Estimates of the time-varying standard deviation of the surface EMG signal (EMGσ) are extensively used in the field of EMG-torque estimation. The use of a whitening filter can substantially improve the accuracy of EMGσ estimation by removing the signal correlation and increasing the statistical bandwidth. However, a subject-specific whitening filter which is calibrated to each subject, is quite complex and inconvenient. To solve this problem, we first calibrated a 60th-order “Universal” FIR whitening filter by using the ensemble mean of the inverse of the square root of the power spectral density (PSD) of the noise-free EMG signal. Pre-existing data from elbow contraction of 64 subjects, providing 512 recording trials were used. The test error on an EMG-torque task based on the “Universal” FIR whitening filter had a mean error of 4.80% maximum voluntary contraction (MVC) with a standard deviation of 2.03% MVC. Meanwhile the subject-specific whitening filter had performance of 4.84±1.98% MVC (both have a whitening band limit at 600 Hz). These two methods had no statistical difference. Furthermore, a 2nd-order IIR whitening filter was designed based on the magnitude response of the “Universal” FIR whitening filter, via the differential evolution algorithm. The performance of this IIR whitening filter was very similar to the FIR filter, with a performance of 4.81±2.12% MVC. A statistical test showed that these two methods had no significant difference either. Additionally, a complete theory of EMG in additive measured noise contraction modeling is described. Results show that subtracting the variance of whitened noise by computing the root difference of the square (RDS) is the correct way to remove noise from the EMG signal.

Nesting problems have been present for as long as mankind exists. Present days these problems occur in many different industries, e.g. textile, paper, wood, metal and glass industry. These industries produce massive amounts of products to answer the global demand. To minimize the material waste making these products, a good cutting and packing layout is beneficial. The last three decades, researchers have focused on developing methods to solve these problems through computing, instead of solving them manually. Many possible solutions have been found, each method focusing on the specifications of the problem. This thesis had two sub-objectives. The first one was to find the best method for nesting optimization, by doing an intensive literature study. The second sub-objective was to work with a previous made program that is capable of doing optimization tests, containing a nesting optimization method, and try to improve this method to get better results, using the literature study. At a certain point in this project, based on the progress of the literature study and knowledge acquired on the in-house developed program, a decision had to be made either to continue with the previous developed method or to try a new method. A lot of ideas from the literature where used and implemented to improve the method leading to improving results. Hence, the choice was made to continue working with the previous developed method. A new placement strategy was introduced in the program. Additional program code to improve stencil evaluation was added. A proper user interface was created. At the end of this project, a nesting optimization method was obtained, capable of producing a feasible solution when solving a nesting problem, within a reasonable amount of time.

No / The synthesis of heat exchanger network (HEN) is a comprehensive approach to optimize energy utilization in process industry. Recent developments in HEN synthesis (HENS) present several heuristic methods, such as Simulated Annealing (SA), Genetic Algorithm (GA), and Differential Evolution (DE). In this work, DE method for synthesis and optimization of HEN has been presented. Using DE combined with the concept of super-targeting, the ΔTmin optimization is determined. Then DE algorithm is employed to optimize the global cost function including the constraints, such as heat balance, the temperatures of process streams. A case study has been optimized using DE, generated structure of HEN and compared with networks obtained by other methods such as pinch technology or mathematical programming. Through the result, the proposed method has been illustrated that DE is able to apply in HEN optimization, with 16.7% increase in capital cost and 56.4%, 18.9% decrease in energy, global costs respectively.

Nowadays modern electric power systems consist of large-scale and highly complex interconnected transmission systems, thus transmission expansion planning (TEP) is now a significant power system optimisation problem. The TEP problem is a large-scale, complex and nonlinear combinatorial problem of mixed integer nature where the number of candidate solutions to be evaluated increases exponentially with system size. The accurate solution of the TEP problem is essential in order to plan power systems in both an economic and efficient manner. Therefore, applied optimisation methods should be sufficiently efficient when solving such problems. In recent years a number of computational techniques have been proposed to solve this efficiency issue. Such methods include algorithms inspired by observations of natural phenomena for solving complex combinatorial optimisation problems. These algorithms have been successfully applied to a wide variety of electrical power system optimisation problems. In recent years differential evolution algorithm (DEA) procedures have been attracting significant attention from the researchers as such procedures have been found to be extremely effective in solving power system optimisation problems. The aim of this research is to develop and apply a novel DEA procedure directly to a DC power flow based model in order to efficiently solve the TEP problem. In this thesis, the TEP problem has been investigated in both static and dynamic form. In addition, two cases of the static TEP problem, with and without generation resizing, have also been investigated. The proposed method has achieved solutions with good accuracy, stable convergence characteristics, simple implementation and satisfactory computation time. The analyses have been performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm (CGA) procedures and a detailed comparison has also been presented. Finally, the sensitivity of DEA control parameters has also been investigated.

