A module-level DC optimization proof of concept architecture is proposed to increase the efficiency of photovoltaic (PV) strings by minimizing the negative effects of shading caused by intermittent cloud cover while reducing cloud induced fast frequency fluctuations. The decentralized inverter approach combines the benefits of string and micro-inverter technology. This device can be affixed to pre-existing or new systems and operates in compliance with IEEE 1547 and California rule 21 standards by operating in maximum power point tracking (MPPT) or curtailment mode whenever necessary. The modular level device encapsulates three individual processes: an optimization engine to determine minimum power requirements, a fuzzy logic controller (FLC) to eliminate the effect of passing cloud cover, and a voltage regulation stage to monitor and appropriately adjust the output voltage of the device. Ramp rate reduction was accomplished using adaptive fuzzy logic control with a heuristic rule base inference engine. The modular design can be affixed to grid connected or islanded systems allowing for operation in regulated and variable load conditions. Matlab/Simulink 2019a was used to design and simulate the proof of concept model to verify the resiliency to partial shading, reduction of ramp rates during passing cloud coverage, and optimal output voltage for each panel while maintaining a constant DC link voltage of 120 V. This proof of concept has been successfully validated therefore further testing will be performed for various irradiance conditions.

Yes / Daily increasing use of tidal power generation proves its outstanding features as a renewable source. Due to environmental concerns, tidal current energy which has no greenhouse emission attracted researchers’ attention in the last decade. Additionally, the significant potential of tidal technologies to economically benefit the utility in long-term periods is substantial. Tidal energy can be highly forecasted based on short-time given data and hence it will be a reliable renewable resource which can be fitted into power systems. In this paper, investigations of effects of a practical stream tidal turbine in Lake Saroma in the eastern area of Hokkaido, Japan, allocated in a real microgrid (MG), is considered in order to solve an environmental/economic bi-objective optimization problem. For this purpose, an intelligent evolutionary multi-objective modified bird mating optimizer (MMOBMO) algorithm is proposed. Additionally, a detailed economic model of storage devices is considered in the problem. Results show the efficiency of the suggested algorithm in satisfying economic/environmental objectives. The effectiveness of the proposed approach is validated by making comparison with original BMO and PSO on a practical MG. / Iran National Science Foundation; Royal Academy of Engineering Distinguished Visiting Fellowship under Grant DVF1617\6\45

No description available.

Trajectory modeling is one of the most important aspects of any mission design. The trajectory should be able to propagate the S/C to the final destination while optimizing the flight duration, the total change in velocity and also the total launch mass. The Spacecraft Trajectory Optimizer (STO) tool described in this report first solves the Gauss Lambert problem and generates initial departure and arrival conditions which can also be expressed as porkchop plots. These initial conditions are then used as input to optimize the flight steps which are based on a patched conic approximation with the elliptical transfer with respect to the Sun and the hyperbolic transfers at the departure and arrival planet's sphere of influence. The tool is completely based on MATLAB 2007 or later and uses ODE45 for trajectory propagation and FMINCON with Active-set algorithm for optimization. The results obtained in house were compared with four Mars Sample return orbits calculated at ESOC and there is a very good correlation between the required change in velocities and transfer duration for e.g. Orbit case: O22S, ESOC values: total Delta V = 3.946 - 4.119 [km/s], TOF = 329 - 342 [days] &amp; STO values: Delta V = 3:986 [km/s] &amp; TOF = 335 [days]. The in house data was also used as an input in the System Tool Kit (a professional trajectory calculation software) for modeling an interplanetary trajectory to Mars and the S/C arrived at Mars without any optimization. Therefore, even though the STO does not have all the capabilities of a professional software it can be used for preliminary mission analysis as it offers quite accurate results for interplanetary transfers. / <p>Validerat; 20131127 (global_studentproject_submitter)</p>

Different medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from all these different datasets is known as image registration, which is the topic of this thesis. Image registration, especially multi-modality and multi-parametric registration, remains a challenging field in medical image analysis. The new registration methods described in this work were all developed in response to genuine challenges in on-going clinical studies. These methods have been evaluated using data from these studies. In order to gain an insight into the building blocks of image registration methods, the thesis begins with a comprehensive literature review of state-of-the-art algorithms. This is followed by a description of the first registration method I developed to help track inflammation in aortic abdominal aneurysms. It registers multi-modality and multi-parametric images, with new contrast agents. The registration framework uses a semi-automatically generated region of interest around the aorta. The aorta is aligned based on a combination of the centres of the regions of interest and intensity matching. The method achieved sub-voxel accuracy. The second clinical study involved cardiac data. The first framework failed to register many of these datasets, because the cardiac data suffers from a common artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I developed a new preprocessing technique that is able to correct the artefacts in the functional data using data from the anatomical scans. The registration framework, with this preprocessing step and new particle swarm optimizer, achieved significantly improved registration results on the cardiac data, and was validated quantitatively using neuro images from a clinical study of neonates. Although on average the new framework achieved accurate results, when processing data corrupted by severe artefacts and noise, premature convergence of the optimizer is still a common problem. To overcome this, I invented a new optimization method, that achieves more robust convergence by encoding prior knowledge of registration. The registration results from this new registration-oriented optimizer are more accurate than other general-purpose particle swarm optimization methods commonly applied to registration problems. In summary, this thesis describes a series of novel developments to an image registration framework, aimed to improve accuracy, robustness and speed. The resulting registration framework was applied to, and validated by, different types of images taken from several ongoing clinical trials. In the future, this framework could be extended to include more diverse transformation models, aided by new machine learning techniques. It may also be applied to the registration of other types and modalities of imaging data.

