Real-time weld geometry prediction based on multi-information using neural network optimized by PCA and GA during thin-plate laser welding

2019 ◽  
Vol 43 ◽  
pp. 207-217 ◽  
Author(s):  
Zhenglong Lei ◽  
Jianxiong Shen ◽  
Qun Wang ◽  
Yanbin Chen
Keyword(s):  
Neural Network ◽  
Laser Welding ◽  
Real Time ◽  
Thin Plate ◽  
Weld Geometry

Real-time penetration state monitoring using convolutional neural network for laser welding of tailor rolled blanks

2020 ◽  
Vol 54 ◽  
pp. 348-360 ◽  
Author(s):  
Zhehao Zhang ◽  
Bin Li ◽  
Weifeng Zhang ◽  
Rundong Lu ◽  
Satoshi Wada ◽  
...  
Keyword(s):  
Neural Network ◽  
Laser Welding ◽  
Convolutional Neural Network ◽  
Real Time ◽  
State Monitoring ◽  
Tailor Rolled Blanks

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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