An Automated Real-Time Produced Water Composition Measurement Device for Scale Risk Prediction and Prevention

2016 ◽  
Author(s):  
N. Ghorbani ◽  
C. Yan ◽  
P. Guraieb ◽  
R. C. Tomson ◽  
D. Abdallah ◽  
...  
Keyword(s):  
Real Time ◽  
Risk Prediction ◽  
Produced Water ◽  
Measurement Device ◽  
Water Composition ◽  
Prediction And Prevention ◽  
Composition Measurement

scholarly journals Socioeconomic Deprivation and the Incidence of 12 Cardiovascular Diseases in 1.9 Million Women and Men: Implications for Risk Prediction and Prevention

PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e104671 ◽  
Author(s):  
Mar Pujades-Rodriguez ◽  
Adam Timmis ◽  
Dimitris Stogiannis ◽  
Eleni Rapsomaniki ◽  
Spiros Denaxas ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Risk Prediction ◽  
Socioeconomic Deprivation ◽  
Prediction And Prevention

A Real-time Risk-Prediction Model for Pediatric Venous Thromboembolic Events

PEDIATRICS ◽  
2021 ◽  
pp. e2020042325
Author(s):  
Shannon C. Walker ◽  
C. Buddy Creech ◽  
Henry J. Domenico ◽  
Benjamin French ◽  
Daniel W. Byrne ◽  
...  
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Thromboembolic Events ◽  
Venous Thromboembolic Events ◽  
Venous Thromboembolic

<i>Real-time Corn Grain Measurement Device Using Stereo Vision</i>

2019 ◽  
Author(s):  
Matthew Benjamin Rogers ◽  
Robert Clark Stevens
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Measurement Device ◽  
Corn Grain

scholarly journals Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1242
Author(s):  
Sihao Zhang ◽  
Jingyang Liu ◽  
Guigen Zeng ◽  
Chunhui Zhang ◽  
Xingyu Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Reference Frame ◽  
Quantum Key Distribution ◽  
Short Term Memory ◽  
Key Distribution ◽  
Transmission Efficiency ◽  
System Stability ◽  
Measurement Device ◽  
Measurement Device Independent

In most of the realistic measurement device-independent quantum key distribution (MDI-QKD) systems, efficient, real-time feedback controls are required to maintain system stability when facing disturbance from either external environment or imperfect internal components. Traditionally, people either use a “scanning-and-transmitting” program or insert an extra device to make a phase reference frame calibration for a stable high-visibility interference, resulting in higher system complexity and lower transmission efficiency. In this work, we build a machine learning-assisted MDI-QKD system, where a machine learning model—the long short-term memory (LSTM) network—is for the first time to apply onto the MDI-QKD system for reference frame calibrations. In this machine learning-assisted MDI-QKD system, one can predict out the phase drift between the two users in advance, and actively perform real-time phase compensations, dramatically increasing the key transmission efficiency. Furthermore, we carry out corresponding experimental demonstration over 100 km and 250 km commercial standard single-mode fibers, verifying the effectiveness of the approach.

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