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. 5. Anomaly Detection Subsystem

5.1 Tensor-Based Anomaly Detection

Utilizing multi-layer convolutional recurrent neural networks (CRNNs) with attention mechanisms, the ADS processes high-dimensional time-series data streams to detect anomalous patterns.

Model Architecture:

  • Input Tensor Shape: (Batch, Channels, Timesteps, Features)

  • Convolutional Layers: Extract localized feature embeddings from multivariate sequences.

  • Bidirectional LSTM Stack: Capture long-range temporal dependencies bidirectionally.

  • Self-Attention Layer: Weight sequence elements by contextual relevance to anomaly likelihood.

  • Output Softmax Layer: Classify sequences into normal and anomalous categories.

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Last updated 12 days ago