Emergency response applications for nuclear or radiological events can be significantly improved via deep feature learning due its ability to capture the inherent complexity of the data involved. In this paper we present a novel methodology for rapid source estimation during radiological releases based on deep feature extraction and weather clustering. Atmospheric dispersions are then calculated based on identified predominant weather patterns and are matched against simulated incidents indicated by radiation readings on the ground. We evaluate the accuracy of our methods over multiple years of weather reanalysis data in the European region. We juxtapose these results with deep classification convolution networks and discuss advantages and disadvantages. We find that deep autoencoder configurations can lead to accurate-enough origin estimation to complement existing systems, while allowing for rapid initial response. A cluster-based method for inverse nuclear release source estimation is proposed.Weather clustering is improved via deep-learning latent representation extraction.Evaluation is performed using multiple years of weather data for Europe.The proposed methods are up to 75 accurate in challenging evaluation conditions.The proposed methodology is suitable for rapid emergency response scenarios.
In its simplest form, when there is a single hidden layer and the number of hidden units equals the number of inputs, the auto-encoder is too successful in replicating the input, leading to overfitting. Various methods have been suggested to avoid overfitting, having fewer hidden than input units, enforcing activation sparsity or introducing noise which the auto-encoder learns to compensate for @cite_10 @cite_0 . An alternative approach is to use deeper configurations of stacked autoencoders, where inner layers encode and decode previously encoded vectors. Encodings generated by stacked autoencoders can capture deeper statistical representations of the input data.
@cite_0 @cite_10
2145094598 2025768430
We explore an original strategy for building deep networks, based on stacking layers of denoising autoencoders which are trained locally to denoise corrupted versions of their inputs. The resulting algorithm is a straightforward variation on the stacking of ordinary autoencoders. It is however shown on a benchmark of classification problems to yield significantly lower classification error, thus bridging the performance gap with deep belief networks (DBN), and in several cases surpassing it. Higher level representations learnt in this purely unsupervised fashion also help boost the performance of subsequent SVM classifiers. Qualitative experiments show that, contrary to ordinary autoencoders, denoising autoencoders are able to learn Gabor-like edge detectors from natural image patches and larger stroke detectors from digit images. This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations. Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite.