Current Projects

ENG: Data-based Software Engineering Methods and Tools

ARBAY

Internet marketplaces such as Amazon or eBay are dominating more and more areas of the retail trade as "digital checkpoints". One exception so far has been the trade with durable, highly varied and customizable consumer goods. The effect of the configurable product on the consumer's own four walls can only be checked after delivery. The ARBAY project brings the consultation and configuration of individualized goods into the living room. Virtual and augmented reality technologies are used in the project to create new digital distribution channels for highly varied goods. The aim is to develop a sales platform that extends the principle of known sales platforms for the sale of these goods. The main focus of the ISSE project is the development of a semantic product model as a basis for the platform.

Contact:
E-Mail: Dr. Christoph Knieke

Biotope

The aim of the project BioTope is to develop a basic technology and engineered methodology to facilitate the creation of emergent services that allow for self-adaptive system platforms. The rules of composition are not centrally and statically predetermined by the
Platform, but can be dynamically configured and demand-driven. This should be  achieved and maintained in an Open IoT ecosystem that Integrates data and services to create processes to satisfy user requirements. The main motivation here is user requirements, which drives any new behaviour created in the system. The ecosystem tends to create the balance between user needs and provided data and services. All this is governed by system guarantees that ensures the correct flow of the ecosystem.

Contact:
E-Mail: Eric Douglas Nyakam Chiadjeu
E-Mail: Christian Schindler

DevOpt

Software ecosystems are complex system groups of interacting, distributed individual systems that require continuous, autonomous optimization. In DevOpt we understand an emergent, distributed system as a three layers architecture: Local IoT ecosystems negotiate their work configuration / resource usage under framework conditions. A control layer can correct local decisions through comprehensive, distributed optimization. A DevOps layer enables analysis, maintenance and further development through human intervention.

DevOpt aims at the development of controlled emergent systems through distributed modelling, local negotiation of device configurations / resource usage, increased development efficiency through model-based design, as well as combination of emergence and DevOps. The demonstration / evaluation takes place as electric grid scenarios using self-learning predictive SmartMeters. The ISSE will implement a component-based emergence framework for the local layer and environments, which enables local optimization and mutual dynamic use of resources. Furthermore, the emergent integration should be monitored and optimized with regard to functional and non-functional requirements.

Contact:
E-Mail: Mohamed Toufik Ailane
E-Mail: Mohammad Abboush

Timing Analysis and Steering Development

The project "Timing Analysis and Steering Development" researches methods for estimating and safeguarding runtime behavior in embedded control systems with hard real-time requirements. This is done in the context of actual development projects. The focus is on the specification, modeling and measurement of runtime behavior at system level.

Contact:
E-Mail: Mohammad Abboush
E-Mail: Dr. Christoph Knieke

V-Modell XT Bund

This project is about improvement and maintenance of V-Modell XT Bund. The V-Modell XT Bund is a company-specific adaptation of the V-Modell XT to the federal authorities. In this project it is developed, maintained and maintained according to V-Modell XT.

Contact:
E-Mail: Dr. Christoph Knieke

V-Modell XT / Digital Projects App (DiPA)

Weit e. V. has commissioned the Institute for Software and Systems Engineering to provide the V-Modell XT in the form of a public REST interface. After the fundamental revision of the V-Modell XT metamodel, an interface will be offered that is specified with OpenAPI and implemented in Java.

First software developments of Weit e. V. implementing the V-Modell XT REST API are the newly developed Web Assistant (formerly Project Assistant) and the Digital Projects App(https://dipa.online). The revision of this software ecosystem is intended to offer a simple and contemporary entry into project management with V-Modell XT.

In this context, there is research interest in the software life cycle as well as the software architecture with the background that there are already several editions in the form of metamodels and derivations for different user groups of the V-Modell.

Contact:

Email: Karlson Hanke E-Mail: Christoph Knieke

DACS: Dependable and Autonomous Cyber-Physical Systems

SafeWahr

Environmental perception systems of autonomous vehicles are nowdays using extensive AI-based algorithms. Established techniques and methods to proof correctness regarding safety are reaching its limits. Even if a lot of driving scenarios and millions of kilometers are used for testing, an overall safety assurance during design time is not possible. The goal for the project SafeWahr is to duly detect violations of safety critical specifications and uncertainities of AI-based environmental perception systems of autonomous vehicles. In case a violation was detected the autonomous vehicle will then continue its driving task with restricted functionality in a so called fail-operational mode.

