Evolving machine learning and deep learning models using evolutionary algorithms

Despite the great success in data mining, machine learning and deep learning models are yet subject to material obstacles when tackling real-life challenges, such as feature selection, initialization sensitivity, as well as hyperparameter optimization. The prevalence of these obstacles has severely constrained conventional machine learning and deep learning methods from fulfilling their potentials. In this research, three evolving machine learning and one evolving deep learning models are proposed to eliminate above bottlenecks, i.e. improving model initialization, enhancing feature representation, as well as optimizing model configuration, respectively, through hybridization between the advanced evolutionary algorithms and the conventional ML and DL methods. Specifically, two Firefly Algorithm based evolutionary clustering models are proposed to optimize cluster centroids in K-means and overcome initialization sensitivity as well as local stagnation. Secondly, a Particle Swarm Optimization based evolving feature selection model is developed for automatic identification of the most effective feature subset and reduction of feature dimensionality for tackling classification problems. Lastly, a Grey Wolf Optimizer based evolving Convolutional Neural Network-Long Short-Term Memory method is devised for automatic generation of the optimal topological and learning configurations for Convolutional Neural Network-Long Short-Term Memory networks to undertake multivariate time series prediction problems. Moreover, a variety of tailored search strategies are proposed to eliminate the intrinsic limitations embedded in the search mechanisms of the three employed evolutionary algorithms, i.e. the dictation of the global best signal in Particle Swarm Optimization, the constraint of the diagonal movement in Firefly Algorithm, as well as the acute contraction of search territory in Grey Wolf Optimizer, respectively. The remedy strategies include the diversification of guiding signals, the adaptive nonlinear search parameters, the hybrid position updating mechanisms, as well as the enhancement of population leaders. As such, the enhanced Particle Swarm Optimization, Firefly Algorithm, and Grey Wolf Optimizer variants are more likely to attain global optimality on complex search landscapes embedded in data mining problems, owing to the elevated search diversity as well as the achievement of advanced trade-offs between exploration and exploitation

Visible Light Communication (VLC)

Visible light communication (VLC) using light-emitting diodes (LEDs) or laser diodes (LDs) has been envisioned as one of the key enabling technologies for 6G and Internet of Things (IoT) systems, owing to its appealing advantages, including abundant and unregulated spectrum resources, no electromagnetic interference (EMI) radiation and high security. However, despite its many advantages, VLC faces several technical challenges, such as the limited bandwidth and severe nonlinearity of opto-electronic devices, link blockage and user mobility. Therefore, significant efforts are needed from the global VLC community to develop VLC technology further. This Special Issue, “Visible Light Communication (VLC)”, provides an opportunity for global researchers to share their new ideas and cutting-edge techniques to address the above-mentioned challenges. The 16 papers published in this Special Issue represent the fascinating progress of VLC in various contexts, including general indoor and underwater scenarios, and the emerging application of machine learning/artificial intelligence (ML/AI) techniques in VLC

