Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"Neural Network Prediction"”.
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Neural Network Prediction of Ultimate Compression After Impact Loads in Graphite-Epoxy Coupons from Ultrasonic C-Scan Images
… was to investigate how accurately an artificial neural network could predict the ultimate compressive loads of impact damaged 24-ply graphite-epoxy coupons from ultrasonic C-scan images. The 24-ply graphite-epoxy coupons were manufactured with bidirectional preimpregnated tape and cut into 21 …
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Neural Network Prediction of Math and Reading Proficiency as Reported in the Educational Longitudinal Study 2002 Based on Non-Curricular Variables
… performance of a three-layer back propagation neural network to that of traditional multiple linear regression in predicting math and reading proficiency from 103 non-curricular variables collected in the National Center for Educational Statistics' 2002 Educational Longitudinal Study. The …
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Aircraft position prediction using neural networks
… under contract with the FAA. AMASS uses a linear prediction system to predict the position of an aircraft 5 to 30 seconds in the future. The system sounds an alarm to warn air traffic controllers if it foresees a potential accident. However, research done at MIT and Volpe National Transportation …
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Deep Representation Learning on Labeled Graphs
… (RCC), a variant of ICA analogous to recurrent neural network prediction. RCC accommodates any differentiable local classifier and relational feature functions. We provide gradient-based strategies for optimizing over model parameters to more directly minimize the loss function. In our …
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Estimating Impervious Surface Cover in Flathead County, Montana
… landscapes. In this study, an Artificial Neural Network model was developed to update NLCD impervious surface product (2011) in Flathead County, Montana. Four Landsat 8 images from 2015 and 2016 were used to characterize imperviousness. This multi-temporal analytical method was designed to …
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The discovery of new functional oxides using combinatorial techniques and advanced data mining algorithms
… is a two stage process. The first stage, forward prediction, is accomplished using an artificial neural network, a Baconian, inductive technique. In a second stage, the artificial neural network is inverted using a genetic algorithm. The artificial neural network prediction, stoichiometry and …
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Cost-based shop control using artificial neural networks
… of a shop consists of three stages: due-date prediction, order release, and job dispatching. The literature has dealt thoroughly with the third stage, but there is a paucity of study on either of the first two stages or on interaction between the stages. This dissertation focuses on the first …
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Peramalan jumlah kandungan elektron menggunakan kaedah suapan ke hadapan rangkaian neural di Semenanjung Malaysia
… TEC Monitor (GISTM) receiver using feed forward neural network (NN). It also aims to investigate the TEC forecasting method for radio wave propagation value during both equinox and solstices periods. Two GISTM locations at Universiti Kebangsaan Malaysia, 2�550 N, 101�460 E and National …
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Airfoil Self-Noise Prediction Using Neural Networks for Wind Turbines
A neural network prediction method has been developed to compute self-noise of airfoils typically used in wind turbines. The neural networks were trained using experimental data corresponding to tests of several different airfoils over a range of flow conditions. The experimental data corresponds …
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On the Origins of Genetic Novelty in Drosophila
… phenotypic level between species. Applying deep neural networks as a tool to investigate the sequence determinants that govern chromatin accessibility, we find that hybrid convolution-attention neural networks can predict ATAC-seq peaks using only local DNA sequences as input. This predictive …
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Complex Vehicle Modeling: A Data Driven Approach
This thesis proposes an artificial neural network (NN) model to predict fuel consumption in heavy vehicles. The model uses predictors derived from vehicle speed, mass, and road grade. These variables are readily available from telematics devices that are becoming an integral part of connected …