Optimized Time Series Feature Selection for Manufacturing AI: Reducing Complexity and Improving Classification Accuracy
Although AI enhances productivity and quality in manufacturing, real-time validation remains difficult due to the complexity and high-dimensionality of time series data under strict inference time requirements. To address this, we propose two time series feature selection strategies. First, we extra...
Saved in:
| Main Authors: | Jaeseok Jang, Chanyoung Jung, Hail Jung |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11045927/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Recipe Based Anomaly Detection with Adaptable Learning: Implications on Sustainable Smart Manufacturing
by: Junhee Lee, et al.
Published: (2025-02-01) -
Multidimensional time series classification with multiple attention mechanism
by: Chen Liu, et al.
Published: (2024-11-01) -
RAT-CC: A Recurrent Autoencoder for Time-Series Compression and Classification
by: Giacomo Chiarot, et al.
Published: (2025-01-01) -
MicroForest: Lightweight Bottleneck Prediction for Manufacturing Processes on Edge Devices
by: Seungmin Yoo, et al.
Published: (2025-07-01) -
Noise robust classification of carbide tool wear in machining mild steel using texture extraction based transfer learning approach for predictive maintenance
by: Ravi Sekhar, et al.
Published: (2024-12-01)