• Fire Science and Engineering
• /
• v.32 no.3
• /
• pp.134-139
• /
• 2018

Extremely fine particles seriously affect people and are becoming a social problem. Conventional methods using the type of beta ray absorption are difficult to have real-time measurements and miniaturization for the acquisition of fine dust. In this paper, a light scattering method was used. The sensors were configured internally with semiconductor laser diodes for miniaturization, low cost and lightweight. The use of the FFT method makes it easier to separate fine dust according to size compared to conventional light scattering sensors. Bluetooth communication also allows the connection, monitoring and control of devices using smart phones.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.20 no.3
• /
• pp.318-322
• /
• 2014

A kind of field bus called Actuator and Sensor interface bus(AS-i) was designed in this paper. The configuration of AS-i network system used Application Specific Integrated Circuit(ASIC) SAP5S chip and PLC S7-200 station, which included CPU 224 and AS-i master module CP 243-2. We also created an example program for PLC S7-200 to control AS-i network. The fire and smoke detection system was made with AS-i network system that was designed. This system had got more advantages than other system such as number of stations, easy installation, wide working area, etc. And designed system can be used as a partner network for higher level field bus networks.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.4
• /
• pp.250-260
• /
• 2023

In this paper, we designed a IoT(Internet of Things) sensor block embedded lightweight house deck structures that can be implemented using Carbon Fiber Reinforced Polymer(CFRP). Deck-Sensor interconnection interface block via IoT connectivity Hub that can mount external environmental sensors such as fire sensors on the Deck body itself was also proposed. Additionally we described the configuration of devices for data acquisition and analysis based on IoT environmental detection sensors that can be commonly installed and used on these deck bodies. On the other hand, received sensing data based monitoring user interface(UI) also developed and used for sensing data analysis for remote monitoring center. Through the implementation of such IoT-based sensor data transmission and collection analysis devices and UI software, this paper confirmed the availability of CFRP based lightweight House deck structure and possibility of CFRP deck-based IoT sensor data networking and analysis functions.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.967-977
• /
• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.2
• /
• pp.67-72
• /
• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.98-100
• /
• 2007

DC Arc Fault Current is an electric discharge which is occurred in two opposite electrode. In this paper, DC arc detection device is designed for the display of DC arc fault current which is occurred in the local electric network with DC Power. This DC arc is one of the main causes of electric fire. Arc fault in electrical network has the characteristics of low current, high impedance and low frequency. DC Arc current detection device is designed for the display of arc fault current which has the modified arc characteristics.

• Fire Science and Engineering
• /
• v.30 no.4
• /
• pp.88-93
• /
• 2016

A fire detector installed in wood cultural properties has not have selected the detector type appropriate for the features of cultural properties and the structure of wood fire after the fire in Sungnyemun-Gate since 2008. Applying wooden cultural properties different from the general architecture of the structure and fire characteristics is difficult. Therefore, buildings were classified into four shape types and field survey and wooden architecture structure characteristics to identify the problems of the detectors installed on wooden cultural property buildings. The problems appeared to lack the adaptability to external fire detection sensor selection and missing fire detectors installed in accordance with the place. To solve the problem, the closed and open space of the rooms used a smoke detector, outdoor select flame or fixed temperature linear detector to solve the problem.

• Fire Science and Engineering
• /
• v.33 no.6
• /
• pp.20-27
• /
• 2019

In this study, a new function with higher accuracy for fire heat release rate prediction was developed. The 'αt²' curve, which is the major exponential function currently used for fire engineering calculations, must be improved to minimize the prediction gap that causes fire system engineering inefficiency and lower cost-effectiveness. The newly developed prediction function was designed to cover the initial fire stage that features rapid growth based on logistic function theory, which has a more logical background and graphical similarity compared to conventional exponential function methods for 'αt²'. The new function developed in this study showed apparently higher prediction accuracy over wider range of fire growth durations. With the progress of fire growth pattern studies, the results presented herein will contribute towards more effective fire protection engineering.

• Journal of the Society of Disaster Information
• /
• v.17 no.4
• /
• pp.890-896
• /
• 2021

Purpose: The called Aero System is important to find the well detected place in the livingroom or bedroom so, it needs to the confirmation through the Fire Dynamics Simulation Method: A fire simulation of a residential space of 59 m² was performed, and in order to find the point where the fire environment was exposed quickly, measuring points were installed at 0.6 m and 1.5 m in height for each bedroom and living room, and the point where the fire was quickly detected was confirmed. Result: It was confirmed that the temperature and carbon monoxide sensor set at a point of 1.5 m was quickly detected at the reference value. Conclusion: The Fire detection would be relatively quick if the product in which the fire extinguishing module and the AQI module were separated was installed on the wall.

• Fire Science and Engineering
• /
• v.19 no.2 s.58
• /
• pp.75-80
• /
• 2005

This dissertation is about the development of PTC(Positive Temperature Coefficient) thermistor by composition method. A multilayer-type PTC samples were fabricated under optimal conditions after setting the experimental composition equation as $(0.90Ba+0.05Sr+0.05Ca)TiO_3+0.01TiO_3+0.01SiO_2+0.0008MnO_2+0.0018Nb_2O_5$ and their testing results were analyzed. The fabrication method of SMD(Surface Mounted Device) multilayer -type sample based on the composition ratio has the advantages in lowering its resistivity at room temperature, considerably, and increasing maximum current level, as needed. Although there is a disadvantage of peak resistivity drop by the multilayer, causing the increasement of thermal capacity. and thereby, increasing the switching delay time, a high applying voltage can increase the peak resistivity and shorten the switching delay time. The voltage-current characteristic showed that the more multilayers increased the initial maximum current and the transition voltage that increased the resistivity abruptly according to the curie point. The element it could be applied with the sensor for the fire detector.