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This paper describes how neural networks and support vector machines can be used to detect errors in a large scale distributed system, specifically the ATLAS Trigger and Data AcQuisition (TDAQ) system. By collecting, analysing and preprocessing some of the data available in the system it is possible to recognize and/or predict error situations arising in the system. This can be done without detailed...
A set of images was compressed using artificial neural networks (ANNs) utilizing error back-propagation and by principal component analysis (PCA). The total sum square error (TSE) was compared for each method using the training data and a second set of test images. The difference between seen and unseen images with respect to TSSE is more pronounced in 32 by 16 pixel segments than in 8 by 8 segments...
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