Current projects

With the Renewable Expansion Act (EAG), the goal set by the federal government to cover 100% of total electricity consumption from 2030 on a national balance sheet from renewable energy sources is to be implemented. For this purpose, the annual electricity generation from renewable sources is to be increased by 27 TWh by 2030 through new construction, expansion and revitalization, with 11 TWh being realized by means of photovoltaic systems (PV). In 2019, the installed PV capacity in Austria was 1.7 GWp (1.7 TWh of generated energy). The annual growth rates in the years 2014-2018 were 150-180 MWp / a. Only in 2019 was it possible to achieve a higher value of 250 MWp / a. To achieve the target of 11 TWh by 2030, an annual expansion rate of 1,000 MWp / a would have to be achieved. Current studies show that the available roof areas with an expansion potential of 3.5 TWh are not sufficient and therefore alternative PV systems are required. For the reasons mentioned above, in addition to the planned roof systems, innovative alternative area potentials for PV systems must be tapped. The reports from other projects show the available space and energy potential, but these were determined using statistical methods, so that no statement is made regarding the actual suitability. Alternative PV areas / yields in a city were specified, but no methodically systematic localization of these places was carried out. The motivation of PV4EAG is to close this gap and to develop a process for the automated discovery of alternative PV areas. This addresses the sub-topic "Creation of a freely accessible data platform with energy-relevant forecasts" In the PV4EAG project, alternative PV areas are to be analyzed using methods from the field of Geographic Information Science & Technology and artificial intelligence for their suitability in terms of shading, yield potential, construction costs, and grid connection options. The starting point is the existing GIS data (digital building heights, vegetation models, terrain models from airborne laser scanning data, cadastral data), in combination with other publicly available data (Open Governmental Data) and other open data sources (OpenStreetMap, Corine Landcover, etc.), as well as remote sensing data and products (ie orthophotos and satellite image data) are used. By using smart data fusion, semantic annotation of the data (for semantic data integration), and spatial analysis techniques paired with artificial intelligence (GeoAI in the broadest sense), the data should be suitable for large-scale facade-integrated systems on high-rise buildings, sealed parking spaces in shopping centers and residential areas , Traffic areas and rail systems as well as floating PV are analyzed. For the development and testing of the analysis method, the project is limited to selected locations in Styria, with the aim of scaling it up. For these locations, a plausibility check is carried out using the existing VR installation in the EAS lab using a virtual 3D process, and detailed project planning is used to specify the realistic PV yield to be expected.

The Austrian government is committed to accelerating the transition of its energy system and achieving CO2 neutrality by 2040. To achieve this goal, Austria must significantly step up its efforts to decarbonise all parts of its energy sector. Buildings account for about a third of the total energy demand. The government plans to phase out oil and coal heating systems by 2035 and limit the use of natural gas for heating in new buildings from 2025. Energy services such as predictive maintenance, demand-side management and model predictive control are central components for reducing the energy consumption of build-ings and transforming buildings into active, intelligent actors in higher-level energy sys-tems. The aim of BEYOND is to develop the technological foundation for “Next Generation En-ergy Services”, which is made possible by the interplay of the following technolo-gies: Virtual Reality for visualization and real-time interaction with the real building; Machine learning and physical simulation to show the real-life effects of interven-tions and decisions. IoT platforms for bidirectional real-time communication be-tween the building and its users. The technological developments are tested and evaluated on the basis of two use cases “Predictive Maintenance and Error Diagnosis” and “Human Aspects in Buildings”. Innovative companies in the field of energy services, building automation, simulation software and VR technology will benefit from the developments in BEYOND. Political decision-makers and end users will also benefit from the new possibilities of actively engaging with these next generation Energy Services.

When recommending videos, the current context of the user plays an important role. The first task in the context of the project is to identify those recommendation approaches that best help to support contextual recommendation of videos. A context is composed of different data sources, such as current search strings of the user, search strings in similar situations, search strings of the "Nearest Neighbors" (users with similar interaction behavior) etc. Analysis and design are carried out in the work package "On-Demand-Clipping" , Implementation and evaluation of a context-based recommendation approach. The solutions developed for "on-demand clipping" form the basis for further developments with the aim of supporting the synthesis of videos ("on-demand clip merging"). On the basis of information such as available time, keywords describing the context and other information (so-called company-related information parameters in the context of the use of knowlede graphs), the system should identify (synthesize) a sequence of partial videos, which the user sees as a "solution video "represents. A simple application is eLearning: here, for example, a corresponding video can be generated by the system on the basis of the learning requirements of a user.

