Rapid Prediction of High Temperature Properties of Furnace Tube Alloys Using Deep Learning Approaches

2021 ◽  
Author(s):  
Shulin Xiang ◽  
Tao Chen ◽  
Zhichao Fan ◽  
Xuedong Chen ◽  
Zhigang Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Temperature ◽  
Learning Approaches ◽  
High Temperature Properties ◽  
Rapid Prediction ◽  
Furnace Tube

Rapid Prediction of High-Temperature Properties of Furnace Tube Alloys Using Deep Learning Approaches

2020 ◽  
Author(s):  
Shulin Xiang ◽  
Tao Chen ◽  
Zhichao Fan ◽  
Xuedong Chen ◽  
Zhigang Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Temperature ◽  
Learning Algorithm ◽  
Engineering Practice ◽  
Good Prediction ◽  
Learning Approaches ◽  
High Temperature Properties ◽  
Heat Resistant ◽  
Wide Range ◽  
Rapid Prediction

Abstract With the development of Materials Genome Initiative (MGI) and data mining technology, machine learning (ML) has emerged as an important tool in the research of materials science. For the heat resistant alloys used in furnace tubes, the rapid prediction of the high-temperature properties is critical but difficult until now. In this work, the ML method based on the deep learning algorithm is developed to establish the direct correlation between microstructure inputs and output stress rupture properties of Fe-Cr-Ni based heat resistant alloys. Two simple convolutional neural networks (CNN) and the complex network with VGG16 architecture are implemented and evaluated. The simple CNN and VGG16 models are trained from scratch and pre-trained, respectively. Due to the relatively few training samples in the dataset, the data augmentation configuration and the improved architecture are effective to mitigate overfitting in simple CNN models. The result also shows that in the case of transfer learning, the features extracted from other datasets can be used directly to this new visual task. It is demonstrated that both the simple CNN and VGG16 models reach the high prediction accuracies (more than 90 %) of high-temperature properties with a wide range of microstructures. In addition, the good prediction performance achieved in the small dataset also reveals the deep learning approaches can be used to construct powerful vision models in engineering practice, where very limited data is the common situation.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

CENTRALLOY G4879 MICRO

Alloy Digest ◽  
2013 ◽  
Vol 62 (5) ◽  
Keyword(s):  
High Temperature ◽  
Physical Properties ◽  
Tensile Properties ◽  
Nickel Alloy ◽  
High Temperature Properties ◽  
Uniform Dispersion ◽  
Dislocation Movement

Abstract Centralloy G4879 Micro is a cast nickel alloy with very good high-temperature properties. The alloy has carbides in a uniform dispersion that impede dislocation movement. This datasheet provides information on composition, physical properties, elasticity, and tensile properties. It also includes information on casting, machining, and joining. Filing Code: Ni-708. Producer or source: Schmidt & Clemens Inc..

Assessment of the Risk Factors of MDD Recurrence Based on Deep Learning Approaches

2019 ◽  
Author(s):  
Qian Wu ◽  
Weiling Zhao ◽  
Xiaobo Yang ◽  
Hua Tan ◽  
Lei You ◽  
...  
Keyword(s):  
Risk Factors ◽  
Deep Learning ◽  
Learning Approaches

The Problem of Fraudulent Content on the Web: Deep Learning Approaches

2020 ◽  
Author(s):  
Priyanka Meel ◽  
Farhin Bano ◽  
Dr. Dinesh K. Vishwakarma
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
The Web

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

High-temperature properties of liquid-phase-sintered α-SiC

2000 ◽  
Vol 282 (1-2) ◽  
pp. 109-114 ◽  
Author(s):  
Robert P. Jensen ◽  
William E. Luecke ◽  
Nitin P. Padture ◽  
Sheldon M. Wiederhorn
Keyword(s):  
High Temperature ◽  
Liquid Phase ◽  
High Temperature Properties

scholarly journals Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shan Guleria ◽  
Tilak U. Shah ◽  
J. Vincent Pulido ◽  
Matthew Fasullo ◽  
Lubaina Ehsan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Accuracy ◽  
High Sensitivity ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Learning Approaches ◽  
Learning Models ◽  
Whole Slide Image ◽  
Slide Image ◽  
Level Model

AbstractProbe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett’s esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between pathologists. We deployed deep-learning-based image and video analysis in order to improve diagnostic accuracy of pCLE videos and biopsy images. Blinded experts categorized biopsies and pCLE videos as squamous, non-dysplastic BE, or dysplasia/cancer, and deep learning models were trained to classify the data into these three categories. Biopsy classification was conducted using two distinct approaches—a patch-level model and a whole-slide-image-level model. Gradient-weighted class activation maps (Grad-CAMs) were extracted from pCLE and biopsy models in order to determine tissue structures deemed relevant by the models. 1970 pCLE videos, 897,931 biopsy patches, and 387 whole-slide images were used to train, test, and validate the models. In pCLE analysis, models achieved a high sensitivity for dysplasia (71%) and an overall accuracy of 90% for all classes. For biopsies at the patch level, the model achieved a sensitivity of 72% for dysplasia and an overall accuracy of 90%. The whole-slide-image-level model achieved a sensitivity of 90% for dysplasia and 94% overall accuracy. Grad-CAMs for all models showed activation in medically relevant tissue regions. Our deep learning models achieved high diagnostic accuracy for both pCLE-based and histopathologic diagnosis of esophageal dysplasia and its precursors, similar to human accuracy in prior studies. These machine learning approaches may improve accuracy and efficiency of current screening protocols.

Sign in / Sign up

Export Citation Format

Share Document