Deep neural network and random forest classifier for source tracking of chemical leaks using fence monitoring data-Reference-Cited by-同舟云学术

Deep neural network and random forest classifier for source tracking of chemical leaks using fence monitoring data

Published:2018-11 Issue: Volume:56 Page:548-558
ISSN:0950-4230
Container-title:Journal of Loss Prevention in the Process Industries
language:en
Short-container-title:Journal of Loss Prevention in the Process Industries

Author:

Cho Jaehoon,Kim Hyunseung,Gebreselassie Addis Lulu,Shin Dongil

Funder

National Research Foundation of Korea

Korea government

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality,General Chemical Engineering,Food Science,Control and Systems Engineering

Reference18 articles.

1. Random forests;Breiman;Mach. Learn.,2001

2. Placement Optimization and Reliability Analysis of Stationary and Mobile Sensors for Chemical Plant Fence Monitoring;Cho,2017

3. The Bhopal SAGA: Causes and Consequences of the World's Largest Industrial Disaster;Eckerman,2005

4. Reducing the dimensionality of data with neural networks;Hinton;J. Sci.,2006

5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift;Ioffe,2015

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