Global environmental issues, social inequality and geopolitical changes will pose numerous problems for our society in the future. To face these new challenges and deal with them, there is a need to understand and appropriately utilize new digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), robotics and biotechnologies. The use of such new digital technologies contributes to a higher degree of digitalization, while also allowing to respond to emergency situations in a sustainable and effective way. This in turn ensures not only civil and societal safety and security, but also improves the operational readiness and efficiency in safety critical domains such as the Search&Rescue (SAR).
A- A-IQ Ready drives the digital era towards Society 5.0 by promoting civil safety, digital health and co-existence between humans and AI. A-IQ Ready presents methods for localization of so-called SAR platforms which are able to navigate in tunnel scenarios without GPS signals. Using a quantum sensor, geomagnetic field measurements and appropriate sensor fusion algorithms, the position of the SAR platform is determined. In addition, new methods will be developed and demonstrated in A-IQ Ready, which allow for a highly precise assessment of both the health and alertness status of individuals in high-risk situations, as would be in the case of a SAR mission. Using modern contact and non-contact sensor technology, it will be possible to capture the cardiorespiratory status in real time, which directly correlates to the individual`s level of fatigue. This represents an innovative paradigm shift from drowsiness monitoring to sleep prediction.
B- A-IQ Ready proposes cutting-edge quantum sensing, edge continuum orchestration of AI and distributed collaborative intelligence technologies to implement the vision of intelligent and autonomous ECS for the digital age. Quantum magnetic flux and gyro sensors enable highest sensitivity and accuracy without any need for calibration, offer unmatched properties when used in combination with a magnetic field map. Such a localization system will enhance the timing and accuracy of the autonomous agents and will reduce false alarms or misinformation by means of AI and multi-agent system concepts. As a priority, the communication guidance and decision making of groups of agents need to be based on cutting-edge technologies. Edge continuum orchestration of AI will allow decentralizing the development of applications, while ensuring an optimal use of the available resources. Combined with the quantum sensors, the edge continuum will be equipped with innovative, multi-physical capabilities to sense the environment, generating “slim” but accurate measurements. Distributed intelligence will enable emergent behavior and massive collaboration of multiple agents towards a common goal. By exploring the synergies of these cutting-edge technologies through civil safety and security, digital health, smart logistics for supply chains and propulsion use cases, A-IQ Ready will provide the basis for the digital society in Europe based on values, moving towards the ideal of Society 5.0.
The European Green Deal defines 4 key elements for a sustainable mobility and automotive industry, namely: climate neutrality, zero pollution Europe, sustainable transport, and the transition to a circular economy. Digital technologies are a significant enabler for attaining the sustainability goals in mobility and transportation. The EC is taking initiatives to ensure that digital technologies such as AI, 5G, IoT and cloud/edge computing can accelerate the transition of the automotive industry to electrical, autonomous, connected, and shared vehicles. The current COVID-19 situation accelerates this trend.
The AI4CSM project will develop advanced electronic components and systems (ECS) and architectures for future massmarket
ECAS vehicles. This fuels the digital transformation in the automotive sector to support the mobility trends and accelerate the transition towards a sustainable green and digital economy. Having assembled some of Europe’s best partners from industry, research and academia, AI4CSM will deliver key innovations in technical areas including: sensor fusion and perception platforms; efficient propulsion and energy modules; advanced connectivity for cooperative mobility applications; vehicle/edge/cloud computing integration concepts; new digital platforms for efficient and federated computing; and intelligent components based on trustworthy AI techniques and methods. ECAS vehicles enabled by embedded
intelligence and functional integration for future mobility, becomes the pivotal factor for the automotive sector to address the Green Deal principles.
AI4CSM consists of 8 collaborative R&D clusters, gathering 41 partners from 10 countries. AI4CSM will reinforce user acceptance and affordability by convenience and services for the major transition to a diverse mobility. AI4CSM addresses the increasing demand of mobility, supporting future traffic concepts and strengthen the European automotive manufacturing base as a global industry leader.
TUG will provide research work on diagnosis, predictive
maintenance, and lifetime estimation. In any case, obtaining
knowledge from available data, which includes real-world and
simulation data, is of importance. In this project, we will extend
previous work in diagnosis utilizing models substantially. Instead of considering hand-crafted models for diagnosis and the prediction of faults, we are considering learning models from available data. In contrast to existing work, where there is often a need for having data that corresponds to the correct and the faulty behaviour, we want to obtain a model solely from correct behaviour that can be used in a similar way for model-based diagnosis avoiding the use of known faulty behaviour. In this way, classical restrictions regarding the use of model learning and other types of machine learning can be avoided. In particular, we expect to use the learned model of the correct behaviour for fault detection and localization directly, not
needing information about the faulty behaviour represented in the data. In the project, TUG will provide the foundations behind the model-learning approach and also apply it to SC2, where the focus is on diagnostics during operation considering powertrains of vehicles.
