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Systems engineering is used in industry. Specific examples and other information can be found in this category.
Modeling in systems engineering has many advantages. However, if an organization does not yet have (much) experience in model-based systems engineering (MBSE), this approach is risky. The European Space Agency (ESA) took an interesting intermediate route with the OPS-SAT: The team developed the satellite traditionally, i.e. document-based. This approach is very mature at ESA. But after...
ChatGPT can be used to generate MBSE models from text. PlantUML can be used for visualization.
The rise of big data and artificial intelligence (AI) has ushered in a new era of technological progress. However, with these advances come new legal challenges that require global cooperation and legal protection. Even though we as systems engineers often focus on technical issues, dealing with intellectual property has always been important...
AI opens up new possibilities, including in MBSE. The MESCONF modeling conference will take place in Munich on 22 and 23 June. However, the topic of AI is dominant: on the one hand through a keynote speech by Jan Seyler from Festo. On the other hand, I will be offering an open space session on AI & MBSE. Together with the participants, I would like to...
Software is now an essential component of many modern products. But the software has to run somewhere. To do this, we need controllers (computers) and embedded operating systems that run on the controllers. And only then comes our software. The following is a brief overview of operating systems for embedded systems.
AI has the potential to bring MBSE into the mainstream. The "Vision Paper" on the challenges for AI in MBSE provides clarity.
By now, word has gotten around that training data plays a central role for AI models. But how do we get good AI data? And since everyone is currently talking about "Artificial General Intelligence", the question arises as to whether we still need training data at all. The short answer is: Of course! Read on for the long answer...
With AI, we can create MBSE models from text and compare requirements and system models. But there are still problems.
When we develop systems for people, we have to make important decisions: Who are the users? What is important to the users? What is valuable to them? What problem will the system solve for these users, and how do we solve it? How do we know if we are ready? We need to answer all these questions, whether...
If you work in the automotive sector, you will certainly have come across the term "ASPICE". The "A" stands for Automotive, because SPICE is also relevant in other industries. But what exactly is SPICE - and what role does it play in systems engineering? This is the subject of the following.
Last week I reported on RE4AI, Requirements Engineering for Artificial Intelligence. Among other things, it dealt with the limitations and challenges of RE4AI. All well and good, but how do we approach the whole thing in practice? One possible approach is Google PAIR, which is presented below.
RE4AI is the development of products that contain AI-based elements and the challenges of requirements management.
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