Signal Design and Machine Learning Assisted Nonlinearity Compensation for Coherent Optical Fibre Communication Links

This thesis investigates low-complexity digital signal processing (DSP) for signal design and nonlinearity compensation strategies to improve the performance of single-mode optical fibre links over different distance scales. The performance of a novel ML-assisted inverse regular perturbation technique that mitigates fibre nonlinearities was investigated numerically with a dual-polarization 64 quadrature amplitude modulation (QAM) link over 800 km distance. The model outperformed the heuristically-optimised digital backpropagation approach with <5 steps per span and mitigated the gain expansion issue, which limits the accuracy of an untrained model when the balance between the nonlinear and linear components becomes considerable. For short reach links, the phase noise due to low-cost, high-linewidth lasers is a more significant channel impairment. A novel constellation optimisation algorithm was, therefore, proposed to design modulation formats that are robust against both additive white Gaussian noise (AWGN) and the residual laser phase noise (i.e., after carrier phase estimation). Subsequently, these constellations were numerically validated in the context of a 400ZR standard system, and achieved up to 1.2 dB gains in comparison with the modulation formats which were optimised only for the AWGN channel. The thesis concludes by examining a joint strategy to modulate and demodulate signals in a partially-coherent AWGN (PCAWGN) channel. With a low-complexity PCAWGN demapper, 8- to 64-ary modulation formats were designed and validated through numerical simulations. The bit-wise achievable information rates (AIR) and post forward error correction (FEC) bit error rates (BER) of the designed constellations were numerically validated with: the theoretically optimum, Euclidean (conventional), and low-complexity PCAWGN demappers. The resulting constellations demonstrated post-FEC BER shaping gains of up to 2.59 dB and 2.19 dB versus uniform 64 QAM and 64-ary constellations shaped for the purely AWGN channel model, respectively. The described geometric shaping strategies can be used to either relax linewidth and/or carrier phase estimator requirements, or to increase signal-to-noise ratio (SNR) tolerance of a system in the presence of residual phase noise

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Technical Design Report for the PANDA Micro Vertex Detector

This document illustrates the technical layout and the expected performance of the Micro Vertex Detector (MVD) of the PANDA experiment. The MVD will detect charged particles as close as possible to the interaction zone. Design criteria and the optimisation process as well as the technical solutions chosen are discussed and the results of this process are subjected to extensive Monte Carlo physics studies. The route towards realisation of the detector is outlined

Improving decision making for incentivised and weather-sensitive projects

The field of project management has originated from the domain of operational research, which focuses on the mathematical optimization of operational problems. However, in recent decades an increasingly broad perspective has been applied to the field of project management. As such, project management has spawned a number of very active sub- domains, which focus not solely on the scheduling of the project’s baseline, but also on the analysis of risk, as well as the controlling of project execution. This dissertation focuses on two areas where existing literature is still lacking. The first area is the use of incentivised contractual agreements between the owner of a project, and the contractor who is hired to execute the project. Whereas this area has received growing attention in recent years, the majority of studies remained strongly descriptive. Hence, the aim of the first part of this dissertation is to develop a more prescriptive approach from both the owner’s and the contractor’s perspective. The second part of this dissertation investigates the use of dedicated weather models to improve operational performance of weather-sensitive projects. During recent decades, significant effort has been made to improve the quality of weather simulation models. Moreover, the amount of available weather data has been steadily increasing. This opens up a lot of new possibilities for using more precise weather models in order to support operational decision making. In spite of this, the number of applications of these weather models in operational research has remained rather limited. As such, the aim of the second part of this dissertation is to leverage these weather models to improve the scheduling of offshore construction projects, as well as preventive maintenance of offshore wind turbines

