scholarly journals Multi-output Deep Learning for Bus Arrival Time Predictions

2019 ◽  
Vol 41 ◽  
pp. 138-145 ◽  
Author(s):  
Niklas Christoffer Petersen ◽  
Filipe Rodrigues ◽  
Francisco Camara Pereira
Keyword(s):  
Deep Learning ◽  
Arrival Time

scholarly journals Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework

2021 ◽  
Vol 13 (9) ◽  
pp. 1738
Author(s):  
Huiyuan Fu ◽  
Yuchao Zheng ◽  
Yudong Ye ◽  
Xueshang Feng ◽  
Chaoxu Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Arrival Time ◽  
Optical Observations ◽  
Feature Maps ◽  
Feature Map ◽  
Learning Framework ◽  
Time Prediction ◽  
Arrival Time Prediction ◽  
Better Than

Fast and accurate prediction of the geoeffectiveness of coronal mass ejections (CMEs) and the arrival time of the geoeffective CMEs is urgent, to reduce the harm caused by CMEs. In this paper, we present a new deep learning framework based on time series of satellites’ optical observations that can give both the geoeffectiveness and the arrival time prediction of the CME events. It is the first time combining these two demands in a unified deep learning framework with no requirement of manually feature selection and get results immediately. The only input of the deep learning framework is the time series images from synchronized solar white-light and EUV observations. Our framework first uses the deep residual network embedded with the attention mechanism to extract feature maps for each observation image, then fuses the feature map of each image by the feature map fusion module and determines the geoeffectiveness of CME events. For the geoeffective CME events, we further predict its arrival time by the deep residual regression network based on group convolution. In order to train and evaluate our proposed framework, we collect 2400 partial-/full-halo CME events and its corresponding images from 1996 to 2018. The F1 score and Accuracy of the geoeffectiveness prediction can reach 0.270% and 75.1%, respectively, and the mean absolute error of the arrival time prediction is only 5.8 h, which are both significantly better than well-known deep learning methods and can be comparable to, or even better than, the best performance of traditional methods.

Deep Learning for Prediction of Bus Arrival Time in Public Transportation

Logistics 4.0 ◽  
2020 ◽  
pp. 126-135
Author(s):  
Faruk Serin ◽  
Suleyman Mete ◽  
Muhammet Gul ◽  
Erkan Celik
Keyword(s):  
Deep Learning ◽  
Public Transportation ◽  
Arrival Time

Ghost imaging for weak light imaging by using arrival time of photon and deep learning

2020 ◽  
Author(s):  
Yasuhiro Mizutani ◽  
Shoma Kataoka ◽  
Tsutomu Uenohara ◽  
Yasuhiro Takaya
Keyword(s):  
Deep Learning ◽  
Arrival Time ◽  
Ghost Imaging ◽  
Weak Light

scholarly journals A Deep Learning-Based Approach for Train Arrival Time Prediction

2021 ◽  
pp. 213-222
Author(s):  
Bas Jacob Buijse ◽  
Vahideh Reshadat ◽  
Oscar Willem Enzing
Keyword(s):  
Deep Learning ◽  
Arrival Time ◽  
Time Prediction ◽  
Arrival Time Prediction

Predicting Liner Arrival Time Based on Deep Learning

2021 ◽  
Author(s):  
Chao Huang ◽  
Yuqi Huang ◽  
Yang Yu ◽  
Bo Xiao
Keyword(s):  
Deep Learning ◽  
Arrival Time

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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