The thesis deals with the design and implementation of algorithms for import maps into the simulator TRASI. These algorithms are capable of import map from map portal OpenStreetMaps to the simulation environment. The work deals with adjusting the internal structure of the imported intersections, so that their structure was correct according to the rules of traffic. The work deals with the design and implementation of differential evolution for the design of the structure of intersections.

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Submitted by Rafael Santana (rafael.silvasantana@ufpe.br) on 2017-04-19T18:10:57Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Versão Final.pdf: 1475057 bytes, checksum: 12f4f23219e2dd8cfd695075bb204ac4 (MD5) / Made available in DSpace on 2017-04-19T18:10:57Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Versão Final.pdf: 1475057 bytes, checksum: 12f4f23219e2dd8cfd695075bb204ac4 (MD5) Previous issue date: 2016-08-23 / CAPES / Este trabalho trata de sistemas reparáveis que sofrem reparo imperfeito, utilizando uma classe de modelos de processos estocásticos conhecida como Processo de Renovação Generalizado (PRG), que é um modelo de idade virtual que determina a classificação do reparo de acordo com o grau de redução que este proporciona sob a idade real do equipamento, mensurada através de um parâmetro de rejuvenescimento, , e este modelo permite inserir uma maior flexibilidade quanto ao tratamento de dados de falhas. Foi proposto um modelo PRG com base na distribuição -Exponencial ( -PRG), onde o sucesso da Exponencial deve-se, em parte, à sua capacidade de exposições a caudas pesadas e fenômenos de lei de potência. Os estimadores de máxima verossimilhança não apresentaram expressões analíticas e, então, a estimação dos parâmetros -PRG foi realizada por meio do algoritmo evolucionário Differential Evolution (DE), que é algoritmo estocástico para resolver problemas de otimização global de funções não lineares, ou seja, é um método para minimizar funções não lineares e não diferenciáveis em um espaço contínuo de busca. Com base no método DE, foram realizadas simulações a partir de dados de falha extraídos da literatura. A partir das simulações executadas utilizando o método bootstrap paramétrico, mesmo existindo valores discrepantes, o processo de simulação manteve as características dos dados iniciais, de modo que informações sobre as falhas não foram perdidas. Com as simulações, concluiu-se que para tamanhos amostrais maiores, as abordagens bootstrap utilizadas tendem a fornecer estimativas intervalares semelhantes para os parâmetros -PRG. Além disso, foi possível obter alguns resultados estatísticos para os estimadores como a ausência de normalidade e estimar o parâmetro de rejuvenescimento do PRG. / This work deals with repairable systems that undergo imperfect repair, using a class of stochastic process models known as Generalized Renewal Process (GRP), which is a virtual age model that determines the classification of the repair according to the degree of reduction that This provides, under the real age of the equipment, measured through a rejuvenation parameter, , and this model allows to insert a greater flexibility in the treatment of data of failures. A GRP model was proposed based on the -Exponential distribution ( -GRP), where -Exponential success is due, in part, to its ability to expose heavy tails and power law phenomena. The maximum likelihood estimators did not present analytical expressions and, therefore, the estimation of the -GRP parameters was performed using the evolutionary algorithm Differential Evolution (DE), which is a stochastic algorithm to solve problems of global optimization of non-linear functions, that is, is a method to minimize non-linear and non-differentiable functions in a continuous search space. Based on the DE method, simulations were performed based on fault data extracted from the literature. From the simulations performed using the parametric bootstrap method, even if there were discrepant values, the simulation process maintained the characteristics of the initial data, so that information about the failures was not lost. With the simulations, it was concluded that for larger sample sizes, the bootstrap approaches used tend to provide similar interval estimates for the -GRP parameters. In addition, it was possible to obtain some statistical results for the estimators such as the absence of normality and to estimate the GRP rejuvenation parameter.

Satellite formation missions allow for scientific measurement opportunities that are only otherwise possible with the use of unrealistically large satellites. This work applies the Evolutionary Algorithm (EA), Differential Evolution (DE), to a 4-satellite mission design that borrows heavily from the mission specifications for Phase 1 of NASA's Magnetospheric Multi-Scale Mission (MMS). This mission specifies goals for formation "quality" and size over the arc when scientific measurements are to be taken known as the Region of Interest (ROI). To apply DE to this problem a novel definition of fitness is developed and tailored to trajectory problems of the parameter scales of this mission. This method uses numerical integration of evolved initial conditions for trajectory determination. This approach allows for the inclusion of gravitational perturbations without altering the method. Here, the J2 oblateness correction is considered but other inclusions such as solar radiation pressure and other gravitational bodies are readily possible by amending the governing equations of integration which are stored outside of the method and called only during evaluation. A set of three launch conditions is evaluated using this method. Due to computational limitation, the design is restricted to only single-impulse maneuvers at launch and the ROI is initially restricted but then expanded through a process known here as "staging". The ROIs of tests are expanded until they fail to meet performance criteria; no result was able to stage to the full MMS specified $\pm20^\circ$ ROI but this is a result of the single-impulse restriction. The number of orbits a launch condition is able to meet performance criteria is also investigated. Revolutions considered and the ROIs therein contained are staged to investigate if the method is able to handle this additional problem space. Evidence of suitable formation trajectories found by this method is here presented.

