Hernandez, Marli de Freitas Gomes
No description available.
2010 May 1900
Clock mesh is widely used in microprocessor designs for achieving low clock skew and high process variation tolerance. Clock mesh optimization is a very diffcult problem to solve because it has a highly connected structure and requires accurate delay models which are computationally expensive. Existing methods on clock network optimization are either restricted to clock trees, which are easy to be separated into smaller problems, or naive heuristics based on crude delay models. A clock mesh sizing algorithm, which is aimed to minimize total mesh wire area with consideration of clock skew constraints, has been proposed in this research work. This algorithm is a systematic solution search through rigorous Sequential Quadratic Programming (SQP). The SQP is guided by an efficient adjoint sensitivity analysis which has near-SPICE(Simulation Program for Integrated Circuits Emphasis)-level accuracy and faster-than-SPICE speed. Experimental results on various benchmark circuits indicate that this algorithm leads to substantial wire area reduction while maintaining low clock skew in the clock mesh. The reduction in mesh area achieved is about 33%.
03 August 2005
Co-generation is an efficient energy system that generates steam and electricity simultaneously. In ordinary operation, fuel cost accounts for more than 60% of the operational cost. As a result, the boiler efficiency and optimization level of co-generation are both high. To achieve further energy conservation, objectives of this thesis are to find the Profit-maximizing dispatch and efficiency enhancing strategy of the co-generation systems under deregulation. In a coexistent environment of both Bilateral and Poolco-based power market, there are bid-based spot dispatch, and purchases and sales agreement-based contract dispatch. For profit-maximizing dispatch, the steam of boilers, fuels and generation output will be obtained by using the SQP(Sequential Quadratic Programming ) method. In order to improve the boiler efficiency, this thesis utilizes artificial neural networks(ANN) and evolutionary programming(EP) methods to search for the optimal operating conditions of boilers. A co-generation system (back-pressure type and extraction type) is used to illustrate the effectiveness of the proposed method.
Sustainability Filtration and Optimization: A Stepwise Integration Approach / Hållbarhetsfiltering och Optimering: En Stegvis IntegrationsmetodJalaei, Soroosh January 2023 (has links)
This thesis explores the integration of sustainability into Modern Portfolio Theory (MPT) optimization by introducing stepwise filtration and optimization. This study acknowledges the growing importance of sustainability in investment strategies and modifies the traditional MPT framework to include environmental, social, and governance (ESG) factors. A systematic filtration process is conducted where each asset undergoes a step-by-step filtration based on different ESG criteria. For each filtration step, portfolio optimization is performed to find the most efficient portfolios under the filtered criteria. The effect of each filtration on portfolio risk and return profile provides insights into the trade-offs between financial performance and sustainability impacts. The findings indicate that investors considering the ethical aspects of ESG can achieve these goals without significantly affecting the portfolio risk and return. However, investors incorporating all aspects of ESG will experience a higher drop in the efficient frontier. Moreover, while investigating an additional index, including more companies, investors can attain a better portfolio profile while incorporating all aspects of ESG. A central ambition of this study has been enlighten investors regarding the different aspects of ESG and the trade-offs of integrating sustainability into investment practices. Thus, this study seeks to refine the investor decision-making process and encourage investors to make more informed sustainable decisions. / Detta examensarbete undersöker hur hållbarhet kan integreras i Modern Portfolio Theory (MPT) genom att introducera en stegvis filtrering och optimering. Denna studie framhäver den växande betydelsen av hållbarhet inom investeringsstrategier och modifierar det tradionella ramverket för MPT för att inkludera miljön, socialt ansvar och bolagsstyrning (ESG). En systematisk filtreringsprocess genomförs där varje tillgång genomför en iterativ filtrering baserad på ESG-kriterier. För varje filtreringssteg utförs portföljdoptimering för att hitta de mest effektiva portföljerna under den filtrerade kriterierna. Denna process ger insikt i avvägningarna mellan finansiell avkastning och hållbarhetseffekter. Resultaten indikerar att investerare som beaktar de etiska aspekter kan uppnå dessa mål utan att nämnvärt påverka portföljens risk och avkastningsprofil. Dock, investerare som beaktar samtliga aspekter av ESG kommer att uppleva en större minskning av den effektiva fronten. Dessutom kan investerare, genom utforskning av ett kompletterande index som innefattar ett större urval av företag, uppnå en förbättrad portföljprofiler samtidigt som alla ESG-aspekter beaktas. En central ambition för denna studie har varit att upplysa investerare om de olika aspekterna av ESG och avvägningarna med att integrera hållbarhet i investeringspraxis. Således, strävar denna studie efter att förbättra investerarnas beslutprocess och uppmuntra investerare till att fatta med informerade hållbara beslut.
