Review of Recent Developments in Fire Detection Technologies

Liu, Z.

doi:10.1177/1042391503013002003

Cited by 120 publications

(60 citation statements)

References 30 publications

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“…One is a paper that reviews fire detection systems (Liu and Kim, 2003), which is discussed later in this chapter, and the second uses fuzzy logic to convert sensor measurements into qualitative air quality categories (Wide et al, 1997).…”

Section: Environmental Sensor Systemsmentioning

confidence: 99%

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Bayesian based design of real-time sensor systems for high-risk indoor contaminants

Sreedharan¹

2007

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The sudden release of toxic contaminants that reach indoor spaces can be hazardous to building occupants. To respond effectively, the contaminant release must be quickly detected and characterized to determine unobserved parameters, such as release location and strength. Characterizing the release requires solving an inverse problem. Designing a robust real-time sensor system that solves the inverse problem is challenging because the fate and transport of contaminants is complex, sensor information is limited and imperfect, and real-time estimation is computationally constrained.This dissertation uses a system-level approach, based on a Bayes Monte Carlo framework, to develop sensor-system design concepts and methods. I describe three investigations that explore complex relationships among sensors, network architecture, interpretation algorithms, and system performance. The investigations use data obtained from tracer gas experiments conducted in a real building.The influence of individual sensor characteristics on the sensor-system performance for binary-type contaminant sensors is analyzed. Performance tradeoffs among sensor 2 accuracy, threshold level and response time are identified; these attributes could not be inferred without a system-level analysis. For example, more accurate but slower sensors are found to outperform less accurate but faster sensors.Secondly, I investigate how the sensor-system performance can be understood in terms of contaminant transport processes and the model representation that is used to solve the inverse problem. The determination of release location and mass are shown to be related to and constrained by transport and mixing time scales. These time scales explain performance differences among different sensor networks. For example, the effect of longer sensor response times is comparably less for releases with longer mixing time scales.The third investigation explores how information fusion from heterogeneous sensors may improve the sensor-system performance and offset the need for more contaminant sensors. Physics-and algorithm-based frameworks are presented for selecting and fusing information from noncontaminant sensors. The frameworks are demonstrated with door-position sensors, which are found to be more useful in natural airflow conditions, but which cannot compensate for poor placement of contaminant sensors.The concepts and empirical findings have the potential to help in the design of sensor systems for more complex building systems. The research has broader relevance to additional environmental monitoring problems, fault detection and diagnostics, and system design. i

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Section: Environmental Sensor Systemsmentioning

confidence: 99%

“…Liu and Kim (2003) provide an excellent summary of recent advances in automated systems used for fire protection, many of which are enabled by advances in sensor technology and by the use of artificial intelligence techniques for sensor information processing.…”

Section: Sensor Systems In the Built Environmentmentioning

confidence: 99%

Bayesian based design of real-time sensor systems for high-risk indoor contaminants

Sreedharan¹

2007

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“…Thus, to control and extinguish fire hazards, many types of firefighting equipment have emerged. In the current building fire protection design, fire detections, such as gas or smoke and temperature sensors [1], and fire extinguishing methods, such as automatic sprinkler systems, are commonly adopted [2,3]. Video-based technology or computer vision methods are also used in some firefighting system [4].…”

Section: Introductionmentioning

confidence: 99%

“…Video detection combining image-processing technology is an effective method in firefighting system, and some work has been done by researchers [2,16,17]. Rossi, Verstockt, and Hsu proposed a video fire detection technology with a huge amount of information for automatic firefighting robots [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Firefighting robot with video full-closed loop control

Wu¹,

Li²,

Ye³

et al. 2016

Int. J. SAFE

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In view of the many problems in firefighting robots, such as complicated flame positioning, poor environmental adaptability, and difficult installation and debugging, a new firefighting robot design is presented with video full closed-loop feedback to extinguish ground fire in this paper. The firefighting robot consists of a 2-DOF robot, a monocular camera, and a controller. The monocular camera installed on the second link of the robot is utilized to detect and locate ground flames. The robot can dynamically adjust the water landing point to track a flame in real-time through motion control, as the camera is specifically designed with an additional infrared narrowband pass filter and a filter-switching mechanism. An algorithm of detecting and positioning for flame and water landing point is proposed based on image processing and robotic kinematics. Experimental results show that the firefighting robot with video full closed-loop feedback can realize real-time flame detection, location, and sprinkler, and can dynamically track fire location within the monitoring scope. The distance error between fire and the landing point of the water jet can be controlled within a narrow range. Moreover, this firefighting robot is easy to install, debug and have good environment adaptability, and provides efficient and safe solutions for complicated firefighting environment. At the same time, due to its small size and convenient calibration features, this firefighting robot is especially suitable for large space environments.

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“…Recently, computer vision techniques for fire monitoring have been researched. These systems allow the vision at a high distance and without latency, and permit to measure more information related to the fires, such as the size and direction [2].…”

Section: Introductionmentioning

confidence: 99%

Wildfire Smoke Detection Using Computational Intelligence Techniques Enhanced With Synthetic Smoke Plume Generation

Labati

Genovese

Piuri

et al. 2013

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-An early wildfire detection is essential in order to assess an effective response to emergencies and damages. In this paper, we propose a low cost approach based on image processing and computational intelligence techniques, capable to adapt and identify wildfire smoke from heterogeneous sequences taken from a long distance. Since the collection of frame sequences can be difficult and expensive, we propose a virtual environment, based on a cellular model, for the computation of synthetic wildfire smoke sequences. The proposed detection method is tested on both real and simulated frame sequences. Results show that the proposed approach obtains accurate results.

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Review of Recent Developments in Fire Detection Technologies

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Cited by 120 publications

References 30 publications

Bayesian based design of real-time sensor systems for high-risk indoor contaminants

Bayesian based design of real-time sensor systems for high-risk indoor contaminants

Firefighting robot with video full-closed loop control

Wildfire Smoke Detection Using Computational Intelligence Techniques Enhanced With Synthetic Smoke Plume Generation

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