A new framework for experimentation-driven analytics

Extreme data characteristics represent a challenge for advanced data-driven analytics and decision-making in critical domains such as crisis management, predictive maintenance, mobility, public safety and cyber-security. Data-driven insights must be timely, accurate, precise, fit-for-purpose and reliable, considering and learning from user intents and preferences. The EU-funded ExtremeXP project will create a next-generation decision support framework that integrates novel research from big data management, machine learning, visual analytics, explainable ΑΙ, decentralised trust, and knowledge engineering. The framework will aim at optimising the properties of complex analytics processes (e.g. accuracy, time-to-answer, specificity, recall, precision, resource consumption) by associating different user profiles with computation variants, promoting a human-centered, experimentation-based approach to AI and complex analytics. The project will perform five pilot demonstrations.

Artificial intelligence (AI) is finding increasingly diverse applications in the physical world, especially in embedded, cyber-physical devices with limited resources and under demanding conditions. This type of AI is referred to as “Frugal AI” and is characterised by low memory requirements, reduced computing power and the use of less data. The FAIRe (Frugal Artificial Intelligence in Resource-limited environments) project of DFKI and the French computer science institute Inria is developing a comprehensive approach for all abstraction layers of AI applications at the edge.

Edge devices such as driver assistance and infotainment systems in cars, medical devices, manufacturing or service robots and mobile phones have nowhere near the resources of huge cloud data centres that modern machine learning applications require. The challenge is to deal with limited computing power, limited storage space and limited power consumption.

FAIRe aims to enable the deployment of AI applications on mobile devices through an innovative approach to reduce model size and computational overhead by quantising the network, optimising the network architecture, optimising the computations and finally executing on specialised hardware (e.g. RISC-V based or FPGAs).

This combines the expertise from several DFKI research areas: the actual AI algorithms, the hardware on which they run and the compiler layer in between, which translates AI algorithms as efficiently as possible for a specific hardware. To demonstrate this approach in practice, the project team led by Prof Dr Christoph Lüth is conducting a case study on human-robot interaction (HRI) that covers all of these aspects.

Edge AI projects such as FAIRe contribute to making AI applications widely usable on mobile devices and open up new potential for applications.

Partners

• Inria Taran
• Inria Cash
• Inria Corse

LUMINOUS aims at the creation of the next generation of Language Augmented XR systems, where natural language-based communication and Multimodal Large Language Models (MLLM) enable adaptation to individual, not predefined user needs and unseen environments. This will enable future XR users to interact fluently with their environment, while having instant access to constantly updated global as well as domain- specific knowledge sources to accomplish novel tasks. We aim to exploit MLLMs injected with domain specific knowledge for describing novel tasks on user demand. These are then communicated through a speech interface and/or a task adaptable avatar (e.g., coach/teacher) in terms of different visual aids and procedural steps for the accomplishment of the task. Language driven specification of the style, facial expressions, and specific attitudes of virtual avatars will facilitate generalisable and situation-aware communication in multiple use cases and different sectors. LLMs will benefit in parallel in identifying new objects that were not part of their training data and then describing them in a way that they become visually recognizable. Our results will be prototyped and tested in three pilots, focussing on neurorehabilitation (support of stroke patients with language impairments), immersive industrial safety training, and 3D architectural design review. A consortium of six leading R&D institutes experts in six different disciplines (AI, Augmented Vision, NLP, Computer Graphics, Neurorehabilitation, Ethics) will follow a challenging workplan, aiming to bring about a new era at the crossroads of two of the most promising current technological developments (LLM/AI and XR), made in Europe.

Partners

1. Deutsches Forschungszentrum für Künstliche Intelligenz GmbH 2. Ludus Tech SL 3. Mindesk Societa a Responsabilita Limita 4. Fraunhofer Gesellschaft zur Förderung der angewandten Forschung e.V., 5. Universidad del Pais Vasco/Euskal Herriko Universitatea, 6. Fundación Centro de Tecnologias de Interacción visual y comunicaciones Vicomtech 7. University College Dublin, National University of Ireland, 8. Hypercliq IKE 9. Ricoh International B.V. – Niederlassung Deutschland, 10. MindMaze SA, 11. Centre Hospitalier Universitaire Vaudois, 12. University College London

The Horizon Europe project BERTHA kicked off on November 22nd-24th in Valencia, Spain. The project has been granted €7,981,799.50 from the European Commission to develop a Driver Behavioral Model (DBM) that can be used in connected autonomous vehicles to make them safer and more human-like. The resulting DBM will be available on an open-source HUB to validate its feasibility, and it will also be implemented in CARLA, an open-source autonomous driving simulator.

The industry of Connected, Cooperative, and Automated Mobility (CCAM) presents important opportunities for the European Union. However, its deployment requires new tools that enable the design and analysis of autonomous vehicle components, together with their digital validation, and a common language between Tier vendors and OEM manufacturers.

