Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems

Development of Hierarchical Optimization-based Models for Multiscale Damage Detection

In recent years, health monitoring of structure and infrastructure systems has become a valuable source of information for evaluating structural integrity, durability and reliability throughout the lifecycle of structures as well as ensuring optimal maintenance planning and operation. Important advances in sensor and computer technologies made possible to process a large amount of data, to extract the characteristic features of the signals, and to link those to the current structural conditions. In general, the process of data feature extraction relates to solving an inverse problem, in either a data-driven or a model-based type setting. This dissertation explores state-of-the-art hierarchical optimization-based computational algorithms for solving multiscale model-based inverse problems such as system identification and damage detection. The basic idea is to apply optimization tools to quantify an established model or system, characterized by a set of unknown governing parameters, via minimizing the discrepancy between the predicted system response and the measured data. We herein propose hierarchical optimization algorithms such as the improved artificial bee colony algorithms integrated with local search operators to accomplish this task. In this dissertation, developments in multiscale damage detection are presented in two parts. In the first part, efficient hybrid bee algorithms in both serial and parallel schemes are proposed for time domain input-output and output-only identification of macro-scale linear/nonlinear systems such as buildings and bridges. Solution updating strategies of the artificial bee colony algorithm are improved for faster convergence, meanwhile, the simplex method and gradient-based optimization techniques are employed as local search operators for accurate solution tuning. In the case of output-only measurements, both system parameters and the time history of input excitations can be simultaneously identified using a modified Newmark integration scheme. The synergy between the proposed method and Bayesian inference are proposed to quantify uncertainties of a system. Numerical and experimental applications are investigated and presented for macro-scale system identification, finite element model updating and damage detection. In the second part, a framework combining the eXtended Finite Element Method (XFEM) and the proposed optimization algorithms is investigated, for nondestructive detection of multiple flaws/defects embedded in meso-scale systems such as critical structural components like plates. The measurements are either static strains or displacements. The number of flaws as well as their locations and sizes can be identified. XFEM with circular and/or elliptical void enrichments is employed to solve the forward problem and alleviates the costly re-meshing along with the update of flaw boundaries in the identification process. Numerical investigations are presented to validate the proposed method in application to detection of multiple flaws and damage regions. Overall, the proposed multiscale methodologies show a great potential in assessing the structural integrity of building and bridge systems, critical structural components, etc., leading to a smart structure and infrastructure management system

Multi-objective optimisation using the Bees Algorithm

In the real world, there are many problems requiring the best solution to satisfy numerous objectives and therefore a need for suitable Multi-Objective Optimisation methods. Various Multi-Objective solvers have been developed recently. The classical method is easily implemented but requires repetitive program runs and does not generate a true "Pareto" optimal set. Intelligent methods are increasingly employed, especially population-based optimisation methods to generate the Pareto front in a single run. The Bees Algorithm is a newly developed population-based optimisation algorithm which has been verified in many fields. However, it is limited to solving single optimisation problems. To apply the Bees Algorithm to a Multi- Objective Optimisation Problem, either the problem is converted to single objective optimisation or the Bees Algorithm modified to function as a Multi- Objective solver. To make a problem into a single objective one, the weighted sum method is employed. However, due to failings of this classical method, a new approach is developed to generate a true Pareto front by a single run. This work also introduces an enhanced Bees Algorithm. A new dynamic selection procedure improves the Bees Algorithm by reducing the number of parameters and new neighbourhood search methods are adopted to optimise the Pareto front. The enhanced algorithm has been tested on Multi-Objective benchmark functions and the classical Environmental/Economic power Dispatch Problem (EEDP). The results obtained compare well with those produced by other population- based algorithms. Due to recent trends in renewable energy systems, it is necessary to have a new model of the EEDP. Therefore, the EEDP was amended in conjunction with the Bees Algorithm to identify the best design in terms of energy performance and carbon emission reduction by adopting zero and low carbon technologies. This computer-based tool supports the decision making process in the design of a Low-Carbon City.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Multi-objective optimisation using the Bees Algorithm

