SBIR Phase I: Intelligent Fire Detection for Indoor Settings

点击翻译按钮获取中文摘要

This Small Business Innovation Research (SBIR) Phase I project will validate the company’s novel fire detection principle and the use of soft computing techniques by means of (1) computational experiments, and (2) a wide range of real fire and nuisance experiments on the company’s fire detector prototypes. This will allow the company to resolve any technical hurdles that may arise from the integration of the software and hardware into a minimum viable fire detector expected to significantly outperform current market solutions. The company has utilized real-world datasets of fire and nuisance experiments conducted by NIST to test and benchmark the innovation. Using temperature data, for example, the company’s algorithm detected fires up to 10 times faster than current technology (fixed-threshold). In addition, no missed detection occurred, and no false alarms were triggered in all NIST experiments. While these results are impressive, they are not sufficient to commercialize the innovation given that they are based on 69 experiments only. The activities proposed in phase I are designed to reduce the technical risk and strengthen the confidence in the company’s technology prior to commercialization. This is especially important because of the high regulatory burden and associated liabilities in the fire detection space.By leveraging ML, erli.ai’s patent-pending technology will process streaming data from heat/smoke sensors to detect the earliest signs of fire and separate fire and nuisance events. We introduce a new alarm triggering principle rooted in the temporal analysis of sensor data instead of the existing fixed-threshold approach. We propose to further develop our computational architecture, which employs deep Long-Short Term Memory (LSTM) neural networks and a variational autoencoder, by developing a theoretical basis for the outstanding performance our algorithm produced. We will develop an anomaly detection evaluation scheme for time streaming clustering using Dynamic Time Warping and Similarity Matrix technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.