One of the shortcomings arises from the lack of a validated and scientifically based Driver Behavioral Model (DBM) to cover the aspects of human driving performance, which will allow to understand and test the interaction of connected autonomous vehicles (CAVs) with other cars in a safer and predictable way from a human perspective.

Therefore, a Driver Behavioral Model could guarantee digital validation of the components of autonomous vehicles and, if incorporated into the ECUs software, could generate a more human-like response of such vehicles, thus increasing their acceptance.

To cover this need in the CCAM industry, the BERTHA project will develop a scalable and probabilistic Driver Behavioral Model (DBM), mostly based on Bayesian Belief Network, which will be key to achieving safer and more human-like autonomous vehicles.

The new DBM will be implemented on an open-source HUB, a repository that will allow industrial validation of its technological and practical feasibility, and become a unique approach for the model’s worldwide scalability.

The resulting DBM will be translated into CARLA, an open-source simulator for autonomous driving research developed by the Spanish partner Computer Vision System. The implementation of BERTHA’s DBM will use diverse demos which allow the building of new driving models in the simulator. This can be embedded in different immersive driving simulators as HAV from IBV.

BERTHA will also develop a methodology which, thanks to the HUB, will share the model with the scientific community to ease its growth. Moreover, its results will include a set of interrelated demonstrators to show the DBM approach as a reference to design human-like, easily predictable, and acceptable behaviour of automated driving functions in mixed traffic scenarios.

Partners

Instituto de Biomecanica de Valencia (ES). Institut Vedecom (FR), Universite Gustave Eiffel (FR), German Research Center for Artificial Intelligence (DE), Computer Vision Center (ES), Altran Deutschland (DE), Continental Automotive France (FR), CIDAUT Foundation (ES), Austrian Institute of Technology (AT), Universitat de València (ES), Europcar International (FR), FI Group (PT), Panasonic Automotive Systems Europe (DE) Korea Transport Institute (KOTI)

AI spreading in the energy sector is expected to dramatically reshape energy value chain in the next years, by improving business processes performance, while increasing environmental sustainability, strengthening social relationships and propagating high social value among citizens. However, uncertain business cases, fragmented regulations, standards immaturity and low-technical SMEs workforce skills barriers are actually hampering the full exploitation of AI along the energy value chain. I-NERGY will deliver: (i) Financing support through Open Calls to third parties SMEs for new energy use cases and technology building blocks validation, as well as for developing new AI-based energy services, while fully aligning to AI4EU service requirements and strengthening the SME competitiveness on AI for energy; (b) An open modular framework for supporting AI-on-Demand in the energy sector by capitalising on state-of-the-art AI, IoT, semantics, federated learning, analytics tools, which leverage on edge-level AI-based cross-sector multi-stakeholder sovereignty and regulatory preserving interoperable data handling. I-NERGY aims at evolving, scaling up and demonstrating innovative energy-tailored AI-as-a-Service (AIaaS) Toolbox, AI energy analytics and digital twins services that will be validated along 9 pilots, which: (a) Span over the full energy value chain, ranging from optimised management of grid and non-grid RES assets, improved efficiency and reliability of electricity networks operation, optimal risk assessment for energy efficiency investments planning, optimising local and virtual energy communities involvement in flexibility and green energy marketplaces; (b) Delivers other energy and non-energy services to realise synergies among energy commodities (district heating, buildings) and with nonenergy sectors (i.e. e-mobility, personal safety/security, AAL), and with non- or low-technical domains end users (i.e. elderly people).

Partners

ENGINEERING – INGEGNERIA INFORMATICA SPA (ENGINEERING – INGEGNERIA INFORMATICA SPA) FUNDACION ASTURIANA DE LA ENERGIA RIGA MUNICIPAL AGENCY FUNDACION CARTIF PQ TECNOLOGICO BOECILLO Rheinisch-Westfälische Technische Hochschule Aachen COMSENSUS, KOMUNIKACIJE IN SENZORIKA, DOO SONCE ENERGIJA D.O.O. VEOLIA SERVICIOS LECAM SOCIEDAD ANONIMA UNIPERSONAL STUDIO TECNICO BFP SOCIETA A RESPONSABILITA LIMITATA ZELENA ENERGETSKA ZADRUGA ZA USLUGE Iron Thermoilektriki Anonymi Etaireia ASM TERNI SPA CENTRO DE INVESTIGACAO EM ENERGIA REN – STATE GRID SA PARITY PLATFORM IDIOTIKI KEFALAIOUXIKI ETAIREIA Institute of Communication & Computer Systems Fundingbox Accelerator SP. Z O.O. Fundingbox Accelerator SP. Z O.O.

