Historically, validation has been perfonned on a case study basis employing visual evaluations, gradually inspiring confidence through continual application. At present, the method of visual evaluation is the most prevalent form of data analysis, as the brain is the best pattern recognition device known. However, the human visual/perceptual system is a complicated mechanism, prone to many types of physical and psychological influences. Fatigue is a major source of inaccuracy within the results of subjects perfonning complex visual evaluation tasks. Whilst physical and experiential differences along with age have an enormous bearing on the visual evaluation results of different subjects. It is to this end that automated methods of validation must be developed to produce repeatable, quantitative and objective verification results. This thesis details the development of the Feature Selective Validation (FSV) method. The FSV method comprises two component measures based on amplitude differences and feature differences. These measures are combined employing a measured level of subjectivity to fonn an overall assessment of the comparison in question or global difference. The three measures within the FSV method are strengthened by statistical analysis in the form of confidence levels based on amplitude, feature or global discrepancies between compared signals. Highly detailed diagnostic infonnation on the location and magnitude of discrepancies is also made available through the employment of graphical (discrete) representations of the three measures. The FSV method also benefits from the ability to mirror human perception, whilst producing infonnation which directly relates human variability and the confidence associated with it. The FSV method builds on the common language of engineers and scientists alike, employing categories which relate to human interpretations of comparisons, namely: 'ideal', 'excellent', 'very good', 'good', 'fair', 'poor' and 'extremely poor' . Quantitative

The doctoral thesis deals with aspects of pre-certification tests of electromagnetic immunity in the automotive industry. The thesis analyses commonly used test methodology and mentions that standardized electromagnetic immunity tests performed by certified testing centres, which are extremely expensive and represents a considerable time load in the development of the vehicle. The thesis therefore proposes innovative ways for replacement of certification tests with an acceptable level of uncertainty. Key methods are seen in numerical simulations and modified test procedures when the vehicle is exposed by electromagnetic field. The proposed methods are deeply explored and their benefits are discussed and support by number of experiments. The results obtained with the pre-certification methods are objectively compared with the results of the certification measurements by FSV method. The work brings a proposal for a comprehensive test methodology including a discussion of risks and uncertainties with related issues.

The purpose of this thesis is to present solutions to overcome several key difficulties that limit the application of numerical modelling in communication cable design and analysis. In particular, specific limiting factors are that simulations are time consuming, and the process of comparison requires skill and is poorly defined and understood. When much of the process of design consists of optimisation of performance within a well defined domain, the use of artificial intelligence techniques may reduce or remove the need for human interaction in the design process. The automation of human processes allows round-the-clock operation at a faster throughput. Achieving a speedup would permit greater exploration of the possible designs, improving understanding of the domain. This thesis presents work that relates to three facets of the efficiency of numerical modelling: minimizing simulation execution time, controlling optimization processes and quantifying comparisons of results. These topics are of interest because simulation times for most problems of interest run into tens of hours. The design process for most systems being modelled may be considered an optimisation process in so far as the design is improved based upon a comparison of the test results with a specification. Development of software to automate this process permits the improvements to continue outside working hours, and produces decisions unaffected by the psychological state of a human operator. Improved performance of simulation tools would facilitate exploration of more variations on a design, which would improve understanding of the problem domain, promoting a virtuous circle of design. The minimization of execution time was achieved through the development of a Parallel TLM Solver which did not use specialized hardware or a dedicated network. Its design was novel because it was intended to operate on a network of heterogeneous machines in a manner which was fault tolerant, and included a means to reduce vulnerability of simulated data without encryption. Optimisation processes were controlled by genetic algorithms and particle swarm optimisation which were novel applications in communication cable design. The work extended the range of cable parameters, reducing conductor diameters for twisted pair cables, and reducing optical coverage of screens for a given shielding effectiveness. Work on the comparison of results introduced ―Colour maps‖ as a way of displaying three scalar variables over a two-dimensional surface, and comparisons were quantified by extending 1D Feature Selective Validation (FSV) to two dimensions, using an ellipse shaped filter, in such a way that it could be extended to higher dimensions. In so doing, some problems with FSV were detected, and suggestions for overcoming these presented: such as the special case of zero valued DC signals. A re-description of Feature Selective Validation, using Jacobians and tensors is proposed, in order to facilitate its implementation in higher dimensional spaces.