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Use of Artificial Intelligence Techniques for Prognostics: New Application of Rough Sets

J. Zalewski, Z. Wojcik

International Journal of Computing, Vol. 11, No. 1, pp. 73-81, March 2012.

Abstract: The objective of this paper is to set the context for the potential application of rough sets in prognostics. Prognostics is a field of engineering, which deals with predicting faults and failures in technical systems. Engineering solutions to respective problems embrace the use of multiple Artificial Intelligence (AI) techniques. The authors, first, review selected AI techniques used in prognostics and then propose the application of rough sets to build the system health prognostication model.

Towards Experimental Assessment of Security Threats in Protecting the Critical Infrastructure

J. Zalewski, S. Drager, A. McKeever, J. Kornecki

Proc. ENASE 2012, 7th Intern. Conference on Evaluation of Novel
Approaches to Software Engineering, Wrocław, Poland, June 29-30, 2012

Abstract: Security is a system and software property essential in protecting infrastructure critical to the nation’s business and everyday operation. It is often related to and overlapping with other trustworthiness properties, such as safety and/or reliability. Mutual relationships of these properties and their interactions in real world systems have been studied by multiple authors in a recent decade; however, they are rarely viewed jointly in the context of critical infrastructure. The objective of this paper is to take a closer look at the relationship of security with safety in computing systems, and present a unified view for further research. In particular, the paper presents an overview of the state-of-the-art and focuses on the discussion of the unifying architecture, which leads to interesting observations how security and safety are related. Preliminary experiments on using safety concepts to assess security in industrial control systems with monitoring tools are discussed.