OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Scientific Advances in STEM: From Professor to Students

This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Annual Research Report, 2010-2011

Annual report of collaborative research projects of Old Dominion University faculty and students in partnership with business, industry and government.https://digitalcommons.odu.edu/or_researchreports/1000/thumbnail.jp

12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"

Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin

Design and Analysis of Electric Powertrains for Offshore Drilling Applications

Doktorgradsavhandling ved Institutt for ingeniørvitenskap, Universitetet i Agder, 2016The global energy market is challenged with an ever increasing need for resources to meet the growing demands for electric power, transportation fuels, etc. Although we witness the expansion of the renewable energy industry, it is still the fossil fuels, with oil and gas dominating the scene of global energy supply sector, that provide majority of worldwide power generation.However, many of the easily accessible hydrocarbon reserves are depleted which requires from the producers of drilling equipment to focus on cost-effective operations and technology to compete in a challenging market. Particularly high level of activity is observed in both industry and academia in the field of electrical actuation systems of drilling machines, as control methods of alternating current (AC) motor drives have become an industrially mature technology over the past few decades. In addition, state-of-the-art AC motors manufacturing processes allow to conform to the strict requirements for safe operation of electrical equipment in explosive atmospheres. These two main reasons made electric actuation systems a tough competitor to hydraulic powertrains used traditionally by the industry. However, optimal design of induction motor drives and systematic analysis of factors associated with operation in harsh offshore conditions are still considered as a major challenge. In this thesis, effective methods for design and analysis of induction motor drives are proposed, including aspects of optimization and simulation-based engineering. The first part of the thesis is devoted to studying methods for modeling, control, and identification of induction machines operating in offshore drilling equipment with the focus to improve their reliability, extend lifetime, and avoid faults and damage, whereas the second part introduces more general approaches to the optimal selection of components of electric drivetrains and to the improvement of the existing dimensioning guidelines. A multidisciplinary approach to design of actuation systems is explored in this thesis by studying the areas of motion control, condition monitoring, and thermal modeling of electric powertrains with an aspiration to reach the level of design sophistication which goes beyond what is currently considered an industrial standard. We present a technique to reproduce operation of a full-scale offshore drilling machine on a scaled-down experimental setup to estimate the mechanical load that the designed powertrain must overcome to meet the specification requirements. The same laboratory setup is used to verify the accuracy of the estimation and control method of an induction motor drive based on the extended Kalman filter (EKF) to confirm that the sensorless control techniques can reduce the number of data acquisition devices in offshore machines, and thus decrease their failure rate without negatively affecting their functionality. To address the challenge of condition monitoring of induction motor drives, we propose a technique to assess the expected lifetime of electric drivetrain components when subjected to the desired duty cycles by comparing the effects of a few popular motion control signals on the cumulative damage and vibrations. As a result, the information about the influence of a given control strategy on drivetrain lifecycle is made available early in the design stage which can significantly affect the choice of the optimal powertrain components. The results show that some of the techniques that have a well-proven track record in other industries can be successfully applied to solve challenges associated with operation of offshore drilling machines. One of the most essential contributions of this thesis, optimal selection of drivetrain components, is based on formulating the drivetrain dimensioning problem as a mixed integer optimization program. The components of powertrain that satisfy the design constraints and are as cost-effective as possible are found to be the global optimum, contrary to the functionality offered by some commercially available drivetrain sizing software products. Another important drawback of the dimensioning procedures recommended by the motor drives manufacturers is the inability to assess if the permissible temperature limits given in the standards do not become violated when the actuation system experiences overloads different than these tabulated in the catalogs. Hence, the second most significant contribution is to propose a method to monitor thermal performance of induction motor drives that is based exclusively on publicly available catalog data and allows for evaluating whether the standard thermal performance limits are violated or not under arbitrary load conditions and at any ambient temperature. Both these solutions can effectively enrich the industrially accepted dimensioning procedures to satisfy the level of conservatism that is demanded by the offshore drilling business but, at the same time, provide improved efficiency and flexibility of the product design process and guarantee optimality (quantitatively, not qualitatively, measurable) of the final solution. An attractive direction for additional development is to further integrate knowledge from different fields relevant to electric powertrains to enable design of tailored solutions without compromising on their cost and performance

JTIT

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Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters