3,609 research outputs found
Machine learning and mixed reality for smart aviation: applications and challenges
The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency
Systemic Circular Economy Solutions for Fiber Reinforced Composites
This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning
The behaviour of an optimization algorithm when attempting to solve a problem depends on the values assigned to its control parameters. For an algorithm to obtain desirable performance, its control parameter values must be chosen based on the current problem. Despite being necessary for optimal performance, selecting appropriate control parameter values is time-consuming, computationally expensive, and challenging. As the quantity of control parameters increases, so does the time complexity associated with searching for practical values, which often overshadows addressing the problem at hand, limiting the efficiency of an algorithm. As primarily recognized by the no free lunch theorem, there is no one-size-fits-all to problem-solving; hence from understanding a problem, a tailored approach can substantially help solve it.
To predict the performance of control parameter configurations in unseen environments, this thesis crafts an intelligent generalizable framework leveraging machine learning classification and quantitative characteristics about the problem in question. The proposed parameter performance classifier (PPC) framework is extensively explored by training 84 high-accuracy classifiers comprised of multiple sampling methods, fitness types, and binning strategies. Furthermore, the novel framework is utilized in constructing a new parameter-free particle swarm optimization (PSO) variant called PPC-PSO that effectively eliminates the computational cost of parameter tuning, yields competitive performance amongst other leading methodologies across 99 benchmark functions, and is highly accessible to researchers and practitioners. The success of PPC-PSO shows excellent promise for the applicability of the PPC framework in making many more robust parameter-free meta-heuristic algorithms in the future with incredible generalization capabilities
Leveraging Manifold Theory for Trajectory Design - A Focus on Futuristic Cislunar Missions
Optimal control methods for designing trajectories have been studied extensively by astro-dynamicists. Direct and indirect methods provide separate approaches to arrive at the optimal solution, each having their associated advantages and challenges. Among the realm of optimized transfer trajectories, fuel-optimal trajectories are typically most sought and characterized by se-quential thrust and coast arcs.
On the other hand, it is well known that a simplified dynamical model like the CR3BP analyzed in a rotating coordinate system, reveal fixed points known as Lagrange points. These spatial points can be orbited, with researchers categorizing periodic orbits around them starting from the simple planar Lyapunov orbits and continuing to the more enigmatic butterfly orbits. Studying linearized dynamics using eigenanalysis in the vicinity of a point on these periodic orbits lead to interesting departures spatially manifesting into the invariant manifolds.
This thesis delves into the novel idea of merging aspects of invariant manifold theory and indirect optimal control methods to provide efficient computation of feasible transfer trajectories. The marriage of these ideas provide the possibility of alleviating the challenges of an end-to end optimization using indirect methods for a long mission by utilizing the pre-computed and analyzed manifolds for insertion points of a long terminal coast arc. In addition to this, realistic and accurate mission scenarios require consideration of a high-fidelity dynamical model as well as shadow constraints. A methodology to use the āmanifold analoguesā in such cases has been discussed and utilized in this thesis along with modelling of eclipses during optimization, providing mission designers a basis for efficient and accurate/mission-ready trajectory design. This overcomes the shortcomings in state of the art software packages such as MYSTIC and COPERNICUS
Short-term forecast techniques for energy management systems in microgrid applications
A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the
global average temperature rise to less than 2Ā°C. Achieving the set targets involves increasing
energy efficiency and embracing cleaner energy solutions. Although advances in computing
and Internet of Things (IoT) technologies have been made, there is limited scientific research
work in this arena that tackles the challenges of implementing low-cost IoT-based Energy
Management System (EMS) with energy forecast and user engagement for adoption by a
layman both in off-grid or microgrid tied to a weak grid.
This study proposes an EMS approach for short-term forecast and monitoring for hybrid
microgrids in emerging countries. This is done by addressing typical submodules of EMS
namely: load forecast, blackout forecast, and energy monitoring module. A short-term load
forecast model framework consisting of a hybrid feature selection and prediction model was
developed. Prediction error performance evaluation of the developed model was done by
varying input predictors and using the principal subset features to perform supervised training
of 20 different conventional prediction models and their hybrid variants. The proposed
principal k-features subset union approach registered low error performance values than
standard feature selection methods when it was used with the ālinear Support Vector Machine
(SVM)ā prediction model for load forecast. The hybrid regression model formed from a fusion
of the best 2 models (ālinearSVMā and ācubicSVMā) showed improved prediction performance
than the individual regression models with a reduction in Mean Absolute Error (MAE) by
5.4%.
In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and
Random Forest (RF) model for short-term power outage prediction was proposed that predicted
accurately almost half of the blackouts (49.16%), thereby performing slightly better than the
stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart
meter was developed to realize the implemented energy forecast and offer user engagement
through monitoring and control of the microgrid towards the goal of increasing energy
efficiency
Improvement of construction process by adopting lean construction principles: a construction model development
Lessons learned from the construction industry have shown that adopting lean principles within construction processes can significantly enhance the overall success of a construction project. However, currently the potential benefits of such an approach are still not being fully realised in a uniform way. The application of lean principles in construction projects has an underlying aim to increase the value of projects and to eliminate construction waste, in order to achieve project targets of time, cost, and quality while reducing damage to the environment consistent with the underlying principles of sustainable development. This research project presents a newly developed framework that contains a set of lean methods and techniques to support the application of lean principles to construction project practice. In so doing the method helps those within the construction industry to more consistently achieve the full benefits that lean construction approaches can offer.
The aim of this research project is to investigate the lean construction techniques currently used in the industry and the principles of lean construction applications, particularly, the problems and challenges, and develop a new construction process model in which lean methods/tools can be integrated. This will provide an effective and efficient way for managing construction projects in the construction industry. A parallel aim is to improve the construction process to better manage construction waste, time and cost and to improve the levels of quality and sustainability achieved. The adaptation of lean principles with identified enablers has been assessed where a combination of different lean principles and techniques were considered as the main enablers to develop a framework for the construction process. The RIBA Plan of Work was used to integrate and incorporate several lean construction principles and techniques to develop a standardised model where both the construction stages and the associated activities of the construction process in projects can be described.
The underlying philosophy of the developed framework is to increase the efficiency of transformation activities (known as value-adding activities ā processing). The innovative construction process models presented in this research are developed based on the core enablers that can be used to identify and eliminate waste in the construction process. These include set-based concurrent engineering (SBCE) integrated with the Last PlannerĀ® System (LPS) and lean thinking (LT) within traditional construction process activities. A number of measurement and control methods and guidelines for implementation of the framework are presented. In addition, case study materials have been collected from the industry in order to test and validate the framework. The results provide useful information and guidance to the construction industry as a whole.
The novelty and contribution to knowledge of the research includes: improvement of construction process and performance through the development and implementation of an integrated lean-enabled pull flow construction process framework (i.e. pull flow control embedded within lean construction management) integrated with measurement and control methods within the RIBA Plan of Works. The research concludes by suggesting that the most effective way to implement lean methods and techniques in construction activities is to use the framework proposed and developed in this research which is integrated with the RIBA Plan of Work
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