Hazard and early warning analysis based on domain specific modeling technologies

An aim of proactive risk management strategies is the timely identification of safety related risks. One way to achieve this is by deploying early warning systems. Early warning systems aim to provide useful information on the presence of potential threats to the system, the level of vulnerability of a system, or both of these, in a timely manner. This information can then be used to take proactive safety measures. The United Nation’s has recommended that any early warning system need to have four essential elements, which are the risk knowledge element, a monitoring and warning service, dissemination and communication and a response capability. This research deals with the risk knowledge element of an early warning system. The risk knowledge element of an early warning system contains models of possible accident scenarios. These accident scenarios are created by using hazard analysis techniques, which are categorised as traditional and contemporary. The assumption in traditional hazard analysis techniques is that accidents are occurred due to a sequence of events, whereas, the assumption of contemporary hazard analysis techniques is that safety is an emergent property of complex systems. The problem is that there is no availability of a software editor which can be used by analysts to create models of accident scenarios based on contemporary hazard analysis techniques and generate computer code that represent the models at the same time. This research aims to enhance the process of generating computer code based on graphical models that associate early warning signs and causal factors to a hazard, based on contemporary hazard analyses techniques. For this purpose, the thesis investigates the use of Domain Specific Modeling (DSM) technologies. The contributions of this thesis is the design and development of a set of three graphical Domain Specific Modeling languages (DSML)s, that when combined together, provide all of the necessary constructs that will enable safety experts and practitioners to conduct hazard and early warning analysis based on a contemporary hazard analysis approach. The languages represent those elements and relations necessary to define accident scenarios and their associated early warning signs. The three DSMLs were incorporated in to a prototype software editor that enables safety scientists and practitioners to create and edit hazard and early warning analysis models in a usable manner and as a result to generate executable code automatically. This research proves that the DSM technologies can be used to develop a set of three DSMLs which can allow user to conduct hazard and early warning analysis in more usable manner. Furthermore, the three DSMLs and their dedicated editor, which are presented in this thesis, may provide a significant enhancement to the process of creating the risk knowledge element of computer based early warning systems.

Modeling power in multi-functionality sensor network applications

Rachit Agarwal, Rafael V. Martinez-Catala, Sean Harte, Cedric Segard, Brendan O'Flynn, "Modeling Power in Multi-functionality Sensor Network Applications," sensorcomm, pp.507-512, 2008 Proceedings of the Second International Conference on Sensor Technologies and Applications, August 25-August 31 2008, Cap Esterel, France

Computational modeling of defects in nanoscale device materials

This PhD thesis concerns the computational modeling of the electronic and atomic structure of point defects in technologically relevant materials. Identifying the atomistic origin of defects observed in the electrical characteristics of electronic devices has been a long-term goal of first-principles methods. First principles simulations are performed in this thesis, consisting of density functional theory (DFT) supplemented with many body perturbation theory (MBPT) methods, of native defects in bulk and slab models of In0.53Ga0.47As. The latter consist of (100) - oriented surfaces passivated with A12O3. Our results indicate that the experimentally extracted midgap interface state density (Dit) peaks are not the result of defects directly at the semiconductor/oxide interface, but originate from defects in a more bulk-like chemical environment. This conclusion is reached by considering the energy of charge transition levels for defects at the interface as a function of distance from the oxide. Our work provides insight into the types of defects responsible for the observed departure from ideal electrical behaviour in III-V metal-oxidesemiconductor (MOS) capacitors. In addition, the formation energetics and electron scattering properties of point defects in carbon nanotubes (CNTs) are studied using DFT in conjunction with Green’s function based techniques. The latter are applied to evaluate the low-temperature, low-bias Landauer conductance spectrum from which mesoscopic transport properties such as the elastic mean free path and localization length of technologically relevant CNT sizes can be estimated from computationally tractable CNT models. Our calculations show that at CNT diameters pertinent to interconnect applications, the 555777 divacancy defect results in increased scattering and hence higher electrical resistance for electron transport near the Fermi level.