Layout optimization of offshore wind farms seeks to automate the design of the wind farm and the placement of wind turbines such that the proposed wind farm maximizes its potential. The optimization of an offshore wind farm layout therefore seeks to minimize the costs of the wind farm while maximizing the energy extraction while considering the effects of wakes on the resource; the electrical infrastructure required to collect the energy generated; the cost variation across the site; and all technical and consenting constraints that the wind farm developer must adhere to. As wakes, electrical losses, and costs are non-linear, this produces a complex optimization problem. This thesis describes the design, development, validation, and initial application of a new framework for the optimization of offshore wind farm layouts using either a genetic algorithm or a particle swarm optimizer. The developed methodology and analysis tool have been developed such that individual components can either be used to analyze a particular wind farm layout or used in conjunction with the optimization algorithms to design and optimize wind farm layouts. To accomplish this, separate modules have been developed and validated for the design and optimization of the necessary electrical infrastructure, the assessment of the energy production considering energy losses, and the estimation of the project costs. By including site-dependent parameters and project specific constraints, the framework is capable of exploring the influence the wind farm layout has on the levelized cost of energy of the project. Deploying the integrated framework using two common engineering metaheuristic algorithms to hypothetical, existing, and future wind farms highlights the advantages of this holistic layout optimization framework over the industry standard approaches commonly deployed in offshore wind farm design leading to a reduction in LCOE. Application of the tool to a UK Round 3 site recently under development has also highlighted how the use of this tool can aid in the development of future regulations by considering various constraints on the placement of wind turbines within the site and exploring how these impact the levelized cost of energy.

Orientadora: Profa. Dra. Patrícia Teixeira Leite Asano / Tese (doutorado) - Universidade Federal do ABC, Programa de Pós-Graduação em Energia, 2014. / O objetivo da pesquisa é apresentar uma metodologia para avaliar os efeitos da transferência de água bruta entre rios ou bacias hidrográficas, na perspectiva de otimizar a geração de energia elétrica. Busca-se ainda refletir sobre a racionalização e otimização do volume transferido para que não prejudiquem os ecossistemas, as atividades e utilizações da região doadora de água e atendam as necessidades da região receptora de água, incluindo seus próprios ecossistemas. A produção hidroelétrica é considerada como um uso não consuntivo da água e não concorre com o abastecimento público, desde que corretamente gerenciada. A transferência de água, se associada à produtibilidade das hidroelétricas, pode contribuir para otimização da produção de energia elétrica diminuindo a utilização das usinas térmicas de custos operativos elevados e enormes restrições ambientais. Portanto, neste trabalho de pesquisa foi implementado um novo operador genético em uma ferramenta computacional desenvolvida na UFABC para controle das vazões e volumes a serem transferidos, chamada Hidro-IA, com o emprego de algoritmos genéticos, que é uma das técnicas de inteligência artificial consolidada para otimização de problemas de grande porte. Sabe-se que a melhor forma de aproveitar os ciclos hidrológicos e os respectivos excedentes de água bruta de uma bacia ou região é por meio de um correto armazenamento em reservatórios que possibilitem seu uso em épocas de escassez. Sendo assim, a transferência de água entre regiões é uma estratégia que permite um ganho de armazenagem em reservatórios subutilizados em suas capacidades máximas de estocagem. Após a implementação do operador proposto nesta pesquisa, pode-se observar nos casos testes realizados que a transferência de água foi feita de forma controlada e otimizada, permitindo o uso adequado do recurso hídrico disponível e consequentemente minimizando a utilização de complementação térmica. Finalizando, podese levantar a hipótese de utilizar o ganho com a geração de energia elétrica para tratamento de água em algumas bacias poluídas de forma a trazer diversos benefícios para a sociedade que não seja apenas a produção de eletricidade, mas também, a disponibilização de água para consumo. / The aim of this project is present a methodology to analyze the impacts of the raw water transfer between rivers or watersheds on a perspective of improving electrical power generation. Searches to reflect about rationalization and optimization of volume transferred so it don¿t damage the environment, the activities or the use of the water donor area and that serves the needs of the receiving area, including its own ecosystems. Hydroelectric production is considered a nonconsumptive water activity, not competing with the public water supply if correctly used. The water transfer can be associated with the hydroelectric yield to increase the production of electric energy and decrease the activities of high cost thermoelectric plants and environment restrictions. Therefore in this project was implemented a new genetic operator to control the transference of volume and water flow between rivers or watersheds in a computational tool created by UFABC called Hidro-IA, an artificial intelligence technic used to optimize large-scale problems using genetic algorithms. It is well known that the better way to maximize the hydrologic cycles and the plant raw water surplus is with the storage in water reservoirs, enabling its use in shortage times. Accordingly, the water transfer between regions is a strategy that allows an increase in the underutilized reservoirs. Underutilization occurs due to different rainfall rates and due to intensity of utilization by the population, with exhaustion of local hydric availability. After implantation of the operator proposed here in this project it was possible to saw, in the tests, that that the transference of water was made in a controlled and optimized way enabling the adequate use of the existing hydric resources and therefor minimizing the use of thermal complement. In conclusion, it is possible to utilize the electrical power generation profit to treat water in some of the polluted watersheds, bringing benefits to the society other than electric generation just but providing water to human consumption too.

