scholarly journals Deep learning model to predict complex stress and strain fields in hierarchical composites

2021 ◽  
Vol 7 (15) ◽  
pp. eabd7416
Author(s):  
Zhenze Yang ◽  
Chi-Hua Yu ◽  
Markus J. Buehler
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Design Space ◽  
Learning Model ◽  
Complex Stress ◽  
Complex Materials ◽  
Material Microstructure ◽  
Hierarchical Composites ◽  
Materials Behavior ◽  
Deep Learning Model

Materials-by-design is a paradigm to develop previously unknown high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design space. Here we report an AI-based approach, implemented in a game theory–based conditional generative adversarial neural network (cGAN), to bridge the gap between a material’s microstructure—the design space—and physical performance. Our end-to-end deep learning model predicts physical fields like stress or strain directly from the material microstructure geometry, and reaches an astonishing accuracy not only for predicted field data but also for derivative material property predictions. Furthermore, the proposed approach offers extensibility by predicting complex materials behavior regardless of component shapes, boundary conditions, and geometrical hierarchy, providing perspectives of performing physical modeling and simulations. The method vastly improves the efficiency of evaluating physical properties of hierarchical materials directly from the geometry of its structural makeup.

scholarly journals A compact deep learning model for Khmer handwritten text recognition

2021 ◽  
Vol 10 (3) ◽  
pp. 584
Author(s):  
Bayram Annanurov ◽  
Norliza Noor
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Ad Hoc ◽  
Recognition Rate ◽  
Learning Model ◽  
Text Recognition ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Fully Connected ◽  
Deep Learning Model

<p>The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres. Data collected for previous experiments were used in this work. The oneagainst-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two convolutional layers and one fully connected layer, was proposed. The recognition rate came out to be within 93-98%. The compact model is performed on par with the state-of-theart models. It was discovered that computational capacity requirements usually associated with deep learning can be alleviated, therefore allowing applications under limited computational power.</p>

scholarly journals To perform road signs recognition for autonomous vehicles using cascaded deep learning pipeline

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Riadh Ayachi ◽  
Yahia ElFahem Said ◽  
Mohamed Atri
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Autonomous Vehicle ◽  
Sensory Information ◽  
Learning Model ◽  
The Road ◽  
Road Signs ◽  
Deep Learning Model

Autonomous vehicle is a vehicle that can guide itself without human conduction. It is capable of sensing its environment and moving with little or no human input. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant road signs. In this paper, we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs. The road signs classifier will be based on an artificial intelligence technique. In particular, a deep learning model is used, Convolutional Neural Networks (CNN). CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection. CNN has been successfully used to solve computer vision problems because of its methodology in processing images which is similar to the human brain decision making. The evaluation of the proposed pipeline is proved using two different datasets. The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8% and a testing accuracy of 99.6%. The proposed method can be easily implemented for real-time application.

scholarly journals TRANSFERABILITY ASSESSMENT OF OPEN-SOURCE DEEP LEARNING MODEL FOR BUILDING DETECTION ON SATELLITE DATA

2021 ◽  
Vol XLVI-4/W4-2021 ◽  
pp. 107-110
Author(s):  
A. Spasov ◽  
D. Petrova-Antonova
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Transfer Learning ◽  
Satellite Data ◽  
High Performance ◽  
Remote Sensing Data ◽  
Learning Model ◽  
Training Data ◽  
Building Detection ◽  
Deep Learning Model

Abstract. A great number of studies for identification and localization of buildings based on remote sensing data has been conducted over the past few decades. The majority of the more recent models make use of neural networks, which show high performance in semantic segmentation for the purpose of building detection even in complex regions like the city landscape. However, they could require a substantial amount of labelled training data depending on the diversity of objects targeted, which could be expensive and time consuming to acquire. Transfer Learning is a technique that could be used to reduce the amount of data and resources needed by applying knowledge obtained solving one problem to another one. In addition, if open-source data and models are used, this process is much more affordable. In this paper, the Transfer Learning challenges and issues are explored by utilizing an open-sourced pre-trained deep learning model on satellite data for building detection.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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