Inference for Unreplicated Factorial and Fractional Factorial Designs.

The ability to determine which factors significantly affect a product or process can help to improve its quality. Usually there are many factors to be considered initially, but a limited amount of time and money, so it is important to screen the numerous factors with a limited number of experimental trials. In this situation, unreplicated factorial and fractional factorial designs are often used, but because these experiments are unreplicated they do not possess a formal estimate of the experimental error variance. Several methods have been proposed by Daniel, Box and Meyer, Benski, Lenth, and Schneider, Kasperski, and Weissfeld to determine the significant effects in these experiments. This research focuses on an in-depth comparison of the aforementioned methods under a variety of practical situations commonly found in industrial experiments. Each method will be critically evaluated, with the culmination of the work being a recommendation for the use of the various methods

The automatic design of experiments : Some practical algorithms.

The purpose of this study was to develop a methodology, represented as a set of programmable algorithms, for the design of experiments of the types that are generally likely to be useful in the physical sciences. This has beenachieved by adding to the established theory and practice of designing factorial experiments for both qualitative and quantitative variables. Algorithms were developed for designing fractional two-level factorial experiments according to a pre-specified model to be fitted, expressed in terms of required effects to be estimated. These algorithms are extended in two ways.One of these is to allow a fractional two-level factorial design to be augmented with extra points so that quadratic effects can be estimated. The second is to enable fractional asymmetric multi-level factorial experiments to be designed: balanced fractions first by applying the theory of cyclic groups; then further reduction in the size of the design by using the trace and determinant of the information matrix. The application of the algorithms is illustrated with examples drawn from the physical sciences, particularly metallurgy. The algorithms developed in the study have been fully implemented using standard Fortran 4 with a few specified exceptions. These programs are listed in three appendices. The programs have been run on computers in research laboratories in Australia' and the United States as well as in Britain. They will benefit research scientists who are planning experiments and have access to interactive computers.The principles of algorithmic development are explained and the whole text is supported by references and by a glossary of more important terms

Design of Experiments

This book is a research publication that covers original research on developments within the Design of Experiments - Applications field of study. The book is a collection of reviewed scholarly contributions written by different authors and edited by Dr. Messias Borges Silva. Each scholarly contribution represents a chapter and each chapter is complete in itself but related to the major topics and objectives. The target audience comprises scholars and specialists in the field

Vision-based neural network classifiers and their applications

A thesis submitted for the degree of Doctor of Philosophy of University of LutonVisual inspection of defects is an important part of quality assurance in many fields of production. It plays a very useful role in industrial applications in order to relieve human inspectors and improve the inspection accuracy and hence increasing productivity. Research has previously been done in defect classification of wood veneers using techniques such as neural networks, and a certain degree of success has been achieved. However, to improve results in tenus of both classification accuracy and running time are necessary if the techniques are to be widely adopted in industry, which has motivated this research. This research presents a method using rough sets based neural network with fuzzy input (RNNFI). Variable precision rough set (VPRS) method is proposed to remove redundant features utilising the characteristics of VPRS for data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable foml for input to an improved BP neural network classifier. The improved BP neural network classifier is improved in three aspects: additional momentum, self-adaptive learning rates and dynamic error segmenting. Finally, to further consummate the classifier, a uniform design CUD) approach is introduced to optimise the key parameters because UD can generate a minimal set of uniform and representative design points scattered within the experiment domain. Optimal factor settings are achieved using a response surface (RSM) model and the nonlinear quadratic programming algorithm (NLPQL). Experiments have shown that the hybrid method is capable of classifying the defects of wood veneers with a fast convergence speed and high classification accuracy, comparing with other methods such as a neural network with fuzzy input and a rough sets based neural network. The research has demonstrated a methodology for visual inspection of defects, especially for situations where there is a large amount of data and a fast running speed is required. It is expected that this method can be applied to automatic visual inspection for production lines of other products such as ceramic tiles and strip steel

An agent-based stochastic volatility model

The behavioral origins of the stylized facts of financial returns have been addressed in a growing body of agent-based models of financial markets. While the traditional efficient market viewpoint explains all statistical properties of returns by similar features of the news arrival process, the more recent behavioral finance models explain them as imprints of universal patterns of interaction among investors. In this thesis, we contribute to this literature by introducing a very simple agent-based model in which the ubiquitous stylized facts (fat tails, volatility clustering) are emergent properties of the interaction among traders. The simplicity of the model allows us to estimate the underlying parameters, since it is possible to derive a closed form solution for the distribution of returns. The big advantage of our model with respect to the models proposed in the financial econometrics is the ability to explain the origin of the randomness present in the market. It is in fact, very clear how the interactions based on herding among agents play the crucial rule in the emergence of the market fluctuations. We can precisely identify the source of the aggregate regularities of the returns in terms of the agents behavioral assumptions

