RER-AILF: A Resonant AI Framework for 3D Rare Earth Localization
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866902189256998912 |
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| author | KIM, KYEONGWOOK |
| author_facet | KIM, KYEONGWOOK |
| contents | <p>RER-AILF v2: A Resonant AI Framework for 3D Rare Earth Localization</p> <p>This work presents a novel software-hardware integrated AI framework designed for ultra-precise localization and estimation of rare earth elements through a resonance-based inference engine. The model incorporates phase-synchronized learning modules, quantum-resonant signal conditioning, and multi-sensor alignment via Kuramoto-style oscillatory behavior modeling.</p> <p>The framework enables a multi-stage pipeline: quantum-resonant detection, signal fusion, AI-based inference, and real-time localization. It introduces the concept of Q-Normalized Learning for enhanced phase coherence in distributed sensor systems.</p> <p>Included in this deposit:<br>- Technical Whitepaper (PDF + Markdown)<br>- System Architecture Image (PNG)<br>- Implementation Summary (README-style)</p> <p>The file is restricted and accessible only to approved collaborators for legal protection during pre-patent publication review.</p> <p>Keywords: Resonant AI, Rare Earth Detection, Quantum Synchronization, Sensor Fusion, Kuramoto Inference, Localization Framework.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16321011 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | RER-AILF: A Resonant AI Framework for 3D Rare Earth Localization KIM, KYEONGWOOK Resonant AI Localization Rare Earth Quantum Inference Sensor Fusion Kuramoto Model <p>RER-AILF v2: A Resonant AI Framework for 3D Rare Earth Localization</p> <p>This work presents a novel software-hardware integrated AI framework designed for ultra-precise localization and estimation of rare earth elements through a resonance-based inference engine. The model incorporates phase-synchronized learning modules, quantum-resonant signal conditioning, and multi-sensor alignment via Kuramoto-style oscillatory behavior modeling.</p> <p>The framework enables a multi-stage pipeline: quantum-resonant detection, signal fusion, AI-based inference, and real-time localization. It introduces the concept of Q-Normalized Learning for enhanced phase coherence in distributed sensor systems.</p> <p>Included in this deposit:<br>- Technical Whitepaper (PDF + Markdown)<br>- System Architecture Image (PNG)<br>- Implementation Summary (README-style)</p> <p>The file is restricted and accessible only to approved collaborators for legal protection during pre-patent publication review.</p> <p>Keywords: Resonant AI, Rare Earth Detection, Quantum Synchronization, Sensor Fusion, Kuramoto Inference, Localization Framework.</p> |
| title | RER-AILF: A Resonant AI Framework for 3D Rare Earth Localization |
| topic | Resonant AI Localization Rare Earth Quantum Inference Sensor Fusion Kuramoto Model |
| url | https://doi.org/10.5281/zenodo.16321011 |