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

SMART2B aims to 1) upgrade smartness levels of existing buildings through coordinated control of legacy equipment and smart appliances, 2) implement interoperability in two existing cloud-based platforms that are currently available in the European market and, as a result of this project, will be integrated into a single building management platform, 3) create a user-centric ecosystem that empowers citizens by simplifying equipment and device control and providing information about overall energy performance. The cloud-based platform will facilitate smartness upgrades of existing buildings, enabling their transition from passive buildings to active elements of the energy system by offering new energy and non-energy services such as increased energy efficiency, improved indoor comfort to the occupants and flexibility to various stakeholders including DSOs, building managers and other third-parties. Thereupon, specifically tailored to the needs of the user, SMART2B will provide new business models for the building energy market combining the savings from energy efficiency measures and gains from the active contribution of the building through flexibility services by exploiting the maximum level of smartness. The experience and maturity of solutions from the consortium partners will ensure market uptake through sound exploitation and replication activities carried out by the strong commercial backbone of SMART2B. SMART2B will develop and deploy non-intrusive IoT sensors and actuators in existing buildings aiming to solve one of the main problems of improving buildings’ indoor comfort and energy efficiency: the structural (physical and financial) limits of installing, monitoring, automating and control existing devices in buildings, by proposing plug & play devices able to interact with the appliances and legacy equipment already installed and communicate the collected data to the cloud for remote monitoring, data analysis based on AI and machine learning and control.

In order to increase trust in robot systems and reduce cognitive load, the project aims to develop well-founded methods for measuring trust in assistance systems and the cognitive load caused by their use. Furthermore, options for intervention in (1) the design of the user interface for input and output, (2) the degree of autonomy and (3) the transparency of decisions by the robot that allow an improvement of these parameters are to be examined. The primary innovation of the project is that trust and the cognitive load as well as measures to improve them are thoroughly investigated in an interdisciplinary team (psychologists, visualization experts, robotics, emergency services). The planned direct coupling of the assessment of trust and cognitive load with possible changes in the interaction design and the behavior of the robot will provide new insights into the nature of trust in assistance robots and enable the development of improved assistance systems. The immediate practical benefit of this knowledge is evaluated in realistic field tests with emergency services.

The SurveyHub project aims to develop a modern web application for the evaluation, visualisation and further work with the results of employee surveys. The main aim is to support large companies in overcoming the content-related and logistical hurdles of national and international employee surveys. SurveyHub pursues the following 5 core objectives. 1: 1. to create a DSGVO-compliant and user-friendly online platform for the evaluation and presentation of employee survey results that takes into account Austrian and Central European specifics. 2. to make results data from any online survey system accessible and retrievable via API. SurveyHub is not a "survey platform" but a survey-neutral "evaluation platform". 3. The quantitative result data of employee surveys should not only be presented statistically, but should also be prepared didactically and visually for the target group "managers and employees" in companies. The target groups should be guided through the results in order to better understand their meaning. 4. The qualitative result data in the sense of open comments should be evaluated by content-analytical algorithms and processed in the same way. We want to make accessible the rich wealth of experience and opinion in companies that otherwise lies fallow. 5. We want to digitalise the process of collecting and working with the results and thus accelerate it. It should be possible for clients to receive the results of surveys in hours, not weeks. Managers in client companies should be able to quickly capture the core content of their surveys and process them in a time-saving manner.

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.

In this project, research will be carried out to document thermal engineering applications in which system improvements can be achieved through innovative control approaches. In doing so, not only methods known from control and system engineering will be considered, but also artificial intelligence (AI) methods in particular. To substantiate the potential for further research activities regarding AI in thermal engineering, a comparison between conventional control strategies and AI-based methods is aimed at.

The overall idea of AIDOaRT is to efficiently support requirements, monitoring, modelling, coding, and testing activities during the software development process. AIDOaRT can be used as a platform to extend existing tools. To this intent, the project proposes the use of Model-Driven Engineering (MDE) principles and techniques to provide a model-based framework offering proper methods and related tooling. The projects’ framework will enable the observation and analysis of collected data from both runtime and design time to provide dedicated AI-augmented solutions that will then be validated in concrete industrial cases involving complex CPSs.

