High-Speed X-Ray Imaging for Correlating Acoustic Signals with Quality Monitoring: A Machine Learning Approach

2018 ◽  
Author(s):  
K. Wasmer
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
Acoustic Signals ◽  
Quality Monitoring ◽  
Learning Approach ◽  
X Ray ◽  
X Ray Imaging ◽  
Machine Learning Approach

scholarly journals Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach

Procedia CIRP ◽  
2018 ◽  
Vol 74 ◽  
pp. 654-658 ◽  
Author(s):  
K. Wasmer ◽  
T. Le-Quang ◽  
B. Meylan ◽  
F. Vakili-Farahani ◽  
M.P. Olbinado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Laser Processing ◽  
High Speed ◽  
Quality Monitoring ◽  
Processing Quality ◽  
X Ray ◽  
X Ray Imaging ◽  
Imaging Approach

Prediction and understanding of soft proton contamination in XMM-Newton: a machine learning approach

2021 ◽  
Author(s):  
Elena Kronberg ◽  
Fabio Gastaldello ◽  
Stein Haaland ◽  
Artem Smirnov ◽  
Max Berrendorf ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Machine Learning ◽  
Solar Wind ◽  
Southern Hemisphere ◽  
Solar Wind Speed ◽  
Flank Region ◽  
Learning Approach ◽  
X Ray ◽  
Machine Learning Approach ◽  
Field Lines

<p>One of the major and unfortunately unforeseen sources of background for the current generation of X-ray telescopes flying mainly in the magnetosphere are soft protons with few tens to hundreds of keV concentrated. One such telescope is the X-ray Multi-Mirror Mission (XMM-Newton) by ESA. Its observing time lost due to the contamination is  about 40%. This affects all the major broad science goals of XMM, ranging from cosmology to astrophysics of neutron stars and black holes. The soft proton background could dramatically impact future X-ray missions such Athena and SMILE missions. Magnetopsheric processes that trigger this background are still poorly understood. We use a machine learning approach to delineate related important parameters and to develop a model to predict the background contamination using 12 years of XMM observations. As predictors we use the location of XMM, solar and geomagnetic activity parameters. We revealed that the contamination is most strongly related to the distance in southern direction, ZGSE, (XMM observations were in the southern hemisphere), the solar wind velocity and the location on the magnetospheric magnetic field lines. We derived simple empirical models for the best two individual predictors and a machine learning model which utilizes an ensemble of the predictors (Extra Trees Regressor) and gives better performance. Based on our analysis, future X-Ray missions in the magnetosphere should minimize observations during  times  associated with high solar wind speed  and avoid closed magnetic field lines, especially at the dusk flank region at least in the southern hemisphere. </p>

A machine learning approach-based power theft detection using GRF optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
A. Prakash ◽  
A. Shyam Joseph ◽  
R. Shanmugasundaram ◽  
C.S. Ravichandran
Keyword(s):  
Machine Learning ◽  
Transmission Lines ◽  
High Speed ◽  
Design Methodology ◽  
Learning Approach ◽  
Smart Meter ◽  
Financial Loss ◽  
Content Type ◽  
Garra Rufa ◽  
Machine Learning Approach

Purpose This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users. Design/methodology/approach In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm. Findings The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method. Originality/value The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems.

scholarly journals Edge Detection and Machine Learning Approach to Identify Flow Structures on Schlieren and Shadowgraph Images

2020 ◽  
pp. paper15-1-paper15-14
Author(s):  
Irina Znamenskaya ◽  
Igor Doroshchenko ◽  
Daria Tatarenkova
Keyword(s):  
Machine Learning ◽  
Shock Wave ◽  
Shock Waves ◽  
Edge Detection ◽  
High Speed ◽  
Gas Flow ◽  
Flow Structures ◽  
Learning Approach ◽  
Wave Detection ◽  
Machine Learning Approach

Schlieren, shadowgraph and other types of refraction-based techniques have been often used to study gas flow structures. They can capture strong density gradients, such as shock waves. Shock wave detection is a very important task in analyzing unsteady gas flows. High-speed imaging systems, including high-speed cameras, are widely used to record large arrays of shadowgraph images. To process large datasets of the high-speed shadowgraph images and automatically detect shock waves, convective plumes and other gas flow structures, two computer software systems based on the edge detection and machine learning with convolutional neural networks (CNN) were developed. The edge-detection software utilizes image filtering, noise removing, background image subtraction in the frequency domain and edge detection based on the Canny algorithm. The machine learning software is based on CNN. We developed two neural networks working together. The first one classifies the image dataset and finds images with shock waves. The other CNN solves the regression task and defines shock wave position (single number) based on image pixels tensor (3-D array of numbers) for each image. The supervised learning code based on example input-output pairs was developed to train models. It was shown, that the machine learning approach gives better results in shock wave detection accuracy, especially for low-quality images with a strong noise level. Software system for automated shadowgraph images processing and x-t curves of the shock wave and convective plume movement plotting was developed.

scholarly journals DoFP-ML: A Machine Learning Approach to Food Quality Monitoring Using a DoFP Polarization Image Sensor

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 150282-150290
Author(s):  
Maen Takruri ◽  
Abubakar Abubakar ◽  
Noora Alnaqbi ◽  
Hessa Al Shehhi ◽  
Abdul-Halim M. Jallad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Food Quality ◽  
Image Sensor ◽  
Quality Monitoring ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Polarization Image
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