System of systems uncertainty quantification using machine learning techniques with smart grid application

2020 ◽  
Vol 23 (6) ◽  
pp. 770-782
Author(s):  
Ali K. Raz ◽  
Paul C. Wood ◽  
Linas Mockus ◽  
Daniel A. DeLaurentis
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Uncertainty Quantification ◽  
System Of Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Grid Application

Detecting false data attacks using machine learning techniques in smart grid: A survey

2020 ◽  
Vol 170 ◽  
pp. 102808 ◽  
Author(s):  
Lei Cui ◽  
Youyang Qu ◽  
Longxiang Gao ◽  
Gang Xie ◽  
Shui Yu
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Machine Learning Techniques ◽  
Learning Techniques

Uncertainty Quantification of Artificial Neural Network Based Machine Learning Potentials

2018 ◽  
Author(s):  
Yumeng Li ◽  
Weirong Xiao ◽  
Pingfeng Wang
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Uncertainty Quantification ◽  
Material Design ◽  
Md Simulations ◽  
Machine Learning Techniques ◽  
Field Potentials ◽  
Analysis And Design ◽  
Learning Techniques ◽  
Study Results

Atomistic simulations play an important role in the material analysis and design by being rooted in the accurate first principles methods that free from empirical parameters and phenomenological models. However, successful applications of MD simulations largely depend on the availability of efficient and accurate force field potentials used for describing the interatomic interactions. As a powerful tool revolutionizing many areas in science and technology, machine learning techniques have gained growing attentions in the field of material science and engineering due to their potentials to accelerate the material discovery through their applications in surrogate model assisted material design. Despite tremendous advantages of employing machine learning techniques for the development of force field potentials as compared to conventional approaches, the uncertainty involved in the machine learning interpolated atomic potential energy surface has not drew much attention although it is an important issue. In this paper, the uncertainty quantification study is performed for the machine learning interpolated atomic potentials, and applied to the titanium dioxide (TiO2), an industrially relevant and well-studies material. The study results indicated that quantifying uncertainties is an indispensable task that must be performed along with the atomistic simulation process for a successful application of the machine learning based force field potentials.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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