IntelliServ is an AI and recommender-based platform that offers intelligent support for the service processes of systems and devices: Service processes must be planned in the office and then carried out on site in the form of maintenance, tests and repairs on devices and systems. The system offers "intelligent" support by analyzing and aggregating all available data in the background in order to process them using technologies from the field of AI and recommender systems. The aim is on the one hand to support those responsible in the office and on the other hand to support the implementation staff in making their work more efficient.

The project UserGRIDs develops two user-centred energy services at city district level, be¬ne¬fit-ting from active user participation and large amounts of real-time data. An ICT platform acts as a mid¬dleware providing seamless interoperability and standardized protocols. The first service is an energy management system (EMS) for districts with strongly fluctuating con¬sump¬tion and generation characteristics. The aim is to minimize emissions through optimal mana¬ge¬ment of en-ergy storage and supply from volatile sources. Various building controllers are ex¬ten¬ded to form a comprehensive, self-learning control system for the entire district. Its de¬vel¬opment is based on detailed thermo-electrical models also used by the second service, energy structure planning. It supports decisions on the transition of the district energy system towards zero greenhouse gas emissions. EMS, ICT-platform and energy structure planning will be im¬ple¬men¬ted, tested and further developed at the INNOVATION DISTRICT INFFELD of TU Graz.

Future intelligent and integrated energy systems must have a high degree of flexibility and efficiency to ensurereliable and sustainable operation. Along with the rapid expansion of renewable energy, this degree of flexibility and efficiency can be achieved by overcoming the clear separation between different sectors and by increasing connectivity and the associated data availability through the integration of sensors and edge/fog computing. All of these developments drivethe transition from towardsso-called Cyber Physical Energy Systems. The cyber technologies (sensors, edge/fog computing, IoT networks, etc.) areable to monitor the physical systems, to enable communication between different subsystems and to control them.Modelling and simulation tools are of central importance for the optimal operation of integrated energy systems. Due to the above-mentionedcharacteristics of future intelligent energy systems, the requirements for modelling and simulation have heightened. Project results and findings in the literature have shown that in the field of modelling and simulation, the combination of three paradigms is of major importance: the description of physical components by differential-algebraic equations, the description of discrete systems by discrete-event models and learning from data by means of machine-learning algorithms. The arguments for the "why" in the field of intelligent, integrated energy systems have been clearly established; however,the "how" remains an open research question. The exploratory project NextHyb2 addresses this research gap by exploring the concept of hybrid-hybrid system simulations for integrated energy systems. Methods, tools and systemic solutions will be developed and evaluated together with experts. Furthermore, the solution will be implemented and tested on the basis of a proof of concept.It can be assumed that in most future research and development projects in the field of integrated energy systems, the tools of modelling and simulation will playan integral part. Their role will assit in optimisingthesetup and operation of energy communities or for building models for prediction and diagnosis in the field of energy networks. The project results should serve to ensure that these future projects can be based on an objective and rationally comprehensible evaluation of the possibilities and fundamental limitations in the field of hybrid-hybrid system simulations. Thus, the project serves as preparation for future research and development projects in the field of intelligent, integrated energy systems.

An urgent task for future energy systems is the design and operation of systems that integrate a large proportion of renewable energies and at the same time improve the overall efficiency of the system. In this context, buildings are of central importance: buildings are responsible for 32% of total global final energy consumption and 19% of energy-related greenhouse gas emissions; In the European Union, buildings are responsible for 40% of total energy consumption. The optimization and design of energy systems for residential and office buildings as well as buildings in a network (city districts, energy communities, etc.) is therefore of great importance within the sustainable energy transition. An essential goal of such intelligent energy systems is not only decentralized, locally produced and sustainable energy to match the requirements of the higher-level network (electricity, heat, cooling) as best as possible, but also to actively involve users in the energy system. In contrast to an outdated energy system, in which users were viewed as passive, ignorant consumers, the energy transition requires active participation and participation by everyone. Energy systems have to be developed and optimized with and for the user. The provision and use of data by users plays a crucial role in this process. The omnipresence of data is said to have great potential for operating buildings and energy systems more economically and ecologically. Technical developments enable ever faster and more comprehensive data acquisition, data transmission and data processing. These systemic changes provide an ideal breeding ground for innovations that use this data for new energy services. The data from and about users play a central role. Although a substantial part of the data can be automatically recorded and processed (e.g. with sensors, smart meters or web scraping), the direct involvement of users and the direct query of personal preferences, behavioral intentions and system assessments (for example with regard to the perceived comfort) Great potential for the development and optimization of innovative energy services to support the energy transition. In the ANSERS project, we combine psychological insights into user activation and user participation with cutting-edge software technologies. This interaction is of central importance for future intelligent energy systems, which promote the reduction of energy consumption and enable energy services such as demand response, peer-2-peer trading or the optimal control of interconnected energy systems. To do this, however, it is necessary, on the one hand, to fundamentally understand and optimally address the psychological determinants of user participation in Energy Services and, on the other hand, to make the best possible use of the data and the system both with regard to the requirements of the higher-level network and with regard to the Optimize user requirements.

