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In recent years, the power industry has experienced significant changes on the distribution power system primarily due to the implementation of smart-grid technology and the incremental implementation of distributed generation. Distributed Generation (DG) is simply defined as the decentralization of power plants by placing smaller generating units closer to the point of consumption, traditionally ten mega-watts or smaller. While DG is not a new concept, DG is gaining widespread interest primarily for the following reasons: increase in customer demand, advancements in technology, economics, deregulation, environmental and national security concerns. The distribution power system traditionally has been designed for radial power flow, but with the introduction of DG, the power flow becomes bidirectional. As a result, conventional power analysis tools and techniques are not able to properly assess the impact of DG on the electrical system. The presence of DG on the distribution system creates an array of potential problems related to safety, stability, reliability and security of the electrical system. Distributed generation on a power system affects the voltages, power flow, short circuit currents, losses and other power system analysis results. Whether the impact of the DG is positive or negative on the system will depend primarily on the location and size of the DG. The objective of this research is to develop indices and an effective technique to evaluate the impact of distributed generation on a distribution power system and to employ the particle swarm optimization technique to determine the optimal placement and size of the DG unit with an emphasis on improving system reliability while minimizing the following system parameters: power losses, voltage deviation and fault current contributions. This research utilizes the following programs to help solve the optimal DG placement problem: Distribution System Simulator (DSS) and MATLAB. The developed indices and PSO technique successfully solved the optimal DG sizing and placement problem for the I 13-Node, 34-Node and 123-Node Test Cases. The multi-objective index proved to be computational efficient and accurately evaluated the impact of distributed generation on the power system. The results provided valuable information about the system response to single and multiple DG units.

<p>Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In this thesis, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on three datasets across different domains. The first dataset consists of posts from the online health forum r/Cancer. The second dataset is a collection of NY Times abstracts and is used to compare</p> <p>the proposed model to LDA. The third is a standard benchmark dataset for topic modeling which consists of a collection of messages posted to 20 different news groups. It is used to compare state-of-the-art generative document models (i.e., ETM and NVDM) to pPSO. The results show that pPSO is able to produce interpretable clusters. Moreover, pPSO is able to capture both common topics as well as emergent topics. The topic coherence of pPSO is comparable to that of ETM and its topic diversity is comparable to NVDM. The assignment parity of pPSO on a document completion task exceeded 90% for the 20News-Groups dataset. This rate drops to approximately 30% when pPSO is applied to the same Skip-Gram embedding derived from a limited, corpus specific vocabulary which is used by ETM and NVDM.</p>

This master thesis covers the dynamic soil structure interaction of soil-steel culverts applyinga methodology based on the frequency domain response. At the first stage of this masterthesis, field tests were performed on one bridge using controlled excitation. Then, themethodology followed uses previous research, the field tests, finite element models (FEM)and perfectly matched layer (PML) elements.Firstly, a 2D model of the analysed bridge, Hårestorp, was made to compare the frequencyresponse functions (FRF) with the ones obtained from the field tests. Simultaneously, a 3Dmodel of the bridge is created for the following purposes: compare it against the 2D modeland the field tests, and to implement a model updating procedure with the particle swarmalgorithm to calibrate the model parameters. Both models use PML elements, which areverified against previous solution from the literature. The verification concludes that thePML behave correctly except for extreme parameter values.In the course of this master thesis, relatively advanced computation techniques were requiredto ensure the computational feasibility of the problem with the resources available.To do that, a literature review of theoretical aspects of parallel computing was performed, aswell as the practical aspects in Comsol. Then, in collaboration with Comsol Support and thehelp given by PDC at KTH it was possible to reduce the computational time to a feasiblepoint of around two weeks for the model updating of the 3D model.The results are inconclusive, in terms of searching for a perfectly fitting model. Therefore,further research is required to adequately face the problem. Nevertheless, there are some accelerometerswhich show a considerable level of agreement. This thesis concludes to discardthe 2D models due to their incapability of facing the reality correctly, and establishes a modeloptimisation methodology using Comsol in connection with Matlab.

