industrial control engineers

Abstract: As AI starts to permeate everyday life, deployment focuses on embedding intelligence in the background, abstracting away controls, and leaving automated decision-making opaque, with few obvious opportunities for human intervention. Where interfaces remain, design has drifted toward abstract, screen-based control, replacing material interaction with menu navigation. These shifts weaken the coupling between human action and system state, eroding operators' ability to understand, anticipate, and intervene in automated systems. We introduce Physically Stateful Interfaces (PSIs), a design concept that re-imagines traditional controls, such as buttons, switches, and knobs, as actuated, bidirectional interface elements. Three foundational behaviors, Self/Reset, Resist/Hide, and Assert/Unhide, unify affordance, feedforward, and feedback into a single interaction point. Rather than static inputs whose impact and consequences must be read elsewhere on a screen, PSIs position physical controls as shared mediators between automated systems and human operators. As automated systems become increasingly capable, their interfaces need to be deliberately designed to center human agency, supporting deliberate choice while keeping automation legible, contestable, and overrideable.

Abstract: Variance penalization is a principled approach to risk-sensitive reinforcement learning (RL) that explicitly trades expected return for policy stability. Existing methods require a dedicated second critic to estimate return variance online, adding architectural complexity and compounding estimation error during learning. We propose a nonparametric variance-penalized actor-critic (VPAC) framework that replaces the variance critic with statistically grounded online estimators based on bootstrapping and random scaling, techniques drawn from the statistical inference literature for stochastic approximation. These estimators require no auxiliary network, maintain a single-critic architecture, and produce variance penalties that are bounded by construction, enabling clean convergence analysis. We establish almost-sure convergence for both a variance-penalized Q-learning algorithm and a two-timescale actor-critic variant via the ordinary differential equation (ODE) method, requiring only that variance estimates remain bounded rather than consistent. Empirically, we evaluate across discrete and continuous stochastic environments, demonstrating that the proposed methods match or exceed the variance reduction achieved by the existing dual-critic VPAC baseline while eliminating the overhead of a second critic. We further validate on a high-temperature superconductor (HTS) manufacturing case study, where VPAC-RS (Random Scaling) achieves a 74% reduction in steady-state critical current variability and a 63% reduction in episode return standard deviation, translating directly to improved yield consistency. Our results establish nonparametric statistical inference as a practical and theoretically sound alternative to auxiliary critics for risk-sensitive RL.

Abstract: Software reuse allows existing components and third-party libraries to be incorporated into new applications, but binary-only components can obscure their origins and implementations. Software composition analysis seeks to identify these reused components and trace their provenance, supporting dependency inventory, vulnerability assessment, and security auditing. For PLC applications, binary2source matching provides a core link in this analysis: given an opaque PLC binary artifact, retrieve its corresponding Structured Text (ST) source file from a collected source repository. However, this task is complicated by cross-platform compilation heterogeneity, the representation gap between PLC binaries and ST source code, and the granularity mismatch between recovered binary units and ST source files. This paper presents PLC-Bin2Src, a cross-platform binary2source matching framework for retrieving corresponding ST source files for binaries produced by CODESYS, GEB, OpenPLC v2, and OpenPLC v3. Platform-aware frontends construct comparable representations, and a shared backend equally combines control--data flow graph (CDFG), function call graph (FCG), and recovered-symbol similarities to rank source candidates. We evaluate PLC-Bin2Src on PLC-BEAD. Results show that PLC-Bin2Src achieves 95.89% Recall@1, 99.66% Recall@5, and an MRR of 0.9769 across four PLC platforms.

Abstract: Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required to meet formal resilience criteria: absorbed adversarial disturbances, graceful degradation under sustained attack, and certified system-level guarantees. Existing resilience frameworks for cyber-physical systems define absorb-recover-adapt trajectories at the architectural level but do not treat machine learning anomaly detectors as first-class components, leaving a gap between component-level robustness evaluation and system-level resilience certification. In this paper, we establish that adversarial robustness in ICS anomaly detection is a specific instantiation of system resilience, and formalise this connection by mapping four resilience constructs, i.e. disturbance class, absorption capacity, recovery trajectory, and degradation function, onto the adversarial machine learning setting. We derive a compositional resilience bound for heterogeneous ICS detection networks, showing that the binding constraint on system-level resilience is the coupling-adjusted absorption capacity of each node along the attack path, not the per-node capacity -- so the binding node need not be the weakest one. Empirical validation on the BATADAL water distribution system benchmark demonstrates that the resulting metrics surface operationally significant phenomena invisible to standard benchmarks: the absorption-degradation divergence under adversarial training, and the paradox that hardening the binding node in isolation reduces system-level resilience. Implications for ICS architecture design and certification standards are discussed.