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Edwin Lughofer

Edwin Lughofer's picture
edwin.lughofer@jku.at FLLL 043 (0) 7236 3343 - 431
  • imPACts (K-Project): Industrial Methods for Process Analytical Chemistry – From Measurement Technologies to Information Systems (Key Researcher of MP3)
  • mvControl (FFG "IKT of the Future"): Generating process feedback from heterogeneous data sources in quality control; in collaboration with the coordinator Profactor and Sony DADC Austria (Key Researcher)
  • useML (FFG "IKT of the Future"): Improving the usability of machine learning in industrial inspection systems; in collaboration with the coordinator Profactor and 2 industrial partners (Key Researcher)
  • Increasing the Transparency of LCM/ACCM in International Research Fora: Organization and Publication Activities on International Level with the support of Linz Center of Mechatronics / Austrian Center of Competence in Mechatronics
  • HOPL (K-Project): Heuristic Optimization in Production and Logistics
  • TransLearn (SRP) - Transfer Learning with Soft Computing Models for Regression Problems: Strategic Research Project with SCCH
  • AEDA (K-Project): Advanced Engineering Design Automation
  • PAC (K-Project): Process Analytical Chemistry - Data Acquistion and Data Processing (Key Researcher in SP1); National K-Project sponsored by the FFG, 9 industrial and 7 academic research partners
  • IREFS (bilateral FWF/DFG research project): Interpretable and Reliable Evolving Fuzzy Systems (Initiator)
  • Condition Monitoring with Data-Driven Models: Strategic Project with ACCM (Area 6) (Key Researcher)
  • Performance Optimization of Electrical Drives: Strategic Project with ACCM (Area 4) (Key Researcher)
  • ASHMOSD (National Research Project):  Austrian Structural Health Monitoring System Demonstrator
  • DynaVis (EU-Project): Dynamically adaptive image classification framework; combining machine learning with image processing techniques: www.dynavis.org; technical representative of JKU (Key Researcher)
  • SynteX (EU-Project): Measuring Feelings and Expectations Associated with Textures: www.syntex.or.at
  • Technology Transfer sponsored by the Upperaustrian technology and research promotion
  • AMPA (EU-Project): Automatic Measurement Plausibility Analysis at engine test benches: research and development in data-based modelling, nonlinear system identification and fault detection; technical representative of JKU (Key Researcher) in AMPA EU-Project; together with 8 partners in Europe
  • Exchange of know-how in data-driven evolving fuzzy systems with Lancaster University, sponsored by the Royal Society Grant, United Kingdom

 
Activities (Organizing, Editing):

 
Activities (Committee Memberships, Reviewing):

 
Books:
 

 
Book Chapters:
 

  • Edwin Lughofer, Evolving Fuzzy Systems --- Fundamentals, Reliability, Interpretability, Useability, Applications (a comprehensive work of reference), in: Handbook on Computational Intelligence, editor: Plamen Parvanov Angelov, World Scientific, pp. 67-135, 2016
     
  • Edwin Lughofer, Flexible Evolving Fuzzy Inference Systems from Data Streams (FLEXFIS++), in: Learning in Non-Stationary Environments: Methods and Applications, editors: Moamar Sayed-Mouchaweh and Edwin Lughofer, Springer, New York, 2012, pp. 205-246
     
  • Edwin Lughofer, Christian Eitzinger and Carlos Guardiola, On-line Quality Control with Flexible Evolving Fuzzy Systems, in: Learning in Non-Stationary Environments: Methods and Applications, editors: Moamar Sayed-Mouchaweh and Edwin Lughofer, Springer, New York, 2012, pp. 375-406
     
  • Davy Sannen, Jean-Michel Papy, Steve Vandenplas, Edwin Lughofer and Hendrik van Brussel, Incremental Classifier Fusion and its Application in Industrial Monitoring and Diagnostics, in: Learning in Non-Stationary Environments: Methods and Applications, editors: Moamar Sayed-Mouchaweh and Edwin Lughofer, Springer, New York, 2012, pp. 153-184
     
