Tutorials

Petri Net, a widely studied mathematical formalism, is a graphical notation for modeling systems. Petri Nets provide the foundation for modeling concurrency, communication, synchronization, and resource sharing constraints that are inherent to many systems. However, Petri Nets do not scale well when it comes to modeling and simulating large systems. Colored Petri Nets (CPNs) extend the vocabulary and constructs of Petri Nets, thereby making them more suitable for modeling large systems. From a practical applications point of view, CPNs support a mechanism of modules that allows one to construct models of large systems in a hierarchical manner. The hierarchy and module concept of CPNs allow different levels of abstraction that are inherent in most systems. The graphical representation makes it easy to see the basic structure of a complex CPN model, that is, understand how the individual subsystems interact with each other.

This tutorial provides adaptability strategies for effective SE execution. Many SEs at different levels of the organization face the dilemma of architecture choices, short term and long-term life-cycle cost trade-offs every day. Deficiencies in SE execution continue to plague system development programs in many organizations.  Efforts to enhance SE at the individual level are often rendered ineffective by group dynamics or organizational limitations.  This tutorial will provide training on basic concept and theory of system adaptability, its application in trade studies to minimize costs from later changes, with examples from automotive, medical device and aerospace applications.

This Workshop will cover the advances on technology ethics and two IEEE initiatives namely IEEE 7000 suite of standards and the Ethics Certification Programme for Autonomous and Intelligent Systems (ECPAIS). The ECPAIS suites of SMART technology ethics criteria comprise Accountability, Transparency, Algorithmic Bias, Privacy, Fairness and Responsible Governance. These provide a basis for raising awareness and offer a comprehensive systematic framework for the innovators, researchers and technologists as well as small and large, public and private enterprises involved in AI and autonomous technology innovation, development and deployment. The main focus is on Autonomous Decision Making and Algorithmic Learning Systems, the emerging regulatory landscape and two complementary approaches to the risk reduction in societal harms and ethical assurance of these technologies.

Risks are an essential tool for decision-making, communication, planning, knowledge management and organisational learning. The quality of Risks can therefore have a drastic impact on all levels of productivity. This Workshop will be used to demonstrate how Risks can be understood using Model Based Systems Engineering (MBSE) and Systems Thinking, and applied to improve productivity. By using a meta-model to illustrate the structure of a Risk, it is possible to understand the core information fields and their inter-relationships, which then provide the basis for a highly effective new Risk development process and template. By explaining the meta-model, process and template, any mystique regarding Risk is lifted. The Systems Approach to Training provides a reliable framework for understanding organisational capabilities through an holistic assessment of Processes, Organisation, Tools and Information (POTI), which then guide the way to efficiently implement the new Risk development approach within target operating models.