The dAIEDGE Network of Excellence (NoE) seeks to strengthen and support the development of the dynamic European cutting-edge AI ecosystem under the umbrella of the European AI Lighthourse and to sistain the development of advanced AI.

dAIEDGE will foster a space for the exchange of ideas, concepts, and trends on next generation cutting-edge AI, creating links between ecosystem actors to help the EC and the peripheral AI constituency identify strategies for future developments in Europe.

Partners

Aegis Rider, Bonseyes Community Association, Blekinge Institute of Technology, Commissariat à l’Energie Atomique et aux énergies alternatives, Centre d’excellence en technologies de l’information et de la communication, Centre Suisse d’Electronique et de Microtechnique, Deutsches Forschungszentrum für Künstliche Intelligenz, Deutsches Zentrum für Luft- und Raumfahrt e.V., ETH Zürich, Fraunhofer Gesellschaft, FundingBox Accelerator SP, Foundation for Research and Technology – Hellas, Haute école spécialisée de Suisse, HIPERT SRL, IMEC, Institut national de recherche en informatique et automatique, INSAIT – Institute for Computer Science, Artificial Intelligence and Technology, IoT Digital Innovation Hub, Katholieke Universiteit Leuven, NVISO SA, SAFRAN Electronics and Defense, SINTEF AS, Sorbonne Université, CNRS, ST Microelectronics, Synopsys International Limited, Thales, Ubotica Technologies Limited, University of Castilla-La Mancha, The University of Edinburgh, University of Glasgow, University of Modena and Reggio Emilia, University of Salamanca, Varjo Technologies, VERSES Global B.V., Vicomtech.

The RACKET project addresses the problem of detecting rare and unknown faults by combining model-based and machine learning methods. The approach is based on the assumption that a physical or procedural model of a manufacturing plant is available, which is not fully specified and has uncertainties in structure, parameters and variables. Gaps and errors in this model are detected by machine learning and corrected, resulting in a more realistic process model (nominal model). This model can be used to simulate system behavior and estimate the future characteristics of a product.

Actual product defects can thus be attributed to anomalies in the output signal and to inconsistencies in the process variables, without the need for a known failure event or an accurate failure model. Errors have a wide range, i.e., geometric errors such as scratches, out-of-tolerance dimensional variables, or dynamic errors such as deviations between estimated and actual product position on a conveyor belt, process steps or incorrect path assignment in the production flow, etc., and can occur at the product and process level.

At 3.2 million tonnes per year, post-consumer packaging waste represents the most significant plastic waste stream in Germany. Despite progress to date, mechanical plastics recycling still has significant potential for improvement: In 2021, only about 27 Ma.-% (1.02 million Mg/a) of post-consumer plastics could be converted into recyclates, and only about 12 Ma.-% (0.43 million Mg/a) served as substitutes for virgin plastics (Conversio Market & Strategy GmbH, 2022).

So far, mechanical plastics recycling has been limited by the high effort of manual material flow characterisation, which leads to a lack of transparency along the value chain. During the ReVise concept phase, it was shown that post-consumer material flows can be characterised automatically using inline sensor technology. The subsequent four-year ReVise implementation phase (ReVise-UP) will explore the extent to which sensor-based material flow characterisation can be implemented on an industrial scale to increase transparency and efficiency in plastics recycling.

Three main effects are expected from this increased data transparency. Firstly, positive incentives for improving collection and product qualities should be created in order to increase the quality and use of plastic recyclates. Secondly, sensor-based material flow characteristics are to be used to adapt sorting, treatment and plastics processing processes to fluctuating material flow properties. This promises a considerable increase in the efficiency of the existing technical infrastructure. Thirdly, the improved data situation should enable a holistic ecological and economic evaluation of the entire value chain. As a result, technical investments can be used in a more targeted manner to systematically optimise both ecological and economic benefits.

Our goal is to fundamentally improve the efficiency, cost-effectiveness and sustainability of post-consumer plastics recycling.

Partners

Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Deutsches Institut für Normung e. V. Human Technology Center der RWTH Aachen University Hündgen Entsorgungs GmbH & Co. KG Krones AG Kunststoff Recycling Grünstadt GmbH SKZ – KFE gGmbH STADLER Anlagenbau GmbH Wuppertal Institut für Klima, Umwelt, Energie gGmbH PreZero Recycling Deutschland GmbH & Co. KG bvse – Bundesverband Sekundärrohstoffe und Entsorgung e. V. cirplus GmbH HC Plastics GmbH Henkel AG Initiative „Mülltrennung wirkt“ Procter & Gamble Service GmbH TOMRA Sorting GmbH

