<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper provides an in-depth study on the application of digital sensor technology in electrical fire detection. It firstly focuses on the design of electrical parameters to improve the accuracy of fire detection. Based on the research results of recurrent neural networks, the article combines the Long Short-Term Memory (LSTM) network and the Gated Recurrent Unit (GRU) neural network. It innovatively proposes an electrical fire feature recognition method based on the LSTMGRU network. The study also incorporates fuzzy inference techniques to optimize the fire alarm decision-making process, which achieves a hierarchical output for electrical fire detection recognition and alarm decision-making. The experimental results show that the LSTM-GRU network achieves an accuracy of 98.83% in electrical fire classification and recognition, significantly better than the results of using LSTM network or GRU network alone. Regarding electrical fire distance recognition, the relative error of the method only ranges from 0.32% to 2.10%, and its output of fire alarm decision is entirely correct. The study not only verifies the high accuracy and reasonableness of the electrical fire feature recognition method based on digital sensing technology, but also provides a brand new idea for recognizing electrical fire detection and alarm decision-making.</p>
</abstract>

This paper provides an in-depth study on the application of digital sensor technology in electrical fire detection. It firstly focuses on the design of electrical parameters to improve the accuracy of fire detection. Based on the research results of recurrent neural networks, the article combines the Long Short-Term Memory (LSTM) network and the Gated Recurrent Unit (GRU) neural network. It innovatively proposes an electrical fire feature recognition method based on the LSTMGRU network. The study also incorporates fuzzy inference techniques to optimize the fire alarm decision-making process, which achieves a hierarchical output for electrical fire detection recognition and alarm decision-making. The experimental results show that the LSTM-GRU network achieves an accuracy of 98.83% in electrical fire classification and recognition, significantly better than the results of using LSTM network or GRU network alone. Regarding electrical fire distance recognition, the relative error of the method only ranges from 0.32% to 2.10%, and its output of fire alarm decision is entirely correct. The study not only verifies the high accuracy and reasonableness of the electrical fire feature recognition method based on digital sensing technology, but also provides a brand new idea for recognizing electrical fire detection and alarm decision-making.

Visualization and analysis of electrical parameter design based on digital sensors

Shandong University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper provides an in-depth study on the application of digital sensor technology in electrical fire detection. It firstly focuses on the design of...

Visualization and analysis of electrical parameter design based on digital sensors

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 02 gen 2024

Accettato: 07 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0349

Parole chiave
LSTM-GRU network, Fuzzy inference, Electrical detection, Digital sensor

© 2024 Xiaohe Wan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Visualization and analysis of electrical parameter design based on digital sensors

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 02 gen 2024

Accettato: 07 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0349

Parole chiaveLSTM-GRU network, Fuzzy inference, Electrical detection, Digital sensor

© 2024 Xiaohe Wan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
LSTM-GRU network, Fuzzy inference, Electrical detection, Digital sensor