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Documents authored by Jung, Daniel

Document
DX Competition
DOI: 10.4230/OASIcs.DX.2025.14
The DX Competition 2025 and Its Benchmarks (DX Competition)

Authors: Ingo Pill, Daniel Jung, Eldin Kurudzija, Anna Sztyber-Betley, Michał Syfert, Kai Dresia, Günther Waxenegger-Wilfing, and Johan de Kleer

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
Fault diagnosis has been addressed in many research communities, leading to a variety of available fault diagnosis techniques. Deciding as a user which fault diagnosis methods are suitable for a specific application is thus a nontrivial task. Benchmarks can provide the community with a holistic understanding of the landscape of newly developed and available fault diagnosis methods when making this decision. After a long hiatus, we revived the DX Competition with three fault diagnosis benchmarks: SLIDe, LUMEN, and LiU-ICE. The purpose of the benchmarks is to inspire fault diagnosis research with challenging problems in cyber-physical systems relevant for industry. The benchmarks share a common code structure and we used similar performance metrics in order to simplify the adaptation of diagnosis system solutions to the different case studies.
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Ingo Pill, Daniel Jung, Eldin Kurudzija, Anna Sztyber-Betley, Michał Syfert, Kai Dresia, Günther Waxenegger-Wilfing, and Johan de Kleer. The DX Competition 2025 and Its Benchmarks (DX Competition). In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 14:1-14:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{pill_et_al:OASIcs.DX.2025.14,
  author =	{Pill, Ingo and Jung, Daniel and Kurudzija, Eldin and Sztyber-Betley, Anna and Syfert, Micha{\l} and Dresia, Kai and Waxenegger-Wilfing, G\"{u}nther and de Kleer, Johan},
  title =	{{The DX Competition 2025 and Its Benchmarks}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{14:1--14:19},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.14},
  URN =		{urn:nbn:de:0030-drops-248030},
  doi =		{10.4230/OASIcs.DX.2025.14},
  annote =	{Keywords: Diagnosis, Algorithms, Evaluation}
}
Document
DOI: 10.4230/OASIcs.DX.2024.4
A Study on Redundancy and Intrinsic Dimension for Data-Driven Fault Diagnosis

Authors: Daniel Jung and David Axelsson

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract
Data-driven fault diagnosis of technical systems use training data from nominal and faulty operation to train machine learning models to detect and classify faults. However, data-driven fault diagnosis is complicated by the fact that training data from faults is scarce. The fault diagnosis task is often treated as a standard classification problem. There is a need for methods to design fault detectors using only nominal data. In model-based diagnosis, the ability construct fault detectors depends on analytical redundancy properties. While analytical redundancy is a model property, it describes the diagnosability properties of the system. In this work, the connection between analytical redundancy and the distribution of observations from the system on low-dimensional manifolds in the observation space is studied. It is shown that the intrinsic dimension can be used to identify signal combinations that can be used for constructing residual generators. A data-driven design methodology is proposed where data-driven residual generators candidates are identified using the intrinsic dimension. The method is evaluated using two case studies: a simulated model of a two-tank system and data collected from a fuel injection system. The results demonstrate the ability to diagnose abnormal system behavior and reason about its cause based on selected signal combinations.
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Daniel Jung and David Axelsson. A Study on Redundancy and Intrinsic Dimension for Data-Driven Fault Diagnosis. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 4:1-4:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{jung_et_al:OASIcs.DX.2024.4,
  author =	{Jung, Daniel and Axelsson, David},
  title =	{{A Study on Redundancy and Intrinsic Dimension for Data-Driven Fault Diagnosis}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{4:1--4:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.4},
  URN =		{urn:nbn:de:0030-drops-220964},
  doi =		{10.4230/OASIcs.DX.2024.4},
  annote =	{Keywords: Data-driven diagnosis, intrinsic dimension, model-based diagnosis, structural methods}
}
Document
DOI: 10.4230/LIPIcs.OPODIS.2022.15
A Unifying Approach to Efficient (Near)-Gathering of Disoriented Robots with Limited Visibility

Authors: Jannik Castenow, Jonas Harbig, Daniel Jung, Peter Kling, Till Knollmann, and Friedhelm Meyer auf der Heide

Published in: LIPIcs, Volume 253, 26th International Conference on Principles of Distributed Systems (OPODIS 2022)

Abstract
We consider a swarm of n robots in a d-dimensional Euclidean space. The robots are oblivious (no persistent memory), disoriented (no common coordinate system/compass), and have limited visibility (observe other robots up to a constant distance). The basic formation task Gathering requires that all robots reach the same, not predefined position. In the related NearGathering task, they must reach distinct positions in close proximity such that every robot sees the entire swarm. In the considered setting, Gathering can be solved in 𝒪(n + Δ²) synchronous rounds both in two and three dimensions, where Δ denotes the initial maximal distance of two robots [Hideki Ando et al., 1999; Michael Braun et al., 2020; Bastian Degener et al., 2011]. In this work, we formalize a key property of efficient Gathering protocols and use it to define λ-contracting protocols. Any such protocol gathers n robots in the d-dimensional space in 𝒪(Δ²) synchronous rounds, for d ≥ 2. For d = 1, any λ-contracting protocol gathers in optimal time 𝒪(Δ). Moreover, we prove a corresponding lower bound stating that any protocol in which robots move to target points inside the local convex hulls of their neighborhoods - λ-contracting protocols have this property - requires Ω(Δ²) rounds to gather all robots (d > 1). Among others, we prove that the d-dimensional generalization of the GTC-protocol [Hideki Ando et al., 1999] is λ-contracting. Remarkably, our improved and generalized runtime bound is independent of n and d. We also introduce an approach to make any λ-contracting protocol collision-free (robots never occupy the same position) to solve NearGathering. The resulting protocols maintain the runtime of Θ (Δ²) and work even in the semi-synchronous model. This yields the first NearGathering protocols for disoriented robots and the first proven runtime bound. In particular, combined with results from [Paola Flocchini et al., 2017] for robots with global visibility, we obtain the first protocol to solve Uniform Circle Formation (arrange the robots on the vertices of a regular n-gon) for oblivious, disoriented robots with limited visibility.
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Jannik Castenow, Jonas Harbig, Daniel Jung, Peter Kling, Till Knollmann, and Friedhelm Meyer auf der Heide. A Unifying Approach to Efficient (Near)-Gathering of Disoriented Robots with Limited Visibility. In 26th International Conference on Principles of Distributed Systems (OPODIS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 253, pp. 15:1-15:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{castenow_et_al:LIPIcs.OPODIS.2022.15,
  author =	{Castenow, Jannik and Harbig, Jonas and Jung, Daniel and Kling, Peter and Knollmann, Till and Meyer auf der Heide, Friedhelm},
  title =	{{A Unifying Approach to Efficient (Near)-Gathering of Disoriented Robots with Limited Visibility}},
  booktitle =	{26th International Conference on Principles of Distributed Systems (OPODIS 2022)},
  pages =	{15:1--15:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-265-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{253},
  editor =	{Hillel, Eshcar and Palmieri, Roberto and Rivi\`{e}re, Etienne},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2022.15},
  URN =		{urn:nbn:de:0030-drops-176350},
  doi =		{10.4230/LIPIcs.OPODIS.2022.15},
  annote =	{Keywords: mobile robots, gathering, limited visibility, runtime, collision avoidance, near-gathering}
}
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