Clayton Cooper
Researcher / author (OpenAlex)
Facts
2 properties- Affiliation
- Miami Universityas of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
🔖 Identifiers · 2
- ORCID iD
- 0000-0001-6212-8788
- OpenAlex ID
- A5085982115, A5129402742as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 10
Anomaly detection in milling tools using acoustic signals and generative adversarial networks,
Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals,
Error homogenization in physics-informed neural networks for modeling in manufacturing,
Function block-based human-robot collaborative assembly driven by brainwaves,
Leveraging multimodal data for intuitive robot control towards human-robot collaborative assembly,
Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm,
Multi-sensor fusion and machine learning-driven sequence-to-sequence translation for interpretable process signature prediction in machining,
Surface roughness prediction through GAN-synthesized power signal as a process signature
+2 more
← Authored by 10
Anomaly detection in milling tools using acoustic signals and generative adversarial networks,
Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals,
Error homogenization in physics-informed neural networks for modeling in manufacturing,
Function block-based human-robot collaborative assembly driven by brainwaves,
Leveraging multimodal data for intuitive robot control towards human-robot collaborative assembly,
Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm,
Multi-sensor fusion and machine learning-driven sequence-to-sequence translation for interpretable process signature prediction in machining,
Surface roughness prediction through GAN-synthesized power signal as a process signature
+2 more
Affiliated with 1
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Summary
Clayton Cooper is a human[1].