scholarly journals An Improved Deep Neural Network Model Based on Searching Space Limitation for Object Localization

2020 ◽  
Vol 790 ◽  
pp. 012146
Author(s):  
Renchao Wu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Object Localization ◽  
Model Based ◽  
Space Limitation

Deep Neural Network Model based on One-Stage Detector for Identifying Fundus Lesions

2021 ◽  
Author(s):  
Carlos Santos ◽  
Marilton Sanchotene De Aguiar ◽  
Daniel Welfer ◽  
Bruno Belloni
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Model Based ◽  
One Stage

Holoscopic 3D Microgesture Recognition by Deep Neural Network Model Based on Viewpoint Images and Decision Fusion

2021 ◽  
Vol 51 (2) ◽  
pp. 162-171
Author(s):  
Yi Liu ◽  
Min Peng ◽  
Mohammad Rafiq Swash ◽  
Tong Chen ◽  
Rui Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Decision Fusion ◽  
Model Based

scholarly journals Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

2020 ◽  
Vol 8 (10) ◽  
pp. 766
Author(s):  
Dohan Oh ◽  
Julia Race ◽  
Selda Oterkus ◽  
Bonguk Koo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Comparison Results ◽  
Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

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