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      Measuring the accessibility of critical facilities in the presence of hurricane-related roadway closures and an approach for predicting future roadway disruptions

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          Face recognition: a convolutional neural-network approach.

          We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.
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            The HRD real-time hurricane wind analysis system

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              Best practices for convolutional neural networks applied to visual document analysis

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                Author and article information

                Journal
                Natural Hazards
                Nat Hazards
                Springer Science and Business Media LLC
                0921-030X
                1573-0840
                February 2019
                October 10 2018
                February 2019
                : 95
                : 3
                : 615-635
                Article
                10.1007/s11069-018-3507-5
                a063aa92-cfa3-4c6f-9aac-cee4d69999d6
                © 2019

                http://www.springer.com/tdm

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