<p>Visualization is a rapidly growing area of computer graphics. Due to the very progressive development of computer technology and especially 3D-graphic boards yesterdays imagination of visualizations, today is a reality. </p><p>At Saab Bofors Dynamics in Linköping missile systems are developed. Through the development, different subsystems are simulated before the manufacturing starts. The results of these simulations have to be visualized to get a meaning. The visualizations can be symbolic or more real looking 3D-visualizations. </p><p>During the work an investigation of available tools for computer visualizations on the market has been conducted. The outcome of the investigation has later been the foundation for the selection of tools to solve the visualization task. </p><p>One 3D-visualization application for general 3D-visualization has then been developed using two different 3D-APIs, and the experiences of these have then been analyzed.</p>

Το πρόβλημα της βελτιστοποίησης ερωτημάτων πολλαπλών κριτηρίων σε βάσεις δεδομένων είναι ένα αρκετά δύσκολο και ενδιαφέρον ερευνητικά πρόβλημα, διότι χαρακτηρίζεται από αντικρουόμενες απαιτήσεις. Κάθε βήμα στην απάντηση ενός ερωτήματος μπορεί να εκτελεστεί με παραπάνω από έναν τρόπους. Για την επίλυση τέτοιου είδους ερωτημάτων έχουν προταθεί διάφοροι αλγόριθμοι, με πιο πρόσφατους τους: Mariposa, M' και Generate Partitions. Ο Mariposa και ο Μ' εφαρμόζονται στην βάση δεδομένων Mariposa, η οποία δίνει την δυνατότητα στον χρήστη να καθορίζει την επιθυμητή εξισορόπηση (tradeoff) καθυστέρησης/κόστους για κάθε ερώτημα που θέτει. Ο αλγόριθμος Mariposa ακολουθεί μία προσέγγιση απληστίας (greedy approach) προσπαθώντας σε κάθε βήμα να μεγιστοποιήσει το «κέρδος» ενώ ο Μ' χρησιμοποιεί σύνολα βέτιστων κατά Pareto λύσεων για την επιλογή του επόμενου βήματος στην θέση του κριτηρίου απληστίας. Τέλος, ο αλγόριθμος Generate Partition χρησιμοποιεί έναν διαχωρισμό του χώρου απαντήσεων χρησιμοποιώντας δομές R-trees πετυχαίνοντας πολύ καλή απόδοση. / The optimization of queries in distributed database systems is known to be subject to delicate trade-offs. For example, the Mariposa database system allows users to specify a desired delay-cost tradeoff (that is to supply a decreasing function u(d) specifying how much the user is willing to pay in order to receive the query results within time d) Mariposa divides a query graph into orizontal strides analyzes each stride, and uses a greedy heuristic to find the best plan for all strides.

Visualization is a rapidly growing area of computer graphics. Due to the very progressive development of computer technology and especially 3D-graphic boards yesterdays imagination of visualizations, today is a reality. At Saab Bofors Dynamics in Linköping missile systems are developed. Through the development, different subsystems are simulated before the manufacturing starts. The results of these simulations have to be visualized to get a meaning. The visualizations can be symbolic or more real looking 3D-visualizations. During the work an investigation of available tools for computer visualizations on the market has been conducted. The outcome of the investigation has later been the foundation for the selection of tools to solve the visualization task. One 3D-visualization application for general 3D-visualization has then been developed using two different 3D-APIs, and the experiences of these have then been analyzed.