Fault tolerant control for distributed systems: theory and applications.
Christophe Aubrun received the Doctorat d’Université in 1993 from the University Henri Poincaré, Nancy I, France. Since 2003 he is a full Professor at University Henri Poincaré, Nancy I, where he teaches Automatic Control. He has been the Director of the Institut Universitaire Professionnalisé in Electrical engineering and head of the department of Electrical Engineering of the Institut Universitaire de Technologie of Nancy. He is a member of the Research Centre In Automatic Control of Nancy (CRAN). His current research interests are focused on model-based fault diagnosis and fault tolerant with emphasis on networked control systems and continuous commissioning of building energy system.
Some recent publications:
- Shaikshavali Chitraganti, Samir Aberkane, Christophe Aubrun. Robust stabilization of a class of state-dependent jump linear systems, Nonlinear Analysis: Hybrid Systems, Elsevier, 2015, 18, pp.48-59.
- Marcin Witczak, Mariusz Buciakowski, Christophe Aubrun. Predictive actuator fault-tolerant control under ellipsoidal bounding. International Journal of Adaptive Control and Signal Processing, Wiley-Blackwell, 2015,
- Shaikshavali Chitraganti, Samir Aberkane, Christophe Aubrun, Guillermo Valencia-Palomo, Vasile Dragan. On control of discrete-time state-dependent jump linear systems with probabilistic constraints: A receding horizon approach, Systems and Control Letters, Elsevier, 2014, 74, pp.81-89.
- Boumedyen Boussaid, Christophe Aubrun, Jin Jiang, Naceur Abdelkrim. FTC approach with actuator saturation avoidance based on reference management, International Journal of Robust and Nonlinear Control, Wiley-Blackwell, 2014, 24 (17), pp.2724-2740.
Book: Co-design Approaches to Dependable Networked Control Systems
Daniel Simon, Ye-Qiong Song, Christophe Aubrun
January 2010, Wiley-ISTE
Engineering and production of advanced electronics for HMI
Lothar Seybold was born in Germany in 1970, received the Dipl-Ing. degree in electronic engineering from Hochschule Ravensburg– Weingarten (Germany) and the M.B.A. degree in business integration from the University of Wurzburg (Germany) in 1998 and 2003, respectively. In 2015 he received a Ph.D. degree in control engineering and robotics from the University of Zielona G´ora (Poland). Lothar Seybold has worked as a design engineer and a consultant for several international companies like ABB AG (Switzerland) and Integrated Systems AG (Germany). Since 2005 he has been in charge of innovation and R&D activities for RAFI GmbH & Co. KG (Germany), currently he serves as a Vice President. His current research interests include industrial controls and automation, artificial intelligence, fault detection and isolation (FDI) and fault-tolerant control (FTC). Lothar Seybold has published more than 20 papers in international journals and conference proceedings.
Classification of Imbalanced Data: Studying Data Characteristics and Improving Performance of Classifiers
Jerzy Stefanowski is a professor of Poznan University of Technology, Institute of Computing Science. He received his Ph.D. and Habilitation degrees from the same University. His research interests include knowledge discovery, data mining, machine learning and intelligent decision support. Major results are concerned with: induction of various types of rules, multiple classifiers, mining class – imbalanced data, incremental learning from evolving data streams, data preprocessing, generalizations of rough set theory, descriptive clustering of documents and medical applications. He is the author and co-author of over 170 research papers and 2 books. In addition to his research activities he served in a number of organizational capacities, as e.g.: Former President of Wielkopolska Regional Branch of Polish Information Processing Society (2006-2011), current member of the Executive Board of Polish Artificial Intelligence Society; co-founder and co-leader of Polish Special Interest Group on Machine Learning. Moreover, since 2012 he is the Editor in Chief of Foundations of Computing and Decision Science journal.