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nist-gov/www.nist.gov/publications/search?ta[0]=248596
Scott Williams 4063bef310 Initital commit.
2025-03-05 18:59:57 +00:00

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value="249466">Advanced communications</option><option
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value="248311">-Quantum communications</option><option
value="2806891">-Spectrum sharing</option><option
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value="248541">-Conventional energy</option><option
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value="248556">-Fuels</option><option
value="248561">Environment</option><option
value="248566">-Air / water / soil quality</option><option
value="2788551">-Climate Measurements</option><option
value="248571">-Environmental health</option><option
value="248576">-Greenhouse gas measurements</option><option
value="248581">-Marine science</option><option
value="2746596">-Sustainability</option><option
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value="248591">-Fire detection</option><option
value="2746901">-Fire dynamics and science</option><option
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value="248601">-Fire modeling</option><option
value="248606">-Fire risk reduction</option><option
value="248611">-Materials flammability</option><option
value="2746906">-Structural fire resistance</option><option
value="2746911">-Wildland urban interface fire</option><option
value="248616">Forensic Science</option><option
value="248626">-Digital evidence</option><option
value="248636">-Drugs and toxicology</option><option
value="248621">-Firearms and toolmarks</option><option
value="248641">-Forensic biometrics</option><option
value="248631">-Forensic genetics</option><option
value="248646">-Trace evidence</option><option
value="248651">Health</option><option
value="248681">-Biopharmaceuticals</option><option
value="2813441">-Cell and gene therapy</option><option
value="248661">-Clinical diagnostics</option><option
value="2816296">--Cancer</option><option
value="248676">--Medical imaging</option><option
value="248671">-Food and nutrition</option><option
value="2813446">-Human microbiome</option><option
value="248686">-Precision medicine</option><option
value="248656">-Regenerative medicine and advanced therapy</option><option
value="2816301">--Flow cytometry</option><option
value="249421">Information technology</option><option
value="2753736">-Artificial intelligence</option><option
value="2800826">--AI measurement and evaluation</option><option
value="2788806">--Applied AI</option><option
value="2788801">--Fundamental AI</option><option
value="2800831">--Hardware for AI</option><option
value="2800991">--Machine learning</option><option
value="2800836">--Trustworthy and responsible AI</option><option
value="248701">-Biometrics</option><option
value="248706">-Cloud computing and virtualization</option><option
value="248711">-Complex systems</option><option
value="248716">-Computational science</option><option
value="248721">-Conformance testing</option><option
value="248726">-Cyber-physical systems</option><option
value="2746861">--Smart cities</option><option
value="248731">-Cybersecurity</option><option
value="248746">--Cryptography</option><option
value="2753741">--Cybersecurity education and workforce development</option><option
value="2788811">--Cybersecurity measurement</option><option
value="248736">--Identity and access management</option><option
value="2788816">--Privacy engineering</option><option
value="248751">--Risk management</option><option
value="2788821">--Securing emerging technologies</option><option
value="2788826">--Trustworthy networks</option><option
value="2788831">--Trustworthy platforms</option><option
value="248756">-Data and informatics</option><option
value="248761">--Human language technology</option><option
value="248766">--Information retrieval</option><option
value="248771">--Natural language processing</option><option
value="248781">-Federal information processing standards (FIPS)</option><option
value="248786">-Health IT</option><option
value="2748441">-Internet of Things (IoT)</option><option
value="248796">-Interoperability testing</option><option
value="2806901">-Location based services</option><option
value="248801">-Mobile</option><option
value="248806">-Networking</option><option
value="2753766">--Mobile and wireless networking</option><option
value="2753756">--Network management and monitoring</option><option
value="248811">--Network modeling and analysis</option><option
value="2753746">--Network security and robustness</option><option
value="2753751">--Network test and measurement</option><option
value="248816">--Next generation networks</option><option
value="2753761">--Protocol design and standardization</option><option
value="2753771">--Software defined and virtual networks</option><option
value="248821">-Privacy</option><option
value="248826">-Software research</option><option
value="248841">--Software testing</option><option
value="248846">-Usability and human factors</option><option
value="248851">--Accessibility</option><option
value="248776">-Video analytics</option><option
value="2788836">-Virtual / augmented reality</option><option
