scholarly journals A Deep Learning Framework Based on Multisensor Fusion Information to Identify the Airplane Wake Vortex

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yi Ai ◽  
Yuanji Wang ◽  
Weijun Pan ◽  
Dingjie Wu
Keyword(s):  
Deep Learning ◽  
Wind Field ◽  
Aviation Industry ◽  
Wake Vortex ◽  
Doppler Lidar ◽  
Multisensor Fusion ◽  
Rapid Improvement ◽  
Learning Framework ◽  
Detection And Identification ◽  
Vortex Detection

Along with the rapid improvement of the aviation industry, flight density also increases with the increase of flight demand, which directly leads to the increasingly prominent influence of wake vortex on flight safety and aviation control. In this paper, we propose a new joint framework—a deep learning framework—based on multisensor fusion information to address the detection and identification of wake vortices in the near-Earth phase. By setting multiple Doppler lidar in near-Earth flight areas at different airports, a large number of accurate wind field data are captured for wake vortex detection. Meanwhile, the airport surveillance radar is used to locate the wake vortex. In the deep learning framework, an end-to-end CNN-LSTM model has been employed to identify the airplane wake vortex from the data detected by Doppler lidar and the airport surveillance radar. The variables including the wind field matrix, positioning matrix, and the variance sequence are used as inputs to the CNN channel and LSTM channel. The identification and location information of the wake vortex in the wind field image will be output by the framework. Experiments show that the joint framework based on a multisensor possesses stronger ability to capture local feature and sequence feature than the traditional CNN or LSTM model.

Simulation of Wake Vortex Detection with Airborne Doppler Lidar

2000 ◽  
Vol 37 (6) ◽  
pp. 984-993 ◽  
Author(s):  
Denis Darracq ◽  
Alexandre Corjon ◽  
Frédéric Ducros ◽  
Mike Keane ◽  
Daniel Buckton ◽  
...  
Keyword(s):  
Wake Vortex ◽  
Doppler Lidar ◽  
Vortex Detection

Research on the Technique of Aircraft Wake Vortex Detection Based on 1.5 μm Doppler Lidar

2011 ◽  
Vol 40 (6) ◽  
pp. 811-817
Author(s):  
吴永华 WU Yong-hua ◽  
胡以华 HU Yi-hua ◽  
戴定川 DAI Ding-chuan ◽  
徐世龙 XU Shi-long ◽  
李今明 LI Jin-ming
Keyword(s):  
Wake Vortex ◽  
Doppler Lidar ◽  
Aircraft Wake ◽  
Vortex Detection

scholarly journals Prediction of Air Flight Cancellation during COVID-19 using Deep Learning Methods

2020 ◽  
Author(s):  
Samir Kumar Bandyopadhyay ◽  
Vishal Goyel ◽  
SHAWNI DUTTA
Keyword(s):  
Deep Learning ◽  
Predictive Model ◽  
Short Term Memory ◽  
Disease Outbreaks ◽  
Air Traffic ◽  
Aviation Industry ◽  
Learning Framework ◽  
History Of ◽  
Flight Cancellations ◽  
Oil Crises

Air traffic is vulnerable to external factors, such as oil crises, natural disasters, economic recessions and disease outbreaks due to COVID-19. This reason seems to have a more severe and more rapid impact on air traffic numbers as sudden increases in flight cancellations, aircraft groundings and travel bans. Various Airways loose revenues and it is difficult for them to sustain for a long period. This problem as been facing the entire world. The reductions in passenger numbers are significant. It is due to flights being cancelled or planes flying empty between airports. It is in turn massively reducing revenues for airlines and forced many airlines to lay off employees or declare bankruptcy. Airways also have to attempt refunding cancelled trips in order to diminish their losses. The airliner manufacturers and airport operators have also laid off employees. According to some commentators, this crisis is the worst ever encountered in the history of the aviation industry. Aircraft cancellation prediction is accomplished by utilising deep learning framework. In this framework, two dissimilar recurrent neural networks are assembled as a single entity while inferring the prediction results. Long-short term memory (LSTM) and Gated Recurrent Unit (GRU) are employed to design the proposed predictive model. This predictive model is compared against traditional neural network based Multi-layer perceptron model. Experimental results indicated an accuracy of 98.7% by the proposed model.

A Deep Learning Framework for Prediction of Retinal Disorders

2020 ◽  
Author(s):  
Raniyaharini R ◽  
Madhumitha K ◽  
Mishaa S ◽  
Virajaravi R
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
Retinal Disorders

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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