Co-organized by CCPS laboratory and Hassan II University of Casablanca
will be held during September 22-24, 2022 in Marrakesh, Morocco. SADASC’22 will bring together researchers and industry professionals contributing towards different phases of designing, exploiting and maintaining Smart Cyber Physical Systems and their Applications. These phases include requirements engineering, data acquisition/cleaning, storage, deployment, exploitation, and visualization. Designing these systems also has to consider issues such as ethics, security and privacy. CCPS’2022 follows the success of the Agadir (2016) , Casablanca (2018) and Marakech (2020)
Extended versions of selected papers are considered for publication in international journals indexed Scopus, MDLP, Elsevier, EI..(Special issues & regular)
Progress in Artificial IntelligenceProspective authors are invited to submit original research papers; which are NOT submitted or published or under consideration anywhere in other conferences or journals. Topics of interest include, but are not limited to:
professor and Head of School of Computing, Macquaire University
Dr. Michael Sheng is a full professor and Head of School of Computing, Macquaire University. Before moving to Macquarie University, Michael has spent 10 years at the School of Computer Science, The University of Adelaide, serving in a number of senior leadership roles including Acting Head and Deputy Head of School of Computer Science. Michael obtained his PhD in Computer Science from the University of New South Wales (UNSW) and did his Postdoc as a Research Scientist at CSIRO ICT Centre. From 1999 to 2001, Michael also worked at UNSW as a Visiting Research Fellow. Prior to that, he spent 6 years as a Senior Software Engineer in industries. Professor Michael Sheng's research interests include IoT, services computing, and big data analytics. Michael is ranked by Microsoft Academic as one of the Most Impactful Authors in Services Computing (ranked Top 5 All Time) and in Web of Things (ranked Top 20 All Time). He is the recipient of the AMiner Most Influential Scholar Award on IoT (2007 - 2017), ARC Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003). Professor Sheng has authored over 450 publications in highly influential journals and conferences, including ACM Computing Surveys, ACM TOIT, TOMM, TKDD, VLDB Journal, IEEE TKDE, TPDS, TMC, TSC, WWWJ, IEEE Computer, IEEE Internet Computing, Communications of the ACM, VLDB, ICDE, ICDM, IJCAI, AAAI, CIKM, EDBT, WWW, ICSE, ICSOC, ICWS, and CAiSE. He has given keynote and invited talks over 50 conferences and has delivered research tutorials at several premier conferences including AAAI, IJCAI, ICSOC, IEEE BigData, and WISE.
Research Professor at Ikerbasque, Basque Foundation for Science, Bilbao, Spain, and the University of the Basque Country UPV/EHU, San Sebastian, Spain.
Fadi Dornaika received the M.S. in Signal, Image and Speech Processing from Grenoble Institute of Technology, Grenoble, France, in 1992, and the Ph.D. in Computer Science from INRIA and Grenoble Institute of Technology, Grenoble, France, in 1995. He has held various research positions in Europe, China and Canada. He is currently a Research Professor at Ikerbasque, Basque Foundation for Science, Bilbao, Spain, and the University of the Basque Country UPV/EHU, San Sebastian, Spain. He has published more than 300 papers in the field of computer vision, pattern recognition and machine learning, including 120 indexed journal articles in (IEEE Trans. Robotics & Automation, IEEE Trans. Cybernetics, IEEE Trans. Neural Networks and Learning Systems, IEEE Trans. CSVT, IEEE Trans. SMC, Information Fusion, Information Sciences, Neural Networks, Pattern Recognition, Artificial Intelligence Review, Knowledge-Based Systems, Int. Journal of Computer Vision, Int. Journal of Robotics Research, etc.). His current research interests include: Manifold Learning, Supervised Learning, Multiview Clustering, Scalable Semi-Supervised Learning, Structured Semi-Supervised Learning, Deep Learning with applications to facial age estimation, facial beauty prediction, emotion recognition, fatigue detection, and kinship verification.
Professor and previous Head of School of Computer Science, at the University of Sydney, Australia.
