Eduardo Carvalho

Comportamento Mecânico e Características Estruturais de Compósitos Poliméricos Reforçados com Fibras Contínuas e Alinhadas de Curauá

Polymeric matrix composites reinforced with natural lignocellulosic fibers are now being used in many fields of practical interest. This has motivated scientific and technological investigations on both, traditional fibers, such as sisal and jute, as well as those, like curaua, which present promising characteristics. Until today, however, the works performed on the behavior of curaua reinforced polymeric composites used short, discontinuous and randomly oriented fibers. As a consequence the attained mechanical strength was relatively small. In the current work the properties of polyester matrix composites reinforced with up to 30 wt. % of continuous and aligned curaua fibers was investigated. These composites were bend tested and the fracture surface was observed by scanning electron microscopy. The results showed strength values higher than those obtained by other researchers in curaua composites with short and non-oriented fibers. Microstructural observations revealed an effective adhesion between the fibers and the matrix, which contributed to the mechanical performance of the present composites.

Efeito da interface fibra/matriz sobre a resistência de compósitos poliméricos reforçados com fibras de coco

Avaliaram-se as caracteristicas da interface fibra/matriz em compositos de poliester reforcado com fibra de coco. Esta avaliacao foi realizada atraves de medidas da tensao interfacial de cisalhamento e tambem por observacao microestrutural da area de contato fibra de coco/resina poliester. A partir de ensaios de arrancamento de fibras de coco embutidas em capsulas de resina poliester analisou-se por microscopia eletronica de varredura regioes das fibras que se romperam ou sofreram escorregamento ao serem extraidas da resina. Os resultados revelaram uma tensao de cisalhamento interfacial similar a de outras fibras lignocelulosicas e mostraram tambem uma razoavel adesao interfacial decorrente da natureza heterogenea das fibras de coco, o que facilita a impregnacao pela resina.

Driver behavior profiling: An investigation with different smartphone sensors and machine learning

Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement.

Production of Synthetic Ornamental Marble as a Marble Waste Added Polyester Composite

The worldwide demand for ornamental stones in building construction is motivating the use of their wastes, generated during fabrication, to produce synthetic stones. This work has as its objective to investigate the production of a synthetic ornamental marble (SOM) under vacuum and vibro-compression processing of a polyester matrix composite with addition of marble waste as a filler. Rectangular SOM composite plates were subjected to compression and flexural mechanical tests. Samples were analyzed to obtain the density, water absorption, and microstructure. The SOM composites presented properties within the expected range of an artificial stone, which indicates that the applied process is suitable for production of this type of material.

Fabrication of Artificial Stone from Marble Residue by Resin Transfer Molding

Artificial stones have recently been worldwide commercialized but are still not produced in Brazil. This has motivated efforts for the local fabrication of a similar stone. Thus, an artificial ornamental stone (AOS) was fabricated by means of a resin transfer molding (RTM) process. Marble residues were placed inside a hermetic mold under vacuum. A still fluid polyester resin, already mixed with a catalyst and a thinner, was injected into the mold. After curing, the density and water absorption of the AOS were evaluated. The material was also subjected to both compression and bend mechanical tests. The AOS microstructure was analyzed by scanning electron microscopy, which was then related to the obtained physical and mechanical properties.

Utilização de gnaisse fino em massa cerâmica para telhas

This work has for objective to evaluate the effect of the replacing of sand by gnaisse sawing waste into a red ceramic body used for roofing tile fabrication. Specimens were then prepared by uniaxial pressing at 20 MPa before firing at 850, 950 and 1050oC in a laboratory furnace. The following properties were evaluated: plasticity, linear shrinkage, water absorption and flexural rupture strength. The results showed that the use of gnaisse fines by replacing the sand did not change the workability of the body and promoted a significant increase on the mechanical strength of the fired ceramic.

Exploiting the use of machine learning in two different sensor network architectures for indoor localization

Indoor localization has been an active research area for the last two decades. This emerged in the context of providing a mobile robot the capability to conduct navigation tasks in indoor environments. Although the sensing technologies and techniques proposed for indoor robot localization have proven to be reliable solutions, these cannot be adopted as a solution to people or object localization for indoor environments, particularly, due to their high computational cost and power requirements. In order to mitigate these issues, a low-power consumption sensing technology, based on the strength of WiFi signals, is being studied. Nevertheless, a concern when working with these signals is their vulnerability to interference. This paper exploits the use of machine learning is two different architectures for localization and present how a data filtering technique can alleviate interferences. A step into a fault tolerance approach is also given, presenting that the system can maintain certain reliability even losing some of its parts.

Anomalous Strain Rate Effect in Ultrafine Grained Titanium

An anomalous effect was found in the strain rate dependence of severe plastic deformed commercially pure titanium with ultrafinegrained structure. A maximum tensile strength was obtained for ϵ ˙ = 10 − 3 s − 1 . This did not allow a single strain rate sensitivity parameter to be defined in the interval from ϵ ˙ = 10 − 5 to 10 − 1 s − 1 . Distinct deformation mechanisms for lower and higher strain rates might be the reason for this anomaly.

Exploiting the use of recurrent neural networks for driver behavior profiling

Driver behavior affects traffic safety, fuel/energy consumption and gas emissions. The purpose of driver behavior profiling is to understand and have a positive influence on driver behavior. Driver behavior profiling tasks usually involve an automated collection of driving data and the application of computer models to classify what characterizes the aggressiveness of drivers. Different sensors and classification methods have been employed for this task, although low-cost solutions, high performance and collaborative sensing remain open questions for research. This paper makes an investigation with different Recurrent Neural Networks (RNN), aiming to classify driving events employing data collected by smartphone accelerometers. The results show that specific configurations of RNN upon accelerometer data provide high accuracy results, being a step towards the development of safer transportation systems.

A Deep Learning Approach to Detect Distracted Drivers Using a Mobile Phone.

Detect distracted driver is an essential factor to maintain road safety and avoid the risk of accidents and deaths. Studies of the World Health Organization shows that the distraction caused by mobile phones can increase the crash risk by up to 400%. This paper proposes a convolutional neural network that is able to monitor drivers video surveillance, more specifically detect and classify when the driver is using a cell phone. The experiments show an impressive accuracy, achieving up 99% of accuracy detecting distracted driver.