  • Edwin Lughofer, Evolving Fuzzy Models - Incremental Learning, Stability and Interpretability Issues, Applications, VDM Verlag, Saarbrücken, 2008 (book issue of PhD thesis)
     
  • Edwin Lughofer, Data-Driven Incremental Learning of Takagi-Sugeno Fuzzy Models, PhD-Thesis, Department of Knowledge-Based Mathematical Systems, University Linz, 2001-2005

     

  • Edwin Lughofer. Towards Robust Evolving Fuzzy Systems, book chapter in Evolving Intelligent Systems - Methodologies and Applications, editors: Plamen Angelov, Dimitar Filev and Nik Kasabov, John Wiley and Sons, 2010, pp. 87-126
     
  • Erich Peter Klement*, Edwin Lughofer, Johannes Himmelbauer and Bernhard Moser, Data-Driven and Knowledge-Based Modelling, chapter in Hagenberg Research, editors: Michael Affenzeller, Bruno Buchberger, Alois Ferscha, Michael Haller, Tudor Jebelean, Erich Peter Klement, Josef Kueng, Peter Paule, Birgit Proell, Wolfgang Schreiner, Gerhard Weiss, Roland Wagner, Wolfram Woess, Robert Stubenrauch and Wolfgang Windsteiger, Springer Verlag, pp. 237-279, 2009
     
  • Christian Eitzinger*, James E. Smith, Edwin Lughofer and Davy Sannen, Lernfaehige Inspektionssysteme, Automatisierungsatlas, SPS Magazin, 2009, pp. 370-372

 

Position Papers and Editorials:

Journal Papers:

  • Mahardhika Pratama* and Jie Lu and E. Lughofer and G. Zhang and Sreenatha Anavatti, Scaffolding Type-2 Classifier for Incremental Learning under Concept Drifts, NeuroComputing, to appear, 2016
     

  • Eva Weigl*, Wolfgang Heidl, Edwin Lughofer, Christian Eitzinger and Thomas Radauer, On Improving Performance of Surface Inspection Systems by On-line Active Learning and Flexible Classifier Updates, Machine Vision and Applications, vol. 27 (1), pp. 103-127, 2016, doi: 10.1007/s00138-015-0731-9
     

  • Edwin Lughofer*, Carlos Cernuda, Stefan Kindermann and Mahardhika Pratama, Generalized Smart Evolving Fuzzy Systems, Evolving Systems, vol. 6 (4), pp. 269-292, 2015, doi: 10.1007/s12530-015-9132-6
     

  • Edwin Lughofer* and Moamar Sayed-Mouchaweh, Autonomous Data Stream Clustering Implementing Split-and-Merge Techniques - Towards a Plug-and-Play Approach, Information Sciences, vol. 204, pp. 54--79, 2015
     

  • Edwin Lughofer*, Eva Weigl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer, Integrating new Classes On the Fly in Evolving Fuzzy Classifier Designs and Its Application in Visual Inspection, Applied Soft Computing, vol. 35, pp. 558-582, 2015, doi:10.1016/j.asoc.2015.06.038
     

  • Jianli Liu*, Edwin Lughofer and Xianyi Zeng, Aesthetic Perception of Visual Textures: A Holistic Exploration using Texture Analysis, Psychological Experiment and Perception Modeling, Frontiers of Computational Neuroscience, vol. 9:134, pp. 1--14, 2015, http://dx.doi.org/10.3389/fncom.2015.00134
     

  • Carlos Cernuda, Edwin Lughofer*, Thomas Röder, Wolfgang Märzinger, Thomas Reischer, Marcin Pawliczek and Markus Brandstätter, Self-Adaptive Non-Linear Methods for Improved Multivariate Calibration in Chemical Processes, Lenzinger Berichte, vol. 92, pp. 12--32, 2015
     
  • Kurt Pichler*, Edwin Lughofer, Markus Pichler, Thomas Buchegger, Erich Peter Klement and Matthias Huschenbett, Fault detection in reciprocating compressor valves under varying load conditions, Mechanical Systems and Signal Processing, vol. 70-71, pp. 104-119, 2016, doi:10.1016/j.ymssp.2015.09.005
     
