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Determination and research of calibration methodology of pressure resistance sensors in specialized diagnostic laboratory

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IEEE Access 2025年 13卷 77642-77655页

作者： Zelnik, Roman Ruzarovsky, Roman Horak, Tibor Csekei, Martin Michal, David Sido, Jan Slovak University of Technology in Bratislava Institute of Manufacturing Technologies Faculty of Materials Science and Technology in Trnava Trnava91724 Slovakia Slovak University of Technology in Bratislava Institute of Applied Informatics Automation and Mechatronics Faculty of Materials Science and Technology in Trnava Trnava91724 Slovakia

The aim of this thesis is to develop algorithms for configuring tactile sensors in the calibration process. The research involves extensive measurements, data analysis, and assessment of sensor behavior during measurement to evaluate their performance and reliability. A key contribution of this work is the application of progressive diagnostics theory and methods for obtaining new calibration data, along with analytical methods for evaluating sensor reliability. The work includes mathematical calculations necessary for accurate calibration and logical methods for analyzing sensor states, applied in the development of a specialized diagnostic laboratory. The research led to the development of a methodology for analyzing sensor characteristics, reliability, and mathematical corrections. The implemented autonomous calibration algorithm enables automated diagnostics and optimization of sensor lifetime, improving accuracy and operability. Results demonstrate a reduction in measurement deviations from 18.1% to 1.1%, with the tolerance range narrowed to 2.6%. These findings confirm the effectiveness of the methodology in improving sensor calibration and enhancing system reliability. Automated correction mechanisms allow sensors to adapt dynamically to changing conditions, increasing the overall reliability and cost-effectiveness of technological systems. This work provides a comprehensive analysis of sensor calibration and diagnostic methods, focusing on pressure sensors in industrial systems essential for optimizing industrial processes. © 2013 IEEE.

关键词： Measurement theory

COMMANDER: A robust cross-machine multi-phase Advanced Persistent Threat detector via provenance analytics

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Journal of Information Security and Applications 2025年 91卷

作者： Liu, Qi Bao, Kaibin Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

Intrusion detection systems (IDS) have traditionally focused on identifying malicious behaviors caused by malware undertaking a series of suspicious activities within a short time. Facing Advanced Persistent Threat (APT) actors employing the so-called low-and-slow strategy, defenders are often blindsided by the poor performance of these IDS. Provenance-based IDS (PIDS) emerged as a promising solution for reducing false alerts, detecting true attacks, and facilitating attack investigation, by causally linking and contextualizing indicative system activities in provenance graphs. However, most existing PIDS can detect neither multi-phase nor cross-machine APT attacks, enabled by persistence and lateral movement techniques, respectively. In the present work, we propose a new PIDS called COMMANDER, which is, to our knowledge, the first system capable of detecting cross-machine multi-phase APT attacks. Further, COMMANDER targets several evasion attacks that can bypass existing PIDS, making it more robust. In addition, COMMANDER can perform whole network tracing for cross-machine multi-phase APT attacks across an industrial-sector organization, for which we additionally develop parsers for system logs of popular industrial controllers. We also develop detection rules with a reference to MITRE's knowledge base for industrial control systems. Our evaluations show that COMMANDER accurately detects attacks, outperforms existing detection systems, and delivers succinct and insightful attack graphs. © 2025

关键词： Computer forensics

Learning the Frequency Dynamics of the Power System Using Higher-order Dynamic Mode Decomposition

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arXiv 2025年

作者： Li, Xiao Wen, Xinyi Schäfer, Benjamin Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

The increasing penetration of renewable energy sources, characterised by low inertia and intermittent disturbances, presents substantial challenges to power system stability. As critical indicators of system stability, frequency dynamics and associated oscillatory phenomena have attracted significant research attention. While existing studies predominantly employ linearized models, our findings demonstrate that linear approximations exhibit considerable errors when predicting frequency oscillation dynamics across multiple time scales, thus necessitating the incorporation of nonlinear characteristics. This paper proposes a data-driven approach based on higher-order dynamical mode decomposition (HODMD) for learning frequency dynamics. The proposed method offers distinct advantages over alternative nonlinear methods, including no prior knowledge required, adaptability to high-dimensional systems, and robust performance. Furthermore, HODMD demonstrates superior capability in capturing system-wide spatio-temporal modes, successfully identifying modal behaviour that remains undetectable through standard Dynamic Mode Decomposition techniques. The efficacy of the proposed methodology is validated through comprehensive case studies on both IEEE 14-bus and WECC systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Dynamic mode decomposition

ECONOMY AND SUSTAINABILITY ANALYSIS WITH A NOVEL MODULAR CONFIGURABLE MULTI-MODAL WHITE-BOX BUILDING MODEL

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arXiv 2025年

作者： Cheng, Haozhen Hagenmeyer, Veit Çakmak, Hüseyin K. Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

This paper presents a novel modeling approach for building performance simulation, characterized as a white-box model with a high degree of modularity and flexibility, enabling direct integration into complex large-scale energy system co-simulations. The introduced model is described in detail, with a focus on its modular structure, and proposes various configurations that include various building insulation, heating methods, occupancy patterns, and weather data to analyze different scenarios, and the energy consumption, CO2 emissions, and heating costs are compared and analyzed across 36 introduced scenarios. The thermodynamic behavior of the model is shown to be consistent with real-world conditions, and the comparison of the scenarios concludes that the use of heat pumps for indoor heating in well-insulated buildings has significant economic and sustainability benefits, whereas the use of natural gas-fueled boilers is more cost-effective for buildings with low energy ratings. Copyright © 2025, The Authors. All rights reserved.

