Increasingly the design of chemical detection alarm algorithms to alert Soldiers of danger grows more complex as new threats emerge. These algorithms need to be robust enough to prevent false alarms to interferents and sensitive enough to alarm to the incredibly small doses that could prove lethal. The design of these algorithms have left a plethora of data that can be leveraged and utilized in a variety of machine learning (ML) techniques. ML is a field of computer science that uses a set of programming and statistical techniques to enable computers to “learn” from input data without being explicitly programmed. Presented is an application of ML to change two independent fielded chemical detectors into an orthogonal system to improve detection algorithms. The approach models the data from an ion-mobility spectrometer (IMS) and a photoionization detector containing electrochemical sensors (PIDECS) to train a ML model (MLA). The semi-supervised MLA is trained using a supervised learning data set, composed of partially labeled data from the heterogeneous instruments, and then fine-tuned using an unsupervised learning algorithm. The MLA correctly identifies two chemical species with over-lapping IMS detection windows. ML can be utilized to improve the ability of currently fielded detectors or future devices to accurately label chemical unknowns given the parameters of detection. The techniques discussed here presents a starting point for improving current and future alarm algorithms with ML.

Date Published: 10 May 2019
PDF: 8 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061X (10 May 2019); doi: 10.1117/12.2518929

Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)