scholarly journals Automatic Diagnosis of Familial Exudative Vitreoretinopathy Using a Fusion Neural Network for Wide-Angle Retinal Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 162-173
Author(s):  
Yu Ye ◽  
Jianbo Mao ◽  
Lei Liu ◽  
Shulin Zhang ◽  
Lijun Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Retinal Images ◽  
Wide Angle ◽  
Automatic Diagnosis ◽  
Familial Exudative Vitreoretinopathy

Artificial neural networks in the problem of determining the position and size of the solar disk in wide-angle photographs of the sky

2021 ◽  
Author(s):  
Mikhail Borisov ◽  
Mikhail Krinitskiy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Disk ◽  
Weather Conditions ◽  
Wide Angle ◽  
Know How ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Moment

<p>Total cloud score is a characteristic of weather conditions. At the moment, there are algorithms that automatically calculate cloudiness based on a photograph of the sky These algorithms do not know how to find the solar disk, so their work is not absolutely accurate.</p><p>To create an algorithm that solves this data, the data used, obtained as a result of sea research voyages, is used, which is marked up for training the neural network.</p><p>As a result of the work, an algorithm was obtained based on neural networks, based on a photograph of the sky, in order to determine the size and position of the solar disk, other algorithms can be used to work with images of the visible hemisphere of the sky.</p>

scholarly journals Interface-targeted seismic velocity estimation using machine learning

2019 ◽  
Vol 218 (1) ◽  
pp. 45-56 ◽  
Author(s):  
C Nur Schuba ◽  
Jonathan P Schuba ◽  
Gary G Gray ◽  
Richard G Davy
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Dimensional Space ◽  
Seismic Profile ◽  
Velocity Estimation ◽  
Seismic Survey ◽  
Wide Angle ◽  
Seismic Velocities ◽  
Lower Dimensional Space ◽  
Lower Dimensional

SUMMARY We present a new approach to estimate 3-D seismic velocities along a target interface. This approach uses an artificial neural network trained with user-supplied geological and geophysical input features derived from both a 3-D seismic reflection volume and a 2-D wide-angle seismic profile that were acquired from the Galicia margin, offshore Spain. The S-reflector detachment fault was selected as the interface of interest. The neural network in the form of a multilayer perceptron was employed with an autoencoder and a regression layer. The autoencoder was trained using a set of input features from the 3-D reflection volume. This set of features included the reflection amplitude and instantaneous frequency at the interface of interest, time-thicknesses of overlying major layers and ratios of major layer time-thicknesses to the total time-depth of the interface. The regression model was trained to estimate the seismic velocities of the crystalline basement and mantle from these features. The ‘true’ velocities were obtained from an independent full-waveform inversion along a 2-D wide-angle seismic profile, contained within the 3-D data set. The autoencoder compressed the vector of inputs into a lower dimensional space, then the regression layer was trained in the lower dimensional space to estimate velocities above and below the targeted interface. This model was trained on 50 networks with different initializations. A total of 37 networks reached minimum achievable error of 2 per cent. The low standard deviation (<300  m s−1) between different networks and low errors on velocity estimations demonstrate that the input features were sufficient to capture variations in the velocity above and below the targeted S-reflector. This regression model was then applied to the 3-D reflection volume where velocities were predicted over an area of ∼400 km2. This approach provides an alternative way to obtain velocities across a 3-D seismic survey from a deep non-reflective lithology (e.g. upper mantle) , where conventional reflection velocity estimations can be unreliable.

ANN-Based Defects' Diagnosis of Industrial Optical Devices

2011 ◽  
pp. 131-137
Author(s):  
Matthieu Voiry ◽  
Véronique Amarger ◽  
Joel Bernier ◽  
Kurosh Madani
Keyword(s):  
Neural Network ◽  
Data Extraction ◽  
Production Quality ◽  
Optical Devices ◽  
Major Step ◽  
Automatic Diagnosis ◽  
Diagnosis System ◽  
Repetitive Nature ◽  
Faults Diagnosis ◽  
Harmful Effects

A major step for high-quality optical devices faults diagnosis concerns scratches and digs defects detection and characterization in products. These kinds of aesthetic flaws, shaped during different manufacturing steps, could provoke harmful effects on optical devices’ functional specificities, as well as on their optical performances by generating undesirable scatter light, which could seriously damage the expected optical features. A reliable diagnosis of these defects becomes therefore a crucial task to ensure products’ nominal specification. Moreover, such diagnosis is strongly motivated by manufacturing process correction requirements in order to guarantee mass production quality with the aim of maintaining acceptable production yield. Unfortunately, detecting and measuring such defects is still a challenging problem in production conditions and the few available automatic control solutions remain ineffective. That’s why, in most of cases, the diagnosis is performed on the basis of a human expert based visual inspection of the whole production. However, this conventionally used solution suffers from several acute restrictions related to human operator’s intrinsic limitations (reduced sensitivity for very small defects, detection exhaustiveness alteration due to attentiveness shrinkage, operator’s tiredness and weariness due to repetitive nature of fault detection and fault diagnosis tasks). To construct an effective automatic diagnosis system, we propose an approach based on four main operations: defect detection, data extraction, dimensionality reduction and neural classification. The first operation is based on Nomarski microscopy issued imaging. These issued images contain several items which have to be detected and then classified in order to discriminate between “false” defects (correctable defects) and “abiding” (permanent) ones. Indeed, because of industrial environment, a number of correctable defects (like dusts or cleaning marks) are usually present beside the potential “abiding” defects. Relevant features extraction is a key issue to ensure accuracy of neural classification system; first because raw data (images) cannot be exploited and, moreover, because dealing with high dimensional data could affect learning performances of neural network. This article presents the automatic diagnosis system, describing the operations of the different phases. An implementation on real industrial optical devices is carried out and an experiment investigates a MLP artificial neural network based items classification.

scholarly journals OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19

2020 ◽  
Author(s):  
Tripti Goel ◽  
R. Murugan ◽  
Seyedali Mirjalili ◽  
Deba Kumar Chakrabartty
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Diagnosis

scholarly journals Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

PLoS ONE ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. e0187336 ◽  
Author(s):  
Joon Yul Choi ◽  
Tae Keun Yoo ◽  
Jeong Gi Seo ◽  
Jiyong Kwak ◽  
Terry Taewoong Um ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Pilot Study ◽  
Retinal Images ◽  
Deep Learning Neural Network
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