arXiv (All)
open access
Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Prediction
Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which can model both temporal and spatial relationships by representing time series data as a sequence of graphs, have shown exceptional performance in RUL p
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration#1078971
- Assessing trends and forecasting meteorological drought in South Africa using Savitzky-Golay enhanced hybrid deep learning.#258450
- Deep learning approaches for time series prediction of renal recovery in medical critically Ill patients with acute kidney injury: LSTM, GRU, and transformer models.#14979
- Time-Varying Graph Learning with Constraints on Graph Temporal Variation#997877
- RSTGCN: Railway-centric Spatio-Temporal Graph Convolutional Network for Train Delay Prediction#665547
Record · ID 1051672
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive