Journal ArticleDOI
DPAI: A Data-driven simulation-assisted-Physics learned AI model for transient ultrasonic wave propagation.
TLDR
In this paper , a deep neural network model is proposed to simulate the transient ultrasonic wave propagation in the 2D domain by implementing the Data driven-simulation-assisted-Physics learned AI (DPAI) model.About:
This article is published in Ultrasonics.The article was published on 2022-01-01. It has received 6 citations till now. The article focuses on the topics: Medicine & Transient (computer programming).read more
Citations
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Journal ArticleDOI
A review of ultrasonic sensing and machine learning methods to monitor industrial processes.
TL;DR: In this article , the authors present a review of machine learning techniques in combination with ultrasonic measurements for in-line and on-line monitoring of industrial processes and other similar applications. But, their implementation requires expertise to extract and select appropriate features from the sensor measurements as model inputs, and find a suitable set of model hyperparameters.
Journal ArticleDOI
Implementing Data-Driven Approach for Modelling Ultrasonic Wave Propagation Using Spatio-Temporal Deep Learning (SDL)
TL;DR: A data-driven spatio-temporal deep learning model, to simulate forward and reflected ultrasonic wave propagation in the 2D geometrical domain, by implementing the convolutional long short-term memory (ConvLSTM) algorithm.
Journal ArticleDOI
Optimizing hyperparameters of Data-driven simulation-assisted-Physics learned AI (DPAI) model to reduce compounding error.
TL;DR: In this paper , the authors proposed a Data-driven Simulation-Aided-Physics learned AI (DPAI) model to simulate the ultrasonic wave propagation for extended depth with a lower error.
Journal ArticleDOI
Physics-informed neural networks for transcranial ultrasound wave propagation.
TL;DR: In this article , the use of physics-informed neural networks (PINNs) is explored for predicting the transcranial ultrasound wave propagation. But the proposed approach suffers from high computational cost and discretization error in predicting the wavefield passing through the skull.
Journal ArticleDOI
Identifying defects on solar cells using magnetic field measurements and artificial intelligence trained by a finite-element-model
TL;DR: In this article , the magnetic field imaging method was used to detect defects in solar cells and modules without contact during operation, and different training data sets were set up in the simulation model by varying the electrical conductivities of the different parts of the solar cell.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Posted Content
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
TL;DR: This paper proposes the convolutional LSTM (ConvLSTM) and uses it to build an end-to-end trainable model for the precipitation nowcasting problem and shows that it captures spatiotemporal correlations better and consistently outperforms FC-L STM and the state-of-the-art operational ROVER algorithm.
Journal ArticleDOI
Recurrent neural networks and robust time series prediction
J. Connor,R.D. Martin,Les Atlas +2 more
TL;DR: A robust learning algorithm is proposed and applied to recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component and are shown to give better predictions than neural networks trained on unfiltered time series.
Journal ArticleDOI
Target Classification Using the Deep Convolutional Networks for SAR Images
TL;DR: A new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used, which can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.