In the real world, there are many problems requiring the best solution to satisfy numerous objectives and therefore a need for suitable Multi-Objective Optimisation methods. Various Multi-Objective solvers have been developed recently. The classical method is easily implemented but requires repetitive program runs and does not generate a true "Pareto" optimal set. Intelligent methods are increasingly employed, especially population-based optimisation methods to generate the Pareto front in a single run. The Bees Algorithm is a newly developed population-based optimisation algorithm which has been verified in many fields. However, it is limited to solving single optimisation problems. To apply the Bees Algorithm to a Multi- Objective Optimisation Problem, either the problem is converted to single objective optimisation or the Bees Algorithm modified to function as a Multi- Objective solver. To make a problem into a single objective one, the weighted sum method is employed. However, due to failings of this classical method, a new approach is developed to generate a true Pareto front by a single run. This work also introduces an enhanced Bees Algorithm. A new dynamic selection procedure improves the Bees Algorithm by reducing the number of parameters and new neighbourhood search methods are adopted to optimise the Pareto front. The enhanced algorithm has been tested on Multi-Objective benchmark functions and the classical Environmental/Economic power Dispatch Problem (EEDP). The results obtained compare well with those produced by other population- based algorithms. Due to recent trends in renewable energy systems, it is necessary to have a new model of the EEDP. Therefore, the EEDP was amended in conjunction with the Bees Algorithm to identify the best design in terms of energy performance and carbon emission reduction by adopting zero and low carbon technologies. This computer-based tool supports the decision making process in the design of a Low-Carbon City

Artificial Intelligence Approach for Seismic Control of Structures

Abstract In the first part of this research, the utilization of tuned mass dampers in the vibration control of tall buildings during earthquake excitations is studied. The main issues such as optimizing the parameters of the dampers and studying the effects of frequency content of the target earthquakes are addressed. Abstract The non-dominated sorting genetic algorithm method is improved by upgrading generic operators, and is utilized to develop a framework for determining the optimum placement and parameters of dampers in tall buildings. A case study is presented in which the optimal placement and properties of dampers are determined for a model of a tall building under different earthquake excitations through computer simulations. Abstract In the second part, a novel framework for the brain learning-based intelligent seismic control of smart structures is developed. In this approach, a deep neural network learns how to improve structural responses during earthquake excitations using feedback control. Abstract Reinforcement learning method is improved and utilized to develop a framework for training the deep neural network as an intelligent controller. The efficiency of the developed framework is examined through two case studies including a single-degree-of-freedom system and a high-rise building under different earthquake excitation records. Abstract The results show that the controller gradually develops an optimum control policy to reduce the vibrations of a structure under an earthquake excitation through a cyclical process of actions and observations. Abstract It is shown that the controller efficiently improves the structural responses under new earthquake excitations for which it was not trained. Moreover, it is shown that the controller has a stable performance under uncertainties

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Design and Optimization of Meta-Ferrites for Antenna Applications

PhDThis thesis aims to study the design and optimization of Metamaterial Ferrite (metaferrite) structures for antenna applications. These techniques are primarily focused on various forms of the Genetic Algorithm methods. The motivation for this is to provide generic design tools that will be transformed to the industry for further development of the field, especially for the design of low profile antennas. Several possible applications of this technology have been investigated. Antenna size reduction on platforms with low radar cross sections (RCS) is essential for many practical applications and through metamaterials antennas and associated platforms can be co-designed and optimized. Metamaterials are artificially designed electromagnetic materials or structures which possess properties not existing in nature. Examples include left-handed materials with simultaneous negative permittivity and permeability and zero index media etc. In this project we are particularly interested in the development of two-dimensional metamaterials or “metasurfaces” with the same function as conventional magnetic or magneto-dielectric materials. Ferrites, often used in the design of non-reciprocal microwave devices such as isolators and circulators, are bulky, heavy and lossy for antenna applications. Impedance matched media have been widely used in the design of low-profile antennas and radar absorbers with losses added. Metamaterials and metasurfaces can mimic ferrites at microwave frequencies. These “metaferrites” have attracted interest from academic and industrial communities. In this thesis, a genetic algorithm (GA) is developed incorporated with commercial electromagnetic modelling tools, namely CST Microwave Studio. Antennas with ferrites have been characterized and unique functionalities have been explored. Sample structures have been made based on conventional printed circuit board (PCB) and ink-jet printing technologies. In addition, these structures have been used to augment U-slot patch antennae and the advantages of doing so have been measured. In addition, a new concept of hyperuniform randomness has been applied to the design of metasurfaces with the intention of comparing metaferrite performance with an alternate method aimed at similar goals. It has been demonstrated by numerical simulations that superior properties such as RCS reduction can be achieved with the consideration of trade-offs between the modelling complexity and the device performance.BAE systems and Engineering and Physical Sciences Research Council

