Bearing Defect Detection with Unsupervised Neural Networks

Additive Manufacturing Defect Detection using Neural Networks

Currently defect detection in additive manufacturing is predominately done by traditional image processing, approximation, and statistical methods. Two important aspects of defect detection are edge detection and porosity detection. Both problems appear to be good candidates to apply machine learning. This project describes the implementation of neural networks as an alternative method for defe...

Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks

We investigate anomaly detection in an unsupervised framework and introduce Long Short Term Memory (LSTM) neural network based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM based structure and obtain fixed length sequences. We then find a decision function for our anomaly detectors based on the One Class Support Vector Machines (...

Apple Defect Detection and Quality Classification with MLP-Neural Networks

The initial analysis of a quality classification system for ‘Jonagold’ and ‘Golden Delicious’ apples is shown. Color, texture and wavelet features are extracted from the apple images. Principal components analysis was applied on the extracted features and some preliminary performance tests were done with single and multi layer perceptrons. Keywordscomputer vision; image processing; defect segme...

Solving nonlinear Lane-Emden type equations with unsupervised combined artificial neural networks

In this paper we propose a method for solving some well-known classes of Lane-Emden type equations which are nonlinear ordinary differential equations on the semi-innite domain. The proposed approach is based on an Unsupervised Combined Articial Neural Networks (UCANN) method. Firstly, The trial solutions of the differential equations are written in the form of feed-forward neural networks cont...

Unsupervised multimodal neural networks