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Search Publications by: Wai Cheong Tam (Fed)

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Displaying 1 - 25 of 42

Building Fire Hazard Predictions Using Machine Learning

January 26, 2024
Author(s)
Eugene Yujun Fu, Wai Cheong Tam, Tianhang Zhang, Xinyan Huang
The lack of information on the fire ground has always been the leading factor in making wrong decisions . Wrong decisions can be made by individual firefighters, their local chiefs, and/or the incident commander. Any wrong decision at any level (scale)

Report on High Energy Arcing Fault Experiments: Experimental Results from Medium-Voltage Bus Duct and Switchgear Enclosures

September 15, 2023
Author(s)
Gabriel Taylor, Anthony D. Putorti Jr., Scott Bareham, Christopher U. Brown, Wai Cheong Tam, Ryan Falkenstein-Smith, Stephen Fink, Michael Heck, Edward Hnetkovsky, Nicholas Melly, Kenneth Hamburger, Kenneth Miller
This report documents an experimental program designed to investigate high energy arcing fault (HEAF) phenomena for medium-voltage, metal-enclosed bus ducts and switchgear. This report covers full-scale laboratory experiments using representative nuclear

A review of thermal exposure and fire spread mechanisms in Large Outdoor Fires and the Built Environment

July 28, 2023
Author(s)
Alex Filkov, Virginie Tihay-Felicelli, Nima Masoudvaziri, David Rush, Andres Valencia, Yu Wang, David Blunck, Mario Valero, Kamila Kempna, Jeruzalemska Jan Smolka, Jacques De Beer, Zakary Campbell-Lochrie, Felipe Centeno, Muhammad Asim Ibrahim Stuvaregatan, Calisa Katiuscia Lemmertz, Wai Cheong Tam
Due to socio-economic and climatic changes around the world, large outdoor fires in the built environment have become one of the global issues that threaten billions of people. The devastating effects of them are indicative of weaknesses in existing

Towards Real-Time Heart Health Monitoring in Firefighting Using Convolutional Neural Networks

June 28, 2023
Author(s)
Jiajia Li, Christopher U. Brown, Dillon Dzikowicz, Mary Carey, Wai Cheong Tam, Michael Xuelin Huang
A machine learning-based heart health monitoring model, named H2M, was developed. 24-hour electrocardiogram (ECG) data from 112 professional firefighters was used to train the proposed model. The model used carefully designed multi-layer convolution neural

Real-Time Flashover Prediction Model for Multi-Compartment Building Structures Using Attention Based Recurrent Neural Networks

March 17, 2023
Author(s)
Wai Cheong Tam, Eugene Yujun Fu, Jiajia Li, Richard D. Peacock, Paul A. Reneke, Thomas Cleary, Grace Ngai, Hong Va Leong, Michael Xuelin Huang
This paper presents the development of an attention based bi-directional gated recurrent unit model, P-Flashv2, for the prediction of potential occurrence of flashover in a traditional 111 m2 single story ranch-style family home. Synthetic temperature data

Real-time Forecast of Compartment Fire and Flashover based on Deep Learning

April 6, 2022
Author(s)
Tianhang Zhang, Zilong Wang, Ho Yin Wong, Wai Cheong Tam, Xinyan Huang, Fu Xiao
Forecasting building fire development and critical fire events in real-time is of great significance for firefighting and rescue operations. This work proposes an artificial intelligence (AI) system to fast forecast the compartment fire development and

Report on High Energy Arcing Fault Experiments - Experimental Results from Low Voltage Switchgear Enclosures

December 29, 2021
Author(s)
Gabriel Taylor, Anthony D. Putorti Jr., Scott Bareham, Christopher U. Brown, Wai Cheong Tam, Edward Hnetkovsky, Andre Thompson, Michael Selepak, Philip Deardorff, Kenneth Hamburger, Nicholas Melly, Kenneth Miller
This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena for low-voltage metal enclosed switchgear containing aluminum conductors. This report covers full-scale laboratory experiments using

Report on High Energy Arcing Fault Experiments - Experimental Results from Open Box Enclosures

December 29, 2021
Author(s)
Gabriel Taylor, Anthony D. Putorti Jr., Scott Bareham, Christopher U. Brown, Wai Cheong Tam, Edward Hnetkovsky, Andre Thompson, Michael Selepak, Philip Deardorff, Kenneth Hamburger, Nicholas Melly, Kenneth Miller, Kenneth Armijo, Paul Clem, Alvaro Cruz-Cabrera, Byron Demosthenous, Austin Glover, Chris LaFleur, Raymond Martinez, James Taylor, Rana Weaver, Caroline Winters
This report documents an experimental program to investigate High Energy Arcing Fault (HEAF) phenomena. The experiments provide data to better characterize the arc to improve the prediction of arc energy emitted during a HEAF event. An open box allows for

Report on High Energy Arc Fault Experiments: Experimental Results from Medium Voltage Electrical Enclosures

November 29, 2021
Author(s)
Gabriel Taylor, Anthony D. Putorti Jr., Scott Bareham, Edward Hnetkovsky, Kenneth Hamburger, Nicholas Melly, Mark Henry Salley, Christopher U. Brown, Wai Cheong Tam, Eric Link, Michael Selepak, Philip Deardorff, Kenneth Miller, Paul Clem, Byron Demosthenous, Austin Glover, Chris LaFleur, Raymond Martinez, Anthony Tanbakuchi
This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena for medium voltage electrical switchgear containing aluminum conductors. This report covers full-scale laboratory experiments using

A Generic Flashover Prediction Model for Residential Buildings Using Graph Neural Network

November 11, 2021
Author(s)
Wai Cheong Tam, Eugene Yujun Fu, Paul A. Reneke, Richard D. Peacock, Thomas Cleary
A generic graph neural network-based model is developed to predict the potential occurrence of flashover for different building structures. The proposed model transforms multivariate temperature data into graph-structure data. Utilizing graph convolution

Sensors and Machine Learning Models to Prevent Cooktop Ignition and Ignore Normal Cooking

July 28, 2021
Author(s)
Amy Mensch, Anthony Hamins, Wai Cheong Tam, John Lu, Kathryn Markell, Christina You, Matthew Kupferschmid
According to a recent NFPA report, 49 % of reported home fires involve cooking equipment, with cooktops accounting for 87 % of cooking-fire deaths and 80 % of the civilian injuries [1, 2]. Between 2014–2018, U.S. fire departments responded to an estimated

The Evolving Temperature Field in a 1 m Methanol Pool Fire

June 4, 2021
Author(s)
Jian Chen, Kunhyuk Sung, Zhigang Wang, Andy Tam, Ki Yong Lee, Anthony Hamins
Thin filament pyrometry is used to measure the time-varying temperature field in a 1 m methanol pool fire. A digital camera with optical filters and zoom lens recorded the emission intensity of an array of 12 µm Silicon-Carbide filaments oriented