Optimización del diseño estructural de pavimentos asfálticos para calles y carreteras

gráficos, tablasThe construction of asphalt pavements in streets and highways is an activity that requires optimizing the consumption of significant economic and natural resources. Pavement design optimization meets contradictory objectives according to the availability of resources and users’ needs. This dissertation explores the application of metaheuristics to optimize the design of asphalt pavements using an incremental design based on the prediction of damage and vehicle operating costs (VOC). The costs are proportional to energy and resource consumption and polluting emissions. The evolution of asphalt pavement design and metaheuristic optimization techniques on this topic were reviewed. Four computer programs were developed: (1) UNLEA, a program for the structural analysis of multilayer systems. (2) PSO-UNLEA, a program that uses particle swarm optimization metaheuristic (PSO) for the backcalculation of pavement moduli. (3) UNPAVE, an incremental pavement design program based on the equations of the North American MEPDG and includes the computation of vehicle operating costs based on IRI. (4) PSO-PAVE, a PSO program to search for thicknesses that optimize the design considering construction and vehicle operating costs. The case studies show that the backcalculation and structural design of pavements can be optimized by PSO considering restrictions in the thickness and the selection of materials. Future developments should reduce the computational cost and calibrate the pavement performance and VOC models. (Texto tomado de la fuente)La construcción de pavimentos asfálticos en calles y carreteras es una actividad que requiere la optimización del consumo de cuantiosos recursos económicos y naturales. La optimización del diseño de pavimentos atiende objetivos contradictorios de acuerdo con la disponibilidad de recursos y las necesidades de los usuarios. Este trabajo explora el empleo de metaheurísticas para optimizar el diseño de pavimentos asfálticos empleando el diseño incremental basado en la predicción del deterioro y los costos de operación vehicular (COV). Los costos son proporcionales al consumo energético y de recursos y las emisiones contaminantes. Se revisó la evolución del diseño de pavimentos asfálticos y el desarrollo de técnicas metaheurísticas de optimización en este tema. Se desarrollaron cuatro programas de computador: (1) UNLEA, programa para el análisis estructural de sistemas multicapa. (2) PSO-UNLEA, programa que emplea la metaheurística de optimización con enjambre de partículas (PSO) para el cálculo inverso de módulos de pavimentos. (3) UNPAVE, programa de diseño incremental de pavimentos basado en las ecuaciones de la MEPDG norteamericana, y el cálculo de costos de construcción y operación vehicular basados en el IRI. (4) PSO-PAVE, programa que emplea la PSO en la búsqueda de espesores que permitan optimizar el diseño considerando los costos de construcción y de operación vehicular. Los estudios de caso muestran que el cálculo inverso y el diseño estructural de pavimentos pueden optimizarse mediante PSO considerando restricciones en los espesores y la selección de materiales. Los desarrollos futuros deben enfocarse en reducir el costo computacional y calibrar los modelos de deterioro y COV.DoctoradoDoctor en Ingeniería - Ingeniería AutomáticaDiseño incremental de pavimentosEléctrica, Electrónica, Automatización Y Telecomunicacione