Machine Learning Outperforms Standard Force-Field Calibration Techniques

Scilight ◽  
2020 ◽  
Vol 2020 (45) ◽  
pp. 451116
Author(s):  
Jodi Ackerman Frank
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Field Calibration ◽  
Calibration Techniques

scholarly journals Enhanced force-field calibration via machine learning

2021 ◽  
Author(s):  
Aykut Argun ◽  
Tobias Thalheim ◽  
Stefano Bo ◽  
Frank Cichos ◽  
Giovanni Volpe
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Field Calibration

scholarly journals Enhanced force-field calibration via machine learning

2020 ◽  
Vol 7 (4) ◽  
pp. 041404
Author(s):  
Aykut Argun ◽  
Tobias Thalheim ◽  
Stefano Bo ◽  
Frank Cichos ◽  
Giovanni Volpe
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Field Calibration

ApneaDetector

2021 ◽  
Vol 5 (2) ◽  
pp. 1-22
Author(s):  
Xianda Chen ◽  
Yifei Xiao ◽  
Yeming Tang ◽  
Julio Fernandez-Mendoza ◽  
Guohong Cao
Keyword(s):  
Machine Learning ◽  
Sleep Apnea ◽  
Wearable Sensors ◽  
Machine Learning Techniques ◽  
Clinical Test ◽  
Learning Techniques ◽  
Apnea Detection ◽  
Hospital Visits ◽  
Calibration Techniques ◽  
Data Calibration

Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, specialized wearable sensors, professional installations, and long waiting lists. To address this problem, we design a smartwatch-based system called ApneaDetector, which exploits the built-in sensors in smartwatches to detect sleep apnea. Through a clinical study, we identify features of sleep apnea captured by smartwatch, which can be leveraged by machine learning techniques for sleep apnea detection. However, there are many technical challenges such as how to extract various special patterns from the noisy and multi-axis sensing data. To address these challenges, we propose signal denoising and data calibration techniques to process the noisy data while preserving the peaks and troughs which reflect the possible apnea events. We identify the characteristics of sleep apnea such as signal spikes which can be captured by smartwatch, and propose methods to extract proper features to train machine learning models for apnea detection. Through extensive experimental evaluations, we demonstrate that our system can detect apnea events with high precision (0.9674), recall (0.9625), and F1-score (0.9649).

Tuning force field parameters of ionic liquids using machine learning techniques

2021 ◽  
Vol 200 ◽  
pp. 110759
Author(s):  
Rafikul Islam ◽  
Md Fauzul Kabir ◽  
Saugato Rahman Dhruba ◽  
Khurshida Afroz
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Force Field ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Force Field Parameters

scholarly journals Energy-free machine learning force field for aluminum

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Ivan Kruglov ◽  
Oleg Sergeev ◽  
Alexey Yanilkin ◽  
Artem R. Oganov
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
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