Particle Swam Optimization (PSO) is especially useful for rapid optimization of problems involving multiple objectives and constraints in dynamic environments. It regularly and substantially outperforms other algorithms in benchmark tests. This paper describes research leading to the application of PSO to the autonomous asset management problem in electronic warfare. The PSO speed provides fast optimization of frequency allocations for receivers and jammers in highly complex and dynamic environments. The key contribution is the simultaneous optimization of the frequency allocations, signal priority, signal strength, and the spatial locations of the assets. The fitness function takes into account the assets' locations in 2 dimensions, maximizing their spatial distribution while maintaining allocations based on signal priority and power. The fast speed of the optimization enables rapid responses to changing conditions in these complex signal environments, which can have real-time battlefield impact. Results optimizing receiver frequencies and locations in 2 dimensions have been successful. Current run-times are between 450ms (3 receivers, 30 transmitters) and 1100ms (7 receivers, 50 transmitters) on a single-threaded x86 based PC. Run-times can be substantially decreased by an order of magnitude when smaller swarm populations and smart swarm termination methods are used, however a trade off exists between run-time and repeatability of solutions. The results of the research on the PSO parameters and fitness function for this problem are demonstrated.

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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>

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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.

[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.

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