scholarly journals Communication: Analysing kinetic transition networks for rare events

2014 ◽  
Vol 141 (4) ◽  
pp. 041104 ◽  
Author(s):  
Jacob D. Stevenson ◽  
David J. Wales
Keyword(s):  
Rare Events ◽  
Kinetic Transition ◽  
Transition Networks

scholarly journals Properties of kinetic transition networks for atomic clusters and glassy solids

2017 ◽  
Vol 19 (37) ◽  
pp. 25498-25508 ◽  
Author(s):  
John W. R. Morgan ◽  
Dhagash Mehta ◽  
David J. Wales
Keyword(s):  
Small World ◽  
Atomic Clusters ◽  
Scale Free ◽  
Kinetic Transition ◽  
Glassy Systems ◽  
Transition Networks

Small-world and scale-free properties are analysed for kinetic transition networks of clusters and glassy systems.

scholarly journals Kinetic Transition Networks for the Thomson Problem and Smale’s Seventh Problem

2016 ◽  
Vol 117 (2) ◽  
Author(s):  
Dhagash Mehta ◽  
Jianxu Chen ◽  
Danny Z. Chen ◽  
Halim Kusumaatmaja ◽  
David J. Wales
Keyword(s):  
Kinetic Transition ◽  
Thomson Problem ◽  
Transition Networks

Refined kinetic transition networks for the GB1 hairpin peptide

2009 ◽  
Vol 11 (18) ◽  
pp. 3341 ◽  
Author(s):  
Joanne M. Carr ◽  
David J. Wales
Keyword(s):  
Kinetic Transition ◽  
Transition Networks

scholarly journals Identifying mechanistically distinct pathways in kinetic transition networks

2019 ◽  
Vol 151 (12) ◽  
pp. 124101 ◽  
Author(s):  
Daniel J. Sharpe ◽  
David J. Wales
Keyword(s):  
Kinetic Transition ◽  
Transition Networks

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.

scholarly journals Wepy: A Flexible Software Framework for Simulating Rare Events with Weighted Ensemble Resampling

ACS Omega ◽  
2020 ◽  
Vol 5 (49) ◽  
pp. 31608-31623
Author(s):  
Samuel D. Lotz ◽  
Alex Dickson
Keyword(s):  
Rare Events ◽  
Software Framework
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