Generalized Renewal Processes are useful for approaching the rejuvenation of dynamical systems resulting from planned or unplanned interventions. We present new perspectives for the Generalized Renewal Processes in general and for the Weibull-based Generalized Renewal Processes in particular. Disregarding from literature, we present a mixed Generalized Renewal Processes approach involving Kijima Type I and II models, allowing one to infer the impact of distinct interventions on the performance of the system under study. The first and second theoretical moments of this model are introduced as well as its maximum likelihood estimation and random sampling approaches. In order to illustrate the usefulness of the proposed Weibull-based Generalized Renewal Processes model, some real data sets involving improving, stable, and deteriorating systems are used.

This article develops a model for dependent and imperfect condition-based preventive and corrective maintenance actions. The approach is based on the combination of the intensity proportional repair alert, a competing risks-based model and the generalized renewal process. Typically, intensity proportional repair alert can identify either how preventive actions may modify the distribution of the time between critical failures or how corrective events may change the frequency of preventive maintenances, but this method fails to analyze the effectiveness of the maintenance actions because they are treated as being perfect. On the other hand, generalized renewal process is able to capture the quality of maintenance, classifying it as perfect, minimal or imperfect depending on the value of a rejuvenation parameter. However, generalized renewal process cannot distinguish how different types of maintenance influence each other as intensity proportional repair alert does. Therefore, the intensity proportional repair alert-generalized renewal process hybrid approach is proposed here to fill this gap. This article also develops the maximum likelihood estimators for the proposed model as well as a Monte Carlo-based algorithm to estimate the expected number of preventive and corrective maintenances over time. The proposed model is validated through two example applications for which the intensity proportional repair alert-generalized renewal process model results show close agreement with the failure datasets.

In practical applications, the presence of delays can deteriorate the performance of the control system or even cause plant instability. However, by properly controlling these delays, it is possible to improve the performance of the mechanism. The present work is based on a proposal to analyze the asymptotic stability and convergence of a quadrotor robot, an unmanned aerial vehicle (UAV), on the performance of a given task, under time delay in the data flow. The effects of the communication delay problem, as well as the response-signal behavior of the quadrotors in the accomplishment of positioning mission are presented and analyzed from the insertion of fixed time delay intervals in the UAVs' data collected by its sensors system. Due to the large search space in the set of parameter combinations and the high computational cost required to perform such an analysis by sequentially executing thousands of simulations, this work proposes an open source GPU-based implementation to simulate the robot behavior. Experimental results show a speedup up to 4900x in comparison to MATLAB® implementation. The implement is available in Colab Google platform.

Os FPGAs oferecem eficiência energética para o desenvolvimento de aceleradores para fluxo de dados na Nuvem. Porém, existem vários desafios para popularizar seu uso. Dentre eles, podemos citar o tempo de compilação (que pode demorar horas) e conhecimento de hardware para uso adequado de linguagens de síntese de alto nível. Recentemente, a ferramenta READY possibilitou a redução do tempo de compilação e configuração para microsegundos. O ambiente foi validado na plataforma em nuvem HARP 2 da Intel/Altera. Apesar da integração com a Linguagem C++ para o desenvolvimento das aplicações, o acelerador é descrito de forma textual como um grafo. Neste trabalho é apresentado a extensão PLAIN, que inclui uma interface online gráfica para descrição dos aceleradores, a automatização do fluxo de projeto, dois níveis de simulação e um nível de execução. A ferramenta também mostra estatísticas de desempenho e permite criação de novos operadores para exploração do espaço de projeto.

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