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2017

Neural Network

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Full-Text Articles in Engineering

Prediction Of Solid Oxide Fuel Cell Performance Using Artificial Neural Network, M. A. Rafe Biswas, Kamwana N. Mwara Oct 2017

Prediction Of Solid Oxide Fuel Cell Performance Using Artificial Neural Network, M. A. Rafe Biswas, Kamwana N. Mwara

M. A. Rafe Biswas

NASA’s Johnson Space Center has recently begun efforts to eventually integrate air-independent Solid Oxide Fuel Cell (SOFC) systems, with landers that can be propelled by LOX-CH4, for long duration missions. Using landers that utilize such propellants, provides the opportunity to use SOFCs as a power option, especially since they are able to process methane into a reactant through fuel reformation. Various lead-up activities, such as hardware testing and computational modelling, have been initiated to assist with this developmental effort.
One modeling approach, currently being explored to predict SOFC behavior, involves the usage of artificial neural networks (ANN). Since SOFC performance …

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Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection, Norman L. Tasfi, Wilson A. Higashino, Katarina Grolinger, Miriam A. M. Capretz Jan 2017

Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection, Norman L. Tasfi, Wilson A. Higashino, Katarina Grolinger, Miriam A. M. Capretz

Electrical and Computer Engineering Publications

The increase in electrical metering has created tremendous quantities of data and, as a result, possibilities for deep insights into energy usage, better energy management, and new ways of energy conservation. As buildings are responsible for a significant portion of overall energy consumption, conservation efforts targeting buildings can provide tremendous effect on energy savings. Building energy monitoring enables identification of anomalous or unexpected behaviors which, when corrected, can lead to energy savings. Although the available data is large, the limited availability of labels makes anomaly detection difficult. This research proposes a deep semi-supervised convolutional neural network with confidence sampling for …

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