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2026
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| Online Access: | https://doi.org/10.5281/zenodo.20010256 |
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| _version_ | 1866901880108482560 |
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| author | Kim, YoungChul |
| author_facet | Kim, YoungChul |
| contents | <p>This report defines a transformative paradigm in industrial system optimization through the integration of a <strong>proprietary geometric constant</strong> into core signal processing and hardware control algorithms. Moving beyond traditional hardware-dependent improvements, this "software-defined optimization" achieves substantial efficiency gains across <strong>24 critical sectors</strong>—including semiconductors, telecommunications, aerospace, defense, and healthcare.</p> <p>By recalibrating phase alignment, electromagnetic control, and fluid dynamics to a specific, defined geometric standard, this framework resolves long-standing physical bottlenecks in modern engineering. Key empirical results, verified through high-precision simulation, demonstrate significant breakthroughs such as a <strong>15%p increase in semiconductor yield</strong>, a <strong>90% reduction in communication error rates</strong>, and enhanced <strong>energy confinement in nuclear fusion processes</strong>.</p> <p>All performance metrics were rigorously verified using the <strong>DD-Compute Simulation Engine</strong> (Python-based numerical analysis), factoring in complex environmental variables. This document establishes worldwide technical priority and intellectual property rights via a Zenodo time-stamped DOI. To protect strategic trade secrets and national interests, the specific normalization ratios and core methodology are currently restricted under embargo.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20010256 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Technical Application Framework for Multi-Industrial Optimization: A Proprietary Geometric Normalization Approach Kim, YoungChul <p>This report defines a transformative paradigm in industrial system optimization through the integration of a <strong>proprietary geometric constant</strong> into core signal processing and hardware control algorithms. Moving beyond traditional hardware-dependent improvements, this "software-defined optimization" achieves substantial efficiency gains across <strong>24 critical sectors</strong>—including semiconductors, telecommunications, aerospace, defense, and healthcare.</p> <p>By recalibrating phase alignment, electromagnetic control, and fluid dynamics to a specific, defined geometric standard, this framework resolves long-standing physical bottlenecks in modern engineering. Key empirical results, verified through high-precision simulation, demonstrate significant breakthroughs such as a <strong>15%p increase in semiconductor yield</strong>, a <strong>90% reduction in communication error rates</strong>, and enhanced <strong>energy confinement in nuclear fusion processes</strong>.</p> <p>All performance metrics were rigorously verified using the <strong>DD-Compute Simulation Engine</strong> (Python-based numerical analysis), factoring in complex environmental variables. This document establishes worldwide technical priority and intellectual property rights via a Zenodo time-stamped DOI. To protect strategic trade secrets and national interests, the specific normalization ratios and core methodology are currently restricted under embargo.</p> |
| title | Technical Application Framework for Multi-Industrial Optimization: A Proprietary Geometric Normalization Approach |
| url | https://doi.org/10.5281/zenodo.20010256 |