Structure of the CNN model. by Dongmei Liu (268523)

“…The integration of computational communication concepts (e.g., signal propagation network topological features) and multi-domain features enables the model to capture comprehensive spatiotemporal and dynamic signal characteristics, further validating its superiority in identifying weak microseismic signals with low signal-to-noise ratios (SNR).This indicates that the combination of time - domain images and computational - communication technology is more suitable as the input data for the CNN model. It can effectively distinguish microseismic waveforms from background noise, opening up a new path for the identification of mine microseismic signals and demonstrating the application potential of computational communication in this field.…”

Background noise 8. by Dongmei Liu (268523)

“…The integration of computational communication concepts (e.g., signal propagation network topological features) and multi-domain features enables the model to capture comprehensive spatiotemporal and dynamic signal characteristics, further validating its superiority in identifying weak microseismic signals with low signal-to-noise ratios (SNR).This indicates that the combination of time - domain images and computational - communication technology is more suitable as the input data for the CNN model. It can effectively distinguish microseismic waveforms from background noise, opening up a new path for the identification of mine microseismic signals and demonstrating the application potential of computational communication in this field.…”

Recognition of obvious microseismic events. by Dongmei Liu (268523)

“…The integration of computational communication concepts (e.g., signal propagation network topological features) and multi-domain features enables the model to capture comprehensive spatiotemporal and dynamic signal characteristics, further validating its superiority in identifying weak microseismic signals with low signal-to-noise ratios (SNR).This indicates that the combination of time - domain images and computational - communication technology is more suitable as the input data for the CNN model. It can effectively distinguish microseismic waveforms from background noise, opening up a new path for the identification of mine microseismic signals and demonstrating the application potential of computational communication in this field.…”

Background noise 11. by Dongmei Liu (268523)

“…The integration of computational communication concepts (e.g., signal propagation network topological features) and multi-domain features enables the model to capture comprehensive spatiotemporal and dynamic signal characteristics, further validating its superiority in identifying weak microseismic signals with low signal-to-noise ratios (SNR).This indicates that the combination of time - domain images and computational - communication technology is more suitable as the input data for the CNN model. It can effectively distinguish microseismic waveforms from background noise, opening up a new path for the identification of mine microseismic signals and demonstrating the application potential of computational communication in this field.…”

Voltage noise and water flow noise. by Dongmei Liu (268523)

“…The integration of computational communication concepts (e.g., signal propagation network topological features) and multi-domain features enables the model to capture comprehensive spatiotemporal and dynamic signal characteristics, further validating its superiority in identifying weak microseismic signals with low signal-to-noise ratios (SNR).This indicates that the combination of time - domain images and computational - communication technology is more suitable as the input data for the CNN model. It can effectively distinguish microseismic waveforms from background noise, opening up a new path for the identification of mine microseismic signals and demonstrating the application potential of computational communication in this field.…”

Clusters obtained from hierarchical cluster analysis with Ward’s linkage. by Myanca Rodrigues (10970895)

SpeLL: An Agent for Natural Language-Driven Intelligent Spectral Modeling by Jiashun Fu (20888176)

“…The core strength of SpeLL lies in its dual RAG pathways. The Code RAG provides specialized code knowledge for spectral data analysis, enabling the LLM to generate robust and domain-specific analytical scripts that address the implementation and optimization of algorithms. …”

Flow chart of the AWGE-ESPCA model. by Rui Miao (1592461)

Prediction of TFs for key anoikis-related genes. by Yufeng He (5673119)

Subjects:

Four potential therapeutic drugs. by Yufeng He (5673119)

Subjects:

Figs 1–11. by Yufeng He (5673119)

Subjects:

Functional analysis of anoikis-related genes. by Yufeng He (5673119)

Subjects:

Immunoassay of key anoikis-related genes. by Yufeng He (5673119)

Subjects:

Validation of the performance of 6 key anoikis-related genes. by Yufeng He (5673119)

Subjects:

Flow chart of the experimental procedure. by Yufeng He (5673119)

Subjects:

PCR primers. by Yufeng He (5673119)

Subjects:

H&E staining results of rats in each group. by Yufeng He (5673119)

Subjects:

Expression levels of spinal cord proteins in each group of rats. by Yufeng He (5673119)

Subjects:

Expression levels of mRNA in the spinal cord of each group of rats. by Yufeng He (5673119)

Subjects:

Selection of key anoikis-related genes. by Yufeng He (5673119)

Subjects: