Gray-Edge-HOG feature based cascaded learning for facial landmark detection

Compared with the traditional statistical models, such as the active shape model and the active appearance model, the facial feature point localization method based on deep learning has improved in accuracy and speed, but there still exist some problems. First, when the traditional deep neural network model targets a data set containing different face poses, it only performs the preprocessing through the initialized face alignment, and does not consider the regularity of the distribution of the feature points corresponding to the face pose during feature extraction. Secondly, the traditional deep neural network model does not take into account the feature space differences caused by the different position distribution of the external contour points and internal organ points (such as eyes, nose and mouth), resulting in inconsistent detection accuracy and difficulty of different feature points. In order to solve the above problems this paper proposes a convolutional neural network (CNN) based on grayedge-HOG (GEH) fusion feature.

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RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽

10.3390/electronics10131514 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1514

Author(s):

Seung-Ho Lim ◽

WoonSik William Suh ◽

Jin-Young Kim ◽

Sang-Young Cho

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

System Level ◽

Neural Network Models ◽

Data Set ◽

Embedded Device ◽

Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

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Using deep learning to identify the severity of pipeline dents

Pipeline Science and Technology ◽

10.28999/2514-541x-2020-4-2-90-96 ◽

2020 ◽

Vol 4 (2) ◽

pp. 90-96

Author(s):

Ishita Charkraborty ◽

◽

Brent Vyvial ◽

Keyword(s):

Neural Network ◽

Machine Learning ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Data Set ◽

Machine Learning Model ◽

Unseen Data ◽

Risk Categories

With the advent of machine learning, data-based models can be used to increase efficiency and reduce cost for the characterization of various anomalies in pipelines. In this work, artificial intelligence is used to classify pipeline dents directly from the in-line inspection (ILI) data according to their risk categories. A deep neural network model is built with available ILI data, and the resulting machine learning model requires only the ILI data as an input to classify dents in different risk categories. Using a machine learning based model eliminates the need for conducting detailed engineering analysis to determine the effects of dents on the integrity of the pipeline. Concepts from computer vision are used to build the deep neural network using the available data. The deep neural network model is then trained on a sub set of the available ILI data and the model is tested for accuracy on a previously unseen set of the available data. The developed model predicts risk factors associated with a dent with 94% accuracy for a previously unseen data set.

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Detection of Tuberculosis from Chest X-Ray Images Based on Modified Inception Deep Neural Network Model

2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) ◽

10.1109/wiecon-ece52138.2020.9397994 ◽

2020 ◽

Author(s):

Omar Tawhid Imam ◽

Moinul Haque ◽

Celia Shahnaz ◽

Sheikh Asif Imran ◽

Md. Tariqul Islam ◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

X Ray ◽

Chest X Ray

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

Journal of Marine Science and Engineering ◽

10.3390/jmse8100766 ◽

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.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119837236 ◽

2019 ◽

Vol 2673 (4) ◽

pp. 108-116 ◽

Cited By ~ 1

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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