Odor identification method based on gas sensors and deep learning

A gas sensor and deep learning technology, applied in the field of odor recognition based on gas sensors and deep learning, can solve problems such as the inability to model long-distance connections of single-channel time series signals, the impact of recognition accuracy, baseline drift, etc., to improve odor recognition Performance, Avoidance of Information Loss, Effect of Specific Odor Recognition

Active Publication Date: 2020-07-24
HUAZHONG UNIV OF SCI & TECH
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  • Abstract
  • Description
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AI Technical Summary

Benefits of technology

The technology described in this patented solution improves on previous techniques used alone or combined together. It involves analyzing signals from various sources like gases within an environment's airspace to identify patterns associated therewith (such as smells) which are indicative of potential hazards such as fires or explosions. By utilizing these extracted featured values, advanced detection systems can recognize potentially dangerous conditions quickly without relying solely upon manual inspections. Additionally, it suggests adding hidden layers between existing models based on their structure to further enhance its effectiveness over previously identified threat scenarios. Overall, this new approach provides better accuracy and reliability in identifying harmful atmospheric pollution levels accurately.

Problems solved by technology

Technical Problem addressed in this patented technical problem is how to develop an electronics nosema system capable of identifying diverse types of volatile substances accurately without requiring extensive training effort and costly equipment. Existing techniques require large amounts of memory capacity but they lack efficiency when dealing with smaller quantities of target molecules like oxygen and nitrogen compounds.

Method used

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  • Odor identification method based on gas sensors and deep learning
  • Odor identification method based on gas sensors and deep learning
  • Odor identification method based on gas sensors and deep learning

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Embodiment Construction

[0036] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0037] The invention provides an odor recognition method based on gas sensor and deep learning, such as figure 1 As shown, the steps specifically include:

[0038] (1) Obtain the response curve cluster of the odor to be measured through the gas sensor array;

[0039] (2) Performing data preprocessing and data amplification on the response curve cluster to obtain a sensor signal;

[0040] (3) Perform featur

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Abstract

The invention discloses an odor identification method based on gas sensors and deep learning, which is characterized in that a response curve cluster of odor to be detected is obtained through a gas sensor array, original data is directly used as an input sample of an odor identification deep neural network, data preprocessing and data amplification are performed on the input sample, time sequenceresponse data hierarchical features are automatically extracted by adoption of deep learning, global feature extraction and long-range dynamic feature extraction are performed at the same time, and an odor label is output through a classifier, so that high-sensitivity and specific odor identification is realized. The odor identification method of the invention has high sensitivity and high reliability, and can be widely applied to the fields of industrial production, medical treatment, environment, safety and the like.

Description

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Claims

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Application Information

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Owner HUAZHONG UNIV OF SCI & TECH
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