Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Lymph Node Yield and Disease-Free Survival After Neoadjuvant Chemoimmunotherapy for Esophageal Squamous Cell Carcinoma: A Multicenter, Retrospective Cohort Study.

Annals of surgical oncology·2026

Same author

Comparative study of the tumor-infiltrating lymphocytes before and after neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.

Journal of cardiothoracic surgery·2026

Same author

Surgery-based treatment and prognostic factors in patients with limited-stage small cell lung cancer: a retrospective cohort study.

Frontiers in oncology·2026

Same author

Applications of Nature-Inspired Water Cycle Algorithm in Antenna Design and Array Synthesis.

Sensors (Basel, Switzerland)·2026

Same author

Enhanced Range Resolution Beamforming for Subarray-Based FDA.

Sensors (Basel, Switzerland)·2026

Same author

Lumbar-sacral giant sparganosis presenting as an intradural mass.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases·2026

Search research articles

Related Experiment Video

Updated: May 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Adaptive signal recognition in mines based on deep learning.

Yi Rong¹, Anyi Wang², Mingbo Wang¹

¹School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.

Scientific Reports

|March 26, 2025

Summary

This summary is machine-generated.

This study introduces an adaptive deep learning method for wireless signal recognition in coal mines. The novel approach improves accuracy and efficiency in complex environments, outperforming existing methods.

Keywords:

Adaptive signal recognition Attention mechanism Channel shuffling Group residual shuffle attention WaveNet Mine wireless communication

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Area of Science:

Electrical Engineering
Computer Science
Signal Processing

Background:

Coal mines present complex wireless environments with signal interference and multiple communication technologies.
Existing methods face challenges in recognition accuracy and system complexity.

Purpose of the Study:

To develop a deep learning-based adaptive signal recognition method for complex wireless environments.
To improve recognition accuracy and reduce system complexity in coal mine communications.

Main Methods:

Proposed a Group Residual Shuffle Attention WaveNet model.
Incorporated grouped residual convolution, channel shuffling, and dilated causal convolution.
Introduced a dynamic channel attention mechanism for adaptive feature weighting.

Main Results:

Achieved average recognition rates of 93.2% (public dataset) and 94.5% (simulated dataset).
Outperformed CTDNN by over 1.5% in recognition accuracy.
Improved inference speed by over 14% compared to other methods.

Conclusions:

The Group Residual Shuffle Attention WaveNet offers an efficient and reliable solution for intelligent mine communication.
The method demonstrates strong performance on general datasets and adapts well to complex signal recognition tasks.
The proposed approach effectively addresses challenges in coal mine wireless environments.