value="248856">-Visualization research</option><option
value="248861">-Voting systems</option><option
value="2748236">Infrastructure</option><option
value="248866">Manufacturing</option><option
value="248871">-Additive manufacturing</option><option
value="2746601">-Factory communications</option><option
value="248886">-Factory operations planning and control</option><option
value="248891">-Interoperability in manufacturing</option><option
value="248901">-Machining</option><option
value="2788841">-Manufacturing economics</option><option
value="248916">-Manufacturing quality assurance</option><option
value="248906">-Manufacturing systems design and analysis</option><option
value="2784921">-Monitoring, diagnostics and prognostics</option><option
value="2748446">-Process improvement</option><option
value="248911">-Process measurement and control</option><option
value="2748451">-Product data</option><option
value="248921">-Robotics in manufacturing</option><option
value="2748476">--Agility and adaptability</option><option
value="2748456">--Collaborative robots</option><option
value="2748461">--Dexterous grasping</option><option
value="2748471">--Manipulation</option><option
value="2748466">--Mobility and industrial autonomous vehicles</option><option
value="2748486">--Navigation / actuation / control</option><option
value="2748481">--Sensing and perception</option><option
value="248926">-Supply chain</option><option
value="248931">-Sustainable manufacturing</option><option
value="2748491">-Systems engineering</option><option
value="2748496">-Systems integration</option><option
value="248936">-Technology commercialization</option><option
value="248946">Materials</option><option
value="248951">-Ceramics</option><option
value="248956">-Composites</option><option
value="248961">-Concrete / cement</option><option
value="248966">-Materials characterization</option><option
value="248971">--Composition and structure</option><option
value="248981">--Mechanical properties</option><option
value="248986">--Thermal properties</option><option
value="248991">-Metals</option><option
value="248996">-Modeling and computational material science</option><option
value="249001">-Polymers</option><option
value="249006">-Superconductors</option><option
value="249011">Mathematics and statistics</option><option
value="249016">-Experiment design</option><option
value="249021">-Image and signal processing</option><option
value="249031">-Modeling and simulation research</option><option
value="249036">-Numerical methods and software</option><option
value="249041">-Statistical analysis</option><option
value="249046">-Uncertainty quantification</option><option
value="249051">Metrology</option><option
value="249061">-Amount of substance</option><option
value="249066">-Dimensional metrology</option><option
value="249071">-Electrical / electromagnetic metrology</option><option
value="249081">-Flow metrology and rheology</option><option
value="249086">-Force metrology</option><option
value="249091">-Humidity metrology</option><option
value="249096">-Ionizing radiation metrology</option><option
value="249101">-Mass metrology</option><option
value="2746606">-Metric</option><option
value="249106">-Optical / photometry / laser metrology</option><option
value="249111">-Pressure and vacuum metrology</option><option
value="249116">-Thermometry metrology</option><option
value="249121">-Time and frequency metrology</option><option
value="2746611">-Weights and measures</option><option
value="249126">Nanotechnology</option><option
value="249131">-Nanobiotechnology</option><option
value="249136">-Nanochemistry</option><option
value="249141">-Nanoelectronics</option><option
value="249146">-Nanofabrication / manufacturing</option><option
value="249151">--Lithography</option><option
value="249156">--Self-assembly</option><option
value="249161">-Nanofluidics</option><option
value="249166">-Nanomagnetics</option><option
value="249171">-Nanomaterials</option><option
value="249176">-Nanomechanics</option><option
value="249181">-Nanometrology</option><option
value="249186">-Nanophotonics</option><option
value="249191">-Nanophysics</option><option
value="249196">-Nanoplasmonics</option><option
value="249451">Neutron research</option><option
value="249426">Performance excellence</option><option
value="249211">-Assessment tools and services</option><option
value="249431">-Baldrige award</option><option
value="249436">-Baldrige examiners</option><option
value="249226">-Baldrige Framework and Criteria</option><option
value="249231">-Best practices</option><option
value="249236">-Leadership development</option><option
value="249241">Physics</option><option
value="249246">-Atomic / molecular / quantum</option><option
value="249251">-Biological physics</option><option
value="249256">-Condensed matter</option><option
value="249261">-Electron physics</option><option
value="249266">-Magnetics</option><option
value="249271">-Nuclear physics</option><option
value="249276">-Optical physics and communications</option><option
value="249281">-Quantum information science</option><option
value="249286">-Radiation</option><option
value="249291">-Spectroscopy</option><option
value="249296">-Thermodynamics</option><option