Multiphase flow modelling for enhanced oil and gas drilling and production

From the exploration to the abandonment of an oil and gas discovery, operators and engineers are constantly faced with the challenge of achieving the best commercial potential of oil fields. Although the petroleum engineering community has significantly contributed towards maximising the potential of discovered prospects, the approach adopted so far has been compartmentalised with little (heuristics-based) or no quality integration. The highly interconnected nature of the decision factors affecting the management of any field requires increased implementation of Computer-Aided Process Engineering (CAPE) methods, thus presenting a task for which chemical engineers have the background to make useful contributions. Drilling and production are the two primary challenging operations of oilfield activities, which span through different time horizons with both fast and slow-paced dynamics. These attributes of these systems make the application of modelling, simulation, and optimisation tasks difficult. This PhD project aims to improve field planning and development decisions from a Process Systems Engineering (PSE) perspective via numerical (fluid dynamics) simulations and modelbased deterministic optimisation of drilling and production operations, respectively. Also demonstrated in this work is the importance of deterministic optimisation as a reliable alternative to classical heuristic methods. From a drilling operation perspective, this project focuses on the application of Computational Fluid Dynamics (CFD) as a tool to understand the intricacies of cuttings transport (during wellbore cleaning) with drilling fluids of non-Newtonian rheology. Simulations of two-phase solid-liquid flows in an annular domain are carried out, with a detailed analysis on the impact of several drilling parameters (drill pipe eccentricity, inclination angle, drill pipe rotation, bit penetration rate, fluid rheology, and particle properties) on the cuttings concentration, pressure drop profiles, axial fluid, and solid velocities. The influence of the flow regime (laminar and turbulent) on cuttings transport efficiency is also examined using the Eulerian-Eulerian and Lagrangian-Eulerian modelling methods. With experimentally validated simulations, this aspect of the PhD project provides new understanding on the interdependence of these parameters; thus facilitating industrial wellbore cleaning operations. The second part of this project applies mathematical optimisation techniques via reduced-order modelling strategies for the enhancement of petroleum recovery under complex constraints that characterise production operations. The motivation for this aspect of the project stems from the observation that previous PSE-based contributions aimed at enhancing field profitability, often apply over-simplifications of the actual process or neglect some key performance indices due to problem complexity. However, this project focuses on a more detailed computational integration and optimisation of the models describing the whole field development process from the reservoir to the surface facilities to ensure optimal field operations. Nonlinear Programs (NLPs), Mixed-Integer Linear Programs (MILPs), and Mixed-Integer Nonlinear Programs (MINLPs) are formulated for this purpose and solved using high-fidelity simulators and algorithms in open-source and commercial solvers. Compared to previous studies, more flow physics are incorporated and rapid computations obtained, thus enabling real-time decision support for enhanced production in the oil and gas industry

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Open Data and Models for Energy and Environment

This Special Issue aims at providing recent advancements on open data and models. Energy and environment are the fields of application.For all the aforementioned reasons, we encourage researchers and professionals to share their original works. Topics of primary interest include, but are not limited to:Open data and models for energy sustainability;Open data science and environment applications;Open science and open governance for Sustainable Development Goals;Key performance indicators of data-aware energy modelling, planning and policy;Energy, water and sustainability database for building, district and regional systems; andBest practices and case studies

Blast Propagation and Damage in Urban Topographies

For many years, terrorism has threatened life, property and business. Targets are largely in urban areas where there is a greater density of life and economic value. Governments, insurers and engineers have sought to mitigate these threats through understanding the effects of urban bombings, increasing the resilience of buildings and improving estimates of financial loss for insurance purposes. This has led to a desire for an improved approach to the prediction of blast propagation in urban cityscapes. Urban geometry has a significant impact on blast wave propagation. Presently, only computational fluid dynamics (CFD) methods adequately simulate these effects. However, for large-scale urban domains, these methods are both challenging to use and are computationally expensive. Adaptive mesh refinement (AMR) methods alleviate the problem, but are difficult to use for the non-expert and require significant tuning. We aim to make CFD urban blast simulation a primary choice for governments, insurers and engineers through improvements to AMR and by studying the performance of CFD in relation to other methods used by the industry. We present a new AMR flagging approach based on a second derivative error norm for compressive shocks (ENCS). This is compared with existing methods and is shown to lead to a reduction in overall refinement without affecting solution quality. Significant improvements to feature tracking over long distances are demonstrated, making the method easier to tune and less obtuse to non-experts. In the chapter that follows, we consider blast damage in urban areas. We begin with a validation and a numerical study, investigating the effects of simple street geometry on blast resultants. We then investigate the sensitivity of their distribution to the location of the charge. We find that moving the charge by a small distance can lead to a significant change in peak overpressures and creates a highly localised damage field due to interactions between the blast wave and the geometry. We then extend the investigation to the prediction of insured losses following a large-scale bombing in London. A CFD loss model is presented and compared with simpler approaches that do not account for urban geometry. We find that the simpler models lead to significant over-predictions of loss, equivalent to several hundred million pounds for the scenario considered. We use these findings to argue for increased uptake of CFD methods by the insurance industry. In the final chapter, we investigate the influence of urban geometry on the propagation of blast waves. An earlier study on the confinement effects of narrow streets is repeated at a converged resolution and we corroborate the findings. We repeat the study, this time introducing a variable porosity into the building facade. We observe that the effect of this porosity is as significant as the confinement effect, and we recommend to engineers that they consider porosity effects in certain cases. We conclude the study by investigating how alterations to building window layout can improve the protective effects of a facade. Maintaining the window surface area constant, we consider a range of layouts and observe how some result in significant reductions to blast strength inside the building

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance