Analysing Gas Data using Deep Learning and 2D Gramian Angular Fields

Date

2023

Advisors

Journal Title

Journal ISSN

ISSN

1530-437X

DOI

Volume Title

Publisher

IEEE SENSORS JOURNAL

Type

Article

Peer reviewed

Yes

Abstract

The notion of employing a Deep Learning (DL) for gas classification has kindled revolution in the field that has both improved data collection measures and classification performance. Yet, the current literature, with its vast contributions, has potential in enhancing the current state-of-the-art by employing both DL and novel visualization methods to boost classification performance and speed. Therefore, this paper presents a dual classification system for high-performance gas classification: on 1D time series data and on 2D Gramian Angular Field (GAF) data. For the GAF case, 1D data is converted into 2D counterparts by means of normalization, segmentation, averaging, and color-coding. The Gas Sensor Array (GSA) dataset is used for evaluating the implemented AlexNet model for classifying 2D GAF data and an improved version of GasNet for 1D time-based data. Using a cloud-based architecture, the two models are evaluated and benchmarked with the state-of-the-art. Evaluation results of the modified GasNet model on time series data signifies state-of-the-art accuracy of 96.0%, while AlexNet achieved 81.3% test accuracy of GAF classification with near real-time performance on edge computing platforms.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Citation

Jaleel, M. et al. (2023) Analysing Gas Data using Deep Learning and 2D Gramian Angular Fields. IEEE Sensors Journal

Rights

Research Institute

Institute of Artificial Intelligence (IAI)