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Traditional methods for locating books and resources in libraries often entail browsing catalogsor manual searching that are time-consuming and inefficient. This thesis investigates thepotential of automated digital services to streamline this process, by utilizing Wi-Fi signal datafor precise indoor localization. Central to this study is the development of a model that employsWi-Fi signal strength (RSSI) and round-trip time (RTT) to estimate the locations of library userswith arm-length accuracy. This thesis aims to enhance the accuracy of location estimation byexploring the complex, nonlinear relationship between Received Signal Strength Indicator(RSSI) and Round-Trip Time (RTT) within signal fingerprints. The model was developed usingan artificial neural network (ANN) to capture the relationship between RSSI and RTT. Besides,this thesis introduces and evaluates the performance of a novel variant of the Particle SwarmOptimization (PSO) algorithm, named Randomized Particle Swarm Optimization (RPSO). Byincorporating randomness into the conventional PSO framework, the RPSO algorithm aims toaddress the limitations of the standard PSO, potentially offering more accurate and reliablelocation estimations. The PSO algorithms, including RPSO, were integrated into the trainingprocess of ANN to optimize the network’s weights and biases through direct optimization, aswell as to enhance the hyperparameters of the ANN’s built-in optimizer. The findings suggestthat optimizing the hyperparameters yields better results than direct optimization of weights andbiases. However, RPSO did not significantly enhance the performance compared to thestandard PSO in this context, indicating the need for further investigation into its application andpotential benefits in complex optimization scenarios.

In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis. In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis.

[pt] A decomposição de sulfatos vem ganhando notoriedade pela sua capacidade de geração limpa de H2 através dos ciclos termoquímicos. O entendimento do mecanismo de decomposição é relevante para futuros planejamentos em aplicações industriais. Além disso, a modelagem desses processos permite obter informações acerca da energia requerida para que os mesmos ocorram. Dentre os diferentes sistemas de reações de decomposição, observa-se que alguns deles são mais complexos do que outros, envolvendo a presença de fases intermediárias e múltiplas reações consecutivas ou simultâneas. Portanto, o presente trabalho se propõe a desenvolver uma metodologia para a modelagem da decomposição térmica de sistemas reacionais com diferentes níveis de complexidade: sulfato de alumínio, alúmen de potássio, mistura de sulfatos de alumínio e potássio, sulfato de zinco e sulfato de ferro (II). Os experimentos foram realizados utilizando análise termogravimétrica (TG) para ter o entendimento dos diferentes estágios de decomposição, utilizando os dados obtidos na etapa de modelagem. O modelo envolveu o uso de um conjunto de equações diferenciais para representar cada uma das reações que ocorrem na decomposição. A estimação dos parâmetros cinéticos feita pelo método de otimização por enxame de partículas. Os resultados indicaram que sistemas envolvendo a decomposição do sulfato de alumínio são catalisados na presença de sulfato de potássio. No caso do zinco, a dessulfatação do sulfato anidro ocorre em duas etapas, com a presença de um oxissulfato como uma fase intermediária. O sulfato de ferro (II) também apresenta uma decomposição complexa ao passar pela fase de sulfato de ferro (III) antes de ser completamento convertido em hematita. Todas as modelagens mostraram excelente ajuste aos dados experimentais, com R2 acima de 0.98 em todos os casos. / [en] The interest over of the decomposition of sulfates has increased due to its capacity of generating clean H2 through the thermochemical cycles. Understanding the decomposition mechanism is relevant to future industrial design and applications. Moreover, the modeling of these processes gives the information needed to know how much energy is required for the occurrence of the reactions. Among the different reaction systems, it is observed a range of complexity, with the presence of intermediate phases, and multiple consecutive or simultaneous reactions. Therefore, the present work proposed to develop a modeling methodology for the thermal decomposition of sulfates systems with different complexity levels: aluminum sulfate, potassium alum, mixture of aluminum sulfate and potassium sulfate, zinc sulfate, and iron (II) sulfate. The experiments were performed using thermogravimetric analysis (TGA) to understand the decomposition stages and use the data in the modeling step. The developed model consisted of a system of differential equations to describe every reaction taking place in the decomposition. The kinetic parameters estimation was made by using particle swarm optimization. The results indicate that potassium sulfate catalyzes the decomposition of aluminum sulfate. In the case of zinc, the desulfation of anhydrous zinc sulfate occurs in two stages, with the presence zinc oxysulfate as an intermediate phase. Iron (II) sulfate also shows a complex decomposition system, as it first decomposes into iron (III) sulfate before it is completely converted into hematite. All the modeling results displayed an excellent agreement with the experimental data, with R2 values above 0.98 for all cases.

