Publications 2019

Recent trends like internet-of-things (IoT) and internet-of-everything (IoE) require new distributed computing and com- munication approaches as size of interconnected devices moves from a cm³ - to the sub-mm³ -scale. Technological advance behind size reduction will facilitate integration of networked computing on material rather than structural level, requiring algorithmic and architectural scaling towards distributed computing. Associated challenges are linked to use of low reliability, large scale computer networks operating on low to very low resources in robotic materials capable of per- forming cluster computing on micro-scale. Networks of this type need superior robustness to cope with harsh conditions of operation. These can be provided by self-organization and -adaptivity. On macro scale, robotic materials afford unified distributed data processing models to allow their connection to smart environments like IoT/IoE. The present study addresses these challenges by applying mobile Multi-agent systems (MAS) and an advanced JavaScript agent processing platform (JAM), realizing self-adaptivity as feature of both data processing and the mechanical system itself. The MAS’ task is to solve a distributed optimization problem using a mechanically adaptive robotic material in which stiffness is increased via minimization of elastic energy. A practical realization of this example necessitates environmental interaction and perception, demonstrated here via a reference architecture employing a decentralized approach to control local property change in service based on identification of the loading situation. In robotic materials, such capabilities can support actuation and/or lightweight design, and thus sustainability.

Der Entwurf und die Simulation komplexer mechatronischer und verteilter intelligenter Systeme erfordern eine einheitliche Systemmodellierungs- und Programmiersprache. Dieser Beitrag stellt JavaScript als eine vereinheitlichte Modellierungs- und Programmiersprache vor, indem JavaScript mit einem semantischen Typsystem JST erweitert wird, um die Lücke zwischen Modellen und Implementierungen zu schließen. Daraus resultiert die JS+ SupersetSprache, die Typisierung, Modellierung und Programmierung kombiniert. Es werden verschiedene Modelldomänen und ihre Beziehung zum JS+ Programmierungsmodell einschließlich einiger generischer Transformationsregeln am Beispiel eines sensorischen Materials gezeigt. Schließlich wird das Multidomain Simulationswerkzeug SEJAM eingeführt, das physikalische und datenverarbeitende Simulation mit Agenten kombiniert.

Modelling and simulation of social interaction and networks is of high interest in multiple disciplines and fields of application ranging from fundamental social science to smart city management. Future smart city infrastructures and management is characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, i.e., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds pose higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by extending simulation worlds with real world interaction and vice-versa. The simulation world interacts with real world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, light) of mobile devices like smart phones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smart phone Apps or integrated in WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and creating a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact of social interaction, mobility, and opinion making, which has to be considered, too. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of challenges and methodologies to study and control large-scale and complex socio-technical systems using agent-based methods.

Augmented reality is well known for extending the real world by adding computer-generated perceptual information and overlaid sensory information. In contrast, simulation worlds are commonly closed and rely on artificial social behaviour and synthetic sensory information generated by the simulator program or using data collected off-line by surveys. Agent-based modelling used for investigation and evaluation of social interaction and networking relies on parameterisable models. Finding accurate and representative parameter settings can be a challenge. In this work, a new simulation paradigm is introduced, providing augmented virtuality by coupling crowd sensing and social data mining with simulation worlds by using mobile agents in an unified way. A simple social network analysis case-study based on the Sakoda social interaction model and mobile crowd sensing demonstrate the capabilities of the new hybrid simulation method.

Machine Learning (ML) techniques are widely used in Structural Health Monitoring (SHM) and Non-destructive Testing (NDT), but the learning process, the learned models. and the prediction consistency are poorly understood. This work investigates and compares a wide range of ML models and algorithms for the detection of hidden damages in materials monitored using low-cost strain sensors. The investigation is performed using a multi-domain simulator imposing a tight coupling of physical and sensor network simulation in the real-time scale. The device under test is approximated by using a mass-spring network and a multi-body physics solver.

Die Sensorierung von Materialien und Strukturen hin zu smarten sensorischen Materialien schreitet durch den technologischen Fortschritt immer weiter voran. Smarte sensorische Materialien bedeuten materialintegrierte Sensornetzwerke, die ganz neue Anforderungen an die verteilte Datenverarbeitung und Kommunikation stellen. Werden diese sensorische Materialien, die intrinsische und extrinsische Perzeption ermöglichen, durch integrierte Aktoren (z.B. Thermoplaste) erweitert entstehen smarte adaptive Materialien. Diese smarten adaptiven Materialien können auf veränderte Umgebungsbedingungen (wie z.B. Lastsituationen) oder Schäden reaktiv ihre Material- und Struktureigenschaften derart ändern dass die Verteilung von mechanischen Größen (Dehnung, Spannung, Kräfte, usw.) für die aktuelle Situation optimiert werden kann. Dazu wird ein Paradigma der verteilten Datenverarbeitung aus der Informatik eingesetzt und vorgestellt: Reaktive Multiagentensysteme. Diese kooperierenden Agenten sollen selbstorganisierend und möglichst autonom die Struktur hinsichtlich einer Zielgröße (z.B. minimale mechanische Energie) bei veränderlichen Lastsituationen optimieren.

Die Freie Hansestadt Bremen ist ein bedeutender Wissenschafts- und Wirtschaftsstandort in Deutschland und für ihre Bürgerinnen und Bürger eine ebenso bedeutende Stadt zum Leben. Wir möchten einen Blick in die Zukunft wagen und fragen, wie sich diese Zukunft aus Sicht der in Bremen beheimateten Wissenschaft und Politik und der Bremer Bevölkerung darstellt. Auch bitten wir die in Bremen ansässigen Medien um Mitwirkung an dieser Studie.