Combined optimization algorithms applied to pattern classification

Accurate classification by minimizing the error on test samples is the main goal in pattern classification. Combinatorial optimization is a well-known method for solving minimization problems, however, only a few examples of classifiers axe described in the literature where combinatorial optimization is used in pattern classification. Recently, there has been a growing interest in combining classifiers and improving the consensus of results for a greater accuracy. In the light of the "No Ree Lunch Theorems", we analyse the combination of simulated annealing, a powerful combinatorial optimization method that produces high quality results, with the classical perceptron algorithm. This combination is called LSA machine. Our analysis aims at finding paradigms for problem-dependent parameter settings that ensure high classifica, tion results. Our computational experiments on a large number of benchmark problems lead to results that either outperform or axe at least competitive to results published in the literature. Apart from paxameter settings, our analysis focuses on a difficult problem in computation theory, namely the network complexity problem. The depth vs size problem of neural networks is one of the hardest problems in theoretical computing, with very little progress over the past decades. In order to investigate this problem, we introduce a new recursive learning method for training hidden layers in constant depth circuits. Our findings make contributions to a) the field of Machine Learning, as the proposed method is applicable in training feedforward neural networks, and to b) the field of circuit complexity by proposing an upper bound for the number of hidden units sufficient to achieve a high classification rate. One of the major findings of our research is that the size of the network can be bounded by the input size of the problem and an approximate upper bound of 8 + √2n/n threshold gates as being sufficient for a small error rate, where n := log/SL and SL is the training set

Development of a machine-tooling-process integrated approach for abrasive flow machining (AFM) of difficult-to-machine materials with application to oil and gas exploration componenets

This thesis was submitted for the degree of Doctor of Engineering and awarded by Brunel UniversityAbrasive flow machining (AFM) is a non-traditional manufacturing technology used to expose a substrate to pressurised multiphase slurry, comprised of superabrasive grit suspended in a viscous, typically polymeric carrier. Extended exposure to the slurry causes material removal, where the quantity of removal is subject to complex interactions within over 40 variables. Flow is contained within boundary walls, complex in form, causing physical phenomena to alter the behaviour of the media. In setting factors and levels prior to this research, engineers had two options; embark upon a wasteful, inefficient and poor-capability trial and error process or they could attempt to relate the findings they achieve in simple geometry to complex geometry through a series of transformations, providing information that could be applied over and over. By condensing process variables into appropriate study groups, it becomes possible to quantify output while manipulating only a handful of variables. Those that remain un-manipulated are integral to the factors identified. Through factorial and response surface methodology experiment designs, data is obtained and interrogated, before feeding into a simulated replica of a simple system. Correlation with physical phenomena is sought, to identify flow conditions that drive material removal location and magnitude. This correlation is then applied to complex geometry with relative success. It is found that prediction of viscosity through computational fluid dynamics can be used to estimate as much as 94% of the edge-rounding effect on final complex geometry. Surface finish prediction is lower (~75%), but provides significant relationship to warrant further investigation. Original contributions made in this doctoral thesis include; 1) A method of utilising computational fluid dynamics (CFD) to derive a suitable process model for the productive and reproducible control of the AFM process, including identification of core physical phenomena responsible for driving erosion, 2) Comprehensive understanding of effects of B4C-loaded polydimethylsiloxane variants used to process Ti6Al4V in the AFM process, including prediction equations containing numerically-verified second order interactions (factors for grit size, grain fraction and modifier concentration), 3) Equivalent understanding of machine factors providing energy input, studying velocity, temperature and quantity. Verified predictions are made from data collected in Ti6Al4V substrate material using response surface methodology, 4) Holistic method to translating process data in control-geometry to an arbitrary geometry for industrial gain, extending to a framework for collecting new data and integrating into current knowledge, and 5) Application of methodology using research-derived CFD, applied to complex geometry proven by measured process output. As a result of this project, four publications have been made to-date – two peer-reviewed journal papers and two peer-reviewed international conference papers. Further publications will be made from June 2014 onwards.Engineering and Physical Sciences Research Council (EPSRC) and the Technology Strategy Board (TSB