01 January 2018
Substance abuse is a serious issue in both modern and traditional societies. Besides health complications such as depression, cancer and HIV, social complications such as loss of concentration, loss of job, and legal problems are among the numerous hazards substance use disorder imposes on societies. Understanding the causes of substance abuse and preventing its negative effects continues to be the focus of much research. Substance use behaviors, symptoms and signs are usually measured in form of ordinal data, which are often modeled under threshold models in Structural Equation Modeling (SEM). In this dissertation, we have developed a general nonlinear optimizer for the software package OpenMx, which is a SEM package in widespread use in the fields of psychology and genetics. The optimizer solves nonlinearly constrained optimization problems using a Sequential Quadratic Programming (SQP) algorithm. We have tested the performance of our optimizer on ordinal data and compared the results with two other optimizers (implementing SQP algorithm) available in the OpenMx package. While all three optimizers reach the same minimum, our new optimizer is faster than the other two. We then applied OpenMx with our optimization engine to a very large population-based drug abuse dataset, collected in Sweden from over one million pairs, to investigate the effects of genetic and environmental factors on liability to drug use. Finally, we investigated the reasons behind better performance of our optimizer by profiling all three optimizers as well as analyzing their memory consumption. We found that objective function evaluation is the most expensive task for all three optimizers, and that our optimizer needs fewer number of calls to this function to find the minimum. In terms of memory consumption, the optimizers use the same amount of memory.
06 September 2012
This thesis evaluates decommissioning strategies for the International Space Station ISS. A permanent solution is attempted by employing energy efficient invariant manifolds that arise in the circular restricted three body problem CRTBP to transport the ISS from its low Earth orbit LEO to a lunar orbit. Although the invariant manifolds provide efficient transport, getting the the ISS onto the manifolds proves quite expensive, and the trajectories take too long to complete. Therefore a more practical, although temporary, solution consisting of an optimal re-boost maneuver with the European Space Agency's automated transfer vehicle ATV is proposed. The optimal re-boost trajectory is found using control parameterization and the sequential quadratic programming SQP algorithm. The model used for optimization takes into account the affects of atmospheric drag and gravity perturbations. The optimal re-boost maneuver produces a satellite lifetime of approximately ninety-five years using a two ATV strategy.
Al-Othman, Abdul Rahman K.
As a special case of the static state estimation problem, the load-flow problem is studied in this thesis. It is demonstrated that the non-linear load-flow formulation may be solved by real-coded genetic algorithms. Due to its global optimisation ability, the proposed method can be useful for off-line studies where multiple solutions are suspected. This thesis presents two methods for estimating the uncertainty interval in power system state estimation due to uncertainty in the measurements. The proposed formulations are based on a parametric approach which takes in account the meter inaccuracies. A nonlinear and a linear formulation are proposed to estimate the tightest possible upper and lower bounds on the states. The uncertainty analysis, in power system state estimation, is also extended to other physical quantities such as the network parameters. The uncertainty is then assumed to be present in both measurements and network parameters. To find the tightest possible upper and lower bounds of any state variable, the problem is solved by a Sequential Quadratic Programming (SQP) technique. A new robust estimator based on the concept of uncertainty in the measurements is developed here. This estimator is known as Maximum Constraints Satisfaction (MCS). Robustness and performance of the proposed estimator is analysed via simulation of simple regression examples, D.C. and A.C. power system models.