value="249301">-Time and frequency</option><option
value="249461">Public safety</option><option
value="249311">-Chemical / Biological / Radiological / Nuclear / Explosives (CBRNE)</option><option
value="249316">-First responder preparedness</option><option
value="249321">-Law enforcement</option><option
value="249331">-Public safety communications research</option><option
value="249336">-Response robots</option><option
value="249456">Resilience</option><option
value="2746576">-Community resilience</option><option
value="2816306">--Recovery</option><option
value="2816311">--Resilience metrics</option><option
value="248456">-Disaster and failure studies</option><option
value="248466">-Earthquake risk reduction</option><option
value="2788846">-Resilience economics</option><option
value="2746891">-Resilient materials</option><option
value="248481">-Windstorm impact reduction</option><option
value="249341">Standards</option><option
value="249346">-Accreditation</option><option
value="249416">-Calibration services</option><option
value="249366">-Conformity assessment</option><option
value="249371">-Documentary standards</option><option
value="2748516">-Frameworks</option><option
value="2813451">-Quality assurance</option><option
value="249376">-Reference data</option><option
value="249381">-Reference instruments</option><option
value="249386">-Reference materials</option><option
value="249391">-Standards education</option><option
value="2813456">Technology transfer</option><option
value="249396">Transportation</option><option
value="249401">-Aerospace</option><option
value="249406">-Automotive</option></select>
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<div class="views-element-container"><div class="js-view-dom-id-484a304c8fe473255e06340c025e29f66496ffb289c4397ab7b7723db6487d67">
<header>
NIST Authors in <strong>Bold</strong>
<p></p>Displaying 1 - 25 of 77
</header>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/cost-effective-data-driven-approach-flashover-prediction-across-diverse-residential">A Cost-Effective Data-driven Approach to Flashover Prediction across Diverse Residential Layouts for Enhanced Firefighters Situational Awareness</a></h3>
<div class="nist-teaser__date">
December 30, 2024
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item">Linhao Fan, <span class="nist-author">Hongqiang Fang</span>, Tianshui Liang, <span class="nist-author">Wai Cheong Tam</span>, Qixing Zhang</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
This paper presents gFlashNet, a generic flashover prediction model, designed to address the limitations of existing models that are restricted to specific residential building layouts. The aim of this research is to improve the scalability and
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/examining-effects-deep-learning-model-structure-model-interpretability-time-series">Examining the Effects of Deep Learning Model Structure on Model Interpretability for Time-Series Classifications in Fire Research</a></h3>
<div class="nist-teaser__date">
September 13, 2024
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Wai Cheong Tam</span>, Fan Linhao, <span class="nist-author">Qi Tong</span>, Fang Hongqiang</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
This present work utilizes an interpretability model to understand and explain the decisions of deep learning models. The use of DeepLIFT is proposed and attributions of a study case are obtained. Benchmarking against two other interpretability models
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/detecting-firefighters-thermal-risks-commercial-building-structure-using-machine">Detecting Firefighter&#039;s Thermal Risks in a Commercial Building Structure Using Machine Learning</a></h3>
<div class="nist-teaser__date">
August 30, 2024
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Qi Tong</span>, <span class="nist-author">David Stroup</span>, <span class="nist-author">Wai Cheong Tam</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
A multi-input and multi-output (MIMO) machine learning model is developed to simultaneously detect firefighter's thermal risks across a commercial building structure. A total of 2000 numerical experiments with a wide range of fire and ventilation scenarios
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/towards-better-understanding-deep-learning-model-time-series-forecasting-fire-research">Towards Better Understanding of Deep Learning Model for Time Series Forecasting in Fire Research</a></h3>
<div class="nist-teaser__date">
July 29, 2024
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item">Linhao Fan, <span class="nist-author">Qi Tong</span>, Hongqiang FANG, Wei Zhong, <span class="nist-author">Wai Cheong Tam</span>, Tianshui Liang</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
Deep learning model has been a viable approach to forecast critical events in fire development. However, prior to its implementation in real-life firefighting, it is imperative to further understand the black box and assess its rationale. In this paper, an
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/effect-mandatory-fitness-requirements-cardiovascular-events-state-state-analysis-using">The Effect of Mandatory Fitness Requirements on Cardiovascular Events: A State-by-State Analysis Using a National Database</a></h3>
