Real-time FPGA-based laser absorption spectroscopy using on-chip machine learning for 10 kHz intra-cycle emissions sensing towards adaptive reciprocating engines

Schwarm, Kevin K.; Spearrin, R. Mitchell

doi:10.1016/j.jaecs.2023.100231

Applications in Energy and Combustion Science

2023

DOI: 10.1016/j.jaecs.2023.100231

|View full text |Cite

Real-time FPGA-based laser absorption spectroscopy using on-chip machine learning for 10 kHz intra-cycle emissions sensing towards adaptive reciprocating engines

Kevin K. Schwarm,

R. Mitchell Spearrin

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

LAS-on-edge: A real-time laser absorption spectroscopic water vapor sensor on edge computing platforms

Xia,

Xiao

et al. 2024

Sensors and Actuators B: Chemical

View full text Add to dashboard Cite

LAS-on-edge: A real-time laser absorption spectroscopic water vapor sensor on edge computing platforms

Xia,

Xiao

et al. 2024

Sensors and Actuators B: Chemical

View full text Add to dashboard Cite

VOC-Certifire: Certifiably Robust One-Shot Spectroscopic Classification via Randomized Smoothing

Sy,

Ibrahim,

Farooq

2024

ACS Omega

View full text Add to dashboard Cite

Spectroscopic methods are advantageous for gas detection with applications ranging from safety to operational efficiency. Despite the potential of laser-based sensors, real-world challenges, such as noise, interference and unseen conditions, hinder the accurate identification of species. The use of conventional machine learning (ML) models is constrained by extensive data requirements and their limited adaptability to new conditions. Although augmentation-based strategies have proven to improve the robustness of machine learning models, they still do not offer a complete defense. To address these challenges, this study focuses on three primary goals: first, to detect pressure-induced spectral broadening using simple yet effective augmentations; second, to bypass the need for extensive data sets by deploying a one-shot learning approach that can identify up to 12 volatile organic compounds (VOCs); and third, to provide a provable certification for the one-shot learning model predictions via randomized smoothing. To assess the effectiveness of our proposed augmentations and randomized smoothing, we perform a comparative study with four distinct models: VOC-net, VOC-lite, VOC-plus, and VOC-certifire. Remarkably, the one-shot learning model proposed herein, VOC-certifire, delivers predictions that match the baseline model VOC-net. The VOC-certifire predictions not only exhibit robustness and reliability but are also certified within a predefined norm radius. Such a certification is particularly useful for gas detection, where the robustness, precision and consistency are key to well-informed decision-making.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Real-time FPGA-based laser absorption spectroscopy using on-chip machine learning for 10 kHz intra-cycle emissions sensing towards adaptive reciprocating engines

Cited by 2 publications

References 40 publications

LAS-on-edge: A real-time laser absorption spectroscopic water vapor sensor on edge computing platforms

LAS-on-edge: A real-time laser absorption spectroscopic water vapor sensor on edge computing platforms

VOC-Certifire: Certifiably Robust One-Shot Spectroscopic Classification via Randomized Smoothing

Contact Info

Product

Resources

About