QoS metrics-in-the-loop for endowing runtime self-adaptation to robotic software architectures

Multimedia Tools and Applications - The design of robots capable of operating autonomously in changing and unstructured environments, requires using complex software architectures in which,...     

A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming

This paper evaluates different forms of rank-based selection that are used with genetic algorithms and genetic programming. Many types of rank based selection have exactly the same expected value in terms of the sampling rate allocated to each member of the population. However, the variance associated with that sampling rate can vary depending on how selection is implemented. We examine two forms of tournament selection and compare these to linear rank-based selection using an explicit formula. Because selective pressure has a direct impact on population diversity, we also examine the interaction between selective pressure and different mutation strategies.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.     

OpenCOPI: middleware integration for Ubiquitous Computing

In this paper, we present OpenCOPI (Open COntext Platform Integration), a Service-Oriented Architecture-based middleware platform that supports the integration of services provided by distinct sources, ranging from services offered by simple systems to more complex services provided by context-provision middleware. OpenCOPI offers selection and composition mechanisms to, respectively, select and compose services provided by different sources, considering applications of both Quality of Service and Quality of Context requirements. It also offers an adaptation mechanism that enables to adapt the application execution due to service failures, service quality fluctuation and user mobility. OpenCOPI allows the definition of applications in a higher abstraction level by the specification of a semantic workflow that contains abstract activities. This paper illustrates the use of OpenCOPI in an application from the Gas & Oil Industry and it also shows the evaluation of the main mechanisms of OpenCOPI: the service selection, composition, adaptation and workflow execution.     

Poly(vinylidene fluoride) with zinc oxide and carbon nanotubes applied to pressure sheath layers in oil and gas pipelines

In this work, PVDF composites containing 0.2% (m/m) of carbon nanotubes (MWCNTs), PVDF with 5.0% (m/m) of zinc oxide (ZnO), and composites containing both particles in the same contents in the matrix were melt processed in a mini-extruder machine with double screws, using the counter-rotation mode. Composites were characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffraction (XRD), dynamic-mechanical analysis (DMA), and contact angle tests (CA). The samples presented the predominance of the α phase, with an increased degree of crystallinity as well as an increase in dimensional stability by incorporating both fillers, showing a synergistic effect between these particles, as shown on FTIR, DSC, and XRD results. SEM images showed a good dispersion of high aspect ratio particles. In general, DMA and TGA analysis showed that composites had not decreased their thermal and mechanical performance when compared to neat PVDF. Results of CA analysis showed an increase in the hydrophobicity of the sample containing MWCNTs. Permeability tests were also performed using a differential pressure system, combining high temperature and pressure, obtaining permeability measures and time lag. This work presents an alternative of composite materials, suggesting its application in the internal pressure sheath layers of oil and gas flexible pipes.     

Analysis of surface integrity for minimum quantity lubricant—MQL in grinding

The quality of machined components is currently of high interest, for the market demands mechanical components of increasingly high performance, not only from the standpoint of functionality but also from that of safety. Components produced through operations involving the removal of material display surface irregularities resulting not only from the action of the tool itself, but also from other factors that contribute to their superficial texture. This texture can exert a decisive influence on the application and performance of the machined component. This article analyzes the behavior of the minimum quantity lubricant (MQL) technique and compares it with the conventional cooling method. To this end, an optimized fluid application method was devised using a specially designed nozzle, by the authors, through which a minimum amount of oil is sprayed in a compressed air flow, thus meeting environmental requirements. This paper, therefore, explores and discusses the concept of the MQL in the grinding process. The performance of the MQL technique in the grinding process was evaluated based on an analysis of the surface integrity (roughness, residual stress, microstructure and microhardness). The results presented here are expected to lead to technological and ecological gains in the grinding process using MQL.     

Spatial and temporal variability of macrophyte cover and productivity in the eastern Amazon floodplain: A remote sensing approach

Herbaceous aquatic macrophytes cover extensive areas on the floodplains of the Amazon basin and are an important habitat and input of organic carbon. These communities have large intra- and inter-annual variability, and characterization of this variability is necessary to quantify the role of macrophytes in the ecology and biogeochemistry of the floodplain. A novel approach for mapping the temporal evolution of aquatic vegetation in the Amazon floodplain, which could be adapted to other spatially and temporally changing environments, is presented. Macrophyte cover varied seasonally and inter-annually, ranging between 104 and 198 km² for the floodplain examined (total area, 984 km²). The observed evolution of plant distribution indicated a spatial and temporal partition of macrophyte communities into short-lived and annual groups. A simulation of macrophyte net primary production (NPP) based on the mapping results indicated that at least 3% of NPP could be attributed to the short-lived communities. The present results suggest that significant changes in the macrophyte's contribution to carbon cycling in the Amazon floodplain could occur as a result of the predicted increase in frequency of drought years for the Amazon system due to climate change.     

Spatial data warehouses and spatial OLAP come towards the cloud: design and performance

Cloud computing systems handle large volumes of data by using almost unlimited computational resources, while spatial data warehouses (SDWs) are multidimensional databases that store huge volumes of both spatial data and conventional data. Cloud computing environments have been considered adequate to host voluminous databases, process analytical workloads and deliver database as a service, while spatial online analytical processing (spatial OLAP) queries issued over SDWs are intrinsically analytical. However, hosting a SDW in the cloud and processing spatial OLAP queries over such database impose novel obstacles. In this article, we introduce novel concepts as cloud SDW and spatial OLAP as a service, and afterwards detail the design of novel schemas for cloud SDW and spatial OLAP query processing over cloud SDW. Furthermore, we evaluate the performance to process spatial OLAP queries in cloud SDWs using our own query processor aided by a cloud spatial index. Moreover, we describe the cloud spatial bitmap index to improve the performance to process spatial OLAP queries in cloud SDWs, and assess it through an experimental evaluation. Results derived from our experiments revealed that such index was capable to reduce the query response time from 58.20 up to 98.89 %.