A predição de estruturas de proteínas (Protein Structure Prediction PSP) é um problema computacionalmente complexo. Para tratar esse problema, modelos simplificados de proteínas, como o Modelo HP, têm sido empregados para representar as conformações e Algoritmos Evolutivos (AEs) são utilizados na busca por soluções adequadas para PSP. Entretanto, abordagens utilizando AEs muitas vezes não tratam adequadamente as soluções geradas, prejudicando o desempenho da busca. Neste trabalho, é apresentada uma formulação multiobjetivo para PSP em Modelo HP, de modo a avaliar de forma mais robusta as conformações produzidas combinando uma avaliação baseada no número de contatos hidrofóbicos com a distância entre os monômeros. Foi adotado o Algoritmo Evolutivo Multiobjetivo em Tabelas (AEMT) a fim de otimizar essas métricas. O algoritmo pode adequadamente explorar o espaço de busca com pequeno número de indivíduos. Como consequência, o total de avaliações da função objetivo é significativamente reduzido, gerando um método para PSP utilizando Modelo HP mais rápido e robusto / Protein Structure Prediction (PSP) is a computationally complex problem. To overcome this drawback, simplified models of protein structures, such as the HP Model, together with Evolutionary Algorithms (EAs) have been investigated in order to find appropriate solutions for PSP. EAs with the HP Model have shown interesting results, however, they do not adequately evaluate potential solutions by using only the usual metric of hydrophobic contacts, hamming the performance of the algorithm. In this work, we present a multi-objective approach for PSP using HP Model that performs a better evaluation of the solutions by combining the evaluation based on the number of hydrophobic contacts with the distance among the hydrophobic amino acids. We employ a Multi-objective Evolutionary Algorithm based on Sub-population Tables (MEAT) to deal with these two metrics. MEAT can adequately explore the search space with relatively low number of individuals. As a consequence, the total assessments of the objective function is significantly reduced generating a method for PSP using HP Model that is faster and more robust

Neste trabalho investiga-se a aplicação de um algoritmo genético multiobjetivo, ferramenta que se destaca por sua flexibilidade e interpretabilidade, na obtenção de fatores de ponderação para aplicação no controle preditivo de torque do motor de indução, ou Model Predictive Torque Control (MPTC). O MPTC busca minimizar a cada instante de atuação uma função custo que representa o sistema, destacando-se pela rápida resposta de torque, facilidade de incorporar restrições e ausência de modulador de tensão. No entanto, essa técnica apresenta fatores de ponderação em sua estrutura de cálculo que não dispõem de métodos analíticos de projeto. Utilizou-se o algoritmo genético de classificação nãodominada, ou Non-dominated Sorting Genectic Algorithm II (NSGA-II), projetado de forma a obter soluções que busquem o compromisso entre o desempenho dinâmico do motor, via minimização das oscilações de torque e fluxo, e a eficiência energética do sistema por meio da minimização da frequência média de chaveamento da eletrônica de potência. Resultados simulados e experimentais mostraram que o conjunto de soluções fornecido pelo NSGA-II é factível e contrapõe as oscilações de torque e de fluxo e a frequência média de chaveamento, cabendo à aplicação desejada a escolha da solução. Com isso, tem-se uma ferramenta de projeto dos fatores de peso do MPTC capaz de incorporar restrições e ajustar vários fatores ao mesmo tempo. / This work investigates the application of a multi-objective genetic algorithm to obtain a set of weighting factors suitable for use in Model Predictive Torque Control (MPTC) of a induction motor variable speed drive. MPTC approach aims at minimizing a cost function at each step, and is highlighted for its fast torque response, facility to incorporate system constraints and the absence of voltage modulators. Nevertheless, MPTC structure presents weighting factors in the cost function which lack of an analytical design procedure. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was designed for a trade-off between torque and flux ripples minimization and minimization of the average switching frequency of the system. Simulated and experimental results showed NSGA-II offered a Pareto set of feasible solutions, so that torque ripple, flux ripple or average switching frequency can be minimized, depending on the solution chosen according to project demand. Thereby, there is a project tool for MPTC weighting factors able to adjust several factor at the same time, incorporating desired restrictions.