Tong, Peter, email@example.com
This study raises some issues for training pilots to fly forced landings and examines the impact that these issues may have on the design of simulators for such training. It focuses on flight trajectories that a pilot of a single-engine general aviation aircraft should fly after engine failure and how pilots can be better simulator trained for this forced landing manoeuvre. A sensitivity study on the effects of errors and an investigation on the effect of tolerances in the aerodynamic parameters as prescribed in the Manual of Criteria for the Qualification of Flight Simulators have on the performance of flight simulators used for pilot training was carried out. It uses a simplified analytical model for the Beech Bonanza model E33A aircraft and a vertical atmospheric turbulence based on the MIL-F-8785C specifications. It was found that the effect of the tolerances is highly sensitive on the nature of the manoeuvre flown and that in some cases, negative transfe r of training may be induced by the tolerances. A forced landing trajectory optimisation was carried out using Genetic Algorithm. The forced landing manoeuvre analyses with pre-selected touchdown locations and pre-selected final headings were carried out for an engine failure at 650 ft AGL for bank angles varying from banking left at 45° to banking right at 45°, and with an aircraft's speed varying from 75.6 mph to 208 mph, corresponding to 5% above airplane's stall speed and airplane's maximum speed respectively. The results show that certain pre-selected touchdown locations are more susceptible to horizontal wind. The results for the forced landing manoeuvre with a pre-selected location show minimal distance error while the quality of the results for the forced landing manoeuvre with a pre-selected location and a final heading show that the results depend on the end constraints. For certain pre-selected touchdown locations and final headings, the airplane may either touchdown very close to the pre-selected touchdown location but with greater final h eading error from the pre-selected final heading or touchdown with minimal final heading error from the pre-selected final heading but further away from the pre-selected touchdown location. Analyses for an obstacle avoidance forced landing manoeuvre were also carried out where an obstacle was intentionally placed in the flight path as found by the GA program developed for without obstacle. The methodology developed successfully found flight paths that will avoid the obstacle and touchdown near the pre-selected location. In some cases, there exist more than one ensemble grouping of flight paths. The distance error depends on both the pre-selected touchdown location and where the obstacle was placed. The distance error tends to increase with the addition of a specific final heading requirement for an obstacle avoidance forced landing manoeuvre. As with the case without specific final heading requirement, there is a trade off between touching down nearer to the pre-selected location and touching down with a smaller final heading error.
Supervisory Control Optimization with Sequential Quadratic Programming for Parallel Hybrid Vehicle with Synchronous Power SourcesJanuary 2017 (has links)
abstract: The thesis covers the development and modeling of the supervisory hybrid controller using two different methods to achieve real-world optimization and power split of a parallel hybrid vehicle with a fixed shaft connecting the Internal Combustion Engine (ICE) and Electric Motor (EM). The first strategy uses a rule based controller to determine modes the vehicle should operate in. This approach is well suited for real-world applications. The second approach uses Sequential Quadratic Programming (SQP) approach in conjunction with an Equivalent Consumption Minimization Strategy (ECMS) strategy to keep the vehicle in the most efficient operating regions. This latter method is able to operate the vehicle in various drive cycles while maintaining the SOC with-in allowed charge sustaining (CS) limits. Further, the overall efficiency of the vehicle for all drive cycles is increased. The limitation here is the that process is computationally expensive; however, with advent of the low cost high performance hardware this method can be used for the hybrid vehicle control. / Dissertation/Thesis / Masters Thesis Engineering 2017
Programação quadratica sequencial e condições de qualificação / Sequential quadratic programming and constraint qualificationNunes, Fernanda Téles 03 September 2009 (has links)
Orientador: Maria Aparecida Diniz Ehrhardt / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-13T08:54:50Z (GMT). No. of bitstreams: 1 Nunes_FernandaTeles_M.pdf: 2400651 bytes, checksum: 206dfad35642a33d2de362510094e78d (MD5) Previous issue date: 2009 / Resumo: Abordando problemas de minimização de funções com restrições nos deparamos com as condições de otimalidade e, ainda, com condições de qualificação das restrições. Nosso interesse é o estudo detalhado de várias condições de qualificação, com destaque para a condição de dependência linear positiva constante, e sua influência na convergência de algoritmos de Programação Quadrática Sequencial. A relevância deste estudo está no fato de que resultados de convergência que têm, em suas hipóteses, condições de qualificação fracas são mais fortes que aqueles baseados em condições de qualificação fortes. Experimentos numéricos serão realizados tanto para investigar a eficiência destes métodos na resolução de problemas com diferentes condições de qualificação, quanto para comparar dois diferentes tipos de busca, monótona e não-monótona. Tentamos confirmar a hipótese de que algoritmos baseados em uma busca não-monótona atuam contra o Efeito: Maratos, de comum ocorrência na resolução de problemas de minimização através de métodos de Programação Quadrática Sequencial. / Abstract: In the context of constrained optimization problems, we face the optimality conditions and also constraint qualification. Our aim is to study with details several constraint qualification, highlighting the constant positive linear dependence condition, and its influence in Sequential Quadratic Programming algorithms convergence. The relevance of this study is in the fact that convergence results having as hypothesis weak constraints qualification are stronger than those based on stronger constraints qualification. Numerical experiments will be done with the purpose of investigating the efficiency of these methods to solve problems with different constraints qualification and to compare two diferent kinds of line search, monotone and nonmonotone. We want to confirm the hypothesis that algorithms based on a nonmonotone line search act against the Maratos Effect, very common while solving minimization problems through Sequential Quadratic Programming methods. / Mestrado / Mestre em Matemática Aplicada
Page generated in 0.153 seconds