Deep Learning Techniques Revolutionize E&P – Two practical applications

In the recent years, intelligent data-driven faultdiagnosis methods on gearboxes have been successfully developedand popularly applied in the industries. Currently, most ofthe machine learning techniques require that the training andtesting data are from the same distribution. However, thisassumption is difficult to be met in the real industries, sincethe gearbox operating conditions usually change in practice,which results in significant data distribution gap and diagnosticperformance deteriorations in applying the learned knowledgeon the new conditions. This paper proposes a deep learning-based domain adaptation method to address this issue. Theraw current signals are directly used as the model inputs fordiagnostics, which are easy to collect in the real industries andfacilitate practical applications. The maximum mean discrepancymetric is introduced to the deep neural network, the optimizationof which guarantees the extraction of generalized machineryhealth condition features across different operating conditions.The experiments on a real-world gearbox condition monitoringdataset validate the effectiveness of the proposed method, whichoffers a promising tool for cross-domain diagnosis in the realindustries.

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Deep Cross-Corpus Speech Emotion Recognition: Recent Advances and Perspectives

Frontiers in Neurorobotics ◽

10.3389/fnbot.2021.784514 ◽

2021 ◽

Vol 15 ◽

Author(s):

Shiqing Zhang ◽

Ruixin Liu ◽

Xin Tao ◽

Xiaoming Zhao

Keyword(s):

Deep Learning ◽

Emotion Recognition ◽

Feature Learning ◽

Learning Ability ◽

Speech Emotion Recognition ◽

Practical Applications ◽

Learning Techniques ◽

Challenges And Opportunities ◽

Comprehensive Survey ◽

Cross Language

Automatic speech emotion recognition (SER) is a challenging component of human-computer interaction (HCI). Existing literatures mainly focus on evaluating the SER performance by means of training and testing on a single corpus with a single language setting. However, in many practical applications, there are great differences between the training corpus and testing corpus. Due to the diversity of different speech emotional corpus or languages, most previous SER methods do not perform well when applied in real-world cross-corpus or cross-language scenarios. Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have increasingly been adopted for cross-corpus SER. This paper aims to provide an up-to-date and comprehensive survey of cross-corpus SER, especially for various deep learning techniques associated with supervised, unsupervised and semi-supervised learning in this area. In addition, this paper also highlights different challenges and opportunities on cross-corpus SER tasks, and points out its future trends.

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GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users

Sensors ◽

10.3390/s21217010 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7010

Author(s):

Xuefei Li ◽

Liangtu Song ◽

Liu Liu ◽

Linli Zhou

Keyword(s):

Risk Assessment ◽

Deep Learning ◽

Supply System ◽

Practical Applications ◽

Learning Framework ◽

Domain Specific ◽

Learning Techniques ◽

System Risk ◽

Score Matrix ◽

Gas Supply

Gas supply system risk assessment is a serious and important problem in cities. Existing methods tend to manually build mathematical models to predict risk value from single-modal information, i.e., pipeline parameters. In this paper, we attempt to consider this problem from a deep-learning perspective and define a novel task, Urban Gas Supply System Risk Assessment (GSS-RA). To drive deep-learning techniques into this task, we collect and build a domain-specific dataset GSS-20K containing multi-modal data. Accompanying the dataset, we design a new deep-learning framework named GSS-RiskAsser to learn risk prediction. In our method, we design a parallel-transformers Vision Embedding Transformer (VET) and Score Matrix Transformer (SMT) to process multi-modal information, and then propose a Multi-Modal Fusion (MMF) module to fuse the features with a cross-attention mechanism. Experiments show that GSS-RiskAsser could work well on GSS-RA task and facilitate practical applications. Our data and code will be made publicly available.

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Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

10.36227/techrxiv.12195993.v2 ◽

2020 ◽

Author(s):

Arafat Al-Dweik ◽

Reza Mohammadi Tamanani ◽

Radu Muresan

Keyword(s):

Deep Learning ◽

Intelligent Transportation Systems ◽

Human Error ◽

Transportation Systems ◽

Practical Applications ◽

Facial Region ◽

Learning Techniques ◽

Haar Cascade ◽

And Performance ◽

Cascade Method

<div>Road accidents caused by human error are among</div><div>the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,</div><div>a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method</div><div>for practical applications in intelligent transportation systems</div>

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Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

10.36227/techrxiv.12195993 ◽

2020 ◽

Author(s):

Arafat Al-Dweik ◽

Reza Mohammadi Tamanani ◽

Radu Muresan

Keyword(s):

Deep Learning ◽

Intelligent Transportation Systems ◽

Human Error ◽

Transportation Systems ◽

Practical Applications ◽

Facial Region ◽

Learning Techniques ◽

Haar Cascade ◽

And Performance ◽

Cascade Method

<div>Road accidents caused by human error are among</div><div>the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,</div><div>a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method</div><div>for practical applications in intelligent transportation systems</div>

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Automatic Image Colorization using Deep Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f7719.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 1592-1595

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Colorization ◽

Practical Applications ◽

Black And White ◽

Video Restoration ◽

Learning Techniques ◽

Fully Automatic ◽

Historical Image ◽

Different Sources

Image colorization is a fascinating topic and has become an area of research in the recent years. In this project, we are going to colorize black and white images with the help of Deep Learning techniques. Some previous approaches required human involvement or resulted in the development of desaturated images. We are building a Deep Convolutional Neural Network (CNN) which will be trained on over a million images. The output generated by the model is fully dependent on the images it has been trained from and requires no human help. The images are taken from different sources like ResNet, Reddit, etc. The model will include many hidden layers to make the output more accurate. This will be a fully automatic model and will produce images with accurate colors and contrast. Finally, the goal of this project is to produce realistic and color accurate images that can easily fool the viewer. The viewer wouldn’t be able to differentiate between the photo which the model produced and the real photo. Our project has wide practical applications like historical image/video restoration, image enhancement for better interpretability, frame by frame colorization of black and white documentaries, etc.

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Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

10.36227/techrxiv.12195993.v1 ◽

2020 ◽

Author(s):

Arafat Al-Dweik ◽

Reza Mohammadi Tamanani ◽

Radu Muresan

Keyword(s):

Deep Learning ◽

Intelligent Transportation Systems ◽

Human Error ◽

Transportation Systems ◽

Practical Applications ◽

Facial Region ◽

Learning Techniques ◽

Haar Cascade ◽

And Performance ◽

Cascade Method

<div>Road accidents caused by human error are among</div><div>the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,</div><div>a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method</div><div>for practical applications in intelligent transportation systems</div>

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A unified framework for automated person re-indentification

Transport and Communication Science Journal ◽

10.25073/tcsj.71.7.11 ◽

2020 ◽

Vol 71 (7) ◽

pp. 868-880

Author(s):

Nguyen Hong-Quan ◽

Nguyen Thuy-Binh ◽

Tran Duc-Long ◽

Le Thi-Lan

Keyword(s):

Deep Learning ◽

Video Analysis ◽

Camera Network ◽

Unified Framework ◽

Person Detection ◽

Practical Applications ◽

Detection And Tracking ◽

Analysis System ◽

Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

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