Seuraa
Daniel Jung
Daniel Jung
Associate Professor, Linköping University, Sweden.
Vahvistettu sähköpostiosoite verkkotunnuksessa liu.se - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
D Eriksson, E Frisk, M Krysander
Automatica 49 (6), 1591-1600, 2013
1012013
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
912017
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
822017
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
742018
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
632017
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
412015
Residual selection for fault detection and isolation using convex optimization
D Jung, E Frisk
Automatica 97, 143-149, 2018
352018
Data-driven fault diagnosis analysis and open-set classification of time-series data
A Lundgren, D Jung
Control Engineering Practice 121, 105006, 2022
34*2022
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
302020
Structural approach for distributed fault detection and isolation
H Khorasgani, D Jung, G Biswas
IFAC-PapersOnLine 48 (21), 72-77, 2015
262015
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
252016
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
23*2022
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
222021
Sensor selection for fault diagnosis in uncertain systems
D Jung, Y Dong, E Frisk, M Krysander, G Biswas
International Journal of Control, 2018
222018
Quantitative stochastic fault diagnosability analysis
D Eriksson, M Krysander, E Frisk
50th IEEE Conference on Decision and Control, 2011
222011
Drive Scenario Generation Based on Metrics for Evaluating an Autonomous Vehicle Speed Controller For Fuel Efficiency Improvement
S Tamilarasan, D Jung, L Guvenc
WCX18: SAE World Congress Experience, April, 10-12, 2018
20*2018
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
192018
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
192015
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
192014
Structural methodologies for distributed fault detection and isolation
H Khorasgani, G Biswas, D Jung
Applied Sciences 9 (7), 1286, 2019
182019
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Artikkelit 1–20