<div class="nist-teaser__date">
March 1, 2024
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item">Dillon Dzikowicz, Sankalp Saoji, <span class="nist-author">Wai Cheong Tam</span>, Wendy Brunner, Mary Carey</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
Background: Cardiovascular events are known to be the leading cause of death among on-duty firefighters. Implementing fitness standards may help reduce the incidence of cardiovascular deaths; however, standards vary between firefighter type and states. We
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/building-fire-hazard-predictions-using-machine-learning">Building Fire Hazard Predictions Using Machine Learning</a></h3>
<div class="nist-teaser__date">
January 26, 2024
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item">Eugene Yujun Fu, <span class="nist-author">Wai Cheong Tam</span>, Tianhang Zhang, Xinyan Huang</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
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)
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/data-driven-wireless-fire-hose-flow-rate-apparatus">Data-Driven Wireless Fire Hose Flow Rate Apparatus</a></h3>
<div class="nist-teaser__date">
January 25, 2024
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Christopher U. Brown</span>, <span class="nist-author">Gregory W. Vogl</span>, <span class="nist-author">Wai Cheong Tam</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/towards-real-time-heart-health-monitoring-firefighting-using-convolutional-neural">Towards Real-Time Heart Health Monitoring in Firefighting Using Convolutional Neural Networks</a></h3>
<div class="nist-teaser__date">
June 28, 2023
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Jiajia Li</span>, <span class="nist-author">Christopher U. Brown</span>, Dillon Dzikowicz, Mary Carey, <span class="nist-author">Wai Cheong Tam</span>, Michael Xuelin Huang</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
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
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/explainable-machine-learning-based-flashover-prediction-model-using-dimension-wise">Explainable Machine Learning Based Flashover Prediction Model Using Dimension-Wise Class Activation Map</a></h3>
<div class="nist-teaser__date">
June 26, 2023
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item">Linhao Fan, <span class="nist-author">Wai Cheong Tam</span>, Qi Tong, Eugene Yujun Fu, Tianshui Liang</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
This paper presents the development of an explainable machine learning based flashover prediction model, named xFlashNet. Synthetic temperature data from more than 17 000 fire cases are used for model development. The effect of missing data due from heat
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/real-time-flashover-prediction-model-multi-compartment-building-structures-using">Real-Time Flashover Prediction Model for Multi-Compartment Building Structures Using Attention Based Recurrent Neural Networks</a></h3>
<div class="nist-teaser__date">
March 17, 2023
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Wai Cheong Tam</span>, Eugene Yujun Fu, Jiajia Li, <span class="nist-author">Richard D. Peacock</span>, <span class="nist-author">Paul A. Reneke</span>, <span class="nist-author">Thomas Cleary</span>, Grace Ngai, Hong Va Leong, Michael Xuelin Huang</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
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
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/measuring-water-flow-rate-fire-hose-using-wireless-sensor-network-smart-fire-fighting-1">Measuring Water Flow Rate for a Fire Hose Using a Wireless Sensor Network for Smart Fire Fighting: An Update Using Various Hose Conditions</a></h3>
<div class="nist-teaser__date">
January 26, 2023
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Christopher U. Brown</span>, <span class="nist-author">Gregory W. Vogl</span>, <span class="nist-author">Wai Cheong Tam</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
A wireless sensor network was created to measure water-flow rate in a fire hose. An integrated electronic piezoelectric (IEPE) accelerometer was chosen as the sensor to measure the flow rate based on the vibrations generated by water flowing through a fire
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/thermal-and-gas-mixture-composition-measurements-preceding-backdrafts-25th-scale">Thermal and gas mixture composition measurements preceding backdrafts in a 2/5th scale compartment</a></h3>
<div class="nist-teaser__date">
September 1, 2022
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Ryan Falkenstein-Smith</span>, <span class="nist-author">Thomas Cleary</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
This report documents real-time and time-averaged temperature, global and local equivalence ratios, and oxygen, carbon dioxide, and carbon monoxide concentration measurements made at various positions in an isolated 2/5th scale compartment prior to a
