Deep learning-based comprehensive monitor for smart power station

2021 ◽  
Vol 12 (4) ◽  
pp. 380
Author(s):  
Yerong Zhong ◽  
Guoheng Ruan ◽  
Jiaming Jiang
Keyword(s):  
Deep Learning ◽  
Power Station ◽  
Smart Power

Deep learning-based comprehensive monitor for smart power station

2021 ◽  
Vol 12 (4) ◽  
pp. 380
Author(s):  
Jiaming Jiang ◽  
Guoheng Ruan ◽  
Yerong Zhong
Keyword(s):  
Deep Learning ◽  
Power Station ◽  
Smart Power

scholarly journals Research on Prediction of Vertical Water Wall Temperature of Power Station Boiler Based on Deep Learning and Expert Experience

2021 ◽  
Vol 1922 (1) ◽  
pp. 012011
Author(s):  
Xian-tao Du ◽  
Yu-jia Cui ◽  
Ming-hao Wang ◽  
Liang-wei Xia ◽  
Qiang Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wall Temperature ◽  
Power Station ◽  
Water Wall ◽  
Power Station Boiler

scholarly journals Thermal station modelling and optimal control based on deep learning

2021 ◽  
Vol 25 (4 Part B) ◽  
pp. 2965-2973
Author(s):  
Min Cao
Keyword(s):  
Deep Learning ◽  
Thermal Power Station ◽  
Control Method ◽  
Learning Algorithm ◽  
Thermal Power ◽  
Heating System ◽  
Utilization Rate ◽  
Power Station ◽  
Deep Learning Algorithm ◽  
Deterministic Strategy

To solve the mismatch between heating quantity and demand of thermal stations, an optimized control method based on depth deterministic strategy gradient was proposed in this paper. In this paper, long short-time memory deep learning algorithm is used to model the thermal power station, and then the depth deterministic strategy gradient control algorithm is used to solve the water supply flow sequence of the primary side of the thermal power station in combination with the operation mechanism of the central heating system. In this paper, a large number of historical working condition data of a thermal station are used to carry out simulation experiment, and the results show that the method is effective, which can realize the on-demand heating of the thermal station a certain extent and improve the utilization rate of heat.

A Privacy-Preserving-Framework-Based Blockchain and Deep Learning for Protecting Smart Power Networks

2020 ◽  
Vol 16 (8) ◽  
pp. 5110-5118 ◽  
Author(s):  
Marwa Keshk ◽  
Benjamin Turnbull ◽  
Nour Moustafa ◽  
Dinusha Vatsalan ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Deep Learning ◽  
Privacy Preserving ◽  
Power Networks ◽  
Smart Power

Deep Learning

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

The Large Power Station

1918 ◽  
Vol 86 (2229supp) ◽  
pp. 185-185
Keyword(s):  
Power Station ◽  
Large Power

Sootblowing as a key part of power-station efficiency

1990 ◽  
Vol 4 (2) ◽  
pp. 103
Author(s):  
Jim Stevenson
Keyword(s):  
Power Station

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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