Artificial intelligence is really the best solution for systems with unpredictable behavior? A comparative study between ANN and Multi-model approach

Ines Chichi
Inès Chihi - University of Luxembourg

Dr. Inès CHIHI was born in Tunis, Tunisia in 1984. She obtained her PhD degree in electrical engineering from the National Engineering School of Tunis in 2013. From the same school, she obtained her “Habilitation Universitaire” degree in electrical engineering in 2019.
Inès was a professor at the Higher Institute of Applied Sciences and Technologies of Gafsa Tunisia, from 2013 until 2016. Then she joined the National Engineering School of Bizerta, Tunisia (2016-2022).

Since 2017, Inès is also the founder and president of the Association of Energy Efficiency and the Environment (AEEE) and she is a member of the first Tunisian Network for Energy Transition. Inès is a member of the Organization for Women in Science for the Developing World (OWSD), Program Unit of UNESCO.

Since January 14, 2022, Inès has joined the Department of Engineering (DoE) at the University of Luxembourg. Her research area is based on smart sensors for estimation and identification for complex systems with unpredictable behaviors. She applies her approaches in various fields: bioengineering, energy, industry 4.0, etc.

Abstract: Artificial intelligence and Data-driven models are well suggested for modeling complex and non-linear processes. However, they require a very large computation time for data preparation, analysis, and learning. Indeed, complex problems require an extended network that can have exceptionally long and tedious computations, especially at inference instants.

To overcome these problems, we propose a hybrid technique based on multi-model structure. This structure is suggested for modeling nonlinear process by decomposing its nonlinear operating domain into a defined number of sub-models, each one representing a well-defined operating point.

Thus, the multi-model concept is considered an interesting method to improve the performance of the model in terms of accuracy and without increasing too much of the complexity of the empirical model, the training time or the number of parameters to be estimated.

As an example, we present a comparative study between Artificial Neural Network (ANN), known as the most used and efficient technique in empirical modeling, and the proposed multimode approach. This comparative study will be applied to estimate the muscle forces of the forearm from the muscle’s activities.

Wearable Brain-Computer Interfaces for measuring mental states: After data, are we loosing also thoughts privacy?

Pasquale Arpaia - University of Napoli Federico II (Italy)

Dr. Pasquale Arpaia received his Master’s Degree and Ph.D. in Electrical Engineering at the University of Napoli Federico II (Italy), where he is a full-time professor of Instrumentation and Measurements. He is Director of the Interdepartmental Center for Research on Management and Innovation of Health (CIRMIS), Head of the Instrumentation and Measurement for Particle Accelerators Laboratory (IMPALab), the Augmented Reality for Health Monitoring Laboratory (ARHeMlab), the Hi-Tech Academic FabLab Unina, as well as Chairman of the Stage Project of the University Federico II. He is Team Leader at European Organization for Nuclear Research (CERN). He was also a professor at the University of Sannio, an associate at the Institutes of Engines and Biomedical Engineering of CNR, and now of INFN Section of Naples.

He is Associate Editor of the Institute of Physics Journal of Instrumentation, Elsevier Journal Computer Standards & Interfaces, MDPI Instruments, and in the past also of IEEE Transactions on Electronics Packaging and Manufacturing. He was Editor at Momentum Press of the Book Collection “Emerging Technologies in Measurements, Instrumentation, and Sensors”. Recently, he was scientific responsible for more than 30 awarded research projects in cooperation with industry, with related patents and international licenses, and funded 4 academic spin-off companies. He acted as scientific evaluator in several international research call panels. He continuously serves as organizing and scientific committee member in IEEE and IMEKO Conferences. He is a plenary speaker in several scientific conferences.

He published 3 books, several book chapters, and about 300 scientific papers in journals and national and international conference proceedings. His PhD students received awards in 2006, 2010, and 2020 at IEEE I2MTC, in 2016 at IMEKO TC-10, and in 2012 and 2018 at World Conferences.

Abstract: In the last two decades, Brain-Computer Interface (BCI) has gained great interest in the technical-scientific community, and more and more effort has been done to overcome its limitations in daily use. In Industry 4.0 framework, the human becomes part of a highly composite automated system and new-generation user interfaces, integrating cognitive, sensorial, and motor skills, are designed. Humans can send messages or decisions to the automation system through BCI by intentional modulation of brain waves.

However, through the same signal, the system (and, hence, also the human being part of it) acquires information on the user status.

In this talk, the most interesting results of this technological research effort, as well as its further most recent developments, are reviewed. In particular, after a short survey on research at the University of Naples Federico II also in cooperation with CERN, the presentation focuses mainly on state-of-the-art research on wearable measurement systems for acting robots and monitoring mental states (emotions, engagement, distraction, stress and so on). Tens of disparate case studies, carried out by Federico II researchers, spacing from children autism rehabilitation to robotic inspection in hazardous sites, are reported. Special attention is given also to ethic and law issues arising from daily use, by leaving puzzling questions to attendees.

Drive me home please! Contributions from Human Factors to Vehicle Automation

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Josef F. Krems - Chemnitz University of Technology

Josef F. Krems, Professor of Cognitive and Engineering Psychology, graduated at the University of Regensburg in 1980. He then joined the group for Cognitive Psychology as a research assistant and did a PhD in Psychology (1984). For his habilitation (second PhD) he worked on Computer modeling and expert systems (1990). From 1991-1993 he was a Visiting Assistant Professor at Ohio State University, Columbus (OH), where he worked on computational models of diagnostic reasoning. Then he became a Visiting Assistant Professor at the Centre for Studies on Cognitive Complexity at the University of Potsdam (1994-1995). Since 1995 he is a full professor at Chemnitz University of Technology. In 2006 he was invited as Visiting Professor to Chung-Keng University, Taiwan. His current research projects are on Electro-mobility, Man-Machine Interaction, Advanced Driver Assistant systems and highly automated driving.

Abstract: Prototypes of highly automated cars are already being tested on public roads in Europe, Japan and the United States. Automated driving promises several benefits such as improved safety, reduced congestions and emissions, higher comfort as well as economic competitiveness and enhanced mobility in the context of demographic changes. These benefits are often claimed on the basis of a technology-centered perspective of vehicle automation, emphasizing technical advances. However, to exploit the potential of vehicle automation, human-machine-related issues are considered a key question, shifting the perspective towards a human-centered view on automation.

Research on human-automation interaction pointed out already “ironies of automation” that can undermine the expected benefits. Relevant issues mainly relate to the role change in various levels of automation, i.e. mode awareness and transitions from manual to automated control, reduced vigilance due to the monotony of supervising tasks in partially automated driving, changes in attention allocation and engagement in non-driving tasks, out-of-the-loop unfamiliarity resulting in reduced situation awareness, mental models of automation, trust calibration as well as misuse and overreliance. For reducing negative automation effects and enabling successful human-automation interaction, feedback on automation states and behaviors is considered a key factor.

In this talk, I will describe new challenges that arise from the technological development for human factors and how human factors can contribute to a “hybrid architecture”. The focus will be on our own research results on take-over-requests, communication between highly automated cars and other road users, and on comfort and acceptance. part of a highly composite automated system and new-generation user interfaces, integrating cognitive, sensorial, and motor skills, are designed. Humans can send messages or decisions to the automation system through BCI by intentional modulation of brain waves.

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