The goal of I-GReta is to develop solutions for planning and operation of highly flexible energy systems benefitting from storage capacities. I-GReta will connect 5 trial sites in 4 countries via a professional ICT platform benefitting from FIWARE components and thereby build a real-world digitalized and decentralized energy system. In recent years, studies and hands-on experience have concluded that the integration and participation of end-users are crucial for the energy transition. Occupants, owners and system operators as key need owners will participate and evaluate the operation of the respective systems in a Virtual Smart Grid which is based on the developed platform. A key use case will be the trading of storage capacities via the platform. The Austrian partners will contribute methodological expertise in the field of psychological and technical aspects of active user participation in smart energy systems with high shares of storages of different energy carriers. Furthermore, they provide mathematical and computational methods to enable large-scale modelling, simulation and optimization of cyber-physical systems. Prototypical developments will be tested and evaluated under laboratory conditions in the Austrian pilots.

Digital twins are very useful for answering or completing inquiries about the past or future behaviour of a complex cyber-physical system (of systems), which is either not yet fully implemented or is used remotely, whereby the creation of a physicallocal copy is not possible or economical. Digital twins are relatively inexpensive to create when they can be derived and simulated directly from artefacts created during development. However, these optimal conditions are often not (yet) given in practice. An automated creation, e.g. by learning methods, would remedy this. In addition, for successful use, the applied models and the insights derived from them must sufficiently reflect the properties of the real system. If this is not the case, they are even counterproductive as they lead to wrong conclusions. Therefore, one must be able to trust digital twins. Hence, they must be correct and reliable and at the same time cost-effective in their creation and maintenance. The use of a new technology is often accompanied by doubts about its reliability and concerns about possible side effects. If digital twins are created automatically, e.g. by learning methods, the process behind it is not easy to understand for the user. For acceptance of the technology by the affected user group, instruments must be available to correctly assess the reliability, traceability and limitations of digital twins and to establish trust. LearnTwins addresses these mentioned challenges by using a combined learn-based testing method. This is based on the insight that the properties of complex systems often cannot be captured concisely in a single model (type). Therefore, the project aims to combine different learning methods to create the digital twin (automatic learning, classical machine learning and deep learning). In addition to already existing data sources, learning data will be gained by executing test cases on the real system, whereby the test cases, in turn, will be created automatically from the (learned) digital twin. The technical work will be embedded in a foresight process. For this purpose, the involvement of stakeholders is planned, who actively work out desired futures and strategies regarding the developed technology. The results of the project will enable the faster and more economical creation of high-quality and reliable digital twins and accelerate the necessary digital transformation of product artefacts. The results on the understandability of automatically learned models should contribute to a higher acceptance and a more focussed use of learning-based methods. The developed methods will be tested and evaluated in three realistic use cases from different domains.

