An odor recognition method based on gas sensor and deep learning

Abstract

The invention discloses an odor recognition method based on a gas sensor and deep learning. The response curve cluster of the odor to be tested is obtained through a gas sensor array, and the original data is directly used as an input sample of a deep neural network for odor recognition, and the data is processed. Preprocessing and data amplification, using deep learning to automatically extract hierarchical features of time series response data, simultaneously extracting global features and long-range dynamic features, and outputting odor labels through classifiers to achieve highly sensitive and specific odor identification. The method of the invention has high sensitivity and high reliability, and can be widely used in fields such as industrial production, medical treatment, environment and safety.

Description

technical field [0001] The invention belongs to the technical field of artificial olfaction, and more specifically, relates to an odor recognition method based on a gas sensor and deep learning. Background technique [0002] The electronic nose based on gas sensors is a typical portable artificial olfactory system, which generates signals through the reaction of the sensor array and the atmosphere, combined with pattern recognition technology, can identify simple and complex odors, and common laboratory gas composition analysis methods (such as electric Compared with chemical methods, optical methods, chromatographic separation methods, etc.), it has the characteristics of convenient use, low price and easy popularization. It is suitable for on-site rapid detection and distributed online monitoring of various gases / odours. It has been widely used in food industry and agricultural production. , environmental monitoring and other fields to obtain practical applications. For e...

AI Technical Summary

Problems solved by technology

[0007] In view of the above defects or improvement needs of the prior art, the present invention provides an odor recognition method based on gas sensors and deep learning, the purpose of which is to deal with the problem of spatial independence among multiple channels of time series signals, and to solve the existing two-based odor recognition method. The artificial olfactory system odor recognition network with a three-dimensional convolution kernel cannot model the long-range connection problem of single-channel time series signals. In addition, it also solves the problem of sensor baseline drift and environmental factor changes on the recognition accuracy.

Images