Uma Arquitetura de Microserviços de Internet das Coisas para Casas Inteligentes

O crescimento de dispositivos conectados à Internet impactou de forma positiva a evolução tecnológica da sociedade. A possibilidade de controlar recursos remotamente e interagir com residências não só facilitou a vida do cidadão como trouxe acessibilidade para quem depende de recursos mais sofisticados para viver. O paradigma de Internet das Coisas surge com diversos cenários de automação e arquiteturas para a relação entre software e objetos conectados. Contudo, ainda existe a necessidade de criar ambientes mais concisos para as interações entre estes dispositivos conectados, unificando e replicando configurações de Casas Inteligentes de forma simples e escalável. Uma arquitetura baseada em microserviços  de Internet das Coisas possui as características de simplicidade de customização e escalabilidade da infraestrutura necessária para atingir este cenário mais conciso. Os resultados apresentados neste artigo encontraram características de manutenabilidade e reproducibilidade promissoras para situações reais de Casas Inteligentes.

Adaptive Clan Particle Swarm Optimization

Abstract-Particle Swarm Optimization has been widely used to solve real world problems, mainly when there are too many variables to be optimized and these variables are continuous. In nature one can observe many examples of cooperative behaviors that lead to complex problem solving. Recently, some Particle Swarm Optimization variations gracefully incorporate such cooperative features with consequent beneficial new abilities. In this paper we put forward the incorporation of auto-adaptation capability in a cooperative Particle Swarm Optimization algorithm, called Clan Particle Swarm Optimization. Next, we present a deep analysis on the adaptation process for one multimodal function and evaluate the performance of our proposal in some well known benchmark problems. The results revealed that our proposal achieved better performance than other approaches, specially in tough multimodal problems

Boolean Operators to Improve Multi-Objective Evolutionary Algorithms for Designing Optical Networks

Abstract The physical topology design (PTD) of optical networks is frequently accomplished by combining several solutions in an iterative way, especially if meta-heuristics are deployed for this purpose. Suitable operators to recombine information of network topologies aiming at creating innovative options for designing networks are very useful. Operators that preserve desired properties can improve the quality of the meta-heuristics utilized for solving the PTD problem. In this paper, we propose new crossover operators by using the OR and XOR operations to improve multi-objective evolutionary algorithms applied to design optical networks. We performed comparisons between the proposed crossover operators and the traditional uniform crossover. The proposed operators showed to be a suitable alternative to design optical networks. We obtained better solutions or at least solutions with the same quality when compared to solutions achieved by traditional approaches, but the execution time required by our proposal is smaller

Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes

Gearboxes are mechanical devices that play an essential role in several applications, e.g., the transmission of automotive vehicles. Their malfunctioning may result in economic losses and accidents, among others. The rise of powerful graphical processing units spreads the use of deep learning-based solutions to many problems, which includes the fault diagnosis on gearboxes. Those solutions usually require a significant amount of data, high computational power, and a long training process. The training of deep learning-based systems may not be feasible when GPUs are not available. This paper proposes a solution to reduce the training time of deep learning-based fault diagnosis systems without compromising their accuracy. The solution is based on the use of a decision stage to interpret all the probability outputs of a classifier whose output layer has the softmax activation function. Two classification algorithms were applied to perform the decision. We have reduced the training time by almost 80% without compromising the average accuracy of the fault diagnosis system