关键词： Sustainable building

NEW CO-SIMULATION VARIANTS FOR EMISSIONS AND COST REDUCTION OF SUSTAINABLE DISTRICT HEATING PLANNING

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arXiv 2025年

作者： Cheng, Haozhen Buccoliero, Verena Kocher, Alexander Hagenmeyer, Veit Çakmak, Hüseyin K. Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

Classical heating of residential areas is very energy-intensive, so alternatives are needed, including renewable energies and advanced heating technologies. Thus, the present paper introduces a new methodology for comprehensive variant analysis for future district heating planning, aiming at optimizing emissions and costs. For this, an extensive Modelica-based modeling study comprising models of heating center, heat grid pipelines and heating interface units to buildings are coupled in co-simulations. These enable a comparative analysis of the economic feasibility and sustainability for various technologies and energy carriers to be carried out. The new modular and highly parameterizable building model serves for validation of the introduced heat grid model. The results show that bio-methane as an energy source reduces carbon equivalent emissions by nearly 70% compared to conventional natural gas heating, and the use of hydrogen as an energy source reduces carbon equivalent emissions by 77% when equipped with a heat pump. In addition, the use of ground source heat pumps has a high economic viability when economic benefits are taken into account. The study findings highlight the importance of strategic planning and flexible design in the early stages of district development in order to achieve improved energy efficiency and a reduced carbon footprint. Copyright © 2025, The Authors. All rights reserved.

关键词： Carbon capture and utilization

A Review on Novel District Concepts in Urban Energy Systems

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SSRN

SSRN 2025年

作者： Galenzowski, Johannes Waczowicz, Simon Çakmak, Hüseyin K. Tajalli-Ardekani, Erfan Beichter, Sebastian Ekin, Ömer Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Institute for Automation and Applied Informatics Germany

Recently, there has been a surge of interest in novel concepts for jointly operating devices in urban areas in clusters, motivated by their potential to support decarbonization, enhance power system flexibility, and promote energy justice. Such clusters encompass multiple devices or buildings but operate on a smaller scale than cities. Examples include Renewable Energy Communities and Positive Energy Districts. These Novel District Concepts (NDCs) integrate interdisciplinary urban planning and social sciences terminology into the energy domain. However, these concept’s precise definitions and practical implementation lack consistency, leading to conceptual ambiguities in the literature. ... (see comment to the editor) © 2025, The Authors. All rights reserved.

关键词： Clean energy

CONSTRUCTION AND CONTROL OF VALIDATED HIGHLY CONFIGURABLE MULTI-PHYSICS BUILDING MODELS FOR MULTI-ENERGY SYSTEM ANALYSIS IN A CO-SIMULATION SETUP

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arXiv 2025年

作者： Cheng, Haozhen Stock, Jan Xhonneux, André Çakmak, Hüseyin K. Hagenmeyer, Veit Forschungszentrum Jülich Jülich52428 Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

Improving energy efficiency by monitoring system behavior and predicting future energy scenarios in light of increased penetration of renewable energy sources are becoming increasingly important, especially for energy systems that distribute and provide heat. On this background, digital twins of cities become paramount in advancing urban energy system planning and infrastructure management. The use of recorded energy data from sensors in district digital twins in collaborative co-simulation platforms is a promising way to analyze detailed system behavior and estimate future scenarios. However, the development and coupling of multi-physics energy system models need to be validated before they can be used for further in-depth analyses. In the present paper, a new multi-physics/-modal and highly configurable building model is presented. Its accuracy and reliability are validated by comparison with data from the TABULA project, ensuring its relevance and applicability to real-world scenarios. The modularity and flexibility with regard to the system configurability of the developed building model is evaluated on various real building types. In addition, the applicability of the building model in a multi-energy system is highlighted by implementing the model in a collaborative co-simulation setup and by coupling it to a district heating grid model in yearly co-simulations. The simulation results for the proposed multi-physical/-modal building modeling concept show a very high level of agreement compared to published reference building data and can therefore be used individually as flexible and modular building models including both thermal and electrical systems for future sector-coupled energy system analyses in view of sustainability. Copyright © 2025, The Authors. All rights reserved.

关键词： District heating

Optimum settings for PID control of nonlinear systems

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arXiv 2025年

作者： Vrabel, R. Slovak University of Technology in Bratislava Institute of Applied Informatics Automation and Mechatronics Bottova 25 Trnava917 01 Slovakia

This paper investigates the application of piecewise linear approximation for the control of nonlinear systems, particularly focusing on the effective linearization of systems modeled by the differential equation y(n) + f(y, y', . . . , y(n-1)) = u(t). We explore the use of PID controllers in conjunction with piecewise linearization, tackling the challenges of nonlinear dynamics by dividing the function f into smaller regions or simplices over a predefined compact set D ⊂ Rn. The parameter h determines the number of linear pieces and influences the approximation accuracy. By increasing h, the system's behavior increasingly mirrors that of the original nonlinear system, leading to enhanced performance of the control system. The paper further discusses the optimization of PID controller parameters through the Particle Swarm Optimization method, with a focus on minimizing the ITAE and ISO criteria. Numerical simulations demonstrate the efficacy of the approach, highlighting the trade-off between computational complexity and approximation accuracy in the context of control system *** Codes 93C10 © 2025, CC BY.

关键词： Three term control systems

Privacy-Preserving Utilization of Distribution System Flexibility for Enhanced TSO-DSO Interoperability: A Novel Machine Learning-Based Optimal Power Flow Approach

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arXiv 2025年

作者： Dindar, Burak Saner, Can Berk Çakmak, Hüseyin K. Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore117581 Singapore

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs)—in our case, distributed generators (DGs)-in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on nonsensitive data, the transfer of sensitive information between stakeholders is prevented. Consequently, even if reverse engineering is applied to these ML models, no sensitive data can be extracted. This allows

关键词： Reverse engineering