GENDER STI will innovatively contribute to solve complex problems associated to the integration of the gender perspective in STI dialogues with third countries. By adopting a design thinking human-centric problem-solving approach, GENDER STI will investigate how gender equality is taken into consideration at different levels of international cooperation dialogues in the area of STI, between the EU Member States and Associated Countries, and a selected set of 10 third countries, including Canada, the United States, Mexico, Brazil, Chile, Argentina, South Africa, India, South Korea and China. This one of a kind consortium of EU and third countries partners will have a meaningful impact in accelerating the integration of gender equality in STI dialogues with third countries. The investigation will be conducted along the three objectives of the Gender Equality strategy in EU R&I, i.e. gender equality in scientific careers, gender in decision making, and gender in R&I content. Built on the work done by ERA-related groups on gender equality and international cooperation, as well as EU funded projects, GENDER STI will provide a mapping and comparative analysis on gender equality in STI bilateral and multilateral agreements with the selected third countries. In addition, the project will deploy a series of Co-Design Lab workshops to create the environment to co-design solutions regarding gender inequalities in STI dialogues. As a result, the project will create the GENDER STI Community of Practice that will help to scale up the experience of gender equality in STI at a European and international level, and the European Observatory on Gender in STI, which is unique of its kind in Europe and will serve as a hub for gender equality in STI dialogues, incorporating all knowledge and materials resulting from the project. These actionable insights will feed the process to formulate recommendations to enhance the integration of gender equality in STI dialogues with third countries.

The construction sector accounts for around 40% of the total final energy consumption and has enormous potential for saving energy and reducing CO2 emissions at low cost. The latest developments in digitization and the establishment of cyber-physical systems have the potential to significantly reduce the costs of building operations. Intelligent systems could access large amounts of data in order to reduce the energy consumption of buildings through optimized regulations that are adapted to actual needs. So far, this potential has only rarely been exploited. The overarching goal of the annex is to improve access to low-cost, high-quality data from buildings and to support the development of data-driven energy efficiency applications and analyzes. This enables the building controls to be optimized in real time and provides energy efficiency data and decision-making aids for building management. In order to achieve the overarching goal of the annex, the annex contains a number of specific goals on the subjects of (i) data acquisition, (ii) digital characterization of buildings, (iii) coding of knowledge in intelligent energy efficiency applications and (iv) support the use of data-driven products and services. These additional goals are 1) Providing knowledge, standards, protocols and procedures for the cost-effective collection and sharing of high quality data in buildings 2) Developing a methodology for creating control-oriented building models that enable testing, development and evaluation of the effects of alternative energy efficient strategies for HVAC - Facilitate control of buildings in a digital environment 3) Develop building energy efficiency (and related) software applications that can be used to reduce energy consumption in buildings and ideally commercialized. 4)Promotion of the transfer of the annex results through case studies, business model innovation and dissemination of results. The aim of Austrian participation in Annex 81 is to network with international project partners in the field of IT and building technology to identify global developments in this field, to actively participate in the cooperative technology development and thus to enhance Austria's competence (on the company side as well as on Side of the research). Through targeted dissemination activities and the involvement of relevant sectors and industries, this knowledge should also be shared with the relevant actors and stakeholders in Austria (control and smart home providers, real estate companies, building technology planners, facility management, IT sectors, architects, etc. ) to get redirected. Subsequently, the aim is to use the international networks gained from the annex, in particular to strengthen Austrian companies, such as permanent cooperation with annex partners in the area of ​​cooperative technology development, possible partnerships in the area of ​​Horizon Europe or at the level of classic business cooperation.

The aim of ROBO-MOLE is to increase the safety for responder and civilians in tunnels or other sub-terrain buildings by the detection and identification of hazard materials, providing an actual situation map and to improve the efficiency of disaster response missions. For instance after a accident with a dangerous goods transporter in a tunnel responder are faced with severe and dangerous challenges due to heat, structural danger, smoke or exposure to hazard material. Thus, a semi-autonomous robot for supporting analyzing tasks will be developed, that is equipped with a broad range of sensors (position, imaging, hazard material). The information of these sensors will be fused to allow safe navigation and control of the robot under challenging conditions (smoke, heat, debris) and to detect and map hazards.

In ParXCel, we will focus on the development of synthesis (e.g., configuration) and analysis algorithms (e.g., conflict detection and diagnosis) that help to tackle the above mentioned challenges. The overall idea of ParXCel is 1) to develop machine learning techniques for boosting the performance and prediction quality of constraint solving and 2) to develop parallelized approaches for efficient analysis operations (conflict detection and diagnosis). Major research contributions of ParXCel will be the following. First, we will focus on the integration of machine learning techniques with adaptive search heuristics. This will make it possible to transfer machine learning based prediction approaches to configuration scenarios and optimize the prediction of future user preferences. Second, we will parallelize existing algorithmic approaches especially in conflict detection and direct diagnosis [JUN2004, FSZ2012]. Parallelization on the algorithmic level, for example, on the level of conflict detection, provides the possibility of exploiting environments such as Java ForkJoin that support the implementation of parallelized algorithms.

