Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder

Detection of Pitting in Gears Using a Deep Sparse Autoencoder

In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised ma...

Transformer fault diagnosis using continuous sparse autoencoder.

This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the con...

Structured Sparse Convolutional Autoencoder

This paper aims to improve the feature learning in Convolutional Networks (Convnet) by capturing the structure of objects. A new sparsity function is imposed on the extracted featuremap to capture the structure and shape of the learned object, extracting interpretable features to improve the prediction performance. The proposed algorithm is based on organizing the activation within and across f...

Parallelizing the Sparse Autoencoder

The objective of this project is to take the Spase Autoencoder algorithm, as presented at: http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/handouts.html , and develop methods for parallelizing, and possibly distributing the computation. In addition, the language Go, was chosen as the language to implement the algorithm, because it is a language designed with parallel computation as ...

Comparison of introduced and local strawberry varieties in relation to yield,vitamin C content,soluble solids and storage life