Research Roadmap for Smart Fire Fighting

Hamins, Anthony P.; Grant, Casey C.; Bryner, Nelson P.; Jones, Albert W.; Koepke, Galen H.

doi:10.6028/nist.sp.1191

Cited by 18 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Applying 'smart' technology to a fire hose could improve 1) the awareness of the hose's current status and 2) the chance of a successful fire attack. Harnessing the power of 'smart' technology to improve situational awareness of the hoses was a part of the vision of Smart Fire Fighting as documented in the Research Roadmap for Smart Fire Fighting [2]. A 'smart' system uses sensors to collect data, provides the data in an understandable format to a user, then allows the user to make decisions.…”

Section: Smart Fire Fighting -Water Flow At the Fire Hose Nozzlementioning

confidence: 99%

Measuring water flow rate for a fire hose using wired accelerometers for smart fire fighting

Brown¹,

Vogl²,

Tam³

2019

View full text Add to dashboard Cite

A wired 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 hose. These sensors are small, lightweight, and they can be attached to the outside of the hose, not obstructing the water's flow path. A relationship was determined between the flow rate of the water and vibration detected by the accelerometer for a range of flow rates. The raw acceleration signal was used to calculate two metrics: the dominant frequency and the standard deviation of acceleration. In a future study, the relationship between the dominant-frequency metric and the flow rate will be applied to a wireless accelerometer network. The relationship will be used to determine the real-time fire hose flow rate critical for improving situational awareness on the fireground.

show abstract

Section: Smart Fire Fighting -Water Flow At the Fire Hose Nozzlementioning

confidence: 99%

Measuring water flow rate for a fire hose using wired accelerometers for smart fire fighting

Brown¹,

Vogl²,

Tam³

2019

View full text Add to dashboard Cite

show abstract

“…In this section, we discuss informally and intuitively the time-related and physical-related incompatibility problems. Let us present the collaborative emergency systems inspired from (Grant et al, 2015), to illustrate the problems that we are aiming to solve.…”

Section: Motivating Example and Problem Statementmentioning

confidence: 99%

Towards the verification of cyber-physical processes based on time and physical properties

Graja

Kallel

Guermouche

et al. 2019

IJBSR

View full text Add to dashboard Cite

A cyber-physical process is a set of cyber and physical activities performed to accomplish a common goal in a given amount of time. The collaboration between cyber-physical processes describes the behaviour of networked cyber-physical systems (NCPS). NCPS have time-related and physical properties, which are crucial to the correctness of their execution. However, when we specify the collaboration between processes, some combination of their corresponding properties can be conflicting. This conflict renders the participating cyber-physical processes incompatible. We observe two types of problems: implicit time-related, and physical-related incompatibilities. In this paper, we propose a novel approach to verify the compatibility of cyber-physical processes with time-related and physical properties. We first define a set of constraints that allow the transformation of the NCPS model into a constraint satisfaction model. Then, we check the compatibility of the processes. Finally, we demonstrate the applicability and effectiveness of our approach through a case study.

show abstract

“…The term 'Cyber-Physical System' was coined by Helen Gill from NSF in 2006 [21]. The federal Networking and Information Technology Research and Development (NITRD) [22] envisions that CPSs can revolutionize agriculture [23]- [26], building control [27]- [30], security [31]- [33], energy management [34], hazard response [35]- [38], healthcare [16], [39], [40], society [41], [42], transportation [43], [44], environment [45] and manufacturing industries [46]- [49]. Even after exploded research interests and investments, the research in CPSs is still in a nascent stage.…”

Section: Modeling Cyber-physical Systemsmentioning

confidence: 99%

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

2020

View full text Add to dashboard Cite

A multitude of cyber-physical system (CPS) applications, including design, control, diagnosis, prognostics, and a host of other problems, are predicated on the assumption of model availability. There are mainly two approaches to modeling: Physics/Equation based modeling (Model-Based, MB) and Machine Learning (ML). Recently, there is a growing consensus that ML methodologies relying on data need to be coupled with prior scientific knowledge (or physics, MB) for modeling CPS. We refer to the paradigm that combines MB approaches with ML as hybrid learning methods. Hybrid modeling (HB) methods is a growing field within both the ML and scientific communities, and are recognized as an important emerging but nascent area of research. Recently, several works have attempted to merge MB and ML models for the complete exploitation of their combined potential. However, the research literature is scattered and unorganized. So, we make a meticulous and systematic attempt at organizing and standardizing the methods of combining ML and MB models. In addition to that, we outline five metrics for the comprehensive evaluation of hybrid models. Finally, we conclude by shedding some light on the challenges of hybrid models, which we, as a research community, should focus on for harnessing the full potential of hybrid models. An additional feature of this survey is that the hybrid modeling work has been discussed with a focus on modeling cyber-physical systems. INDEX TERMS Cyber-physical systems, deep learning, deep neural networks, hybrid models, model-based, machine learning, physics guided, physics informed, physics prior, theory guided.

show abstract

Research Roadmap for Smart Fire Fighting

Cited by 18 publications

References 5 publications

Measuring water flow rate for a fire hose using wired accelerometers for smart fire fighting

Measuring water flow rate for a fire hose using wired accelerometers for smart fire fighting

Towards the verification of cyber-physical processes based on time and physical properties

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

Contact Info

Product

Resources

About