High-accuracy rebar position detection using deep learning–based frequency-difference electrical resistance tomography

2022 ◽  
Vol 135 ◽  
pp. 104116
Author(s):  
Dongho Jeon ◽  
Min Kyoung Kim ◽  
Yeounung Jeong ◽  
Jae Eun Oh ◽  
Juhyuk Moon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrical Resistance ◽  
High Accuracy ◽  
Frequency Difference ◽  
Electrical Resistance Tomography ◽  
Position Detection

Detecting embedded rebar in cement mortar by frequency-difference electrical resistance tomography

2021 ◽  
Vol 132 ◽  
pp. 103974
Author(s):  
Dongho Jeon ◽  
Min Kyoung Kim ◽  
Hayoung Woo ◽  
Yeounung Jeong ◽  
Jae Eun Oh ◽  
...  
Keyword(s):  
Electrical Resistance ◽  
Cement Mortar ◽  
Frequency Difference ◽  
Electrical Resistance Tomography

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Detecting the pulmonary trunk in CT scout views using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aydin Demircioğlu ◽  
Magdalena Charis Stein ◽  
Moon-Sung Kim ◽  
Henrike Geske ◽  
Anton S. Quinsten ◽  
...  
Keyword(s):  
Deep Learning ◽  
Validation Cohort ◽  
High Accuracy ◽  
Pulmonary Trunk ◽  
Reference Standard ◽  
Bolus Tracking ◽  
Wilcoxon Rank Sum Test ◽  
Scout View ◽  
Axial Ct ◽  
Anterior Posterior

AbstractFor CT pulmonary angiograms, a scout view obtained in anterior–posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of localizing the pulmonary trunk in CT scout views by deep learning methods. In 620 eligible CT scout views of 563 patients between March 2003 and February 2020 the region of the pulmonary trunk as well as an optimal slice (“reference standard”) for bolus tracking, in which the pulmonary trunk was clearly visible, was annotated and used to train a U-Net predicting the region of the pulmonary trunk in the CT scout view. The networks’ performance was subsequently evaluated on 239 CT scout views from 213 patients and was compared with the annotations of three radiographers. The network was able to localize the region of the pulmonary trunk with high accuracy, yielding an accuracy of 97.5% of localizing a slice in the region of the pulmonary trunk on the validation cohort. On average, the selected position had a distance of 5.3 mm from the reference standard. Compared to radiographers, using a non-inferiority test (one-sided, paired Wilcoxon rank-sum test) the network performed as well as each radiographer (P < 0.001 in all cases). Automated localization of the region of the pulmonary trunk in CT scout views is possible with high accuracy and is non-inferior to three radiographers.

scholarly journals Optimisation of Sensor Electrode Size for in Electrical Resistance Tomography Implementing Conducting Boundary Strategy

2021 ◽  
Vol 1874 (1) ◽  
pp. 012077
Author(s):  
Suzanna Ridzuan Aw ◽  
Ruzairi Abdul Rahim ◽  
Fazlul Rahman Mohd Yunus ◽  
Mohd Hafiz Fazalul Rahiman ◽  
Yasmin Abdul Wahab ◽  
...  
Keyword(s):  
Electrical Resistance ◽  
Electrical Resistance Tomography ◽  
Electrode Size ◽  
Sensor Electrode

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

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