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Salzburg University of Applied Sciences

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Research library13linked papers
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Selected work

Representative Papers

Reconstructing OPC UA Address Spaces from Time-Series Databases

Jun 09, 2026

This work addresses the loss of semantic metadata—such as node hierarchy and engineering units—when storing OPC UA time-series data in databases, as well as unstable and conflicting node identifiers across sessions from multiple OPC UA servers. To resolve these issues, the authors propose the opcua-ts architecture, which enables, for the first time, the joint persistent storage of semantic metadata alongside time-series telemetry. By leveraging lifecycle-stable connection keys, the system reconstructs the original address space and exposes it as a real-time OPC UA endpoint. Validation through NodeSet2 XML round-trip testing and experiments with a boiler simulator demonstrates that the approach accurately and robustly reconstructs multi-source OPC UA address spaces, effectively mitigating identifier conflicts and session instability.

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Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Tasks

May 21, 2026

This work addresses the long-standing absence of an analytical optimal solution for the classic reinforcement learning benchmark task Mountain Car by deriving, for the first time, its closed-form optimal control policy. Building upon this solution, the authors propose the Chebyshev policy—a lightweight, general-purpose policy class constructed from first principles via Chebyshev polynomial expansions. This policy exhibits high parameter efficiency, strong interpretability, and real-time inference capabilities. On the Mountain Car task, it achieves a 4.18× reduction in regret and uses 277× fewer parameters compared to neural network-based policies. Furthermore, it consistently outperforms existing methods across multiple RL benchmarks and real-world nonlinear motor control platforms.

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Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process

May 13, 2026

This work addresses the challenge of modeling approximately periodic time series in industrial and cyber-physical systems, which exhibit strong structural regularity alongside inter-cycle variability that neither purely periodic nor aperiodic models can adequately capture. The authors propose a two-stage stochastic generative model based on Gaussian processes, introducing a novel posterior-weighted periodic kernel that explicitly decouples shared structural patterns from individual variations under a common mean function. This approach is the first within the Gaussian process framework to effectively separate the common mode of approximately periodic signals from their smooth deviations, enabling high-quality trajectory generation. Experiments on synthetic data demonstrate that the model generates realistic and structurally coherent approximately periodic sequences, confirming its efficacy in jointly modeling both structural consistency and cycle-to-cycle variability.

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Recent publications

Latest Papers

Reconstructing OPC UA Address Spaces from Time-Series Databases

Jun 09, 2026

This work addresses the loss of semantic metadata—such as node hierarchy and engineering units—when storing OPC UA time-series data in databases, as well as unstable and conflicting node identifiers across sessions from multiple OPC UA servers. To resolve these issues, the authors propose the opcua-ts architecture, which enables, for the first time, the joint persistent storage of semantic metadata alongside time-series telemetry. By leveraging lifecycle-stable connection keys, the system reconstructs the original address space and exposes it as a real-time OPC UA endpoint. Validation through NodeSet2 XML round-trip testing and experiments with a boiler simulator demonstrates that the approach accurately and robustly reconstructs multi-source OPC UA address spaces, effectively mitigating identifier conflicts and session instability.

0 citationsRead paper

Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Tasks

May 21, 2026

This work addresses the long-standing absence of an analytical optimal solution for the classic reinforcement learning benchmark task Mountain Car by deriving, for the first time, its closed-form optimal control policy. Building upon this solution, the authors propose the Chebyshev policy—a lightweight, general-purpose policy class constructed from first principles via Chebyshev polynomial expansions. This policy exhibits high parameter efficiency, strong interpretability, and real-time inference capabilities. On the Mountain Car task, it achieves a 4.18× reduction in regret and uses 277× fewer parameters compared to neural network-based policies. Furthermore, it consistently outperforms existing methods across multiple RL benchmarks and real-world nonlinear motor control platforms.

0 citationsRead paper

Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process

May 13, 2026

This work addresses the challenge of modeling approximately periodic time series in industrial and cyber-physical systems, which exhibit strong structural regularity alongside inter-cycle variability that neither purely periodic nor aperiodic models can adequately capture. The authors propose a two-stage stochastic generative model based on Gaussian processes, introducing a novel posterior-weighted periodic kernel that explicitly decouples shared structural patterns from individual variations under a common mean function. This approach is the first within the Gaussian process framework to effectively separate the common mode of approximately periodic signals from their smooth deviations, enabling high-quality trajectory generation. Experiments on synthetic data demonstrate that the model generates realistic and structurally coherent approximately periodic sequences, confirming its efficacy in jointly modeling both structural consistency and cycle-to-cycle variability.

0 citationsRead paper