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/spatial-temporal-graph-neural-network-model-predicting-flashover-arbitrary-building">A Spatial Temporal Graph Neural Network Model for Predicting Flashover in Arbitrary Building Floorplans</a></h3>
<div class="nist-teaser__date">
August 3, 2022
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Wai Cheong Tam</span>, Eugene Yujun Fu, <span class="nist-author">Jiajia Li</span>, Xinyan Huang, Jian Chen, Michael Xuelin Huang</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
Rapid fire progression, such as flashover, has been one of the leading causes for firefighter deaths and injuries in residential building environments. Due to long computational time of and the required prior knowledge about the fire scene, existing models
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/real-time-forecast-compartment-fire-and-flashover-based-deep-learning">Real-time Forecast of Compartment Fire and Flashover based on Deep Learning</a></h3>
<div class="nist-teaser__date">
April 6, 2022
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item">Tianhang Zhang, Zilong Wang, Ho Yin Wong, <span class="nist-author">Wai Cheong Tam</span>, Xinyan Huang, Fu Xiao</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
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
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/development-flashover-prediction-model-compartment-fires-using-support-vector">Development of a Flashover Prediction Model for Compartment Fires Using Support Vector Regression</a></h3>
<div class="nist-teaser__date">
April 1, 2022
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Wai Cheong Tam</span>, Jun Wang, <span class="nist-author">Richard D. Peacock</span>, <span class="nist-author">Paul A. Reneke</span>, Eugene Yujun Fu, <span class="nist-author">Thomas Cleary</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
This report provides additional technical details to an article entitled P-Flash – A Machine Learning-based Model for Flashover Prediction using Recovered Temperature Data. Research was conducted to examine the use of Support Vector Regression (SVR) to
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/generic-flashover-prediction-model-residential-buildings-using-graph-neural-network">A Generic Flashover Prediction Model for Residential Buildings Using Graph Neural Network</a></h3>
<div class="nist-teaser__date">
November 11, 2021
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Wai Cheong Tam</span>, Eugene Yujun Fu, <span class="nist-author">Paul A. Reneke</span>, <span class="nist-author">Richard D. Peacock</span>, <span class="nist-author">Thomas Cleary</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
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
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/p-flash-machine-learning-based-model-flashover-prediction-using-recovered-temperature">P-Flash - A Machine Learning-based Model for Flashover Prediction using Recovered Temperature Data</a></h3>
<div class="nist-teaser__date">
April 4, 2021
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item">Jun Wang, <span class="nist-author">Andy Tam</span>, Youwei Jia, <span class="nist-author">Richard Peacock</span>, <span class="nist-author">Paul A. Reneke</span>, Eugene Yujun Fu, <span class="nist-author">Thomas Cleary</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
Research was conducted to examine the use of Support Vector Regression (SVR) to build a model to forecast the potential occurrence of flashover in a single-floor, multi-room compartment fire. Synthetic temperature data for heat detectors in different rooms
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/predicting-flashover-occurrence-using-surrogate-temperature-data">Predicting Flashover Occurrence using Surrogate Temperature Data</a></h3>
<div class="nist-teaser__date">
February 9, 2021
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Andy Tam</span>, Eugene Yujun Fu, <span class="nist-author">Richard Peacock</span>, <span class="nist-author">Paul A. Reneke</span>, Jun Wang, Grace Ngai, Hong Va Leong, <span class="nist-author">Thomas Cleary</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
Fire fighter fatalities and injuries in the U.S. remain too high and fire fighting too hazardous. Until now, fire fighters rely only on their experience to avoid life-threatening fire events, such as flashover. In this paper, we describe the development of
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/measuring-water-flow-rate-fire-hose-using-wireless-sensor-network-smart-fire-fighting-0">Measuring Water Flow Rate for a Fire Hose Using a Wireless Sensor Network for Smart Fire Fighting</a></h3>
<div class="nist-teaser__date">
January 2, 2021
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Christopher U. Brown</span>, <span class="nist-author">Gregory W. Vogl</span>, <span class="nist-author">Andy Tam</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
A wireless sensor network was created to measure water-flow rate in a fire hose. An integrated electronic piezoelectric accelerometer was chosen as the sensor to measure the flow rate based on the vibrations generated by water flowing through a fire hose
