5 research outputs found

    Optimising multimodal fusion for biometric identification systems

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    Biometric systems are automatic means for imitating the human brain’s ability of identifying and verifying other humans by their behavioural and physiological characteristics. A system, which uses more than one biometric modality at the same time, is known as a multimodal system. Multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. This thesis addresses some issues related to the implementation of multimodal biometric identity verification systems. The thesis assesses the feasibility of using commercial offthe-shelf products to construct deployable multimodal biometric system. It also identifies multimodal biometric fusion as a challenging optimisation problem when one considers the presence of several configurations and settings, in particular the verification thresholds adopted by each biometric device and the decision fusion algorithm implemented for a particular configuration. The thesis proposes a novel approach for the optimisation of multimodal biometric systems based on the use of genetic algorithms for solving some of the problems associated with the different settings. The proposed optimisation method also addresses some of the problems associated with score normalization. In addition, the thesis presents an analysis of the performance of different fusion rules when characterising the system users as sheep, goats, lambs and wolves. The results presented indicate that the proposed optimisation method can be used to solve the problems associated with threshold settings. This clearly demonstrates a valuable potential strategy that can be used to set a priori thresholds of the different biometric devices before using them. The proposed optimisation architecture addressed the problem of score normalisation, which makes it an effective “plug-and-play” design philosophy to system implementation. The results also indicate that the optimisation approach can be used for effectively determining the weight settings, which is used in many applications for varying the relative importance of the different performance parameters

    Sustainable Human Resource Management

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    The concept of sustainability is important for companies both in the case of SMEs and worldwide multinational companies. Some key factors to help a company achieve its sustainability objectives are based on human resource management. Sustainable human resource management is a typical cross-functional task that becomes increasingly important at the strategic level of a company. Industry 4.0 technologies, Internet of Things, and competitive demands, as signs of globalization, have led to significant changes across the organizational structures and human resource strategies of companies. The increasing importance of sophisticated human resource strategies in the life of companies and the intention to find optimal design and operation strategies for sustainable human resource management were a motivation for launching this book. This book offers a selection of papers which explain the impact of smart human resource management on economy. Authors from 14 countries published working examples and case studies resulting from their research in this field. The aim of this book is to help students at the level of BSc, MSc, and PhD level, as well as managers and researchers, to understand and appreciate the concept, design, and implementation of sustainable human resource management solutions

    Dynamic data driven investigation of petrophysical and geomechanical properties for reservoir formation evaluation

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    Petrophysical and geomechanical properties of the formation such as Young’s modulus, bulk modulus, shear modulus, Poisson’s ratio, and porosity provide characteristic description of the hydrocarbon reservoir. It is well-established that static geomechanical properties are good representatives of reservoir formations; however, they are non-continuous along the wellbore, expensive and determining these properties may lead to formation damage. Dynamic geomechanical formation properties from acoustic measurements offer a continuous and non-destructive means to provide a characteristic description of the reservoir formation. In the absence of reliable acoustic measurements of the formation, such as sonic logs, the estimation of the dynamic geomechanical properties becomes challenging. Several techniques like empirical, analytical and intelligent systems have been used to approximate the property estimates. These techniques can also be used to approximate acoustic measurements thus enable dynamic estimation of geomechanical properties. This study intends to explore methodologies and models to dynamically estimate geomechanical properties in the absence of some or all acoustic measurements of the formation. The present work focused on developing empirical and intelligent systems like artificial neural networks (ANN), Gaussian processes (GP), and recurrent neural networks (RNN) to determine the dynamic geomechanical properties. The developed models serve as a cost-effective, reliable, efficient, and robust methods, offering dyanmic geomechanical analysis of the formation. This thesis has five main contributions: (a) a new data-driven empirical model of estimating static Young’s modulus from dynamic Young’s modulus, (b) a new data-driven ANN model for sonic well log prediction, (c) a new data-driven GP model for shear wave transit time prediction, (d) a new dynamic data-driven RNN model for sonic well log reproduction, and (e) an assessment on the ANN as a reliable sonic logging tool

    Arquiteturas de hardware para aceleração de algoritmos de controle preditivo não-linear