The project COOL-QUARTER-PLUS was developed to counteract the current trend towards inefficient individual units with coordinated cooling concepts at neighbourhood level. The focus is on office and research quarters, because here central measures are more likely to be implemented than in heterogeneous residential or mixed-use quarters due to the ownership structure and central management.

The increasing digitalization and automation through the use of Artificial Intelligence (AI) of the working world (industry 4.0, Smart Logistics, Big Data, etc.) and our everyday life(assistance systems, smart devices, social media, etc.) posts great challenges for society and education. This ranges from building awareness, increasing acceptance and teaching thefoundations of the technology, to fostering a meaningful, creative usage as well as an assessment of threats, risks as well as opportunities and potentials. The program area ischaracterized by a few urban centers and a large number of rural regions. To prevent a brain drain from the program area as well as to ensure a sustainable and responsible usageof technology, young people with skills to understand and use these new technologies are required. AI applications in particular allow an added value away from urban centers orwithout access to natural resources. Stimulating enthusiasm as well as facilitating a basic understanding has to be done at an early age. This provides a sound basis for youngpeople’s decision to pursue a career (job, training, study) in an AI related sector. The project addresses these challenges at two levels: On the one hand, fostering young people’s(aged 10-14) interest in AI and facilitating a basic technical understanding at an early age. In this context, the integration of teachers and instructors, using a train-the-trainerapproach is the key and ensures a broad and sustainable dissemination. On the other hand, building awareness regarding social, economic and technical aspects and potentials of AIamong the general public (children, parents, apprentices, working persons, etc.) using open, low-threshold activities. Since the described challenges concern the entire program area,a cross-border project implementation is vital.

Automated robot systems that are able to navigate in remote and challenging environments like alpine regions be a significant help end users mountain or constructors or of infrastructure (e.g paths or installation for protection). Such robots can perform automated transport of materials, tools, and persons as well as the automated execution of construction or maintenance actions (although the development of the actual manipulation actions is beyond the scope of this project). Endurance and payload issues renders the deployment of ground robots more realistically. Two main issues arise when deploying such robots in remote and difficult terrain. First in contrast to humans robots usually need a rather detailed maps of their environment to perform navigation; deriving and execution an efficient and safe path to a given goal. In engineered environments like urban areas or highways such detailed maps are available. In remote or unstructured areas these maps needed to be generated beforehand either by the robot itself or by other means like airborne systems. The fact that detailed maps for remote areas do not exist and the expensive pre-mapping step are obstacle for fast and efficient deployment. Second humans are capable to navigate in a challenging environment even with a less detailed map and a rough given route because of their superior perception and motion skills. Robots in contrat still need very detailed map and a high accuracy in their localization to perform challenging navigation task. In order to allow a ground robot to navigate efficiently and safely in remote areas to support end use activities RoboNav will aim at three main goals. First in close cooperation with the end users use cases will be defined that are relevant for the users but are also realistic and helpful for the participating users. The definition of promising use cases andrealistic requirements will maintain realistic expectations and acceptance by the users. The second goal of RoboNav is the development of a pipeline to convert earth observation data into routing data that can be used for the navigation. The important goal is here to avoid extensive preparation campaigns such as detailed mapping of the environment with the robot system itself or other systems like UAVs. Animportant innovation is here that the obtained routing data will be generated depending on the robot’s locomotion capabilities in order to allow broad and easy application of the approach. The third goal of RoboNav is to develop an integrated navigation concept that is suitable for automated navigation of a robot in the envisioned challenging environments. In order to achieve this goal the views and competences of two research disciplinesneed to be combined: geodesy and robotics. A suitable navigation concept including localization, routing and guidance will be replicated for the purpose of an automatically moving robot. The proposed navigation system will be implemented and integrated into a robot platform demonstrator. The integrated system will be evaluated in realistic field trials defined in cooperation with the end users. Deployments in the field but will also form a base for an economic exploitation by young participating companies from both disciplines.