This diploma thesis deals with application optimization method in circular economy branch. The introduction is focused on explaining main features of the issue and its benefits for economy and environment. Afterwards are mentioned some obstacles, which are preventing transition from current waste management. Mathematical apparatus, which is used in practical section, is described in the thesis. Core of the thesis is mathematical optimization model, which is implemented in the GAMS software, and generator of input data is made in VBA. The model includes all of significant waste management options with respect to economic and enviromental aspect, including transport. Functionality is then demostrated on a small task. Key thesis result is application of the model on real data concerning Czech Republic. In conclusion an analysis of computation difficulty, given the scale of the task, is accomplished.

Stochastic methods are present in our daily lives, especially when we need to make a decision based on uncertain events. In this thesis, we present basic approaches used in stochastic tasks. In the first chapter, we define the stochastic problem and introduce basic methods and tasks which are present in the literature. In the second chapter, we present various problems which are non-linearly dependent on the probability measure. Moreover, we introduce deterministic and non-deterministic multicriteria tasks. In the third chapter, we give an insight on the concept of stochastic dominance and we describe the methods that are used in tasks with multidimensional stochastic dominance. In the fourth chapter, we capitalize on the knowledge from chapters two and three and we try to solve the role of portfolio optimization on real data using different approaches. 1

<div>Neuromorphic systems promise a novel alternative to the standard von-Neumann architectures that are computationally expensive for analyzing big data, and are not efficient for learning and inference. This novel generation of computing aims at ``mimicking" the human brain based on deploying neural networks on event-driven hardware architectures. A key bottleneck in designing such brain-inspired architectures is the complexity of co-optimizing the algorithm’s speed and accuracy along with the hardware’s performance and energy efficiency. This complexity stems from numerous intrinsic hyperparameters in both software and hardware that need to be optimized for an optimum design.</div><div><br></div><div>In this work, we present a versatile hierarchical pseudo agent-based multi-objective hyperparameter optimization approach for automatically tuning the hyperparameters of several training algorithms (such as traditional artificial neural networks (ANN), and evolutionary-based, binary, back-propagation-based, and conversion-based techniques in spiking neural networks (SNNs)) on digital and mixed-signal neural accelerators. By utilizing the proposed hyperparameter optimization approach we achieve improved performance over the previous state-of-the-art on those training algorithms and close some of the performance gaps that exist between SNNs and standard deep learning architectures.</div><div><br></div><div>We demonstrate >2% improvement in accuracy and more than 5X reduction in the training/inference time for a back-propagation-based SNN algorithm on the dynamic vision sensor (DVS) gesture dataset. In the case of ANN-SNN conversion-based techniques, we demonstrate 30% reduction in time-steps while surpassing the accuracy of state-of-the-art networks on an image classification dataset (CIFAR10) on a simpler and shallower architecture. Further, our analysis shows that in some cases even a seemingly minor change in hyperparameters may change the accuracy of these networks by 5‑6X. From the application perspective, we show that the optimum set of hyperparameters might drastically improve the performance (52% to 71% for Pole-Balance control application). In addition, we demonstrate resiliency of different input/output encoding, training neural network, or the underlying accelerator modules in a neuromorphic system to the changes of the hyperparameters.</div>

This dissertation models human intent for a robot navigation task, managed by a human and undertaken by a robot in a dynamic, multi-objective environment. Intent is expressed by a human through a user interface and then translated into a robot trajectory that satisfies a set of human-specified objectives and constraints. For a goal-based robot navigation task in a dynamic environment, intent includes expectations about a path in terms of objectives and constraints to be met. If the planned path drifts from the human's intent as the environment changes, a new path needs to be planned. The intent framework has four elements: (a) a mathematical representation of human intent within a multi-objective optimization problem; (b) design of an interactive graphical user interface that enables a human to communicate intent to the robot and then to subsequently monitor intent execution; (c) integration and adoption of a fast online path-planning algorithms that generate solutions/trajectories conforming to the given intent; and (d) design of metric-based triggers that provide a human the opportunity to correct or adapt a planned path to keep it aligned with intent as the environment changes. Key contributions of the dissertation are: (i) design and evaluation of different user interfaces to express intent, (ii) use of two different metrics, cosine similarity and intent threshold margin, that help quantify intent, and (iii) application of the metrics in path (re)planning to detect intent mismatches for a robot navigating in a dynamic environment. A set of user studies including both controlled laboratory experiments and Amazon Mechanical Turk studies were conducted to evaluate each of these dissertation components.

