Daniel Jung

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Erik FriskProfessor, Department of Electrical Engineering, Linköping University, SwedenVerified email at liu.se
Mattias KrysanderAssociate Professor, Department of Electrical Engineering, Linköping University, SwedenVerified email at liu.se
Gautam BiswasCornelius Vanderbilt Professor of Engineering; Professor of Computer Science and EngineeringVerified email at vanderbilt.edu
Hamed KhorasganiWorkdayVerified email at workday.com
Qadeer AhmedAssociate Professor of Research, The Ohio State University, Center for Automotive ResearchVerified email at ieee.org
Christofer SundströmAssociate professor, Vehicular Systems, Department of Electrical Engineering, Linköping UniversityVerified email at liu.se
Lars ErikssonProfessor at Vehicular Systems, Linköping UniversityVerified email at liu.se
Kok Yew Ng (Mark Ng)Senior Lecturer at School of Engineering, Engineering Research Institute, Ulster UniversityVerified email at ulster.ac.uk
Giorgio RizzoniProfessor of Mechanical and Aerospace Engineering, The Ohio State UniversityVerified email at osu.edu
Pierpaolo PolverinoUniversità degli Studi di SalernoVerified email at unisa.it
Cesare PianeseUniversity of Salerno - Dept. of Industrial Engineering - eProLab (Energy and Propulsion Laboratory)Verified email at unisa.it
Pradeep Sharma OrugantiThe Ohio State UniversityVerified email at osu.edu
Eeshan DeosthaleThe Ohio State UniversityVerified email at osu.edu
Xieyuan ZhangWayveVerified email at wayve.ai
Shreshta Rajakumar DeshpandeResearch Engineer, Ford Motor CompanyVerified email at osu.edu
Marcello CanovaThe Ohio State UniversityVerified email at osu.edu
Levent GuvencProfessor of Mechanical and Aerospace Engineering, Ohio State UniversityVerified email at osu.edu
Santhosh TamilarasanDoctoral Scholar at Automated Driving Lab, OSUVerified email at osu.edu
Mukilan ArasuThe Ohio State UniversityVerified email at buckeyemail.osu.edu
Oscar GustafssonLinköping UniversityVerified email at liu.se

Daniel Jung

Associate Professor, Linköping University, Sweden.

Verified email at liu.se - Homepage

Model-based diagnosis data-driven diagnosis Machine learning Optimal control of hybrid electric vehicles


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A method for quantitative fault diagnosability analysis of stochastic linear descriptor models D Eriksson, E Frisk, M Krysander Automatica 49 (6), 1591-1600, 2013	98	2013
A toolbox for analysis and design of model based diagnosis systems for large scale models E Frisk, M Krysander, D Jung IFAC-PapersOnLine 50 (1), 3287-3293, 2017	72	2017
A combined data-driven and model-based residual selection algorithm for fault detection and isolation D Jung, C Sundström IEEE Transactions on Control Systems Technology 27 (2), 616-630, 2017	71	2017
Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation D Jung, KY Ng, E Frisk, M Krysander Control Engineering Practice 80, 146-156, 2018	64	2018
Model-based diagnosis through structural analysis and causal computation for automotive polymer electrolyte membrane fuel cell systems P Polverino, E Frisk, D Jung, M Krysander, C Pianese Journal of Power Sources 357, 26-40, 2017	47	2017
Development of misfire detection algorithm using quantitative FDI performance analysis D Jung, L Eriksson, E Frisk, M Krysander Control Engineering Practice 34, 49-60, 2015	40	2015
Residual selection for fault detection and isolation using convex optimization D Jung, E Frisk Automatica 97, 143-149, 2018	32	2018
Structural approach for distributed fault detection and isolation H Khorasgani, D Jung, G Biswas IFAC-PapersOnLine 48 (21), 72-77, 2015	25	2015
A combined diagnosis system design using model-based and data-driven methods D Jung, KY Ng, E Frisk, M Krysander 2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol), 177-182, 2016	23	2016
Quantitative stochastic fault diagnosability analysis D Eriksson, M Krysander, E Frisk 50th IEEE Conference on Decision and Control, 2011	22	2011
Data-driven open-set fault classification of residual data using Bayesian filtering D Jung IEEE Transactions on Control Systems Technology 28 (5), 2045-2052, 2020	20	2020
Sensor selection for fault diagnosis in uncertain systems D Jung, Y Dong, E Frisk, M Krysander, G Biswas International Journal of Control, 2018	20	2018
Automated design of grey-box recurrent neural networks for fault diagnosis using structural models and causal information D Jung Learning for Dynamics and Control Conference, 8-20, 2022	19*	2022
Mission-based design space exploration for powertrain electrification of series plugin hybrid electric delivery truck D Jung, Q Ahmed, X Zhang, G Rizzoni SAE Technical Paper, 2018	18	2018
Analysis of fault isolation assumptions when comparing model-based design approaches of diagnosis systems D Jung, H Khorasgani, E Frisk, M Krysander, G Biswas IFAC-PapersOnLine 48 (21), 1289-1296, 2015	18	2015
Data-driven fault diagnosis analysis and open-set classification of time-series data A Lundgren, D Jung Control Engineering Practice 121, 105006, 2022	17*	2022
Robust residual selection for fault detection H Khorasgani, DE Jung, G Biswas, E Frisk, M Krysander 53rd IEEE Conference on Decision and Control, 5764-5769, 2014	17	2014
Integrated Approximate Dynamic Programming and Equivalent Consumption Minimization Strategy for Eco-Driving in a Connected and Automated Vehicle S Rajakumar Deshpande, D Jung, M Canova IEEE Transactions on Vehicular Technology 70 (11), 11204 - 11215, 2021	16	2021
Structural methodologies for distributed fault detection and isolation H Khorasgani, G Biswas, D Jung Applied Sciences 9 (7), 1286, 2019	15	2019
Using quantitative diagnosability analysis for optimal sensor placement D Eriksson, M Krysander, E Frisk Proceedings of IFAC Safe Process 2012, 2012	13	2012

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