Independent validation is fundamental to emphasise the capability and safety of any solution in the electric, connected and automated (ECA) vehicles space. It is vital that appropriate and audited testing takes place in a controlled environment before any deployment takes place. As the software and hardware components come from multiple vendors and integrate in numerous ways, the various levels of validation required must be fully understood and integration with primary and secondary parts must be considered. The key targets of ArchitectECA2030 are - the robust mission-validated traceable design of electronic components and systems (ECS) - the quantification of an accepted residual risk of ECS for ECA vehicles to enable type approval, and - an increased end-user acceptance due to more reliable and robust ECS. The proposed methods include automatic built-in safety measures in the electronic circuit design, accelerated testing, residual risk quantification, virtual validation, and multi-physical and stochastic simulations. The project will implement a unique in-vehicle monitoring device able to measure the health status and degradation of the functional electronics empowering model-based safety prediction, fault diagnosis, and anomaly detection. A validation framework comprised of harmonized methods and tools able to handle quantification of residual risks using data different sources (e.g. monitoring devices, sensor/actuators, fleet observators) is provided to ultimately design safe, secure, and reliable ECA vehicles with a well-defined, quantified, and acceptable residual risk across all ECS levels. The project brings together stakeholders from ECS industry, standardization and certification bodies, test field operators, insurance companies, and academia closely interacting with ECSEL lighthouse initiative Mobility.E to align and influence emerging standards and validation procedures for ECA vehicles.

Automatic tests allow software developers to detect a considerable amount of bugs before a software is released. However, the step after the detection of a bug, i.e. debugging, is still done manually in most software companies. This requires many man-hours of expensive developers. While there exist numerous academic approaches to ease the debugging process, these approaches are rarely used in practice. Therefore, automatic debugging has the potential to reduce time and costs of the software development cycle. This project aims to close the gap between academic research and debugging in practice. We will focus our research on the improvement of existing academic debugging techniques (in particular Slicing, Model-based Software Debugging, and Spectrum-based Fault Localization) in order to prepare them for use in practice. Thereby, we will address the scalability, accuracy and practicability of the approaches. The project is divided into three phases: the assessment phase, the improvement phase, and the integration phase. In the assessment phase, we examine the reasons for the lack of acceptance of academic debugging approaches in practice. This phase includes observations of software developers when they are debugging a program and follow-up interviews. This observational study is used to develop hypotheses about the current state of debugging in practice. The obtained hypotheses are tested with the data we will collect through a large-scale online survey. We will furthermore identify the weaknesses of the individual debugging approaches and identify possible improvements by laboratory experiments. These experiments include both evaluations of approaches with existing benchmarks and user studies where participants have to use the different debugging approaches to find bugs in given programs. In the next phase, we will use the insights gained from the assessment phase to improve the individual debugging approaches. There are two particularly interesting research questions in this project phase: (1) Can the combination of debugging approaches help to improve the overall debugging experience? (2) Is it possible to automatically select the best suited debugging method for a given program and test suite? In the third phase, we will focus on the practical integration of the approaches into development environments and processes. Essential parts of this phase are (1) the integration of the academic debugging approaches into IDEs (e.g. Eclipse), (2) the co-evolution of tests for a given debugging program, and (3) the implementation of additional debugging support (e.g. Cause-Effect-Chains). We plan to conduct extensive laboratory experiments to evaluate the usefulness of the developed debugging tools.

Industrial energy systems for manufacturing are mainly designed for single supply technologies, not designed for the fluctuation of energy demand and energy supply and thus can only react to a volatile demand and supply (thermal and electric) to a limited extent. From this, the need for the best possible support in optimizing the operation of the industrial energy system (demand and supply), the interaction of different renewable (volatile) and conventional energy sources and the design for industrial energy systems can be derived. The demand for products from the printed circuit board industry is continuously on the rise. Besides the increase of companies in Austria have to face the challenge of frequent change and adaptation to end-user requirements, causing significant changes in the energy demand and supply and by this, energy capacity limits onsite. This will be further increased by digitalisation and in terms of site security the need to increase productivity. The flexibility of the system makes it almost impossible for industry to plan and assess necessary adaptations and investment in the process and supply system and these challenges will increase significantly in the upcoming years. The overall objective of DigitalEnergyTwin is to support the industry with the development of a methodology and software tool to optimize the operation and design of industrial energy systems. The core of the project is the development of a holistic optimization approach, based on (near-) real production data, historical and predictions of the existing system, both the process demand and supply level. By this, industry will be supported for the first time with reliable solutions in terms of fluctuating, volatile and renewable energy supply well designed for efficient process technologies. The methodology of the digital twin will be developed and validated for single use cases and more importantly implemented in the manufacturing industry (PCB industry). For selected processes (energy relevant) and renewable as well as conventional supply technologies also the product quality will be addressed within this approach. Simplification and the development of technical standard solutions will lead to costeffective exploitation in other industrial sectors. Thus, the DigitalEnergyTwin builds on other areas of digitisation that are currently being developed. The use of the digital twin methodology will also make it possible to use the augmented and virtual reality (AR/VR) approach, which enables efficient production and system monitoring as well training and will support the EnergyManager4.0 in the future. By this, a maximum impact and multiplication in other industrial companies and sectors will be achieved and the industry benefits from a reduction of costs and risk of investment decision, which will lead to a significant increase of the implementation of renewable energy technologies as well as technologies for higher energy efficiency in industrial production.

