임베디드 플랫폼을 위한 화재 조기 감지 시스템: 오경보 최소화를 위한 딥러닝 접근 방식
Early Fire Detection System for Embedded Platforms: Deep Learning Approach to Minimize False Alarms
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In Korea, fires are the second most common type of disaster, causing large-scale damages. The installation of fire detectors is legislated to prevent fires and minimize damage. Conventional fire detectors have limitations in initial suppression of failures because theydetect fires when large amounts of smoke and heat are generated. Additionally, frequent malfunctions in fire detectors may cause usersto turn them off. To address these issues, recent studies focus on accurately detecting even small-scale fires using multi-sensor and deeplearning technologies. They also aim at quick fire detection and thermal decomposition using gas. However, these studies are not practical because they overlook the heavy computations involved. Therefore, we propose a fast and accurate fire detection system based onmulti-sensor and deep-learning technologies. In addition, we propose a computation-reduction method for selecting sensors suitable fordetection using the Pearson correlation coefficient. Specifically, we use a moving average to handle outliers and two-stage labeling toreduce false detections during preprocessing. Subsequently, a deep-learning model is selected as LSTM for analyzing the temporalsequence. Then, we analyze the data using a correlation analysis. Consequently, the model using a small data group with low correlationachieves an accuracy of 99.88% and a false detection rate of 0.12%.
Abstract
In Korea, fires are the second most common type of disaster, causing large-scale damages. The installation of fire detectors is legislated to prevent fires and minimize damage. Conventional fire detectors have limitations in initial suppression of failures because theydetect fires when large amounts of smoke and heat are generated. Additionally, frequent malfunctions in fire detectors may cause usersto turn them off. To address these issues, recent studies focus on accurately detecting even small-scale fires using multi-sensor and deeplearning technologies. They also aim at quick fire detection and thermal decomposition using gas. However, these studies are not practical because they overlook the heavy computations involved. Therefore, we propose a fast and accurate fire detection system based onmulti-sensor and deep-learning technologies. In addition, we propose a computation-reduction method for selecting sensors suitable fordetection using the Pearson correlation coefficient. Specifically, we use a moving average to handle outliers and two-stage labeling toreduce false detections during preprocessing. Subsequently, a deep-learning model is selected as LSTM for analyzing the temporalsequence. Then, we analyze the data using a correlation analysis. Consequently, the model using a small data group with low correlationachieves an accuracy of 99.88% and a false detection rate of 0.12%.
참고문헌 (25)
- Ministry of the Interior and Safety, "2022 Disaster Yearbook", Sejong Printing Information of the Korea Culture and Sports Education Association, ~ (2022)
- National Fire Agency, "2022 Fire Statistical Yearbook", Yun Planning, ~ (2022)
- S. Y. Jeong, "A new strategy for detecting plant hormone ethylene using oxide semiconductor chemiresistors : exceptional gas selectivity and response tailored by nanoscale Cr2O3 catalytic overlayer", Adv. Sci., 7(7), pp.1903093~ (2020)
- S. Y. Jeong, "Rational design of semiconductor-based chemiresistors and their libraries for next-generation artificial olfaction", Adv. Materials, 32(51), pp.2002075~ (2020)
- S. Li, "Au nanoparticles decorated ZnIn2S4", WO3 core-shell heterostructures as highly sensitive and selective ethylene glycol gas sensors, 2024, pp.998, 175027~
- W. Ku, "Rational design of hybrid sensor arrays combined synergistically with machine learning for rapid response to a hazardous gas leak environment in chemical plants", J. Hazard Mater. Adv., 466, pp.133649~ (2024)
- A. Gaur, "Fire sensing technologies: A review", IEEE Sens. J., 19(9), pp.3191~3202 (2019)
- G. Son, "A Study on Fire Alarm Test of IoT Multi-Fire Detector Combined Smoke", CO and Smoke, J. Soc. Disaster Inf., pp.2021, 17(2)[DOI]
- A. Solórzano, "Early fire detection based on gas sensor arrays : Multivariate calibration and validation", Sens. Actuators B Chem., 352, pp.130961~ (2022)
- J. Baek, "Real-time fire detection system based on dynamic time warping of multichannel sensor networks", Fire Saf. J., 123, pp.103364~ (2021)
- J. Baek, "Intelligent multi-sensor detection system for monitoring indoor building fires", IEEE Sens. J., 21(24), pp.27982~27992 (2021)
- L. Wu, "Multi-sensor data fusion algorithm for indoor fire early warning based on BP neural network", Inf., 12(2), pp.59~ (2021)
- X. Huang, "Fire detection and recognition optimization based on virtual reality video image", IEEE Access, 8, pp.77951~77961 (2020)
- B. Venuste, "Smart Approach for Fire Detection Systems in Kigali", J. Appropriate Technol., 9(3), pp.152~161 (2023)
- L. Kou, "Deep learning based inverse model for building fire source location and intensity estimation", Fire Saf. J., 121, pp.103310~ (2021)
- P. Narkhede, "Gas detection and identification using multimodal artificial intelligence based sensor fusion", Appl. Syst. Innov., 4(1), pp.3~ (2021)
- S. Mekruksavanich, "Lstm networks using smartphone data for sensor-based human activity recognition in smart homes", Sens., 21(5), pp.1636~ (2021)
- S. Kim, "LSTM-based Early Fire Detection System using Small Amount Data", J. Semicond. Display Technol., 23(1), pp.110~116 (2024)
- H. Cheng, "Data prediction model in wireless sensor networks based on bidirectional LSTM", EURASIP J. Wirel. Commun. Netw., 2019, pp.203(1)~203(12) (2019)
- L. Dong, "Prediction of Streamflow Based on Dynamic Sliding Window LSTM", Water, 12(11), pp.3032~ (2020)
- D. Tomar, "Deep learning neural network prediction system enhanced with best window size in sliding window algorithm for predicting domestic power consumption in a residential building", Comput. Intell. Neurosci., 2022(1), pp.7216959~ (2022)
- M. Jaén-Vargas, "Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models", PeerJ. Comput. Sci., 8, pp.e1052~ (2022)
- S. Dong, "Fracture identification in reservoirs using well log data by window sliding recurrent neural network", Geoenergy Sci. Eng., 230, pp.212165~ (2023)
- P. Schober, "Correlation coefficients : appropriate use and interpretation", Anesth. Analg., 126(5), pp.1763~1768 (2018)
- J. H. Lee, "Deriving Optimal Health Indicators Based on Fuzzy Logic using Lithium-ion Battery EIS Measurement Data and Developing SOH Estimation Based on LSTM Algorithm", Trans. Korean Inst. Power Electron., 29(4), pp.308~315 (2024)