Abstract

Abstract
Bad weather conditions can reduce visibility on images acquired outdoors, decreasing their visual quality. The image processing task concerned with the mitigation of this effect is known as image dehazing. In this paper we present a new image dehazing technique that can remove the visual degradation due to haze without relying on the inversion of a physical model of haze formation, but respecting its main underlying assumptions. Hence, the proposed technique avoids the need of estimating depth in the scene, as well as costly depth map refinement processes. To achieve this goal, the original hazy image is first artificially under-exposed by means of a sequence of gamma-correction operations. The resulting set of multiply-exposed images is merged into a haze-free result through a multi-scale Laplacian blending scheme. A detailed experimental evaluation is presented in terms of both qualitative and quantitative analysis. The obtained results indicate that the fusion of artificially under-exposed images can effectively remove the effect of haze, even in challenging situations where other current image dehazing techniques fail to produce good-quality results. An implementation of the technique is open-sourced for reproducibility (https://github.com/agaldran/amef_dehazing).

Abstract
The power demand uncertainties and intrinsic intermittent characteristics of wind and photovoltaic (PV) distributed energy resources (DERs) make the conventional load flow methods inefficient in active distribution networks (ADNs) and microgrids. Some statistical tools such as Monte Carlo simulation (MCS) are always a reliable solution. However, statistical tools are time-consuming and rather useless in large power systems. In this paper, a new method is proposed for robust probabilistic load flow (PLF) in microgrids and ADNs, including renewable energy resources (RERs), based on singular value decomposition (SVD) unscented Kalman filtering. The probability density functions (PDFs) and cumulative distribution functions (CDFs) for some of the ADN variables are compared with the other reported PLF methods for different test systems and the results validate the robustness, efficiency and accuracy of the proposed method.

Abstract
The wavelength sensitivity and spectral resolution of Mach-Zehnder fiber interferometers based on uncoated and TiO2 coated LPFGs is presented and compared with TiO2 coated single LPFGs optical fiber sensors.

Abstract
The present paper addresses the use of evolutionary based algorithms for off-line fractional-order controller tuning. In particular, a linearized model of a motor-rotor propulsion device was assumed whose representativeness is supported by laboratorial measurements. Initially, the controller was calibrated, using the devised linear model, by a procedure that uses a cost function defined as the linear combination between the sum of the squared error and the sum of the absolute error. In this work, it was shown that this process can be improved by using an evolutionary based algorithm in order to find the best controller parameters. This strategy allows a more automatic tuning procedure isolating it from the user intervention. Moreover, the results achieved by this process, lead to an improved rotational speed regulation. © 2018 IEEE.

Abstract
Capacity generation expansion problems have traditionally been represented with low time resolution models due to their high computational cost, very often using blocks of hours with similar demand. However, the current transformation of the power system with the new generation and consumption technologies, the flexibility and reserve requirements, and the expected new behavioral consumption patterns, requires more complex and detailed models with higher time resolution to provide accurate investment decisions and allow for closer analyses. In particular, these challenges require chronological hourly models with constraints linking all the years of the planning horizon, compromising in most cases the computational feasibility. This paper presents a new approach to synthetize a reduced representative time period for capacity expansion problems, for being used in detailed chronological hourly models, while keeping them computationally feasible. The representative period is synthetized by selecting, with a genetic algorithm, those real days that minimizes the distance between the duration curves of a set of relevant variables (such as demand, renewable generation, ramps, etc.) computed for the original and for the representative periods. Results show that investments decisions with the representative period are very similar to those obtained with the full planning horizon, while computational times are strongly reduced.