Recently, there has been a great deal of research into improving AI methods and their application. The main focus is on tracking progress, enabling transparent comparisons, and fostering a more profound understanding of AI. In that process, different organizations generate and use plenty of assets that need to be tracked, traced and managed. Moreover, it is important to discover assets relevant for the task at hand. This paper presents research aiming to contribute to answering the question of what is required to exchange and manage AI models and related assets effectively without semantic gaps in an industrial context. We introduce a platform for AI model exchange, which facilitates the usage, exchange, and analysis of AI models and datasets. The platform incorporates an ontology that can foster a more profound common understanding of what is required in these tasks and help tackle the issues mentioned above. Finally, we elucidate the utility of the platform through the illustration of a use case in the context of real-time critical systems.

In recent years artificial neural networks (NNs) have been at the center of research on data processing. However, their high computational demand often prohibits deployment on resource-constrained Industrial IoT Systems. Custom hardware accelerators can enable real-time NN processing on small-scale edge devices but are generally hard to develop and integrate. In this paper we present a hardware generation approach to rapidly create, test, and deploy entire SoC platforms with application-specific NN hardware accelerators. The feasibility of the approach is demonstrated by the generation of a condition monitoring system for high-speed valves.

The paper presents an efficient approach to the semantic enrichment of measured sensor data in Wireless Sensor Networks (WSNs), by bridging techniques from Model-driven Software Development (MDSD) and Semantic Web Technology (SWT). Our approach reinforces data interoperability, fostering data sharing and reuse, by utilizing SWT. Model-based and type-agnostic configuration reduces the overall effort for WSN setup and maintenance, which are traditionally complex and time-consuming tasks. The presented approach addresses the problem of large-scale WSN management through the application of SWT in WSN configuration and management without requiring expert knowledge. Additionally, we present a generic architecture and an implementation which is also supplemented by hands-on descriptions of an illustrative use case. Our experimental results demonstrate that our model-based approach provides non-intrusive semantic enrichment with sub-millisecond computational overhead, as well as partially automated configuration of WSNs.

A large variety of data is serialized and exchanged using XML. Model-driven activities can benefit from XML data, as shown by various approaches to IP-XACT and UML integration. However, these approaches are inherently time consuming, error-prone, and inflexible due to the manual effort involved. We propose an automated framework for integrating arbitrary XML data into UML models using an automatically generated UML profile corresponding to the structure of the XML data. User-defined XML-to-UML mappings further enhance this integration. Our approach mitigates the aforementioned issues while providing the same benefits.

Security by Design becomes a significant aspect for establishing the Internet-of-Things (IoT) paradigm. In this paper, we present an approach to utilize virtual prototypes (VP) at system level to enable security evaluation along the design process. The proposed VP-based penetration testing framework provides an approach for attack surface and attack behavior modeling. By utilizing a modular, reconfigurable system simulation, an attack scenario can be assessed with different system alternatives. As the VP simulates both hardware (HW) and software (SW) of a single IoT-device as well as the interconnections of different devices a comprehensive system analysis can be executed. Our framework is based on a model-driven approach, which underlines the achieved degree of automation and its potential for industrial application. A comprehensive system analysis tool is the enabler to apply penetration testing, for identifying weak points in the system design and implementation, from early stages in the design flow. The overall approach is demonstrated by an automotive use case derived from real-world security flaws.

This paper presents an approach on extending virtual prototyping, commonly used for system verification and design space exploration, for safety analysis. Virtual prototyping will enhance safety analysis, overcoming the challenges resulting from the ever-increasing number of safety-related, complex, interconnected electronic systems. The presented integral fault injection framework enables safety analysis in combination with established system verification methods. It consists of a fault behavior specification methodology and the corresponding, reusable injection tool, with focus on seamless applicability in between functional models in early concept phases and low-level structural models in late design phases. Our approach works with third party compilers and simulators while providing a minimal intrusive approach using existing models. Selected use cases at gate, register-transfer and functional level demonstrate the usage of the approach.

Intelligent automotive electronics significantly improved driving safety in the last decades. With the increasing complexity of automotive systems, dependability of the electronic components themselves and of their interaction must be assured to avoid any risk to driving safety due to unexpected failures caused by internal or external faults.

To support the reliability assessment of safety-relevant distributed automotive systems and reduce its complexity, this paper presents a novel approach that extends virtual prototyping towards error effect simulation. Besides the common functional and timed system simulation, error injection is used to stress error tolerance mechanisms. A quantitative assessment of the overall system reliability is performed by observing the system reactions and identifying incorrect system behavior. To foster the industrial application, the analysis is integrated in a model-based design flow, starting at the modeling level to assemble and parameterize the virtual prototype and to configure the analysis. The feasibility of the proposed approach is demonstrated by analyzing a representative safety-relevant automotive use case.