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• 254 Downloads • Abstract A blood spot detection neural network was trained, tested, and evaluated entirely on eggs with blood spots and grade A eggs. The neural network could accurately distinguish between grade A eggs and blood spot eggs. However, when eggs with other defects were included in the sample, the accuracy of the neural network was reduced. The accuracy was also reduced when evaluating eggs from other poultry houses. To minimize these sensitivities, eggs with cracks and dirt stains were included in the training data as examples of eggs without blood spots. The training data also combined eggs from different sources.
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Similar inaccuracies were observed in neural networks for crack detection and dirt stain detection. New neural networks were developed for these defects using the method applied for the blood spot neural network development. The neural network model for blood spot detection had an average accuracy of 92.8%.
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The neural network model for dirt stained eggs had an average accuracy of 85.0%. The average accuracy of the crack detection neural network was 87.8%. These accuracy levels were sufficient to produce graded samples that would exceed the USDA requirements.
An artificial neural network (ANN)‐based model was developed to analyse high‐cycle fatigue crack growth rates (d a/d N ) as a function of stress intensity ranges (Δ K ) for dual phase (DP) steel. The training data consisted of d a/d N at Δ K ranges between 5 and 16 MPa √ for DP steel with martensite contents in the range 32 to 76%. The ANN back‐propagation model with Gaussian activation function exhibited excellent agreement with the experimental results. The fatigue crack growth rate predictions were made to demonstrate its practical significance in a given real‐life situation. Because of the wide range of data points used during training of the model, it will provide a useful predictor for fatigue crack growth in DP steels.
2008 7th World Congress on Intelligent Control and Automation Chongqing, China 2008 7th World Congress on Intelligent Control and Automation IEEE, (2008). 978-1-4244-2113-8 Binbin Dan, Kuisheng Chen, Ling Xiong, Zhijun Rong and Jiangang Yi Research on multi-BP NN-based control model for molten iron desulfurization, (2008).
6133 61, 10.1109/WCICA.20 • T. Srpčič, Fire analysis of steel frames with the use of artificial neural networks, Journal of Constructional Steel Research, 63, 10, (1396), (2007).