This paper suggests a smart direct power control (SDpC) strategy based on the m5-pruned algorithm for a threephase (pWm) rectifier. (pWm) rectifier is a non-linear system due to its structure of semiconductor switches...
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This paper suggests a smart direct power control (SDpC) strategy based on the m5-pruned algorithm for a threephase (pWm) rectifier. (pWm) rectifier is a non-linear system due to its structure of semiconductor switches that distort the current line, produce DC link fluctuations, degrade the unity power factor (UpF) and system stability. Conventional methods as the DpC model based on proportional-integral (pI) controllers and hysteresis comparators cannot effectively address the aforementioned drawbacks due to the limited bandwidth operations, overshoot response, tuning sensitivity, sluggish reaction to sudden changes, and variable switching frequency. This paper proposes a new DpC strategy that replaces the intelligent m5-pruned algorithms instead with the DC bus voltage controller and the active and reactive hysteresis comparators. Fast prediction, large-scale control, update learning, and robustness are powerful features enabling m5pmodel accomplish the high and stable performance of the DC bus voltage and compensate for the hysteresis comparator defects. Under mATLAB/ Simulink, WEKA software, and DSpace DS1103 board ordered by the graphical interface of the control desk, the simulation and experimental results achieved and proved the effectiveness, efficiency, and robustness of the proposed control in both steady and transient states.
Em diferentes aspectos da vida cotidiana, o ser humano é forçado a escolher entre v5;rias opç5;es, esse processo é conhecido como tomada de decisão. No nível do negócio, a to...
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Em diferentes aspectos da vida cotidiana, o ser humano é forçado a escolher entre várias opções, esse processo é conhecido como tomada de decisão. No nível do negócio, a tomada de decisões desempenha umpapel muito importante, porque dessas decisões depende o sucesso ou o fracasso das organizações. No entanto, emmuitos casos, tomar decisões erradas pode gerar grandes custos. Desta forma, alguns dos problemas de tomada de decisão que um gerente enfrenta comumente são, por exemplo, a decisão para determinar umpreço, a decisão de comprar ou fabricar, emproblemas de logística, problemas de armazenamento, etc. por outro lado, a coleta de dados tornou-se uma vantagem competitiva, pois pode ser utilizada para análise e extração de resultados significativos por meio da aplicação de diversas técnicas, como estatística, simulação, matemática, econometria e técnicas atuais, como aprendizagem de máquina para a criação de modelos preditivos. Além disso, há evidências na literatura de que a criação de modelos com técnicas de aprendizagem de máquina têm um impacto positivo na indústria e em diferentes áreas de pesquisa. Nesse contexto, o presente trabalho propõe o desenvolvimento de ummodelo preditivo para tomada de decisão, usando as técnicas supervisionadas de aprendizado de máquina, e combinando o modelo gerado com as restrições pertencentes ao processo de otimização. O objetivo da proposta é treinar ummodelo matemático com dados históricos de umprocesso decisório e obter os preditores compostos por funções empíricas que serão posteriormente utilizadas e modeladas de acordo com as restrições do problema. Assim, este trabalho pode ser classificado como uma pesquisa aplicada, com objetivos empíricos descritivos e experiência prática que explicarão o modelo e suas vantagens. A maneira de abordar o problema deste trabalho será quantitativa, sendo os procedimentos técnicos de modelagem e simulação. A sistemática proposta é validada aplicando-se a umproblema real em uma empres
For service providers and operators, successful root cause analysis is essential for satisfactory service provisioning. However, reasons for sudden trend changes of the instantaneous Quality of Experience (QoE) may no...
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ISBN:
(纸本)9781538682128
For service providers and operators, successful root cause analysis is essential for satisfactory service provisioning. However, reasons for sudden trend changes of the instantaneous Quality of Experience (QoE) may not always be immediately visible from underlying service- or network-level monitoring data. Thus, there is the challenge to pinpoint such moments of change in provisioning, and model the impact on instantaneous QoE, as a lead in root cause analysis. This work investigates the potential of machine Learning (mL) of deriving time-interval-based models for instantaneous QoE ratings, obtained from a set of publicly available rating traces. In particular, the paper demonstrates the capability of the mL algorithmm5p to model trends of instantaneous QoE through model trees, consisting of piecewise linear functions over time. It is shown how and to which extent these functions can be used to estimate moments of change. Furthermore, the model trees support earlier assumptions about exponential shapes of instantaneous QoE over time as reactions to sudden changes of provisioning, such as video freezes.
The uniaxial compressive strength (UCS) and Young's modulus (E) of rock are important parameters for evaluating the strength, deformation, and stability of rock engineering structures. Direct measurement of these ...
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The uniaxial compressive strength (UCS) and Young's modulus (E) of rock are important parameters for evaluating the strength, deformation, and stability of rock engineering structures. Direct measurement of these parameters is expensive, time-consuming, and even infeasible in some circumstances due to the difficulty involved in obtaining core samples. Recently, soft computing tools have been used to predict UCS and E based on index tests. most of these tools are not as transparent and easy to use as empirical regression-based models. This study presents another soft computing approach-model trees-for predicting the UCS and E of carbonate rocks. The main advantages of model trees are that they are easier to use than other data learning tools and, more importantly, they represent understandable mathematical rules. In this study, the m5p algorithm was employed to build and evaluate model trees (UCS and E model trees). First, the models were developed in an unpruned form, and then they were pruned to avoid overfitting. The data used to train and test the model trees were collected from quarries in southwestern Turkey. model trees included Schmidt hammer, effective porosity, dry unit weight, paEurowave velocity, and slake durability index as input variables. When the models were assessed using a number of statistical indices (RmSE, mAE, VAF, and R (2)), it was found that unpruned and pruned model trees provide acceptable predictions of UCS and E, although the pruned models are simpler and easier to understand.
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