Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems (1902.02432v3)

Abstract: Cyber Physical Systems (CPS) have increasingly started using Learning Enabled Components (LECs) for performing perception-based control tasks. The simple design approach, and their capability to continuously learn has led to their widespread use in different autonomous applications. Despite their simplicity and impressive capabilities, these models are difficult to assure, which makes their use challenging. The problem of assuring CPS with untrusted controllers has been achieved using the Simplex Architecture. This architecture integrates the system to be assured with a safe controller and provides a decision logic to switch between the decisions of these controllers. However, the key challenges in using the Simplex Architecture are: (1) designing an effective decision logic, and (2) sudden transitions between controller decisions lead to inconsistent system performance. To address these research challenges, we make three key contributions: (1) \textit{dynamic-weighted simplex strategy} -- we introduce ``weighted simplex strategy" as the weighted ensemble extension of the classical Simplex Architecture. We then provide a reinforcement learning based mechanism to find dynamic ensemble weights, (2) \textit{middleware framework} -- we design a framework that allows the use of the dynamic-weighted simplex strategy, and provides a resource manager to monitor the computational resources, and (3) \textit{hardware testbed} -- we design a remote-controlled car testbed called DeepNNCar to test and demonstrate the aforementioned key concepts. Using the hardware, we show that the dynamic-weighted simplex strategy has 60\% fewer out-of-track occurrences (soft constraint violations), while demonstrating higher optimized speed (performance) of 0.4 m/s during indoor driving than the original LEC driven system.

• The paper introduces a dynamic-weighted simplex strategy that optimizes decision-making in learning-enabled cyber-physical systems.
• It details a methodology that balances real-time system safety with computational efficiency during adaptive operations.
• The findings suggest significant potential for integrating advanced computational models to improve control in cyber-physical and high-energy systems.

Analysis of the Provided Academic Document

The document in question does not provide specific content typical of an academic paper, such as a title, author information, abstract, keywords, detailed sections, or any substantive bibliographic data. As such, there are no explicit research results, data, figures, or theoretical advancements presented within the content provided. However, given that it is formatted for potential submission to "Nuclear Physics B," some inferred insights can be drawn about the intended scope and field of research.

Structural Components

The document uses the structure typical of a submission to an academic journal, with placeholders for essential elements like the abstract, graphical abstract, highlights, and keywords. These components usually serve to:

• Abstract: Summarizes the primary findings or contributions of the paper.
• Graphical Abstract: Visualizes the core concept or result.
• Highlights: Concisely list the significant findings or claims of the research.
• Keywords: Provide searchable terms that signify the scope and focus of the research.

Inferences on Potential Content

Given the typical focus of "Nuclear Physics B," papers submitted to this journal usually address topics within theoretical and experimental high-energy physics, quantum field theory, and the mathematical and conceptual underpinnings of physical models.

1. Theoretical/Computational Focus: The document likely pertains to advanced topics in quantum field theory, perhaps involving computational methods or novel theoretical models. Given the presence of a graphical abstract placeholder, there might be emphasis on visual data representation or computational models.
2. Research Highlights: These would contain concise takeaways from the research, potentially involving breakthrough methodologies or confirmations of theoretical hypotheses concerning nuclear physics' complex interactions.
3. Implications: If complete, the paper might discuss how the findings contribute to theoretical models' refinement or provide novel insights that could influence future experimental setups or measurements.

Speculations on Future Directions

In the field of high-energy and nuclear physics, ongoing developments may involve:

• Increased Precision in Measurements: The continual refinement of models to increase prediction accuracy in high-energy experiments.
• Quantum Computing Applications: Leveraging quantum computational approaches to solve complex simulations otherwise infeasible on classical systems.
• Interdisciplinary Integration: Incorporating insights from adjacent fields such as condensed matter physics to address questions in nuclear physics.

In summary, while the incomplete document does not provide explicit findings or methodologies, the structure aligns with submissions intended for a reputable physics journal focusing on high-energy physics and theoretical constructs. The finalized content would be expected to offer substantial theoretical insight or computational advancements pertinent to nuclear physics.