Electron Beam Processing of Materials

This Special Issue reprint presents articles from researchers working on materials processing via electron beams as well as on their characterization, properties, and applications. The articles presented cover various topics, including metal melting and welding, additive manufacturing, electron beam irradiation, electron beam lithography, process modeling, etc

Application of Multidisciplinary Design Optimisation Frameworks for Engine Mapping and Calibration

With ever-increasing numbers of engine actuators to calibrate within increasingly stringent emissions legislation, the engine mapping and calibration task of identifying optimal actuator settings is much more difficult. The aim of this research is to evaluate the feasibility and effectiveness of the Multidisciplinary Design Optimisation (MDO) frameworks to optimise the multi-attribute steady state engine calibration optimisation problems. Accordingly, this research is concentrated on two aspects of the steady state engine calibration optimisation: 1) development of a sequential Design of Experiment (DoE) strategy to enhance the steady state engine mapping process, and 2) application of different MDO architectures to optimally calibrate the complex engine applications. The validation of this research is based on two case studies, the mapping and calibration optimisation of a JLR AJ133 Jaguar GDI engine; and calibration optimisation of an EU6 Jaguar passenger car diesel engine. These case studies illustrated that: -The proposed sequential DoE strategy offers a coherent framework for the engine mapping process including Screening, Model Building, and Model Validation sequences. Applying the DoE strategy for the GDI engine case study, the number of required engine test points was reduced by 30 – 50 %. - The MDO optimisation frameworks offer an effective approach for the steady state engine calibration, delivering a considerable fuel economy benefits. For instance, the MDO/ATC calibration solution reduced the fuel consumption over NEDC drive cycle for the GDI engine case study (i.e. with single injection strategy) by 7.11%, and for the diesel engine case study by 2.5%, compared to the benchmark solutions.UK Technology Strategy Board (TSB

Kinetics, Technology and Characterisation of Impurity-Free Vacancy Disordering for Photonic Devices in GaAs-AlGaAs

The work presented in this thesis studies the kinetics, technology, and characterisation methods used for the impurity-free vacancy disordering process using dielectric cap annealing technique. Statistical models for defect diffusion have successfully described the kinetics of compositional intermixing in GaAs. Order of magnitude agreement between the predicted and experimentally measured PL shifts was obtained. Various dielectric caps have been investigated when studying the technology of dielectric cap annealing induced intermixing, of which SrF2, SiO2, SiO2:P are most important. A selective intermixing process using only SiO2 was also developed, by processing the caps in oxygen plasma to suppress intermixing. Differential shifts in excess of 100 meV at anneal temperature of 92

A theory of dissociative ground state lasers

Imperial Users onl

Biological Protein Patterning Systems across the Domains of Life: from Experiments to Modelling

Distinct localisation of macromolecular structures relative to cell shape is a common feature across the domains of life. One mechanism for achieving spatiotemporal intracellular organisation is the Turing reaction-diffusion system (e.g. Min system in the bacterium Escherichia coli controlling in cell division). In this thesis, I explore potential Turing systems in archaea and eukaryotes as well as the effects of subdiffusion. Recently, a MinD homologue, MinD4, in the archaeon Haloferax volcanii was found to form a dynamic spatiotemporal pattern that is distinct from E. coli in its localisation and function. I investigate all four archaeal Min paralogue systems in H. volcanii by identifying four putative MinD activator proteins based on their genomic location and show that they alter motility but do not control MinD4 patterning. Additionally, one of these proteins shows remarkably fast dynamic motion with speeds comparable to eukaryotic molecular motors, while its function appears to be to control motility via interaction with the archaellum. In metazoa, neurons are highly specialised cells whose functions rely on the proper segregation of proteins to the axonal and somatodendritic compartments. These compartments are bounded by a structure called the axon initial segment (AIS) which is precisely positioned in the proximal axonal region during early neuronal development. How neurons control these self-organised localisations is poorly understood. Using a top-down analysis of developing neurons in vitro, I show that the AIS lies at the nodal plane of the first non-homogeneous spatial harmonic of the neuron shape while a key axonal protein, Tau, is distributed with a concentration that matches the same harmonic. These results are consistent with an underlying Turing patterning system which remains to be identified. The complex intracellular environment often gives rise to the subdiffusive dynamics of molecules that may affect patterning. To simulate the subdiffusive transport of biopolymers, I develop a stochastic simulation algorithm based on the continuous time random walk framework, which is then applied to a model of a dimeric molecular motor. This provides insight into the effects of subdiffusion on motor dynamics, where subdiffusion reduces motor speed while increasing the stall force. Overall, this thesis makes progress towards understanding intracellular patterning systems in different organisms, across the domains of life