Abstract
Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.

Abstract
This paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers.

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Abstract
Net Present Value (NPV), Weighted Average Cost of Capital (WACC), Internal Rate of Return (IRR), and Total-Life Cost of Capital (TLCC) are economic concepts widely used in capital budgeting to measure and compare the profitability of investments. More specifically, in the electricity sector these measures, with the Levelized Cost of Energy (LCOE), are very often used to assess investments in generation assets. At the same time, electricity generation models based on mathematical programming and game theory have also been developed to determine optimal expansion plans of the generation capacity for long-term horizons. Though these two techniques have both been applied in the literature to assess generation investments in the electricity sector, taking into account, among others, investment, maintenance and operation costs, their mathematical relationships have been rarely reported or even understood. Here we provide some insight on the mathematical links existing between both approaches. © 2015 IEEE.

Abstract
This article presents a work-in-progress version of a Dublin Core Application Profile (DCAP) developed to serve the Social and Solidarity Economy (SSE). Studies revealed that this community is interested in implementing both internal interoperability between their Web platforms to build a global SSE e-marketplace, and external interoperability among their Web platforms and external ones. The Dublin Core Application Profile for Social and Solidarity Economy (DCAP-SSE) serves this purpose. SSE organisations are submerged in the market economy but they have specificities not taken into account in this economy. The DCAP-SSE integrates terms from well-known metadata schemas, Resource Description Framework (RDF) vocabularies or ontologies, in order to enhance interoperability and take advantage of the benefits of the Linked Open Data ecosystem. It also integrates terms from the new essglobal RDF vocabulary which was created with the goal to respond to the SSE-specific needs. The DCAP-SSE also integrates five new Vocabulary Encoding Schemes to be used with DCAP-SSE properties. The DCAP development was based on a method for the development of application profiles (Me4MAP). We believe that this article has an educational value since it presents the idea that it is important to base DCAP developments on a method. This article shows the main results of applying such a method.

Abstract