SYSTEMS ENGINEERING RESOURCES

Distilling Reference Architectures in the High-tech Equipment Industry

by Richard Doornbos, Jelena Marincic, Alexandr Vasenev, and Jacco Wesselius

Abstract: Companies in the high-tech equipment industry are continuously looking for ways to optimize their business. A notoriously difficult part of optimizing is the R&D activities, as risks and uncertainties are inherent. In our experience, creating and using a reference architecture for a product or portfolio to guide future developments is a good way to improve R&D effectiveness and efficiency. But developing a reference architecture by capturing the relevant information and establishing the structure, the models and their interrelations, the tools, and secondly, getting clarity on how to use such reference is not easy. In this article, we describe a method to ‘distill’ a reference architecture using the knowledge built-up in years of developing products and using the customer and business values to capture the key architectural decisions for future products. We explain the purpose and usage of a reference architecture and how to organize it. The experiences obtained in Thermo Fisher Scientific have proven the importance and practicality of this approach.

A section on Artificial Intelligence and Machine Learning addresses leading edge enablers of digital transformation. Papers include:

Pairing Bayesian Methods and Systems Theory to Enable Test and Evaluation of Learning-Based Systems

by Paul Wach, Justin Krometis, Atharva Sonanis, Dinesh Verma, Jitesh Panchal, Laura Freeman, and Peter Beling

Abstract: Modern engineered systems, and learning-based systems, in particular, provide unprecedented complexity that requires advancement in our methods to achieve confidence in mission success through test and evaluation (T&E). We define learning-based systems as engineered systems that incorporate a learning algorithm (artificial intelligence) component of the overall system. A part of the unparalleled complexity is the rate at which learning-based systems change over traditional engineered systems. Where traditional systems are expected to steadily decline (change) in performance due to time (aging), learning-based systems undergo a constant change which must be better understood to achieve high confidence in mission success. To this end, we propose pairing Bayesian methods with systems theory to quantify changes in operational conditions, changes in adversarial actions, resultant changes in the learning-based system structure, and resultant confidence measures in mission success. We provide insights, in this article, into our overall goal and progress toward developing a framework for evaluation through an understanding of equivalence of testing.

Human Models for Future Mobility

by Andreas Lüdtke, Jan-Patrick Osterloh, Jakob Suchan, and Alexander Trende

Abstract: The new DLR Institute of Systems Engineering for Future Mobility (DLR SE) opened its doors at the beginning of 2022. As the new DLR institute emerged from the former OFFIS Division Transportation, it can draw on more than 30 years of experience in the research field on safety critical systems. With the transition to the German Aerospace Center (DLR), the institute has developed a new research roadmap focusing on technical trustworthiness for highly automated and autonomous systems, as described in the article “DLR Institute of Systems Engineering for Future Mobility –Technical Trustworthiness as a Basis for Highly Automated and Autonomous Systems” in this journal. In this paper, we describe how the Group Human Centered Engineering (HCE) contributes to this roadmap with our methods of “virtual test drivers” and “virtual co-drivers.”

NeuroRAN Rethinking Virtualization for AI-native Radio Access Networks in 6G

by Paris Carbone, György Dán, James Gross, Bo Göransson, and Marina Petrova