  • Mahardhika Pratama*, Sreenatha Anavatti, Edwin Lughofer, C.P. Lim, An Incremental Meta-cognitive-based Scaffolding Fuzzy Neural Network, NeuroComputing, vol. 171, pp. 89-105, 2016, doi:10.1016/j.neucom.2015.06.022
     
  • Alexandru-Ciprian Zavoianu*, Edwin Lughofer, Werner Koppelstaetter, Günther Weidenholzer, Wolfgang Amrhein, Erich Peter Klement, Performance Comparison of Generational and Steady-State Asynchronous Multi-Objective Evolutionary Algorithms for Computationally-Intensive Problems, Knowledge-Based Systems, vol. 87, pp. 47-60, 2015, doi:10.1016/j.knosys.2015.05.029
     
  • Jianli Liu*, Edwin Lughofer, Xianyi Zeng, Could Linear Model Bridge the Gap between Low-level Statistical Features and Aesthetic Emotions of Visual Textures?, NeuroComputing, vol. 168 (30), pp. 947-960, 2015, doi:10.1016/j.neucom.2015.05.030
     
  • Francisco Serdio, Edwin Lughofer*, Kurt Pichler, Markus Pichler, Thomas Buchegger and Hajrudin Efendic, Fuzzy Fault Isolation using Gradient Information and Quality Criteria from System Identification Models, Information Sciences, vol. 316, pp. 18-39, 2015, doi:10.1016/j.ins.2015.04.008
     

* corresponding author(s)
 
 
Selected Conference Papers (2010-2015):  (Full List)
 

  • E. Lughofer, E. Weigl, W. Heidl, C. Eitzinger and T. Radauer, Drift Detection in Data Stream Classification without Fully Labelled Instances, Proceedings of the Evolving and Adaptive Intelligent Systems Conference (EAIS) 2015, Douai, France, pp. 1-8, 2015
  • J. Liu, E. Lughofer, X. Zeng and L. Wang, Affective Property Computation of Visual Texture, Proceedings of the 10'th International Conference on Intelligent Systems and Knowledge Engineering (ISKE'15), Taiwan, November 2015, to appear
     
  • A.-C. Zavoianu, E. Lughofer, G. Bramerdorfer, W. Amrhein and S. Saminger-Platz, A Surrogate-Based Strategy for Multi-Objective Tolerance Analysis in Electrical Machine Design, Proceedings of the 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) 2015, Timisoara, 2015, to appear
     
  • C. Cernuda, E. Lughofer, T. Reischer, W. Kantner, M. Pawliczek and M. Brandstetter, Dynamically Slided Chemometric Models for Robust On-line Prediction of Cloud Point in Melamine Resin Production, Proceedings of the Conferentia Chemometrica, Budapest, September 2015
     
  • C. Cernuda, E. Lughofer, H. Klein, C. Forster, M. Pawliczek and M. Brandstetter, Quantification of quality parameters in unfermented beer using a flexible support vector regression variation, Proceedings of the SSC14 conference, 2015
     
  • E. Lughofer, Efficient Sample Selection in Data Stream Regression using Evolving Generalized Fuzzy Models, Proceedings of the International FUZZ-IEEE Conference 2015, Istanbul, pp. 1-9, 2015
     
  • E. Lughofer, E. Weigl, W. Heidl, C. Eitzinger and T. Radauer, Fast and Economic Integration of New Classes On the Fly in Evolving Fuzzy Classifiers using Class Decomposition, Proceedings of the International FUZZ-IEEE Conference 2015, Istanbul, pp. 1-8, 2015
     
  • G. Bramerdorfer, A.-C. Zavoianu, S. Silber, E. Lughofer, W. Amrhein,  Speed Improvements for the Optimization of Electrical Machines - a Survey, Proceedings of the 2015 IEEE International Conference on Electric Machines Drives Conference (IEMDC), Coeur d'Alene, Idaho, USA, to appear, 2015
     