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/use-machine-learning-models-forecast-flashover-occurrence-compartment">On the Use of Machine Learning Models to Forecast Flashover Occurrence in a Compartment</a></h3>
<div class="nist-teaser__date">
September 15, 2020
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item">Jun Wang, <span class="nist-author">Andy Tam</span>, <span class="nist-author">Paul A. Reneke</span>, <span class="nist-author">Richard Peacock</span>, <span class="nist-author">Thomas Cleary</span>, Eugene Yujun Fu, Grace Ngai, Hong Va Leong</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
This paper presents a study to examine the potential use of machine learning algorithms to build a model to forecast the likelihood of flashover occurrence for a single-floor multi-room compartment. Synthetic temperature data for heat detectors from
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/time-series-feature-extraction-and-selection-tool-fire-data">Time Series Feature Extraction and Selection Tool for Fire Data</a></h3>
<div class="nist-teaser__date">
September 15, 2020
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item">Jun Wang, Youwei Jia, Eugene Yujun Fu, Jiajia Li, <span class="nist-author">Andy Tam</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
This paper aims to facilitate the use of machine learning to carry out supervised classification/regression tasks for time series data in fire research. Specifically, a feature engineering tool, FAST (Feature extrAction and Selection for Time-series), is
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/generating-synthetic-sensor-data-facilitate-machine-learning-paradigm-prediction-0">Generating Synthetic Sensor Data to Facilitate Machine Learning Paradigm for Prediction of Building Fire Hazard</a></h3>
<div class="nist-teaser__date">
July 10, 2020
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Wai Cheong Tam</span>, Eugene Yujun Fu, <span class="nist-author">Richard D. Peacock</span>, <span class="nist-author">Paul A. Reneke</span>, Jun Wang, Jiajia Li, <span class="nist-author">Thomas G. Cleary</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
This paper presents a learning-by-synthesis approach to facilitate the utilization of a machine learning paradigm to enhance situational awareness for fire fighting in buildings. An automated Fire Data Generator (FD-Gen) is developed. The overview of FD
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/prevention-cooktop-ignition-using-detection-and-multi-step-machine-learning-algorithms">Prevention of Cooktop Ignition Using Detection and Multi-Step Machine Learning Algorithms</a></h3>
<div class="nist-teaser__date">
April 27, 2020
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Wai Cheong Tam</span>, Eugene Yujun Fu, <span class="nist-author">Amy E. Mensch</span>, <span class="nist-author">Anthony P. Hamins</span>, Christina Yu, Grace Ngai, Hong va Leong</div>
</div>
</div>
</header>
<div class="nist-teaser__content">
This paper presents a study to examine the potential use of machine learning models to build a real-time detection algorithm for prevention of unattended cooking fires. 16 sets of time- dependent sensor signals were obtained from 60 normal/ignition cooking
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/voices-first-responders151nationwide-public-safety-communication-survey-methodology">Voices of First Responders&amp;#151;Nationwide Public Safety Communication Survey Methodology: Development, Dissemination, and Demographics, Phase 2, Volume 1</a></h3>
<div class="nist-teaser__date">
March 20, 2020
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">Kristen Greene</span>, <span class="nist-author">Shanee T. Dawkins</span>, <span class="nist-author">Sandra S. Prettyman</span>, <span class="nist-author">Pamela J. Konkol</span>, <span class="nist-author">Mary F. Theofanos</span>, <span class="nist-author">Kevin C. Mangold</span>, <span class="nist-author">Susanne M. Furman</span>, <span class="nist-author">Michelle P. Steves</span></div>
</div>
</div>
</header>
<div class="nist-teaser__content">
With the newly created Nationwide Public Safety Broadband Network (NPSBN), the public safety community is in the process of supplementing the use of land mobile radios (LMR) to a technology ecosystem that will include a variety of new communication tools
</div>
</div>
</article>
</div>
<div>
<article
class="nist-teaser"
>
<div class="nist-teaser__content-wrapper">
<header>
<h3 class="nist-teaser__title"><a href="/publications/economics-firefighter-injuries-united-states-executive-summary">The Economics of Firefighter Injuries in the United States: Executive Summary</a></h3>
<div class="nist-teaser__date">
December 23, 2019
</div>
<div class="nist-teaser__type">
<div class="nist-field nist-field--label-inline">
<div class="nist-field__label">Author(s)</div>
<div class="nist-field__item"><span class="nist-author">David T. Butry</span>, <span class="nist-author">David H. Webb</span>, <span class="nist-author">Stanley W. Gilbert</span>, Jennifer Taylor</div>
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<div class="nist-teaser__content">
</div>
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