@article{WeidlMadsenDahlquist2002, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Condition Monitoring, Root Cause Analysis and Decision Support on Urgency of Actions}, series = {Book Series FAIA (Frontiers in Artificial Intelligence and Applications), Soft Computing Systems - Design, Management and Applications}, volume = {2002}, journal = {Book Series FAIA (Frontiers in Artificial Intelligence and Applications), Soft Computing Systems - Design, Management and Applications}, number = {87}, pages = {221 -- 230}, year = {2002}, abstract = {We discuss the use of a hybrid system utilizing Object Oriented Bayesian networks and influence diagrams for probabilistic reasoning under uncertainties in industrial process operations. The Bayesian networks are used for condition monitoring and root cause analysis of process operation. The recommended decision sequence of corrective actions and observations is obtained following the "myopic" approach. The BN inference on most probable root cause is used in an influence diagram for taking decisions on urgency of corrective actions vs. delivery deadline. The build-in chain of causality from root cause to process faults can provide the user with explanation facility and a simulation tool of the effect of intended actions.}, subject = {Prozesssteuerung}, language = {en} } @inproceedings{ValenciaNormannSapsaietal.2023, author = {Valencia, Yeimy and Normann, Marc and Sapsai, Iryna and Abke, J{\"o}rg and Madsen, Anders L. and Weidl, Galia}, title = {Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style}, series = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, June 2023}, booktitle = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, June 2023}, doi = {10.1145/3593663.3593685}, pages = {73 -- 82}, year = {2023}, subject = {Lernstil}, language = {en} } @inproceedings{WeidlMadsenDahlquist2003, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Object Oriented Bayesian Networks for Industrial Process Operation}, series = {Proceedings of the first Bayesian Application Modeling Workshop at the 19th Conference in Uncertainty in Artificial Intelligence, 2003}, booktitle = {Proceedings of the first Bayesian Application Modeling Workshop at the 19th Conference in Uncertainty in Artificial Intelligence, 2003}, year = {2003}, abstract = {We present an application, where extensions of existing methods for decision-theoretic troubleshooting are used for industrial process operation and asset management. The extension includes expected average cost of asset management actions, prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand.}, subject = {Prozesssteuerung}, language = {en} } @article{WeidlMadsenIsraelson2005, author = {Weidl, Galia and Madsen, Anders L. and Israelson, S.}, title = {Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes}, series = {Computers \& Chemical Engineering}, volume = {2005}, journal = {Computers \& Chemical Engineering}, number = {29/9}, doi = {10.1016/j.compchemeng.2005.05.005}, pages = {1996 -- 2009}, year = {2005}, abstract = {The increasing complexity of large-scale industrial processes and the struggle for cost reduction and higher profitability means automated systems for processes diagnosis in plant operation and maintenance are required. We have developed a methodology to address this issue and have designed a prototype system on which this methodology has been applied. The methodology integrates decision-theoretic troubleshooting with risk assessment for industrial process control. It is applied to a pulp digesting and screening process. The process is modeled using generic object-oriented Bayesian networks (OOBNs). The system performs reasoning under uncertainty and presents to users corrective actions, with explanations of the root causes. The system records users' actions with associated cases and the BN models are prepared to perform sequential learning to increase its performance in diagnostics and advice.}, subject = {Prozess{\"u}berwachung}, language = {en} } @inproceedings{WeidlMadsenDahlquist2003, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Applications of object-oriented Bayesian networks for causal analysis of process disturbances}, series = {SIMS'2003 (44th International Conference of the Scandinavian Simulation Society)}, booktitle = {SIMS'2003 (44th International Conference of the Scandinavian Simulation Society)}, year = {2003}, abstract = {We discuss a hybrid approach for causal analysis of disturbances in industrial process operation. It represents a combination of OOBN with first level diagnostic packages and physical models serving as agents in the system design and providing evidence for automated reasoning on abnormality in process operation. The aim is causal analysis of non-measurable disturbances as a decision advice complement to the distributed control system (DCS). The approach includes prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand. The methodology has been applied on a screening process with a pressure-flow network in a Pulp Mil.