The research will be carried out until reaching a technology readiness level of 4 comprising experimental proofs of the concepts using parts of the powertrain and technology to be validated in the lab.
In order to strengthen European aviation industry for the future and to increase its competitiveness the European Commission released its vision for aviation Flightpath 2050 in 2011. Among other goals, it aims at the reduction of CO2 emissions by 75 % compared to 2000. In order to achieve this goal the efficiency of modern aero-engines has to be improved considerably, whereas artificial intelligence (AI) and digitalization will play a key role (BMK, 2020).
The Institute for Thermal Turbomachinery and Machine Dynamics at Graz University of Technology has been investigating the aerodynamics of intermediate turbine ducts, a key component of modern aero-engines, for many years. This research provides the institute with a large and well evaluated data basis. It shall be used for AI application in the project ARIADNE. Together with an informatics institute and two Austrian SMEs following goals shall be pursued to provide tools for the optimization of future intermediate turbine ducts in aero-engines:
• Setup of a data bank of the aeronautics of intermediate turbine ducts, based on measurements and simulation of different designs at various inflow conditions. The structure of the data bank shall allow a fast and efficient utilization for AI application.
• Development of methods for data reduction for efficient AI application based on POD methods and Machine Learning
• Development of a method for the fast flow prediction of new designs observing the physics of fluid mechanics
• Development of a tool for the evaluation of measurements in turbine ducts in order to find possible sensor errors
• Development of a tool for the evaluation of flow simulations of turbine ducts in order to find possible model errors or computational mesh problems
• Application of the developed tools to obtain innovative knowledge of principles in the flow of intermediate turbine ducts
• Finally, the developed tools shall be combined with an optimizer with the goal of fast and efficient design optimization, much faster than with flow simulation based optimizing methods
The Quality Assurance Methodologies for autonomous Cyber-Physical Systems (QAMCAS) aims at methods for enabling substantial quality improvements of interacting communicating systems that interact with humans and the physical environment. QAMCAS is intended to carry out research improving quality assurance methods during development together with methods that assure quality criteria like safety as well as reliability and also robustness during operation. The latter deals with fail-operational methodologies based on artifacts obtained during development and also measurements gained from previous similar systems during operation. In QAMCAS we treat quality assurance from a holistic point of view investigating methods to be used at development time as well as methods to be applied during operation of the cyber-physical system. To solve the challenges corresponding to the holistic view, we suggest to integrate testing methodologies like combinatorial testing and model-based testing and to combine them with machine learning approaches for model and test data generation. Furthermore, we carry out research for transferring development artifacts like models to fail-operational systems that follow the model-based reasoning paradigm. For this purpose, we have to work on smart monitoring systems that are capable of identifying failures and triggering fault localization and repair procedures for obtaining truly fail-operational systems. Although, the main ingredients are available, their integration is challenging and requires several research issues to be solved. In this proposal we discuss these issues in detail. In order to show that the proposed methods and techniques contribute to quality assurance of cyberphysical systems, we carry out the development of prototypical implementations that are further on used for providing an experimental evaluation in the context of autonomous driving and other mobile and autonomous systems.
In Austria, the building sector requires 25 % of the total final energy demand amounting to 316 TWh in 2019. Currently Heat Pumps (HPs) generate 6 % of the energy in the building sector (6.3 TWh or 478000 t CO2eq) and over 34 TWh are still provided by non-renewable sources Invalid source specified.. Austria targets to replace oil, gas, and coal in heating systems with renewable heat sources until 2040. HPs will play a decisive role in the future of heating systems for their low carbon footprint and ability to provide both heating and cooling. Assuming HPs with a reasonable average COP of 3.5 will replace 70 % of all existing non-renewable heating systems until 2040, their projected total electricity consumption only in buildings will be about 9 TWh/a with CO2eq emissions of about 2x106 t/a. Fault detection and intelligent control measures enable energy savings at least 10 % in HPs Invalid source specified.Invalid source specified.. This would result in a potential for CO2eq savings of 200000 t/a due to methods to be developed in this project.