Semantic-driven modeling and reasoning for enhanced safety of cyber-physical systems

This dissertation is concerned with the development of new methodologies and semantics for model-based systems engineering (MBSE) procedures for the behavior modeling of cyber-physical systems (CPS). Our main interest is to enhance system-level safety through effective reasoning capabilities embedded in procedures for CPS design. This class of systems is defined by a tight integration of software and physical processes, the need to satisfy stringent constraints on performance, safety and a reliance on automation for the management of system functionality. Our approach employs semantic–driven modeling and reasoning : (1) for the design of cyber that can understand the physical world and reason with physical quantities, time and space, (2) to improve synthesis of component-based CPS architectures, and (3) to prevent under-specification of system requirements (the main cause of safety failures in software). We investigate and understand metadomains, especially temporal and spatial theories, and the role ontologies play in deriving formal, precise models of CPS. Description logic-based semantics and metadomain ontologies for reasoning in CPS and an integrated approach to unify the semantic foundations for decision making in CPS are covered. The research agenda is driven by Civil Systems design and operation applications, especially the dilemma zone problem. Semantic models of time and space supported respectively by Allen’s Temporal Interval Calculus (ATIC) and Region Connectedness Calculus (RCC-8) are developed and demonstrated thanks to the capabilities of Semantic Web technologies. A modular, flexible, and reusable reasoning-enabled semantic-based platform for safety-critical CPS modeling and analysis is developed and demonstrated. The platform employs formal representations of domains (cyber, physical) and metadomains (temporal and spatial) entities using decidable web ontology language (OWL) formalisms. Decidable fragments of temporal and spatial calculus are found to play a central role in the development of spatio-temporal algorithms to assure system safety. They rely on formalized safety metrics developed in the context of cyber-physical transportation systems and collision avoidance for autonomous systems. The platform components are integrated together with Whistle, a small scripting language (under development) able to process complex datatypes including physical quantities and units. The language also enables the simulation, visualization and analysis of safety tubes for collision prediction and prevention at signalized and non-signalized traffic intersections

CHAPTER 3 Data Mining in Scientific Data

Knowledge discovery in scientific data, i.e. the extraction of engineering knowledge in form of a mathematical model description from experimental data, is currently an important part in the industrial re-engineering effort for an improved knowledge reuse. Despite the fact that large collections of data have been acquired in expensive investigations from numerical simulations and experiments in the past, the systematic use of data mining algorithms for the purpose of knowledge extraction from data is still in its infancy. In contrary to other data sets collected in business and finance, scientific data possess additional properties special to their domain of origin. First, the principle of cause and effect has a strong impact and implies the completeness of the parameter list of the unknown functional model more rigorous than one would assume in other domains, such as in financial credit-worthiness data or client behavior analyses. Secondly, scientific data are usually rich in physical unit information which represents an important piece of structural knowledge in the underlying model formation theory in form of dimensionally homogeneous functions. Based on these features of scientific data, a similarity transformation using the measurement unit information of the data can be performed. This similarity transformation eliminates the scale-dependency of the numerical data values and creates a set of dimensionless similarity numbers. Together with reasoning strategies from artificial intelligence such as case-based reasoning, these 62 DATA MINING FOR DESIGN AND MANUFACTURING similarity numbers may be used to estimate many engineering properties of the technical object or process under consideration. Furthermore, the employed similarity transformation usually reduces the remaining complexity of the resulting unknown similarity function which can be approximated using different techniques