Indiana University-Purdue University Indianapolis (IUPUI) / In the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030--this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with the increasing energy demand. To that end, a design methodology to accelerate the LIB development is needed. This can be achieved through the integration of electro-chemical numerical simulations and machine learning algorithms. To help this cause, this study develops a design methodology and framework using Simcenter Battery Design Studio® (BDS) and Bayesian optimization for design and optimization of cylindrical cell type 18650. The materials of the cathode are Nickel-Cobalt-Aluminum (NCA)/Nickel-Manganese-Cobalt-Aluminum (NMCA), anode is graphite, and electrolyte is Lithium hexafluorophosphate (LiPF6). Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. The black-box functions are simulations of the cyclic performance test in Simcenter Battery Design Studio. The physics model used for this study is based on full system model described by Fuller and Newman. It uses Butler-Volmer Equation for ion-transportation across an interface and solvent diffusion model (Ploehn Model) for Aging of Lithium-Ion Battery Cells. The BDS model considers effects of SEI, cell electrode and microstructure dimensions, and charge-discharge rates to simulate battery degradation. Two objectives are optimized: maximization of the specific energy and minimization of the capacity fade. We perform global sensitivity analysis and see that thickness and porosity of the coating of the LIB electrodes that affect the objective functions the most. As such the design variables selected for this study are thickness and porosity of the electrodes. The thickness is restricted to vary from 22microns to 240microns and the porosity varies from 0.22 to 0.54. Two case studies are carried out using the above-mentioned objective functions and parameters. In the first study, cycling tests of 18650 NCA cathode Li-ion cells are simulated. The cells are charged and discharged using a constant 0.2C rate for 500 cycles. In the second case study a cathode active material more relevant to the electric vehicle industry, Nickel-Manganese-Cobalt-Aluminum (NMCA), is used. Here, the cells are cycled for 5 different charge-discharge scenarios to replicate charge-discharge scenario that an EVs battery module experiences. The results show that the design and optimization methodology can identify cells to satisfy the design objective that extend and improve the pareto front outside the original sampling plan for several practical charge-discharge scenarios which maximize energy density and minimize capacity fade.

Solar Tower Power Plants with thermal energy storage are a promising technology for dispatchable renewable energy in the near future. Storage integration makes possible to shift the electricity production to more profitable peak hours. Usually two tanks are used to store cold and hot fluids, but this means both higher related investment costs and difficulties during the operation of the variable volume tanks. Another solution can be a single tank thermocline storage in a multi-layered configuration. In such tank both latent and sensible fillers are employed to decrease the related cost by up to 30% and maintain high efficiencies.  The Master thesis hereby presented describes the modelling and implementation of a thermocline-like multi-layered single tank storage in a STPP. The research work presents a comprehensive methodology to determine under which market structures such devices can outperform the more conventional two tank storage systems. As a first step the single tank is modelled by means of differential energy conservation equations. Secondly the tank geometrical design parameters and materials are taken accordingly with the applications taken into consideration. Both the steady state and dynamic models have been implemented in an existing techno-economic tool developed in KTH, in the CSP division (DYESOPT). The results show that under current cost estimates and technical limitations the multi-layered solid PCM storage concept is a better solution when peaking operating strategies are desired, as it is the case for the two-tier South African tariff scheme. In this case the IRR of an optimal designed power plant can be decreased by 2.1%. However, if a continuous operation is considered, the technology is not always preferred over the two tank solution, yet is a cheaper alternative with optimized power plants. As a result the obtained LCOE can be decreased by 2.4%.