Athman Bouguettaya is Professor and previous Head of School of Computer Science, at the University of Sydney, Australia. He was previously Professor and Head of School of Computer Science and IT at RMIT University, Melbourne, Australia. He received his PhD in Computer Science from the University of Colorado at Boulder (USA) in 1992. He was previously Science Leader in Service Computing at the CSIRO ICT Centre (now DATA61), Canberra. Australia. Before that, he was a tenured faculty member and Program director in the Computer Science department at Virginia Polytechnic Institute and State University (commonly known as Virginia Tech) (USA). He is a founding member and past President of the Service Science Society, a non-profit organization that aims at forming a community of service scientists for the advancement of service science. He is or has been on the editorial boards of several journals including, the IEEE Transactions on Services Computing, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Internet Technology, The ACM Computing Surveys, the International Journal on Next Generation Computing, VLDB Journal, Distributed and Parallel Databases Journal, and the International Journal of Cooperative Information Systems.
EAH Jena University of Applied Sciences, Jena, Germany, Manufacturing and assembly Professor
Tobias Pfeifroth is a professor at EAH Jena at the SchooI of Industrial Engineering and heads the “Assembly Technology” research group. He is also the manager of the Steinbeis "Smart Production" research center. His research focuses on assisting workers in manual assembly operations using new digital tools. Prior to becoming a professor, he held various positions in industry, including at ZF and Bosch Rexroth. He defended his doctoral thesis at the Institute for Machine Tools at the University of Stuttgart and then worked as a group director at the Fraunhofer Institute for Manufacturing Engineering and Automation IPA.
Laval University, Canada
Abstract : This tutorial will introduce and compare model-based and data-driven approaches used for predictive maintenance (PdM) in the Industry 4.0. In model-based approaches, the system degradation is modelled as a stochastic process using historical reliability data. Data-driven approaches have recently emerged and are suitable for the systems where it is possible to obtain monitoring data that represent the behavior of the degradation phenomenon. These data can be used for example to predict the system remaining useful life without knowing the physical nature of the degradation mechanism. The performance of a data-driven approach strictly depends on signal processing and feature engineering techniques. Besides, the revolution of Industry 4.0 provides more convenient supports for the wide development of the data-driven PdM in practice. As sensor technology continue to gain importance, the data-driven perspective has become a relevant approach for improving the quality of maintenance for machines and processes in industrial environments. The tutorial will also present a comprehensive outlook of the current PdM issues, challenges and opportunities, and will outline some research directions for the successful development and deployment of IoT-enabled PdM in industry.
Professeur à ULCO
Abstract : Clustering algorithms are based on implicit assumptions about the properties of data, in particular the similarity between data pairs and the separability between data groups. So, the performance of clustering methods strongly depends on the representation space of these data. For this, feature extraction/selection methods have been widely used as pre-clustering steps to map input data into a feature space where separation is easier. However, recent works show that jointly optimizing for both can yield better results. We propose to use convolutional autoencoder to learn clustering representations of the data by optimizing it with a two-phased training procedure. In the first phase, the autoencoder is trained with a standard reconstruction loss while, in the second phase, the autoencoder is fine-tuned with a combined loss function consisting of the autoencoder reconstruction loss and a clustering-specific loss.
Former director of QUARTZ EA 7393 Centrale Nantes
Abstract : This tutorial will deals with the design of an active fault-tolerant control for a twin wind turbine consisting of two wind turbines mounted on the same tower. When one of the turbines is impacted by electrical faults, precisely a stator inter-turn short circuits fault or a demagnetization fault, the sparse recovery diagnosis method is used for the detection, isolation and estimation of the active fault. An active fault-tolerant control law is designed for the supervision of the faulty machine. Both methods, concerning diagnosis and control, are based on homogeneous theory and consequently converge in finite time. Simulation results highlighting the relevance of the proposed approach are also presented.
director of the Chair on Electric Vehicle (EV) and Hybrid EV (HEV) performances between Renault and Centrale Nantes (CN)
Abstract : The aim of this plenary talk is to present advanced methodologies and technologies we developed in the control of propulsion systems at Central Nantes in collaboration with Renault. The principle objectives behind that developments are to reduce the cost of electric and hybrid powertrains in order to rendre them affordable while respecting a high level of requirements on their performance, operational safety and reliability. This talk will focus on the developments made within three main reacherach axis : Control of AC machines and static converters for EV powertrains; Energy management and control of EV charging and Energy optimization of hybrid powertrain systems. Moreover, a program training E-PICo lunched in 2020 (founded in 2019 by EU) will be prensted as well as the test benches we setup for real-time implementation with scale 1 of the developed advanced methodologies.