Im Bereich Wearable Computing liegt der Schwerpunkt traditionell auf der Verwendung Kleidungsstücke als Plattformen für das On-Body-Sensing. Die Funktionalität solcher Systeme wird durch Abtastung und Berechnung definiert. Gegenwärtig sind Überlegungen zum Modedesign nur Mittel zum Zweck: die Optimierung der Sensor-/Berechnungsleistung bei gleichzeitiger Minimierung des Unbehagens für den Benutzer. Mit anderen Worten, innerhalb des traditionellen Wearable Computing-Ansatzes ist das Kleidungsstück im Wesentlichen ein einfaches Behältnis für hochentwickelte digitale Intelligenz, aber es schließt nicht die Lücke zwischen der Funktion und den tatsächlichen Bedürfnissen des Benutzers. Parallel dazu hat die High-Tech-Fashion-Gemeinschaft nach Möglichkeiten gesucht, die Elektronik in neue Designkonzepte einzubinden. Hier lag der Schwerpunkt auf Design-Aspekten, wobei die digitale Funktion oft ziemlich einfach ist: typischerweise eine Art von Lichteffekten, die durch einfache Signale wie die Menge an Bewegung, Impuls oder Umgebungsbedingungen (Licht, Ton, Temperatur) mit wenig intelligenter Verarbeitung gesteuert werden. Mit anderen Worten, im traditionellen High-Tech-Fashion-Ansatz ist der digitale Teil ein einfaches “Add-on” zu anspruchsvollem Design. Aufbauend auf einer einzigartigen Reihe von Kompetenzen der verschiedenen beteiligten DFKI-Gruppen wollen wir eine neue Generation intelligenter Mode entwickeln, die anspruchsvolle künstliche Intelligenz mit anspruchsvollem Design verbindet. Um dies zu erreichen, müssen wir den gesamten klassischen Prozess der Entwicklung sowohl von Kleidungsstücken als auch der zugehörigen tragbaren Elektronik neu überdenken: Mode- und Elektronikdesign-Kriterien sowie Implementierungsprozesse müssen nahtlos integriert werden können. Wir werden Signalverarbeitungs- und Lernmethoden entwickeln, die es solchen intelligenten Kleidungsstücken ermöglichen, komplexe soziale Umgebungen zu verstehen und auf sie zu reagieren, und neue Interaktionsparadigmen entwerfen, um die soziale Interaktion auf neue, subtile und reichhaltige Weise zu verbessern und zu vermitteln.Dabei werden wir ein breites Spektrum entlang der Größe der sozialen Gruppe und des Übergangs zwischen impliziter und expliziter Interaktion berücksichtigen.

Partners

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With 587.4 million t/a of aggregates used, the construction industry is one of the most resource-intensive sectors in Germany. By substituting primary aggregates with recycled (RC) aggregates, natural resources are conserved and negative environmental impacts such as greenhouse gas emissions are reduced by up to 85%. So far, RC building materials cover only 12.5 wt% of the aggregate demand with 73.3 million t/a. With an use of 53.9 million t/a (73.5 wt%), their use has so far been limited mainly to underground construction applications. In order to secure and expand the ecological advantages of RC building materials, it is therefore crucial that in future more demanding applications in building construction can also be covered by RC building materials. For this purpose, on the one hand, a sufficient quality of RC building materials must be guaranteed, and on the other hand, the acceptance of the customers must be ensured by a guaranteed compliance with applicable standards for building construction applications. An essential quality criterion for RC building materials is the particle size distribution (PSD) according to DIN 66165-1, which is determined in the state-of-the-art by manual sampling and sieve analyses which is time-consuming and costly. In addition, analysis results are only available with a considerable time delay. Consequently, it is neither possible to react to quality changes at an early stage, nor can treatment processes be parameterized directly to changed material flow properties. This is where the KIMBA project steps in: Instead of time-consuming and costly sampling and sieve analyses, the PSD analysis in construction waste processing plants shall be automated in the future by sensor-based inline monitoring. The RC material produced will be measured inline during the processing stage using imaging sensor technology. Subsequently, deep-learning algorithms segment the measured heap into individual particles, whose grain size is predicted and aggregated to a digital PSD. The sensor-based PSDs are then to be used intelligently to increase the quality and thus acceptance of RC building materials and hence accelerate the transition to a sustainable circular economy. Based on the proof of concept, two applications will be developed and demonstrated on a large scale: An automated quality management system continuously records the PSD of the produced RC product in order to document it to the customers and to be able to intervene in the process at an early stage in case of deviations. An AI-based assistance system is to enable adaptive control of the preparation process on the basis of sensor-based monitored PSDs and machine parameters to enable consistently high product qualities to be produced even in the event of fluctuating input qualities.

Partners

MAV Krefeld GmbH Institut für Anthropogene Stoffkreisläufe (ANTS) Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI) KLEEMANN GmbH Lehrstuhl für International Production Engineering and Management (IPEM) der Universität Siegen Point 8 GmbH vero – Verband der Bau- und Rohstoffindustrie e.V Verband Deutscher Maschinen- und Anlagenbau e.V. (VDMA)