Safety Analysis of Level Crossing Surveillance Systems Using Fuzzy Petri Nets

[[abstract]]A backward safety analysis model that can deal with dangerous status by virtue of fuzzy theory is proposed in this paper. Fuzzy Petri Nets (FPN) is developed and applied to the safety analysis for three types of level crossing surveillance systems of Taiwan Railway Administration. The numerical results of proposed FPN models are fairly plausible.[[conferencetype]]國際[[conferencedate]]20081219~20081221[[conferencelocation]]Taipei, Taiwa

Petri Networks in the Planning of Discrete Manufacturing Processes

This chapter puts forward characteristics of selected issues of manufacturing processes planning using the Petri networks technique. It includes references to the extensive literature concerning the use of Petri networks in computer aided planning of discrete production processes. Diversity of these problems is high as it refers both to the methods of modeling and simulation of the course of manufacturing processes, the issue of optimizing these processes and production systems, representation of knowledge on production parts of equipment and integration of planning and production activities in general. The work puts forward example use of a temporary, priority Petri network for modeling and optimizing production systems and manufacturing operations as well as an example of fuzzy interference using the Petri network mechanism

Measurements, Models, Systems and Design

531 s.

Improving Human-Machine Interaction

This thesis studies human and machine interaction. For better interaction between humans and machines, this thesis aims to address three issues that remain unanswered in literature. Three objectives are proposed in this thesis to address the three issues, and the objectives are: (i) identification of the core capabilities of a Human Assistance System (HAS) and study of implementation strategy of the core capabilities; (ii) development of a framework for improving the accuracy of human mind state inference; (iii) study of the effect of representation of the machine’s state (which is represented in a “natural” way) on the user’s actions. By a natural way, it is meant a way that contains emotions known to be always present in humans (or human emotions in short). The study includes theoretical development, experimentation, and prototype implementation. This thesis has concluded: (1) the core capabilities to be addressed in designing a HAS are transparency, communication, rationale, cognition and task-sharing and they can be implemented with the existing technologies including fuzzy logics, Petri Net and ACT-R (Adaptive Control of Thought-Rational); (2) expert opinion elicitation technique is a promising method to construct a more general framework for integrating various algorithms on human state inference; (3) there is a significant effect of the representation of the machine’s state on the user’s actions. The main contributions of this thesis are: (1) provision of a case study for the proof-of-concept of HAS in the area of Computer Aided Design (CAD); (2) provision of an integrated framework for fatigue inference for improved accuracy, being readily generalized to inference of other mind states; (3) generation of a new knowledge regarding the effect of the natural representation of a machine’s states on the user’s actions. These contributions are significant in human-machine science and technology. The first contribution may lead to the development of a new generation CAD system in the near future. The second contribution provides a much powerful technology for human mind inference, which is a key capability in HAS, and the third contribution enriches the science of human-machine interaction and will give impact to the field of Artificial Intelligence (AI) as well. The application of the result of this thesis is rehabilitation, machine learning, etc

Compositional dependability analysis of dynamic systems with uncertainty

Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based safety assessment (MBSA), which has attracted a significant amount of interest from industry, academia, and government agencies. Different model-based safety analysis methods, such as Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS), are increasingly applied by industry for dependability analysis of safety-critical systems. Such systems may feature multiple modes of operation where the behaviour of the systems and the interactions between system components can change according to what modes of operation the systems are in.MBSA techniques usually combine different classical safety analysis approaches to allow the analysts to perform safety analyses automatically or semi-automatically. For example, HiP-HOPS is a state-of-the-art MBSA approach which enhances an architectural model of a system with logical failure annotations to allow safety studies such as Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA). In this way it shows how the failure of a single component or combinations of failures of different components can lead to system failure. As systems are getting more complex and their behaviour becomes more dynamic, capturing this dynamic behaviour and the many possible interactions between the components is necessary to develop an accurate failure model.One of the ways of modelling this dynamic behaviour is with a state-transition diagram. Introducing a dynamic model compatible with the existing architectural information of systems can provide significant benefits in terms of accurate representation and expressiveness when analysing the dynamic behaviour of modern large-scale and complex safety-critical systems. Thus the first key contribution of this thesis is a methodology to enable MBSA techniques to model dynamic behaviour of systems. This thesis demonstrates the use of this methodology using the HiP-HOPS tool as an example, and thus extends HiP-HOPS with state-transition annotations. This extension allows HiP-HOPS to model more complex dynamic scenarios and perform compositional dynamic dependability analysis of complex systems by generating Pandora temporal fault trees (TFTs). As TFTs capture state, the techniques used for solving classical FTs are not suitable to solve them. They require a state space solution for quantification of probability. This thesis therefore proposes two methodologies based on Petri Nets and Bayesian Networks to provide state space solutions to Pandora TFTs.Uncertainty is another important (yet incomplete) area of MBSA: typical MBSA approaches are not capable of performing quantitative analysis under uncertainty. Therefore, in addition to the above contributions, this thesis proposes a fuzzy set theory based methodology to quantify Pandora temporal fault trees with uncertainty in failure data of components.The proposed methodologies are applied to a case study to demonstrate how they can be used in practice. Finally, the overall contributions of the thesis are evaluated by discussing the results produced and from these conclusions about the potential benefits of the new techniques are drawn

A Fuzzy Spatio-Temporal-based Approach for Activity Recognition

International audienceOver the last decade, there has been a significant deployment of systems dedicated to surveillance. These systems make use of real-time sensors that generate continuous streams of data. Despite their success in many cases, the increased number of sensors leads to a cognitive overload for the operator in charge of their analysis. However, the context and the application requires an ability to react in real-time. The research presented in this paper introduces a spatio-temporal-based approach the objective of which is to provide a qualitative interpretation of the behavior of an entity (e.g., a human or vehicle). The process is formally supported by a fuzzy logic-based approach, and designed in order to be as generic as possible