Investigating rare events with nonequilibrium work measurements. I. Nonequilibrium transition path probabilities

2014 ◽  
Vol 140 (3) ◽  
pp. 034114 ◽  
Author(s):  
Mahmoud Moradi ◽  
Celeste Sagui ◽  
Christopher Roland
Keyword(s):  
Rare Events ◽  
Transition Path

scholarly journals Transition path sampling of rare events by shooting from the top

2017 ◽  
Vol 147 (15) ◽  
pp. 152716 ◽  
Author(s):  
Hendrik Jung ◽  
Kei-ichi Okazaki ◽  
Gerhard Hummer
Keyword(s):  
Rare Events ◽  
Path Sampling ◽  
Transition Path ◽  
Transition Path Sampling

scholarly journals Using Bayes formula to estimate rates of rare events in transition path sampling simulations

2015 ◽  
Vol 143 (13) ◽  
pp. 134121 ◽  
Author(s):  
Pierre Terrier ◽  
Mihai-Cosmin Marinica ◽  
Manuel Athènes
Keyword(s):  
Rare Events ◽  
Path Sampling ◽  
Transition Path ◽  
Transition Path Sampling ◽  
Bayes Formula

Improved transition path sampling methods for simulation of rare events

2008 ◽  
Vol 128 (14) ◽  
pp. 144104 ◽  
Author(s):  
Manan Chopra ◽  
Rohit Malshe ◽  
Allam S. Reddy ◽  
J. J. de Pablo
Keyword(s):  
Sampling Methods ◽  
Rare Events ◽  
Path Sampling ◽  
Transition Path ◽  
Transition Path Sampling

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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