相关论文: Towards Industry 4.0: Gap Analysis between Current…
The four Industry 4.0 design principles information transparency, technical assistance, interconnection, and decentralized decisions pose challenges in integrating information technology (IT) and operational technology (OT) solutions in…
This paper introduces a GenAI-empowered approach to automated development of automotive software, with emphasis on autonomous and Advanced Driver Assistance Systems (ADAS) capabilities. The process starts with requirements as input, while…
In this paper we outline a software development process for safety-critical systems that aims at combining some of the specific strengths of model-based development with those of programming language based development using safety-critical…
The manufacture of an information product (IP) is akin to the manufacture of a physical product. Current approaches to model such information manufacturing systems (IMS), lack the ability to systematically represent the dynamic changes…
Systems tend to become more and more complex. This has a direct impact on system engineering processes. Two of the most important phases in these processes are requirements engineering and quality assurance. Two significant complexity…
Business process modeling is used by most organizations as an essential framework for ensuring efficiency and effectiveness of the work and workflow performed by its employees and for ensuring the alignment of such work with its strategic…
For many decades, formal methods are considered to be the way forward to help the software industry to make more reliable and trustworthy software. However, despite this strong belief and many individual success stories, no real change in…
Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial…
This paper describes the comprehensive safety framework that underpinned the development, release process, and regulatory approval of BMW's first SAE Level 3 Automated Driving System. The framework combines established qualitative and…
Machine Learning (ML) has been integrated into various software and systems. Two main components are essential for training an ML model: the training data and the ML algorithm. Given the critical role of data in ML system development, it…
Due to the fourth industrial revolution, industrial applications make use of the progress in communication and embedded devices. This allows industrial users to increase efficiency and manageability while reducing cost and effort.…
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned…
Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting…
Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning…
Model-based engineering promises to boost productivity and quality of complex systems development. In the context of safety-critical systems, a traditionally highly regulated and conservative domain, the use of models gained importance in…
An ever shorter technology lifecycle engendered the need for assessing new technologies w.r.t. their market readiness. Knowing the Technology readiness level (TRL) of a given target technology proved to be useful to mitigate risks such as…
Requirements Engineering (RE) is a critical phase in software development including the elicitation, analysis, specification, and validation of software requirements. Despite the importance of RE, it remains a challenging process due to the…
The concept of Industry 4.0 brings a disruption into the processing industry. It is characterised by a high degree of intercommunication, embedded computation, resulting in a decentralised and distributed handling of data. Additionally,…
Demand forecasting of hierarchical components is essential in manufacturing. However, its discussion in the machine-learning literature has been limited, and judgemental forecasts remain pervasive in the industry. Demand planners require…
Industrial automation systems (IASs) are commonly developed using the languages defined by the IEC 61131 standard and are executed on PLCs. In this paper, a system-based approach for the development of IASs is adopted. A framework is…