Deep neural network-based strategy for optimal sensor placement in data assimilation of turbulent flow

2021 ◽  
Vol 33 (2) ◽  
pp. 025119
Author(s):  
Zhiwen Deng ◽  
Chuangxin He ◽  
Yingzheng Liu
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Turbulent Flow ◽  
Deep Neural Network ◽  
Sensor Placement ◽  
Optimal Sensor Placement

Optimal sensor placement for variational data assimilation of unsteady flows past a rotationally oscillating cylinder

2017 ◽  
Vol 823 ◽  
pp. 230-277 ◽  
Author(s):  
Vincent Mons ◽  
Jean-Camille Chassaing ◽  
Pierre Sagaut
Keyword(s):  
Data Assimilation ◽  
Sensor Placement ◽  
Computational Cost ◽  
Unsteady Flows ◽  
Variational Data Assimilation ◽  
Model Parameters ◽  
Oscillating Cylinder ◽  
Optimal Sensor Placement ◽  
Initial Flow ◽  
Placement Procedure

An optimal sensor placement procedure is proposed within the framework of variational data assimilation (DA) for unsteady flows, with the aim of maximizing the efficiency of the DA procedure. It is dedicated to the a priori design of a sensor network, and relies on a first-order adjoint approach. The proposed methodology first consists in identifying, via optimal control, the locations in the flow that have the greatest sensitivity with respect to a change in the initial condition, boundary conditions or model parameters. In a second step, sensors are placed at these locations for DA purposes. The use of this optimal sensor placement procedure does not require extra development in the case where a variational DA suite is available. The proposed methodology is applied to the reconstruction of unsteady bidimensional flows past a rotationally oscillating cylinder. More precisely, the possibilities of reconstructing the rotational speed of the cylinder and the initial flow, which here encompasses upstream conditions, from various types of observations are investigated via variational DA. Then, the observation optimization procedure is employed to identify optimal locations for placing velocity sensors downstream of the cylinder. Both reduction in the computational cost and improvement in the quality of the reconstructed flow are achieved through optimal sensor placement, encouraging the application of the proposed methodology to more complex and realistic flows.

Deep neural network-based cost function for metal cutting data assimilation

2020 ◽  
Vol 107 (1-2) ◽  
pp. 385-398
Author(s):  
Takashi Misaka ◽  
Jonny Herwan ◽  
Seisuke Kano ◽  
Hiroyuki Sawada ◽  
Yoshiyuki Furukawa
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Cost Function ◽  
Deep Neural Network ◽  
Metal Cutting

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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