Segmental rotor switched reluctance machines for use in automotive traction

PhD ThesisThis thesis explores the development of the Segmental Rotor Switched Reluctance Machine (SRM) to provide electric vehicle traction. This electrical machine, which has a topology distinct from the conventional SRM and has been previously shown to offer enhanced torque density, is selected based on its potential to offer a low cost, sustainable alternative to today’s state-of-the-art electric vehicle traction motors. With the launch, as long ago as 1997, of the Toyota Prius Hybrid Electric Vehicle and of the more recent Nissan Leaf Electric Vehicle in 2010, volume produced vehicle traction drives are an established reality. However hurdles remain in order to reduce the cost of electric and hybrid electric vehicles so that they become cost-competitive with more conventional vehicles. From an electrical machine perspective, one clear cost driver stands out; the rare-earth metals which form the key ingredient in today’s class leading electrical machines. These materials are both expensive (>100USD/kg) and, as was seen in 2011 / 2012, subject to significant price volatility. Equally the mining and refinement of rare-earth materials, such as Neodymium, Dysprosium and Samarium, has been shown to have a much higher environmental footprint than that of the other materials typically used in electrical machines. Beyond the elimination of rare-earths, the thesis looks to further improve the sustainability and cost of the Segmental Rotor SRM. Copper conductors, expensive and difficult to recycle at an electrical machine’s end-of-life, are replaced by more easily recycled aluminium. Aluminium windings are compressed, prior to assembly with the electrical machine, in order to achieve very high fill factors to overcome their relatively low electrical conductivity. Methods are also sought to reduce overall material waste and simplify assembly processes; these include computer based optimisation of the motor structure along with the use of modular manufacturing techniques. With the Nissan Leaf’s Neodymium Iron Boron based Interior Permanent Magnet machine selected as a comparator, an 80kW Segmental Rotor SRM is constructed and tested. The design is shown to have promise and a number of industrially funded follow-on projects are now underway in order to develop the technology further for use in a volume electric vehicle application

Failure analysis and mechanical behaviors of metamaterials

In recent years, mechanical metamaterials have been explored for their tunable nature with the continual development of Additive Manufacturing (AM) technologies. As a result, the failure mechanisms of the metamaterials and their mechanical behaviours under different boundary and environmental conditions have been investigated. Firstly, failure mechanisms of AM originated imperfections in the metamaterials have been investigated. In this, three types of imperfection have been considered in the numerical modelling of the metamaterials: distorted struts, missing struts, and strut diameter variation. Then a novel numerical framework was developed to overcome computational difficulties within the existing numerical approaches beyond the elastic region. Three modes of microscopic localisation were observed in metamaterials before failure: crushing band, shear band and void coalescence. The results showed that a clear separation exists between the three modes of localisation depending upon the type and level of defects and loading condition. Under compressive loading, all metamaterials failed due to the crushing band; the distorted lattices are prone to shear band localisation with increased distortion, whereas missing lattices majorly fail due to void coalescence at high missing struts defect. The study on imperfect metamaterials has suggested that it can exhibit either ductile, damage-tolerant behaviours or sudden, catastrophic failure mode, depending on the distribution of the introduced disorderliness. Thus, a data-driven approach has been developed, combining deep-learning and global optimisation algorithms, to tune the distribution of the disorderliness/imperfections to achieve damage-tolerant metamaterial designs. A case study on the metamaterial created from a periodic Face Centred Cubic (FCC) lattice has demonstrated that the optimised metamaterials can generate high-quality designs with improved ductility, enabling them to sustain larger deformations without failure at a lower cost to strength and stiffness. This has been validated by an experimental study on an optimised metamaterial design. The results showed that the optimized designs can achieve up to 100% increase in ductility at the expense of less than 5% stiffness and 8-15% tensile strength. Finally, the creep behaviour of Inconel 718 metamaterial has been investigated at an elevated temperature to understand the effects of the microstructural defects. A Kachanov's damage modelling has been used to predict the creep performance of the metamaterials. The analysis and experimental results indicated that the creep resistance of the metamaterials is dependent on the microstructure and loading conditions. The creep behaviour of the metamaterials is significantly different from that of the bulk material due to their complex microstructure. Overall, this study contributes to the development of mechanical metamaterials with improved mechanical properties using AM technologies. The neural network-based data-driven methodology offers a promising avenue for designing high-quality metamaterials that are cost-effective and have desirable mechanical properties. The results of this study have significant implications for various applications, including structural engineering, biomechanics, and aerospace engineering, including in understanding, and designing for the creep behavior of Inconel 718 metamaterials