Daniel Jung

882 total citations
58 papers, 603 citations indexed

About

Daniel Jung is a scholar working on Control and Systems Engineering, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Daniel Jung has authored 58 papers receiving a total of 603 indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Control and Systems Engineering, 16 papers in Artificial Intelligence and 9 papers in Automotive Engineering. Recurrent topics in Daniel Jung's work include Fault Detection and Control Systems (40 papers), Machine Fault Diagnosis Techniques (20 papers) and Anomaly Detection Techniques and Applications (9 papers). Daniel Jung is often cited by papers focused on Fault Detection and Control Systems (40 papers), Machine Fault Diagnosis Techniques (20 papers) and Anomaly Detection Techniques and Applications (9 papers). Daniel Jung collaborates with scholars based in Sweden, United States and Malaysia. Daniel Jung's co-authors include Erik Frisk, Mattias Krysander, Gautam Biswas, Kok Yew Ng, Hamed Khorasgani, Qadeer Ahmed, Pierpaolo Polverino, Cesare Pianese, Lars Eriksson and Giorgio Rizzoni and has published in prestigious journals such as Journal of Power Sources, Automatica and Mechanical Systems and Signal Processing.

In The Last Decade

Daniel Jung

51 papers receiving 582 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel Jung Sweden 13 439 130 107 85 82 58 603
Manuel Arias Chao Switzerland 8 362 0.8× 88 0.7× 73 0.7× 107 1.3× 61 0.7× 17 584
Setu Madhavi Namburu United States 10 283 0.6× 80 0.6× 116 1.1× 55 0.6× 92 1.1× 17 466
Zhenyu Wang China 13 290 0.7× 46 0.4× 111 1.0× 71 0.8× 57 0.7× 76 538
Mansour Hajji Tunisia 14 426 1.0× 165 1.3× 293 2.7× 99 1.2× 63 0.8× 42 769
Radhia Fezai Qatar 11 358 0.8× 82 0.6× 137 1.3× 158 1.9× 29 0.4× 31 506
Stefan Byttner Sweden 11 194 0.4× 108 0.8× 60 0.6× 64 0.8× 82 1.0× 43 479
Muhammad Abid Pakistan 18 719 1.6× 96 0.7× 162 1.5× 174 2.0× 45 0.5× 76 943
Leïla Hayet Mouss Algeria 12 259 0.6× 155 1.2× 73 0.7× 92 1.1× 41 0.5× 67 518
Kihoon Choi United States 10 266 0.6× 66 0.5× 66 0.6× 53 0.6× 25 0.3× 22 404
Cunsong Wang China 10 292 0.7× 70 0.5× 72 0.7× 83 1.0× 73 0.9× 37 471

Countries citing papers authored by Daniel Jung

Since Specialization
Citations

This map shows the geographic impact of Daniel Jung's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Jung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Jung more than expected).

Fields of papers citing papers by Daniel Jung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Jung. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Jung. The network helps show where Daniel Jung may publish in the future.

Co-authorship network of co-authors of Daniel Jung

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Jung. A scholar is included among the top collaborators of Daniel Jung based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Jung. Daniel Jung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jung, Daniel, et al.. (2025). Uncertainty-aware fault diagnosis of unknown faults using ensemble-based NODE residuals. Mechanical Systems and Signal Processing. 242. 113599–113599.
2.
Jung, Daniel, et al.. (2025). Learning robust residuals for attack diagnosis of advanced driver assist systems. Control Engineering Practice. 162. 106366–106366.
3.
Krysander, Mattias, et al.. (2025). Consistency-based diagnosis using data-driven residuals and limited training data. Control Engineering Practice. 159. 106283–106283. 1 indexed citations
4.
Jung, Daniel, et al.. (2024). Fuel injection fault diagnosis using structural analysis and data-driven residuals. IFAC-PapersOnLine. 58(4). 360–365. 1 indexed citations
5.
Jung, Daniel, et al.. (2024). The Electrochemical Commercial Vehicle (ECCV) Platform. Energies. 17(7). 1742–1742. 3 indexed citations
6.
Jung, Daniel, et al.. (2023). Analysis of Tariffs and the Impact on Voltage Variations in Low-Voltage Grids with Smart Charging and Renewable Energy. Energies. 16(22). 7648–7648. 3 indexed citations
7.
Ahmed, Qadeer, et al.. (2023). Enhancing the Security of Automotive Systems Using Attackability Index. IEEE Transactions on Intelligent Vehicles. 9(1). 315–327. 2 indexed citations
8.
Jung, Daniel, et al.. (2021). Data-driven fault diagnosis analysis and open-set classification of time-series data. Control Engineering Practice. 121. 105006–105006. 59 indexed citations
9.
Jung, Daniel, et al.. (2021). Discrete Fault Diagnosis of Structurally Reconfigurable Systems. Journal of Dynamic Systems Measurement and Control. 143(10).
10.
Jung, Daniel, et al.. (2020). A forest-based algorithm for selecting informative variables using Variable Depth Distribution. Engineering Applications of Artificial Intelligence. 97. 104073–104073. 4 indexed citations
11.
Jung, Daniel. (2020). Distributed Feature Selection for Multi-Class Classification Using ADMM. IEEE Control Systems Letters. 5(3). 821–826. 7 indexed citations
12.
Jung, Daniel. (2020). Data-Driven Open-Set Fault Classification of Residual Data Using Bayesian Filtering. IEEE Transactions on Control Systems Technology. 28(5). 2045–2052. 29 indexed citations
13.
Krausmann, Elisabeth, et al.. (2020). Core industrial and energy facilities. Biblos-e Archivo (Universidad Autónoma de Madrid). 268–284. 9 indexed citations
14.
Khorasgani, Hamed, Gautam Biswas, & Daniel Jung. (2019). Structural Methodologies for Distributed Fault Detection and Isolation. Applied Sciences. 9(7). 1286–1286. 9 indexed citations
15.
Jung, Daniel. (2019). Isolation and Localization of Unknown Faults Using Neural Network-Based Residuals. Annual Conference of the PHM Society. 11(1). 8 indexed citations
16.
Jung, Daniel, Yi Dong, Erik Frisk, Mattias Krysander, & Gautam Biswas. (2018). Sensor selection for fault diagnosis in uncertain systems. International Journal of Control. 93(3). 629–639. 18 indexed citations
17.
Jung, Daniel, et al.. (2017). A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation. IEEE Transactions on Control Systems Technology. 27(2). 616–630. 62 indexed citations
18.
Jung, Daniel, et al.. (2016). Analysis of optimal energy management in smart homes using MPC. 5. 2066–2071. 7 indexed citations
19.
Jung, Daniel. (2015). Diagnosability performance analysis of models and fault detectors. Linköping studies in science and technology. Dissertations. 8 indexed citations
20.
Lee, Jong‐Seok, et al.. (2008). Preventing ELF(Executable and Linking Format)-File-Infecting Malware using Signature Verification for Embedded Linux. Jeongbo gwahaghoe nonmunji. keompyuting ui silje. 14(6). 589–593. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026