Spreadsheets are omnipresent in organizations. They are used for a variety of purposes and in many cases, calculations in spreadsheets directly serve as a basis for reporting and for subsequent managerial decision making. One reason for the widespread use of spreadsheets is that they can be developed (“programmed”) by end users, e.g., an employee in the controlling department. Being able to develop one’s own data aggregation and decision support tools increases the flexibility for the end users. However, one potential problem with spreadsheets is that they are typically not subject to quality assurance (QA) processes that are common in traditional software development, like code inspections or systematic testing. In many cases, spreadsheet developers also lack the awareness regarding the importance of software testing. Moreover, today’s spreadsheet environments provide only limited functionality for fault avoidance, detection, and removal. As a result, faults are not uncommon in spreadsheets and numerous cases have been reported where such faults led to substantial financial losses for companies1 or to miscalculations in scientific investigations, for a prominent example in the field of economics. Due to the risks that can arise from faulty spreadsheets, researchers have proposed a variety of approaches to provide better tool support for spreadsheet developers. These techniques range from advanced spreadsheet visualizations, over algorithmic spreadsheet testing and debugging methods, to solutions that aim at the automated repair of erroneous spreadsheets. The proposed project continues these lines of research and specifically focuses on improved debugging support for spreadsheets. While existing work in that area often focused on algorithmic fault localization, recent research indicates that to be truly helpful for users, debuggers should offer more functionality than only providing ranked lists of fault candidates. In this project, we will therefore explore novel mechanisms for interactive spreadsheet debugging, which, for example, proactively guide the user to the true location of the fault. From a methodological perspective, one key aspect of our joint research project is that we follow a dual approach. Besides traditional computational experiments that, e.g., compare the fault ranking performance and the computation times of different techniques, we plan to run different controlled user studies, where study participants solve debugging tasks with the help of the tools that will be developed in the project.

In the area of digital transformation, when Flexibility in manufacturing of complex products becomes the key competitive advantage, Artificial Intelligence (AI) is the accepted method to drive the digitalization for the transformation of the industry and their industrial prod-ucts. These products with highest complexity based on multi-dimensional requirements need flexible and adaptive manufacturing lines as well as flexible com-ponents. One of the examples is the automotive indus-try and the products based on high semiconductor con-tent for functional integration, such as highly auto-mated cars, aircrafts, and also the related industrial and manufacturing itself. This change will enable new in-novative industrial and manufacturing models. New process management approaches emerge with the use of data science as a core strategy in the organization and management of these networking manufacturing sites. With deterministic data, the classical approach performs best. However, when only empirical data are available, AI be-comes the option of choice, especially in networking manufacturing sites which are typical in the semiconductor manufacturing and the novel ISLAND approach of the automotive industry away from the linear production with less potential for further flexibility and faster response times.

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.

It is predicted that over 50 billion intelligent objects - smart things - will communicate with each other in the Internet of Things by 2020, allowing for numerous everyday applications. For example, cars will be able to communicate with each other on the streets to prevent accidents, and tailor-made furniture will be able to tell industrial production machines what exactly needs to be done to them. One day, the Internet of Things will be as important as the power grid is today. There is, however, still much research to be done, especially regarding the reliability of the Internet of Things. In particular, critical applications in health, traffic and production need to function perfectly at all times. Lead project researchers in the Field of Expertise Information, Communication & Computing at TU Graz are working on fundamental aspects that will enable computers embedded into everyday objects to function reliably, even under the most difficult conditions.