Alois Zoitl holds a PhD degree in Electrical Engineering with focus on dynamic reconfiguration of real-time constrained control applications and a Master degree in Electrical Engineering with the focus on distributed industrial automation systems from Vienna University of Technology. 
Currently he is a Professor for cyber-physical systems for engineering and production with the LIT | CPS Lab at Johannes Kepler University, Linz. Before that he was the scientific research group leader for Industrial Automation at the research institute fortiss in Munich, Germany. Before that he was the head of the research field Distributed Intelligent Automation Systems (Odo Struger Laboratory) at the Automation and Control Institute (ACIN), Vienna University of Technology. 
He is co-author of more than 150 publications (3 books, 6 book chapters, 19 journal articles) and the co-inventor of 4 patents in the mentioned areas. His
research interests are in the area adaptive production systems, distributed control architectures, and dynamic reconfiguration of control applications as well as software development and software quality assurance methods for industrial automation. Alois Zoitl conducted and lead several industry funded R&D projects as well as coordinated and participated in several public funded (national as well as European) R&D projects. 
He is a founding member of the open source initiatives Eclipse 4diac, providing a complete IEC 61499 solution, and OpENer. Furthermore, he is a member of the IEEE, the PLCopen user organization, and GMA. Since 2009 he is an active member of the IEC SC65B/WG15 for the distributed automation standard IEC 61499. He was named convenor of the group in May 2015.
Speech Title: Hic sunt dracones? Developing software for networked production  automation systems
Industry faces major challenges as product life-cycles shorten, product variability increases, and global markets become more volatile. To remain competitive, production facilities and equipment must be adaptable to respond quickly and efficiently to these changes. A key success factor in achieving these goals is the control and automation infrastructure. New distributed architectures are a possible approach to address these requirements. The amount of software in production automation systems is constantly increasing. This is reinforced by the demand for increased networking of these systems. Current technologies are already reaching their limits. This leads to increasing development efforts and costs. It seems as if control software turns into an indomitable beast which is very difficult to control. New interaction and communication patterns as well as new ways of programming automation systems consisting of networked control units are required. In the context of this talk we would like to give an overview of the current and future requirements for production automation systems. The current approaches to programming production automation systems will be considered. In particular, it will be shown how model-driven or low code software development can help to tame the beast and reduce development efforts. An important aspect here is Open Source Software, which still has great potential especially in the production automation system environment.


Shaoying Liu holds a B.Sc and a M.Sc degree in Computer Science from Xi'an Jiaotong University, China, and the Ph.D in Computer Science from the University of Manchester, U.K. He worked as Assistant Lecturer and then Lecturer at Xi'an Jiaotong University, Research Associate at the University of York, and Research Assistant in the Royal Holloway and Bedford New College at the University of London, respectively, in the period of 1982 -1994. He joined the Department of Computer Science at Hiroshima City University as Associate Professor in April 1994, and the Department of Computer Science in the Faculty of Computer and Information Sciences at Hosei University in April 2000. In April 2001 he was promoted to a Professor. From 1st April 2020, he has been working at Hiroshima University as a Professor.
He was invited as a Visiting Research Fellow to The Queen's University of Belfast from December 1994 to February 1995, a Visiting Professor to the Computing Laboratory at the University of Oxford from December 1998 to February 1999, and a Visiting Professor to the Department of Computer Science at the University of York from April 2005 to March 2006. From 2003 he is also invited as an Adjunct Professor to Shanghai Jiaotong University, Xi'an Jiaotong University, Xidian University, and a Visiting Professor to Shanghai University, Xi'an Polytechnic University, Bejing Jiaotong University, and Beijing University in China, respectively.
He is IEEE Fellow, British Computer Society (BCS) Fellow, and member of Japan Society for Software Science and Technology.


Title: Agile Formal Engineering Methods for High Productivity and Reliability

Abstract: With the rapid development and spreading applications of IoT systems and information systems, how to ensure software productivity and reliability has become a tremendous challenge to conventional software engineering. To overcome this challenge, we have developed the “Agile Formal Engineering Methods’’ (AFEM) as a research area since 1989 to study how formal methods can be effectively integrated into conventional software engineering technologies and process models so that formal techniques can be tailored, revised, or extended to fit the need for improving software productivity and reliability in practice (e.g., through the enhancement of the usability of formalism and the tool supportability of the relevant methods). As a result of our efforts, we have developed a specific AFEM called Agile Structured Object-Oriented Formal Language (Agile-SOFL) that offers a Three-Step Specification Approach, Specification Animation for Validation, Incremental Specification-Based Implementation, and Specification-Based Testing techniques. In this talk, after reviewing the commonly used development methods, I will focus on the introduction of Agile-SOFL and explain how it can be used to improve software productivity and reliability. Finally, I will describe several important and new research directions and topics for future software engineering.




As a research engineer with CERN, Manfred worked on lowering turn-around times for the implementation of numerical algorithms on FPGAs. He received his PhD in Electronics Engineering in 2007 from Graz University of Technology. Subsequently, he joined the Research Lab Computational Technologies and Applications at the University of Vienna as a post-doc. Since 2014, Manfred is with the Materials Center Leoben as a Key Researcher in Embedded Computing and Machine Learning. His research interests include the creation of hybrid models for condition monitoring, uncertainty quantification, automatic code generation and embedded inference.

Speech Title: Probabilistic Hybrid Models for Effective Design of Condition Monitoring Systems

Abstract: As we push manufacturing to ever smaller lots, the operating conditions of both production lines and products are getting more and more diverse. Consequently, we need to automate the design process of quality control and condition monitoring systems to cope with the increasing variability. Relevant steps in this design process include machine-readable specification of knowledge, machine-readable system specification, design of experiments, hybrid model construction and reasoning about the model (to specialise a generic model for different tasks and evaluate the costs of different variants). In order to reason about a hybrid model, a common representation of uncertainty is required. Using random variables for this task, we arrive at probabilistic hybrid models. I will introduce different model construction approaches and point out open issues in respective tooling (reasoning as well as code generation).




Bio: Barbara Mayer holds a PhD in control theory with focus on model predictive control of hybrid systems from TU Vienna and a Master degree in Technical Mathematics of TU Graz. Since 2017, she is a Assoc. Professor for automation and digital production at the Institute Industrial Management at the university of applied sciences FH JOANNEUM in Kapfenberg, Austria. She is Head of Smart Production Lab, the learning and research factory for digital production of the Institute, and the research group Digital Shopfloor. Before she was software designer and later leader of software development of a middle-sized Austrian company in the field of Automation, where she collected industrial experience. Her fields of research are model predictive control, non-linear modeling, IoT, and distributed control. Barbara Mayer conducted and led several industry funded R&D projects as well as coordinated and participated in several public funded national R&D projects. She is participating on initiatives of the Platform Industry 4.0 and is member of the scientific advisory board of the European R&D project EuProGigant.

Speech Title: Learning and research factory for digital production: the Smart Production Lab of FH Joanneum

Abstract: The Institute of Industrial Management has conducted interdisciplinary developments on smart production in numerous projects. The results can especially be experienced by students as well as industrial companies in the learning and research factory for digital production, the Smart Production Lab at the FH JOANNEUM in Kapfenberg, Austria. More than 30 interdisciplinary use cases developed over the last three years show vivid examples and a holistic view on smart production in the research fields of Digital Shopfloor, ERP and MES; Supply Chain Management, Future of Work and Management Control. In this talk an insight on activities as well as in selected use cases is given. Furthermore, the development of a paperless vertically integrated production is presented.