}, subject = {Prozessmodell}, language = {en} } @incollection{WeidlMadsenDahlquist2008, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Decision Support on Complex Industrial Process Operation}, series = {Bayesian Networks: A Practical Guide to Applications}, booktitle = {Bayesian Networks: A Practical Guide to Applications}, publisher = {Wiley}, doi = {10.1002/9780470994559.ch18}, pages = {313 -- 328}, year = {2008}, abstract = {Introduction: A methodology for Root Cause AnalysisPulp and paper applicationThe ABB Industrial IT platformConclusion}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{WeidlMadsenKasparetal.2014, author = {Weidl, Galia and Madsen, Anders L. and Kaspar, Dietmar and Breuel, Gabi}, title = {Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements}, series = {The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014)}, booktitle = {The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014)}, doi = {10.1109/ISIC.2014.6967630}, year = {2014}, abstract = {An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their impact on the memory and time requirements of an ECU-processor for automotive applications. New challenges are faced, when the prediction horizon is to be further extended.}, subject = {Fahrerassistenzsystem}, language = {en} } @article{WeidlMadsenWangetal.2018, author = {Weidl, Galia and Madsen, Anders L. and Wang, Stevens Ruixi and Kaspar, Dietmar and Karlsen, Martin}, title = {Early and Accurate Recognition of Highway Traffic Maneuvers Considering Real-World Application : A Novel Framework Using Bayesian Networks}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2018}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {10/3}, doi = {10.1109/MITS.2018.2842049}, pages = {146 -- 158}, year = {2018}, abstract = {This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires efficient and effective analysis of high volume and frequency data streams. The maneuver recognition uses features, analyzing the observed vehicles behavior and available free space on the target lane. Dynamic Bayesian networks (DBN) capture the motion of the own and surrounding vehicles as a dynamic process, following the trend development of lateral motion features. The static and dynamic models for maneuver recognition are statistically evaluated with real highway driving data sequences. The DBN demonstrates earlier recognition (∼ 1.1 seconds) and higher accuracy (1.12\% error rate) with more stable performance than the static models. The system is deployed on an experimental vehicle, where a Divide-and-conquer approach to inference in object-oriented Bayesian networks is introduced and its implementation is shown to require computation time of 0.15 milliseconds and ROM memory ∼ 10 2 kilobytes. The promising performance evaluation results are confirmed by test drives in real highway traffic.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{WeidlMadsenTereshchenkoetal.2018, author = {Weidl, Galia and Madsen, Anders L. and Tereshchenko, Viacheslav and Zhang, Wei and Stevens Ruixi, Wang and Kasper, Dietmar}, title = {Situation Awareness and Early Recognition of Traffic Maneuvers}, series = {Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016}, booktitle = {Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016}, doi = {10.3384/ecp171428}, year = {2018}, abstract = {We outline the challenges of situation awareness with early and accurate recognition of traffic maneuvers and how to assess them. This includes also an overview of the available data and derived situation features,handling of data uncertainties, modelling and the approach for maneuver recognition. An efficient and effective solution, meeting the automotive requirements, is successfully deployed and tested on a prototype car. Test driving results show that earlier recognition of intended maneuver is feasible on average 1 second (and up to 6.72 s) before the actual lane-marking crossing. The even earlier maneuver recognition is dependent on the earlier recognition of surrounding vehicles.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{WeidlMadsenTereshchenkoetal.2015, author = {Weidl, Galia and Madsen, Anders L. and Tereshchenko, Viacheslav and Kaspar, Dietmar and Breuel, Gabi}, title = {Early Recognition of Maneuvers in Highway Traffic}, series = {European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015}, booktitle = {European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015}, doi = {10.1007/978-3-319-20807-7_48}, year = {2015}, abstract = {This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive target platform, which has severe constraints on time and space performance of the system. The test driving has been performed with encouraging results. Even earlier recognition is possible by considering the trend development of features, characterizing the dynamic driving process. The preliminary test results confirm feasibility.}, subject = {Fahrerassistenzsystem}, language = {en} }