RoboFlux Whitepaper
  • RoboFlux AI: Comprehensive Technical Whitepaper
  • 1. Introduction
  • 2. System Overview
  • 3. Modular Architecture
  • 4. Data Ingestion and Preprocessing Layer
    • 4.1 Data Stream Typologies
    • 4.2 Data Pipeline Orchestration
    • 4.3 Data Normalization & Feature Engineering
  • 5. Anomaly Detection Subsystem
    • 5.1 Tensor-Based Anomaly Detection
    • 5.2 Online Learning Adaptation
  • 6. Quantum-Inspired Path and Task Optimization
  • 7. Secure Webhook Integration Framework
  • 8. AI Knowledge Hub Implementation
  • 9. Report Generation and Distribution Mechanism
  • 10. Deployment Modalities
  • 11. Cybersecurity and Compliance Protocols
  • 12. Future Roadmap and Extensibility
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  1. 4. Data Ingestion and Preprocessing Layer

4.3 Data Normalization & Feature Engineering

Prior to AI inference, all data undergoes a rigorous normalization and transformation protocol:

  • Zero-Center Scaling: Converts numeric telemetry to zero-mean, unit-variance for numerical stability in gradient-based learning systems.

  • Temporal Windowing: Segments time-series data into overlapping sliding windows to capture temporal correlations.

  • Derived Feature Construction: Generates secondary metrics (e.g., moving averages, variance trends, spectral entropy) to enhance anomaly detection sensitivity.

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Last updated 12 days ago