The objectives of this work were to investigate, design and implement beam steering antenna arrays for mobile and wireless applications using the genetic algorithm (GA) and particle swarm optimisation (PSO) techniques as optimisation design tools. Several antenna designs were implemented and tested: initially, a printed dipole antenna integrated with a duplex RF switch used for mobile base station antenna beam steering was investigated. A coplanar waveguide (CPW) to coplanar strip (CPS) transition was adopted to feed the printed dipole. A novel RF switch circuit, used to control the RF signal fed to the dipole antenna and placed directly before it, was proposed. The measured performance of the RF switch was tested and the results confirmed its viability. Then two hybrid coupled PIN diode phase shifters, using Branchline and Rat-Race ring coupler structures, were designed and tested. The generation of four distinct phase shifts was implemented and studied. The variations of the scattering parameters were found to be realistic, with an acceptable ±2 phase shift tolerance. Next, antenna beam steering was achieved by implementing RF switches with ON or OFF mode functions to excite the radiating elements of the antenna array. The switching control process was implemented using a genetic algorithm (GA) method, subject to scalar and binary genes. Anti-phase feeding of radiating elements was also investigated. A ring antenna array with reflectors was modelled and analysed. An antenna of this type for mobile base stations was designed and simulation results are presented. Following this, a novel concept for simple beam steering using a uniform antenna array operated at 2.4 GHz was designed using GA. The antenna is fed by a single RF input source and the steering elements are reactively tuned by varactor diodes in series with small inductors. The beam-control procedure was derived through the use of a genetic algorithm based on adjusting the required reactance values to obtain the optimum solution as indicated by the cost function. The GA was also initially used as an optimisation tool to derive the antenna design from its specification. Finally, reactive loading and time modulated switching techniques are applied to steer the beam of a circular uniformly spaced antenna array having a source element at its centre. Genetic algorithm (GA) and particle swarm optimisation (PSO) processes calculate the optimal values of reactances loading the parasitic elements, for which the gain can be optimised in a desired direction. For time modulated switching, GA and PSO also determine the optimal on and off times of the parasitic elements for which the difference in currents induced optimises the gain and steering of the beam in a desired direction. These methods were demonstrated by investigating a vertically polarised antenna configuration. A prototype antenna was constructed and experimental results compared with the simulations. Results showed that near optimal solutions for gain optimisation, sidelobe level reduction and beam steering are achievable by utilising these methods. In addition, a simple switching process is employed to steer the beam of a horizontally polarised circular antenna array. A time modulated switching process is applied through Genetic Algorithm optimisation. Several model examples illustrate the radiation beams and the switching time process of each element in the array.

Dealing with the electromagnetic issue might bring a sort of discontinuous and nondifferentiable regions. Thus, it is of great interest to implement an appropriate optimisation approach, which can preserve the computational resources and come up with a global optimum. While not being trapped in local optima, as well as the feasibility to overcome some other matters such as nonlinear and phenomena of discontinuous with a large number of variables. Problems such as lengthy computation time, constraints put forward for antenna requirements and demand for large computer memory, are very common in the analysis due to the increased interests in tackling high-scale, more complex and higher-dimensional problems. On the other side, demands for even more accurate results always expand constantly. In the context of this statement, it is very important to find out how the recently developed optimization roles can contribute to the solution of the aforementioned problems. Thereafter, the key goals of this work are to model, study and design low profile antennas for wireless and mobile communications applications using optimization process over a computational electromagnetics numerical solution. The numerical solution method could be performed over one or hybrid methods subjective to the design antenna requirements and its environment. Firstly, the thesis presents the design and modelling concept of small uni-planer Ultra- Wideband antenna. The fitness functions and the geometrical antenna elements required for such design are considered. Two antennas are designed, implemented and measured. The computed and measured outcomes are found in reasonable agreement. Secondly, the work is also addressed on how the resonance modes of microstrip patches could be performed using the method of Moments. Results have been shown on how the modes could be adjusted using MoM. Finally, the design implications of balanced structure for mobile handsets covering LTE standards 698-748 MHz and 2500-2690 MHz are explored through using firefly algorithm method. The optimised balanced antenna exhibits reasonable matching performance including near-omnidirectional radiations over the dual desirable operating bands with reduced EMF, which leads to a great immunity improvement towards the hand-held. / General Secretariat of Education and Scientific Research Libya