One approach for handling situations, which are not known during design time is to partially shift the safety assurance to the operating time. Ultimately, some kind of "operation time certification" is aimed. For this purpose an operation time monitoring architecture for self-diagnosis will be developed in SafeWahr. Within this operation time monitoring architecture three types of monitors will be included: (1) A Situation Monitor, which determines if the current situation was considered during design time, (2) a Validity Monitor, which determines if the AI-based perception is safe regarding its results and (3) a Function Monitor, which determines if the target function acts corretly regarding safety specifications.

Contact person:
E-Mail: Andreas Vorwald

autoMoVe

The autoMoVe project focuses on highly modular vehicle concepts based on a universally usable basic vehicle module with interchangeable vehicle superstructures, e.g. for passenger or goods transport. Taking into account the requirements of the selected application scenarios, the vehicle concepts are developed with a focus on vehicle design, vehicle functions, energy management and control as well as software architectures.

Furthermore, a development and simulation platform will be implemented to support the virtual development work. Here the functions of the developed vehicle concepts are demonstrated. In addition, individual innovative subsystems are physically implemented and tested.

Contact:
E-Mail: Iqra Aslam

Mobility Lab

The Mobility Laboratory is a cross-sectional internal project that aims to connect the current dynamically adaptive software platform with safeguarding mechanisms. The platform to be developed should be used in as many projects as possible. Furthermore, the mobility laboratory is a place where mainly students come together to work on topics of autonomous driving and machine learning. The laboratory has two RaspberryPi vehicles, a 1:8 model vehicle and an indoor positioning system.

Contact:
E-Mail: Andreas Vorwald

mobil-e-Hub

The mobil-e-Hub project aims to meet the challenges of increasing logistics traffic on the last mile to the customer, especially due to e-commerce. The technological focus and central project innovation of mobil-e-Hub is a new logistics system that can tie drones with transport boxes via carrier systems to (electric) vehicles - the mobile e-hubs - for passenger mobility, e.g. public transport buses. The drones themselves autonomously pick up the boxes optimized for food transport at automated picking stations, touch down on the vehicles equipped for this purpose and take off directly at the delivery point to autonomously hand over the box to the customer.

There are legal and technological challenges in drone operation as a delivery service. ISSE is addressing the technological challenges. To enable reliable, robust and safe operation of the delivery drone system, an online monitoring system for the drones is being developed and implemented. For this purpose, the dependability cage approach developed at ISSE for runtime monitoring of functional requirements of autonomous vehicles is adapted to flight systems. In addition, for optimal control planning of an e-mobility system, energy management is crucial, therefore artificial intelligence methods are used to predict the energy demand. Challenging is the coupled energy management between the electric carrier vehicle and the delivery drone considering the logistics system requirements (e.g., time), the path information, the current local conditions (e.g., temperature), and the previous observations. The information from the energy management can be linked to the online monitoring system to ensure that sufficient energy is available to reach the target and make a planned landing.

Contact: E-Mail: Adina Aniculaesei

 

Future Lab Mobility

The project of “Zukunftslabor Mobilität” (in English: Future Mobility Lab) focuses on the application-oriented research work by using different mobility-related technologies. In four well-defined interdisciplinary fields (Collaborative Research Fields (CRF 1-4)), potential environmentally and socially compatible mobility solutions such as connection of systems, human users and infrastructures relying on the digitalization technology will be investigated and developed, with consideration of concrete use cases about future mobility in Lower Saxony of Germany as well as related methods and approaches for the development of innovative mobility solutions.

In the scope of this project, TU Clausthal is involved together with other project partners in CRF 2, which focuses on relevant topics of smart mobility data handling. As known, the data plays a significantly important role as the basis of autonomous driving (CRF 1) and additionally as the precondition of the development and implementation of appropriate mobility services and corresponding business models (CRF 4). In CRF2, scientists from the research fields of communication technology, information and software engineering work closely together to develop methods to realize a safe and reliable data acquisition, evaluation and fusion of mobility data. For this purpose, the approaches for reliable (safety, security and privacy) data collection and processing, which fulfill the legal framework like DSGVO, will be developed and demonstrated with consideration of both phases for system development and operation. The approaches for the development of a reliable data architecture, methods and approaches for the standardization of data handling and fusion, as well as the assessment and assurance of data quality will be also developed and tested in the project.