Concentrated Solar Power (CSP) constitutes one suitable solution for exploiting solar resources for power generation. In this context, parabolic dish systems concentrate the solar radiation onto a point focusing receiver for small-scale power production. Given the modularity feature of such system, the scale-up is a feasible option; however, they offer a suitable solution for small scale off-grid electrification of rural areas. These systems are usually used with Stirling engines, nevertheless the coupling with micro-gas turbines presents a number of advantages, related to the reliability of the system and the lower level of maintenance required. The OMSoP project, funded by the European Union, aims at the demonstration of a parabolic dish coupled with an air-driven Brayton cycle. By looking at the integrated system, a key-role is played by the solar receiver, whose function is the absorption of the concentrated solar radiation and its transfer to the heat transfer fluid. Volumetric solar receivers constitute a novel and promising solution for such applications; the use of a porous matrix for the solar radiation absorption allows reaching higher temperature within a compact volume, while reducing the heat transfer losses between the fluid and the absorption medium. The aim of the present work is to deliver a set of optimal design specifications for a volumetric solar receiver for the OMSoP project. The work is based on a Multi-Objective Optimization algorithm, with the objective of the enhancement of the receiver thermal efficiency and of the reduction of the pressure drop. The optimization routine is coupled with a detailed analysis of the component, based on a Computational Fluid Dynamics model and a Mechanical Stress Analysis. The boundary conditions are given by the OMSoP project, in terms of dish specifications and power cycle, whilst the solar radiation boundary is modelled by means of a Ray Tracing routine. The outcome of the analysis is the assessment of the impact on the receiver performance of some key design parameters, namely the porous material properties and the receiver geometrical dimensions. From the results, it is observed a general low pressure drop related to the nominal air mass flow, with several points respecting the materials limitations. One design point is chosen among the optimal points, which respects the OMSoP project requirements for the design objectives, i.e. a minimum value of efficiency of 70%, and pressure losses below 1%. The final receiver configuration performs with an efficiency value of 86%, with relative pressure drop of 0.5%, and it is based on a ceramic foam absorber made of silicon carbide, with porosity value of 0.94.  Moreover, the detailed analysis of one volumetric receiver configuration to be integrated in the OMSoP project shows promising results for experimental testing and for its actual integration in the system.