Process-based models are used in a diverse array of fields, including environmental engineering to provide supporting information to engineers, policymakers and stakeholdes. Recent advances in remote sensing and data storage technology have provided opportunities for improving the application of process-based models and visualizing data, but also present new challenges. The availability of larger quantities of data may allow models to be constructed and calibrated in a more thorough and precise manner, but depending on the type and volume of data, it is not always clear how to incorporate the information content of these data into a coherent modeling framework. In this context, using process-based models in new ways to provide decision support or to produce more complete and flexible predictive tools is a key task in the modern data-rich engineering world. In standard usage, models can be used for simulating specific scenarios; they can also be used as part of an automated design optimization algorithm to provide decision support or in a data-assimilation framework to incorporate the information content of ongoing measurements. In that vein, this thesis presents and demonstrates extensions and refinements to leverage the best of what process-based models offer using Differential Evolution (DE) the Extended Kalman Filter (EKF). Coupling multi-objective optimization to a process-based model may provide valuable information provided an objective function is constructed appropriately to reflect the multi-objective problem and constraints. That, in turn, requires weighting two or more competing objectives in the early stages of an analysis. The methodology proposed here relaxes that requirement by framing the model optimization as a sensitivity analysis. For demonstration, this is implemented using a surface water model (HEC-RAS) and the impact of floodplain access up and downstream of a fixed bridge on bridge scour is analyzed. DE, an evoutionary global optimization algorithm, is wrapped around a calibrated HEC-RAS model. Multiple objective functions, representing different relative weighting of two objectives, are used; the resulting rank-orders of river reach locations by floodplain access sensitivity are consistent across these multiple functions. To extend the applicability of data assimilation methods, this thesis proposes relaxing the requirement that the model be calibrated (provided the parameters are still within physically defensible ranges) before performing assimilation. The model is then dynamically calibrated to new state estimates, which depend on the behavior of the model. Feasibility is demonstrated using the EKF and a synthetic dataset of pendulum motion. The dynamic calibration method reduces the variance of prediction errors compared to measurement errors using an initially uncalibrated model and produces estimates of calibration parameters that converge to the true values. The potential application of the dynamic calibration method to river sediment transport modeling is proposed in detail, including a method for automated calibration using sediment grain size distribution as a calibration parameter.

The increasing penetration of distributed generation, responsive loads and emerging smart metering technologies will continue the transformation of distribution systems from passive to active network conditions. In such active networks, State Estimation (SE) tools will be essential in order to enable extensive monitoring and enhanced control technologies. In future distribution management systems, the novel electrical power distribution system SE requires development in a scalable manner in order to accommodate small to massive size networks, be operable with limited real time measurements and a restricted time frame. Furthermore, a significant phase of new sensor deployment is inevitable to enable distribution system SE, since present-day distribution networks lack the required level of measurement and instrumentation. In the above context, the research presented in this thesis investigates five SE optimization solution methods with various case studies related to expected scenarios of future distribution networks to determine their suitability. Hachtel's Augmented Matrix method is proposed and developed as potential SE optimizer for distribution systems due to its potential performance characteristics with regard to accuracy and convergence. Differential Evolution Algorithm (DEA) and Overlapping Zone Approach (OZA) are investigated to achieve scalability of SE tools; followed by which the network division based OZA is proposed and developed. An OZA requiring additional measurements is also proposed to provide a feasible solution for voltage estimation at a reduced computation cost. Realising the requirement of additional measurements deployment to enable distribution system SE, the development of a novel meter placement algorithm that provides economical and feasible solutions is demonstrated. The algorithm is strongly focused on reducing the voltage estimation errors and is capable of reducing the error below desired threshold with limited measurements. The scalable SE solution and meter placement algorithm are applied on a multi-processor system in order to examine effective reduction of computation time. Significant improvement in computation time is observed in both cases by dividing the problem into smaller segments. However, it is important to note that enhanced network division reduces computation time further at the cost of accuracy of estimation. Different networks including both idealised (16, 77, 356 and 711 node UKGDS) and real (40 and 43 node EG) distribution network data are used as appropriate to the requirement of the applications throughout this thesis.