Contact: e-mail: Behzad Sezari

 

ML4E: Machine Learned Models for Engineers

Sensorentwicklung für Produkte des baulichen Brandschutzes zur Sicherstellung deren Funktion, für Smart Maintenance und I 4.0

Elements of safety-relevant solutions, such as fire doors, differ very significantly from machines in industrial plants in terms of their costs and connection to the energy supply, a variety of challenges need to be tackeld to turn safety-relevant systems into Cyber Physical Systems. The project deals with the necessary developments. These range from the design of specific sensors and actuators, which can also be produced cost-effectively in small quantities, to IT solutions for condition monitoring and automated functional testing of safety-relevant systems, and the establishment of methodological competencies for the development process of such Cyber Physical Systems.

Contact: Stefan Wittek

Matures Ölfeld

Der Einsatz von KI soll dazu dienen, die spezifischen Kosten der Ölförderung zu senken, ohne Kompromisse in der Sicherheit, des Umweltschutzes oder der Integrität einzugehen. Die Studie soll vor diesem Hintergrund aufzeigen, welche Einsatzbereiche für KI in einem maturen Ölfeld bestehen und wie diese wirtschaftlich zu bewerten sind. Die Einsatzmöglichkeiten zur Mehrwertbestimmung künstlicher Intelligenz im maturen Ölfeld sollen in einer Literaturstudie durchgeführt werden. Betrachtungsgegenstand ist hierbei der abgegrenzte Bereich von der Sonde selbst (inkl. Pumpe) bis zur Übergabestation inkl. Pipelinesystem. Hierbei werden die Einsatzmöglichkeiten und Potentiale von KI-Techniken aus den Bereichen Knowledge Discovery in Databases, Predictive Maintenance, Zeitreihenprognose, Modellbildung, modellbasierten und modellfreien Optimalsteuerungen und -regelungen untersucht und beschrieben.

Ansprechpartner: Stefan Wittek

AI-based flood warning system for the city of Goslar

The project "AI-based flood warning system" is a practical application of AI forecasting systems to safety-critical areas. The application domain is the catchment area of Goslar with the existing sensor infrastructure and its historical data. The task of the artificial intelligence is to observe the current status of weather such as precipitation, soil moisture, solar radiation over a past period of time and put it in relation to the current water level in the settlement. A possible correlation is assumed, so that dangerous, sudden peaks can be predicted at an early stage. The forecasting method is thus a core element for the coordination of a series of structural measures for the prevention of sudden flooding events.

Contact: Dimitri Bratzel,Stefan Wittek

 

KIKI

Der Einsatz von KI im Kontext der prädiktiven Instandhaltung von Abwasserkanälen birgt Vorteile, sowohl im Sinne der Kostenersparnis als auch der Vorbeugung von Totalausfällen und daraus ggf. resultierenden Verunreinigungen des Grundwassers. Ziel ist es hierbei, bisher analog ausgeführte Tätigkeiten, so beispielsweise die Klassifikation von Schäden am Kanal, in Zukunft automatisiert erfolgen zu lassen. Darauf aufbauend lässt sich insbesondere eine Strategie ableiten, mit der sich der zukünftige Instandhaltungsprozess optimieren lässt. Zum Zwecke der Nutzerfreundlichkeit wird zusätzlich ein 3D-Modell des gesamten Netzes entwickelt, mit der sich sowohl die Klassifikation, als auch die Prognose des Zustandes visuell einsehen lässt. Die geplanten Schritte hierbei reichen von der theoretischen Erarbeitung eines Konzepts, bis hin zu einem einsatzfähigen Prototypen. Die eingesetzten Techniken umfassen klassische statistische Methoden, bis hin zu maschinellen Verfahren im Sinne des Predictive Maintenance und der Klassifikation.

Ansprechpartner: Benjamin Acar

National Research Data Infrastructure for Engineering Sciences (NFDI4Ing)

NFDI4Ing brings together the engineering communities and fosters the management of engineering research data. The consortium represents engineers from all walks of the profession. It offers a unique method-oriented and user-centred approach in order to make engineering research data FAIR – findable, accessible, interoperable, and re-usable.The consortium represents researchers from all engineering disciplines. It offers a unique method-oriented and user-centered approach to make research data FAIR - discoverable, accessible, interoperable and re-usable.
NFDI4Ing defines a total of 7 archetypes for scientists as users of research infrastructure.The ISSE is mainly active in the BETTY archetype: engineering research software.  This includes in particular the code of simulation models, and questions concerning the integration of heterogeneous models, as well as their approximation with the help of AI methods.

Contact: Stefan Wittek