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    Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2018.O Controle Preditivo Baseado em Modelos (MPC) é uma técnica avançada de controle que vem ganhando espaço tanto na academia quanto na indústria ao longo das últimas décadas. O fato de incorporar restrições em sua lei de controle e de poder ser aplicada tanto para sistemas lineares simples quanto para sistemas não-lineares complexos com múltiplas entradas e múltiplas saídas tornam seu emprego bastante atraente. Porém, seu alto custo computacional muitas vezes impede sua aplicação a sistemas com dinâmicas rápidas, principalmente a sistemas não-lineares embarcados onde há restrições computacionais e de consumo de energia. Baseado nisso, este trabalho se propõe a desenvolver algoritmos e arquiteturas em hardware capazes de viabilizar a aplicação do Controle Preditivo Não-Linear (NMPC) para sistemas embarcados. Duas abordagens são desenvolvidas ao longo do trabalho. A primeira aplica técnicas de aprendizado de máquina utilizando Redes Neurais Artificiais (RNAs) e Máquinas de Vetor de Suporte (SVMs) para criar soluções que aproximam o comportamento do NMPC em hardware. Neste caso, técnicas para o treinamento das RNAs e SVMs são exploradas com o intuito de generalizar uma solução capaz de lidar com uma ampla faixa de referências de controle. Em seguida, arquiteturas de hardware em ponto-flutuante para a implementação de RNAs do tipo RBF (Radial Basis Functions) e SVMs são desenvolvidas juntamente com configurador automático capaz de gerar os códigos VHDL (VHSIC Hardware Description Language) das respectivas arquiteturas baseado nos resultados de treinamento e sua topologia. As arquiteturas resultantes são testadas em um FPGA (Field-Programmable Gate Array) de baixo custo e são capazes de computar soluções em menos de 1 s. Na segunda abordagem, o algoritmo heurístico de Otimização por Enxame de Partículas (PSO), é estudado e adaptado para etapa de busca da sequência de controle ótima do NMPC. Dentre as modificações estão incluídas a adição de funções de penalização para obedecer às restrições de estados do sistema, o aprimoramento da técnica KPSO (Knowledge-Based PSO), denominada KPSO+SS, onde resultados de períodos de soluções de períodos amostragem anteriores são combinados com informações sobre o sinal de controle em estado estacionário e seus valores máximos e mínimos para agilizar a busca pela solução ótima. Mais uma vez, arquiteturas de hardware em ponto-flutuante são desenvolvidas para viabilizar a aplicação do controlador NMPC-PSO a sistemas embarcados. Um gerador de códigos da solução NMPC-PSO é proposto para permitir a aplicação da mesma arquitetura a outros sistemas. Em seguida, a solução é testada para o procedimento de swing-up do pêndulo invertido utilizando uma plataforma hardware-inthe- loop (HIL) e apresentou bom desempenho em tempo-real calculando a solução em menos de 3 ms. Finalmente, a solução NMPC-PSO é validada em um sistema de pêndulos gêmeos e outro sistema de controle de atitudes de um satélite.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) e Decanato de Pesquisa e Inovação -(DPI/ UnB).Model-based Predictive Control (MPC) is an advanced control technique that has been gaining adoption in industry and the academy along the last few decades. Its ability to incorporate system constraints in the control law and be applied from simple linear systems up to more complex nonlinear systems with multiple inputs and outputs attracts its usage. However, the high computational cost associated with this technique often hinders its use, especially in embedded nonlinear systems with fast dynamics with computational and restrictions. Based on these facts, this work aims to study and develop algorithms and hardware architectures that can enable the application of Nonlinear Model Predictive Control (NMPC) on embedded systems. Two approaches are developed throughout this work. The first one applies machine learning techniques using Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) to create solutions that approximate the NMPC behavior in hardware. In this case, ANN and SVM training techniques are explored with the aim to generalize the control solution and work on a large range of reference control inputs. Next, floating-point hardware architectures to implement Radial Basis Function ANNs and SVM solutions are developed along with an automatic architectural configuration too, capable of generating the VHDL (VHSIC Hardware Description Language) codes based on the training results and its topology. Resulting architectures are tested on a low-cost FPGA (Field-Programmable Gate Array) and are capable of computing the solution in under 1 s. In a second approach, the Particle Swarm Optimization (PSO), which is a heuristic algorithm, is studied and adapted to perform the optimal control sequence search phase of the NMPC. Among the main optimizations performed are the addition of penalty functions to address the controlled system state constraints, an improved KPSO (Knowledge-Based PSO) technique named KPSO+SS, where results from previous sampling periods are combined with steady-state control information to speed-up the optimal solution search. Hardware architectures with floating-point arithmetic to enable the application of the NMPC-PSO solution on embedded systems are developed. Once again, a hardware description configuration tool is created to allow the architecture to be applied to multiple systems. Then, the solution is applied to a real-time inverted pendulum swing-up procedure tested on a hardware-in-the-loop (HIL) platform. The experiment yielding good performance and control results and was able to compute the solutions in under 3 ms. Finally, the NMPC-PSO solution is further validated performing a swing-up procedure on a Twin Pendulum system and then on a satellite control platform, a system with multiple inputs and output
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