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March 26, 202641 citationsOpen Access

DSFB Structural Semiotics Engine: A Survey of Applications Across Safety-Critical Domains - Prior Art Establishment and Domain Instantiation Framework for Deterministic Residual-Based Structural Interpretation

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RBRiaan De Beer

Key Points

  • This survey aims to establish the application scope of the DSFB Structural Semiotics Engine across various safety-critical domains.
  • Identified 120 safety-critical domains and organized them into twenty groups.
  • Defined the residual object and slow drift problem relevant to each domain.
  • Outlined admissibility envelope construction and certification standards for each application.
  • Analyzed the DSFB as a supervisory layer that enhances existing probabilistic systems.
  • The DSFB framework is applicable across all identified domains, enhancing existing systems.
  • Formal claims made about structural detectability and inference capabilities in each domain.
  • No empirical validation provided, focusing solely on application scope and conceptual framework.

Abstract

The DSFB Structural Semiotics Engine 1 is a domain-agnostic deterministic framework forresidual-based structural interpretation in dynamic systems. It treats residual trajectories asprimary inferential objects — reading drift direction, slew magnitude, and admissibility gram-mar state — rather than as scalar discrepancies to be thresholded. This survey establishesprior art for the application of the framework across 120 safety-critical and commercially sig-nificant domains, organized into twenty domain groups: energy systems, manufacturing andprocess control, transportation and propulsion, aerospace and defense, infrastructure and civilengineering, medical and biological systems, computing and software, finance and informationsystems, and scientific and theoretical applications. DSFB is designed as a supervisory aug-mentation layer operating on residual and diagnostic signals already produced by incumbentprobabilistic sensing, estimation, and fusion systems. It is not proposed as a replacement forthose systems, but as a deterministic interpretive layer that increases the operational value oftheir residual outputs. In supervisory or advisory-path deployments, the DSFB layer can beevaluated without altering the primary sensing or control architecture.For each domain, the paper identifies the natural residual object, the slow drift problem thatmotivates the application, the admissibility envelope construction, and the applicable certifi-cation or qualification standard. The augmentation architecture — in which the DSFB enginereads residuals already produced by existing estimators without modifying them — is shownto be universally applicable across all 120 domains.This paper does not provide empirical validation for each domain. Its purpose is to formallyestablish the application scope of the DSFB framework as a matter of prior art, and to providethe conceptual instantiation that deep-dive domain papers will elaborate. Each domain sectionconstitutes a formal claim that the DSFB framework’s formal results — Law 1 (StructuralDetectability Principle), Theorem 1 (Finite-Time Envelope Exit), Theorem 9 (DeterministicInterpretability), and Theorem 10 (Certification-Pathway-Eligible Early-Warning Inference) —apply to that domain’s residual structure.

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Cite This Study

Riaan De Beer (2026) studied this question.

synapsesocial.com/papers/69c4ccc9fdc3bde448918637https://doi.org/10.5281/zenodo.19204366
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