Municipal energy systems in Nordic environments face multiple challenges: the cold climate, large-scale industries, a high share of electric heating and long distances drive energy consumption. While actions on the demand side minimize energy use, decarbonization efforts in mining, industries, the heating and the transport sector can increase the consumption of electricity and biofuels. Continued growth of intermittent wind and solar power increases supply, but the planned phase out of Swedish nuclear power will pose challenges to the reliability of the electricity system in the Nordic countries. Bottlenecks in the transmission and distribution grids may restrict a potential growth of electricity use in urban areas, limit new intermittent supply, peak electricity import and export. Environmental concerns may limit growth of biomass use. Local authorities are committed in contributing to national goals on mitigating climate change, while considering their own objectives for economic development, increased energy self-sufficiency and affordable energy costs. Given these circumstances, this thesis investigates existing technical and economic potentials of renewable energy (RE) resources in the Nordic countries with a focus on the northern counties of Finland, Norway and Sweden. The research further aims to provide sets of optimal solutions for sustainable Nordic municipal energy systems, where the interaction between major energy sectors are studied, considering multiple objectives of minimizing annual energy system costs and reducing carbon emissions as well as analyzing impacts on peak electricity import and export. This research formulates an integrated municipal energy system as a multi-objective optimization problem (MOOP), which is solved by interfacing the energy system simulation tool EnergyPLAN with a multi-objective evolutionary algorithm (MOEA) implemented in Matlab. In a first step, the integration or coupling of electricity and heating sectors is studied, and in a second step, the study inquires the impacts of an increasingly decarbonized transport sector on the energy system. Sensitivity analysis on key economic parameters and on different grid emission factors is performed. Piteå (Norrbotten County, Sweden) is a typical Nordic municipality, which serves as a case study for this research. The research concludes that significant techno-economic potentials exist for the investigated resources. Optimization results show that CO2 emissions of a Nordic municipal energy system can be reduced by about 60% without a considerable increase in total energy system costs and that peak electricity import can be reduced by up to 38%. The outlook onto 2030 shows that the transport sector could be composed of high electrification shares and biofuels. Technology choices for optimal solutions are highly sensitive to electricity prices, discount rates and grid emission factors. The inquiries of this research provide important insights about carbon mitigation strategies for integrated energy sectors within a perspective on Nordic municipalities. Future work will refine the transport model, develop and apply a framework for multi-criteria decision analysis (MCDA) enabling local decision makers to determine a technically and economically sound pathway based on the optimal alternatives provided, and analyze the existing policy framework affecting energy planning of local authorities. / Kommunala energisystem i nordiska miljöer möter flera utmaningar: det kalla klimatet, storskaliga industrier, en stor andel elvärme och långa distanser driver energiförbrukningen. Medan åtgärder vidtas på efterfrågesidan för att minimera energianvändningen, kan utsläppsminskande åtgärder inom gruvdrift, industrier, uppvärmningen och transportsektorn öka förbrukningen av el och biobränslen. Fortsatt tillväxt av intermittent vind- och solkraft ökar elproduktion, men den planerade avvecklingen av svensk kärnkraft kommer att utmana tillförlitligheten i elsystemet i de nordiska länderna. Flaskhalsar i överförings- och distributionsnäten kan begränsa en potentiell tillväxt av elanvändningen i stadsområden, begränsa ny intermittent utbud, och påverka elutbyte mellan länderna. Miljöhänsyn kan begränsa ökad användning av biomassa. Lokala myndigheter är engagerade i att bidra till nationella klimatmål, samtidigt som de följer sina egna mål för ekonomisk utveckling, ökad självförsörjning av energi och överkomliga energikostnader. Mot bakgrund av dessa omständigheter undersöker denna avhandling befintliga tekniska och ekonomiska potentialer för förnybar energi i Norden med fokus på de nordliga länen i Finland, Norge och Sverige. Forskningen syftar vidare till att utveckla optimala lösningar för hållbara nordiska kommunala energisystem, där samspelet mellan stora energisektorer studeras, med tanke på att minimera årliga energisystemkostnader och samtidigt minska koldioxidutsläppen samt analysera påverkan på elimport till och export från kommunen. Denna forskning formulerar ett integrerad kommunalt energisystem som multimåloptimeringsproblem (multi-objective optimisation problem - MOOP), som löses genom att kombinera simuleringsverktyget EnergyPLAN med en evolutionär algoritm implementerad i Matlab. I ett första steg studeras kopplingen av el- och värmesektorerna, och i ett andra steg effekterna av en integrerad och alltmer förnybar transportsektor på energisystemet. Känslighetsanalys på viktiga ekonomiska parametrar och på olika utsläppsfaktorer utförs. Piteå (Norrbottens län, Sverige) är en typisk nordisk kommun som fungerar som en fallstudie för detta arbete. Forskningens slutsatser innebär att det finns betydande teknisk-ekonomiska potentialer för de undersökta förnybara resurserna. Optimeringsresultaten visar att koldioxidutsläppen från ett nordiskt kommunalt energisystem kan minskas med cirka 60% utan en avsevärd ökning av de totala energisystemkostnaderna och att den högsta elimporten kan minskas med upp till 38%. Resultat för år 2030 visar att transportsektorn kan ha en mycket hög elektrifieringsgrad och samtidigt används biobränslen i tunga fordon. Optimala lösningar är mycket känsliga för elpriser, räntor och utsläppsfaktorer. Detta arbete ger viktiga insikter om strategier för koldioxidminskning för integrerade energisektorer i ett perspektiv på nordiska kommuner. Min framtida forskning kommer att förfina transportmodellen, utveckla och tillämpa ett ramverk för beslutsanalys med flera kriterier (multi-criteria decision analysis - MCDA) som ska stödja lokala myndigheter att fastställa tekniskt och ekonomiskt hållbara lösningar i deras energiplanering.