LISIC- IMAP team Université du Littoral Cote d'Opale
Abstract : Optimal transport is a old problem, formulated by Monge in the 18th century. Originaly, It consists in looking for the most economical way to transport objects between a set of departure and arrival points. At that time, Monge had concerns rather focused on civil engineering and formulated this problem as finding the minimum cost to move piles of sand from one area and fill holes in another area. However, when dimensionality increases, this problem becomes untractable.
Honorary Chairs | Program Cochairs | General Chair |
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Prof. Houssine AZEDDOUG
President of Hassan II University of Casablanca (UH2C) Prof. Ahmed MOUCHTACHIDirector of ENSAM, Casablanca Prof. EL Mouden El HassanDEAN of FSTM, Marakech Prof. Abdelilah BENNISDirector of HESTIM Prof. Omar BouattaneDirector of ENSET Mohammedia |
Ali Siadat
ENSAM, METZ Sebastian VenturaUniversity of Cordoba, Spain Ladjel BellatrecheNational Engineering School for Mechanics and Aerotechnics (ENSMA), Poitiers. |
Mohamed Hamlich
ENSAM, Hassan II University, Morocco Director of CCPS LAB |
Chair of XAI Workshop "Explainable Artificial Intelligence" | |
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Mohamed RAMDANI, FSTM, UH2C, MOROCCO |
Demo and Video Chairs | |
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Abdelhafid AITELMAHJOUB, ENSAM, UH2C, MOROCCO | |
Mourad ZEGRARI, ENSAM, UH2C, MOROCCO |
Doctoral Consortium Chairs | |
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Djamal BENSLIMANE, IUT, Université Claude Bernard Lyon, FRANCE | |
Abdelwahed EL HASSAN, Cadi Ayyad University, MOROCCO |
Publicity Chairs | |
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Jaafar GABER, UTBM, FRANCE | |
Soad TAYANE, ENSAM, UH2C, MOROCCO | |
Nabila RABBAH, UH2C, MOROCCO |
Proceedings Chairs | |
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Ladjel Bellatreche, ISAE-ENSMA, FRANCE | |
Hamad DENIS, ULCO, FRANCE |
Sponsorship Chairs | |
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Sanaa ELFILALI, FSBM, MOROCCO | |
Nadia MACHKOUR, ENSAM, UH2C, MOROCCO | |
Mohamed ENNAJI, ENSAM, UH2C, MOROCCO |
Local and Registration Chair | |
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Hicham MOUTACHAOUIK, ENSAM, UH2C, MOROCCO | |
Mohamed MOUTCHOU, ENSAM, UH2C, MOROCCO |
Social media chairs | |
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Redouane MAJDOUL, ENSAM, UH2C, MOROCCO | |
Aziz EL AFIA, ENSAM, UH2C, MOROCCO | |
Abdelwahed TOUATI, ENSAM, UH2C, MOROCCO |
Organizing Local Committee | |
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Juniors | Seniors |
A. ELMOUTAOUKKIL
ENSAM, Casablanca M. ChrissENSAM, Casablanca I. YoubENSAM, Casablanca M. AyadENSAM, Casablanca R. MadihaENSAM, Casablanca S. HajarENSAM, Casablanca O. BEN-SAIDENSAM, Casablanca Hamlich EliasENSAM, Casablanca |
M.Hamlich
ENSAM, Casablanca N.MachkourENSAM, Casablanca M.ZegrariENSAM, Casablanca A.AitelmahjoubENSAM, Casablanca Radouane MajdoulENSAM, Casablanca Nabila RabbahENSAM, Casablanca M.ENNAJIENSAM, Casablanca Abdessamad belangourFSBM, UH2C, Morocco Abderrahim FATIHIENSAM, Casablanca Sara QASSIMIFST, Cadi Ayyad University Houda MOURADICERIM, HESTIM, Casablanca Benaissa KissiENSAM, Casablanca A.Abourriche
ENSAM, Casablanca H.MoutachaouikENSAM, Casablanca M.MoutchouENSAM, Casablanca M.RamdaniFST, Mohammedia S.TayaneFSBM, Casablanca Aziz El AfiaENSAM, Casablanca Abdelwahed TouatiENSAM, Casablanca Mme. Kaoutar NajadCCoA/Steinbeis, Germany Bouchra BESSASENSAM, Casablanca El Hassan AbdelwahedFSS, Cadi Ayyad University Chiraz BRIGUICERIM, HESTIM, Casablanca Raihani AbdelhadiENSET, UH2C, MOROCCO |