of stereo couples thanks to horizontal intensity gradient;an algorithm to construct 3D triangles with circular ed...
ISBN:
(纸本)9780769539591
the proceedings contain 74 papers. the topics discussed include: speeding up dense disparity estimation of stereo couples thanks to horizontal intensity gradient;an algorithm to construct 3D triangles with circular edges;facial expression recognition by automatic facial parts position detection with boosted-LBP;implementation of frontal-centroid moment invariants in thermal-based face identification system;face recognition using enhanced Fisher linear discriminant;challenges in computational histopathology: the feasibility of FTIR spectroscopy in clustering;a comparative study and an evaluation framework of multi/hyperspectral image compression;parametric and non-parametric models of linear prediction error for color texture segmentation;capturing image outlines using spline computing;and a novel shape descriptor based on extreme curvature scale space map approach for efficient shape similarity retrieval.
Accurate modelling of driver behaviour in evacuations is vitally important in creating realistic training environments for disaster management. However, few current models have satisfactorily incorporated the variety ...
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ISBN:
(纸本)9783642198748
Accurate modelling of driver behaviour in evacuations is vitally important in creating realistic training environments for disaster management. However, few current models have satisfactorily incorporated the variety of factors that affect driver behaviour. In particular, the interdependence of driver behaviours is often seen in real-world evacuations, but is not represented in current state-of-the art traffic simulators. To address this shortcoming, we present an agent-based behaviour model based on the social forces model of crowds. Our model uses utility-based path trees to represent the forces which affect a driver's decisions. We demonstrate, by using a metric of route similarity, that our model is able to reproduce the real-life evacuation behaviour whereby drivers follow the routes taken by others. the model is compared to the two most commonly used route choice algorithms, that of quickest route and real-time re-routing, on three road networks: an artificial "ladder" network, and those of Lousiana, USA and Southampton, UK. When our route choice forces model is used our measure of route similarity increases by 21%-93%. Furthermore, a qualitative comparison demonstrates that the model can reproduce patterns of behaviour observed in the 2005 evacuation of the New Orleans area during Hurricane Katrina.
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