  • E. Lughofer, E. Weigl, W. Heidl and T. Radauer, Dynamic Inclusion of New Event Types in Visual Inspection using Evolving Classifiers, Proceedings of the International Conference on Machine Learning and Applications (ICMLA 2014), to appear, Detroit, U.S.A., 2014
     
  • F. Serdio, C. Zavoianu, E. Lughofer, K. Pichler, T. Buchegger and H. Efendic, Hybrid Genetic-Fuzzy Systems for Improved Performance in Residual-Based Fault Detection, Proceedings of the 2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 91-96, Porto, Portugal, 2014
     
  • F. Serdio, E. Lughofer, K. Pichler, T. Buchegger, M. Pichler and H. Efendic, Reducing False Positives for Residual-Based On-line Fault Detection by Means of Adaptive Filters, Proceedings of the IEEE SMC 2014 Conference, San Diego, pp. 2803-2808, 2014
     
  • K. Pichler, M. Pichler, E. Lughofer, E.P. Klement, M.Huschenbett, T. Buchegger, On the robustness of fault detection in reciprocating compressor valves, Proceedings of the IEEE SMC 2014 Conference, San Diego, to appear, 2014
     
  • F. Serdio, E. Lughofer, K. Pichler, T. Buchegger, M. Pichler and H. Efendic. Gradient-based Fuzzy Fault Isolation in Residual-based Fault Detection Systems. Proceedings of the WCCI 2014 Conference, Bejing, China, pp. 1428-1435, 2014
     
  • M. Pratama, S.G. Anavatti, M.J. Er and E. Lughofer. A Novel Meta-Cognitive-based Scaffolding Classifier to Sequential Non-stationary Classification Problems.
    Proceedings of the WCCI 2014 Conference, Bejing, China, pp. 369-376, 2014
     
  • C. Cernuda, E. Lughofer, P. Hintenaus, W. Märzinger and J. Kasberger.
    Genetic Hybridization (Hybridgen): A Cooperative Coevolution Algorithm for Variable Selection. Proceedings of the CAC 2014 conference, Richmond, Virginia, 2014, to appear.
     
  • M. Pratama, S.G. Anavatti and E. Lughofer. An Incremental Classifier from Data Streams. in: A. Likas, K. Blekas, and D. Kalles (Eds.): SETN 2014, LNCS 8445, pp. 15--28, Springer International Publishing Switzerland, 2014.
     
  • C. Cernuda, E. Lughofer, P. Hintenaus, W. Maerzinger, T. Reischer and J. Kasberger, Fuzzy Finite State Machine for Multivariate Calibration. Application to Near-Infrared Spectroscopy, Proceedings of the EuroPACT conference, to appear Barcelona, Spain, 2014.
     
  • E. Lughofer, C. Cernuda and M. Pratama, Generalized Flexible Fuzzy Inference Systems from Data Streams, Proceedings of the International Conference on Machine Learning and Applications (ICMLA 2013), pp 1-7, 2013
     
  • A.-C. Zavoianu, E. Lughofer, G. Bramerdorfer, W. Amrhein, and E.P. Klement,  Effective Ensemble-based Method for Creating On-the-Fly Surrogate Fitness Functions for Multi-Objective Evolutionary Algorithms, Proceedings of the 5th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) 2013, Timisoara, Romania, 2013
     
  • A.-C. Zavoianu, E. Lughofer, W. Amrhein and E.P. Klement, Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution, Proceedings of the EUROCAST 2013 Conference, Las Palmas de Gran Canaria, Spain, 2013
     
  • C. Cernuda, E. Lughofer, G. Mayr, T. Röder, P. Hintenaus, W. Märzinger and J. Kasberger, Decremental Active Learning for Optimized Self-Adaptive Calibration in Viscose Production, Proceedings of the SSC 2013 Conference, Stockholm, 2013
     
  • F. Serdio, E. Lughofer, K. Pichler, T. Buchegger, M. Pichler and H. Efendic, Multivariate Fault Detection in the Residual Space using VARMA and Orthogonal Transformations, PHM 2013 Conference, pp. 548-555, New Orleans, 2013