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Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

2019

Operations Research, Systems Engineering and Industrial Engineering

Convolutional neural network (CNN)

Articles 1 - 3 of 3

Full-Text Articles in Engineering

Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli Aug 2019

Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli

Cihan H. Dagli

Automated segmentation in brain magnetic resonance image (MRI) plays an important role in the analysis of many diseases and conditions. In this paper, we present a new architecture to perform MR image brain segmentation (MRI) into a number of classes based on type of tissue. Recent work has shown that convolutional neural networks (DenseNet) can be substantially more accurate with less number of parameters if each layer in the network is connected with every other layer in a feed forward fashion. We embrace this idea and generate new architecture that can assign each pixel/voxel in an MR image of ...


Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli Aug 2019

Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli

Cihan H. Dagli

In this paper, we investigate the diagnostic data from patients suffering with Parkinson's disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson's research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson's Disease diagnosis ...


Weld Penetration Identification Based On Convolutional Neural Network, Chao Li Jan 2019

Weld Penetration Identification Based On Convolutional Neural Network, Chao Li

Theses and Dissertations--Electrical and Computer Engineering

Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like fatigue for example. It also requires much of human intelligence, which either complex modeling or rich of welding experience. Therefore, weld penetration status identification has become the obstacle for intelligent welding system. In this dissertation, an innovative method has been proposed to detect the weld joint penetration status using machine-learning algorithms.

A GTAW welding system is firstly built. Project a dot-structured laser pattern onto the weld pool surface during welding process, the reflected laser pattern ...