Fire Detection Module Research Articles

Pembuatan Alat dan Simulasi Sistem Engine Fire Protection Pada Pesawat Terbang

The manufacture of engine fire protection system simulation tool on aircraft has been carried out. After process of making the simulation tool is complete, the test is carried out to measure the ability of the simulation tool to work, especially the ability of the detector and extinguisher. Testing is done by turning on the burner contained in the engine nacelle, the detector will sense heat and then send a signal of fire trough engine fire detection module to engine fire control panel and aural warning unit, the pilot who receive the signal will operate extinguisher to put out the fire. From the test result it can be concluded the detector is able to detect fires in less than 45 seconds, and extinguisher is able to burnout the fire less than 2 seconds.

Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km2 (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans.

Application Model Analysis on Automatic System of Fire Detection, Alarming and Extinguishment in Crude Oil Storage Tank

In order to realize functions of automatic monitoring, alarming and extinguishment on fire in crude oil storage tank, the paper came up with a fire detection model with multisensor, including selective module with fire feature combination, supervised training module and fire detection module. By regarding PNN as a classifier to carry out tests on effectiveness of the model, the conclusions that the model can reduce the influence of fire parameters’ fluctuation on detection results was drew. Moreover, an excellent fault-tolerant ability was possessed at the same time. Through some confirmatory experiments, The phenomenon was reached that two kinds of parameters in adoptive four parameters have no normal fire signal, but the model still can greatly distinguish from correct fire state.

Development of Intelligent Fire Detection Module Using Optic-Sensors

The paper proposes an adaptive fusion algorithm using competitiveness sensors for fire detection module, and uses computer simulation results to select the optimal weight values for each optic-sensor. Then we design the fire detection module using the tuned weight values of optic-sensors. The competitiveness flame sensor type is ultra-violet sensor (R2868). The controller of the module is HOLTEK microchip, and acquires the detection signals from the optic-sensors through I/O pins, and transmits the detection signals of all sensors to the computer via wire series interface. The adaptive fusion algorithm can tunes weight values according to decision output of the fusion center. The fusion algorithms of the fusion center use Bayesian estimated method to decide the fire event to be true or not. We set the improved weight values in the module for each optic-sensor. From the simulation and experimental implementation results, it demonstrates that the proposed algorithms can compute the adequate weight values.

Assessment of the Contribution of Wildfires to Ozone Concentrations in the Central US-Mexico Border Region

The annual trends and spatiotemporal patterns of monthly 8-hour maximum ozone (8-hr max O3) concentrations in the Paso del Norte region were analyzed, and their associations with fires were examined for the 2001–2010 period. Hourly O3 measurements were retrieved from the Environmental Protection Agency (EPA) Air Quality System, while the times and locations of fires were acquired from the MODIS fire detection module. The absolute 8-hr max O3 concentrations were comparable in urban, rural and background sites. Time series analysis of deseasonalized monthly 8-hr max O3 levels showed statistically significant declining trends for most of the sites located in populated areas, and high correlation coefficients among these. Conversely, a 0.12 ppbv/yr increase of 8-hr max O3 concentration was computed for Chiricahua, a background site located in a Class I protected area. Strong relationships between the monthly 8-hr max O3 concentrations and categorical variables representing the number of fire detections for each month in six buffer zones were computed. Fire incidents near the sites (within 400 km) in central Arizona, central Texas and western Mexico triggered a decrease in the 8-hr max O3 concentration by 1 to 12 ppbv in urban and rural sites, and an increase of 3 ppbv in Chiricahua. Conversely, fire incidents in southeast US, Cuba and central Mexico contributed from 5 up to 19 ppbv. These findings indicated that regional fire incidents may trigger high O3 episodes, which may exceed air quality standards.

An integrated fire detection and suppression system based on widely available video surveillance

An integrated system based on video surveillance is presented for automatic fire detection and suppression. The system is composed of two modules, including fire detection and fire suppression. The fire detection module makes full use of traditional CCD cameras for fire recognition. Some spatio-temporal features, such as color and motion, are extracted to detect fire in real time by utilizing sequential image processing techniques. Once a fire is detected, the system will control the fire suppression module to extinguish the fire automatically. It mainly consists of control device, mobile device, and water gun. Experiments performed in a large space hall show that the integrated system can detect a fire about a few seconds after ignition and automatically suppress the fire quickly.

Detection system of a security robot using multisensor fusion algorithms

This article develops a detection system with a module-based intelligent security robot that has a uniform interface. The detection system contains a power detection module, a gas detection module, an environment detection module, and a fire detection module, etc. The control unit of these modules is a HOLTEK microchip. These modules can communicate with a master module via an I2C interface. The master module communicates with the main controller of the security robot via an RS232 interface. The main controller of the security robot system is an industry personal computer (IPC). It can display the status of these modules on the monitor. These detection modules can enhance the detection results using multisensory fusion algorithms. The user can add or remove the detection modules in any time, and the main controller can acquires sensor signals from these detection modules in real-time. Finally, we present some experimental results using these detection modules, and integrate these modules in a module-based intelligent security robot that executes several scenarios.

Intelligent Home-Automation Security System

The objective of the home security detection and appliance control system is to protect occupants against events -- many of which are attributable to human error. We have developed a module based security system for home automation. The structure of the security system is divided into many modules. Each module in the home -- automation security system we develop has a wireless RF and a speech interface. They contain fire detection module, intruder detection module, environment detection module, gas detection module, (AC) power detection and diagnosis module, appliance control module and team robots. In the security detection module, we use multisensor fusion algorithms to decide an exact output. In the application control module, we use two-wire communication via wireless RF interface. These modules have event-condition voice alarms, and send the real-time status to the master controller. In the smart team robot system, we have designed many smart robots for the security system. We have designed a general user interface (GUI) for the intelligent security system. The user interface can supervise these modules and team robots via wireless RF interface, and use wireless Internet and mobile phone for remote supervision.

Autonomous Fire-Detection System Using Adaptive Sensory Fusion for Intelligent Security Robot

The multisensor fire-detection algorithm is one of the current important issues in the field of fire-detection systems for intelligent buildings. This paper proposes an adaptive fusion algorithm for fire detection, and uses a smoke sensor, flame sensor, and temperature sensor to detect fire incident. In reality, the phenomenon of the fire incident may have smoke, flame, and high-temperature situations. However, these signals may happen simultaneously or sequentially. We use adaptive fusion algorithms to a more reliable decision. However, the adaptive fusion algorithm is more complex in real conditions. Therefore, we use a Taylor expression to modify the adaptive fusion algorithm and simulate to compare with results on first-order, second-order, and third-order expressions. The modified adaptive fusion method can provide adequate reliable fusion for fire detection. We use computer simulation to improve the adaptive fusion algorithm that is accurate and adequate. Then, we design a fire-detection module using an ionization smoke sensor (TG-135), temperature semiconductor sensor (AD590), and ultraviolet sensor (R2868). The experimental results of the fire-detection module demonstrate that it can detect fire incidents in a variety of conditions. Finally, we implement the real-time fire-detection module in an intelligent security robot (Chung Cheng I). If a fire incident occurs, the security robot can find the fire source using the fire-detection module and transmit the detected message to the user via the Internet and a GSM modem.