Abstract
Area coverage is an inherent and important topic when dealing with wireless visual sensor networks, since it may be desired when addressing availability and fault tolerance in critical applications. This problem arises because more than one visual sensor may cover the same area, generating overlapped regions that can be exploited for different kinds of optimization and quality enhancement approaches. Actually, some methods to compute the resulted covered area by a set of sensors have been proposed, and they are initial steps to compute availability metrics that are necessary for many monitoring scenarios. Particularly, approximate approaches are promising when computing area coverage, potentially achieving good results, although such methods lack proper evaluation and analysis about complexity, performance and precision. In this context, we perform an evaluation of a recent algorithm based on approximation for area coverage computing, comparing it with a precise algorithm developed in this work for this purpose. Doing so, it is desired to assess performance and accuracy of both algorithms, indicating the most appropriate approach when addressing availability in visual sensor networks.

Abstract
As already noticed in other telescopes, the presence of large telescope spiders and of a segmented deformable mirror in an Adaptive Optics system leads to pupil fragmentation and may create phase discontinuities. On the ELT telescope, a typical effect is the differential piston, where all disconnected areas of the pupil create their own piston, unseen locally but drastically degrading the final image quality. The poor sensitivity of the Pyramid WFS to differential piston will lead to these modes been badly seen and therefore badly controlled by the adaptive optics (AO) loop. In close loop operation, differential pistons between segments will start to appear and settle around integer values of the average sensing wavelength. These additional differential pistons are artificially injected by the adaptive optics control loop but do not have any real physical origin, contrary to the Low Wind Effect. In an attempt to reduce the impact of unwanted differential pistons that are injected by the AO loop, we propose a novel approach based on the pair-wise coupling of the actuators sitting on the edges of the deformable mirror segments. In this paper, we present the correction principle, its performance in nominal seeing condition, and its robustness relative to changing seeing conditions, wind speed and natural guide star magnitude. We show that the edge actuator coupling is a simple and robust solution and that the additional quadratic error relative to the reference case (i.e. no spiders) is of only 40 nm RMS, well within the requirements for HARMONI.

Abstract
High penetration of renewable energy sources, especially weather-dependent sources, has increased the power systems uncertainties. For any analysis in power systems such as planning and operation, it is essential to confront the stochastic nature of these sources in order to get much more precise results. Since operators need proper strategies and methods to decline negative effects of the stochastic behaviour of renewable power generators, such as total operation cost growth, this paper provides a review of different state-of-the-art approaches from the operator's viewpoint for handling the stochastic behaviour of renewable sources. Hence, in this paper, three different strategies are categorized for stochastic analysis of these sources. The first strategy is mathematical modelling including stochastic dependency and independency, multi-dimensional dependence, forecast and scenarios. Afterwards, demand side management, which is one of the other approaches for dealing with these uncertainties, is investigated and different demand response programs and some methods to model them are presented. Finally, the effect of different electricity market schemes and relevant optimization methods to mitigate the variations of renewable energy sources are discussed. The study demonstrates that an operator should choose one or a combination of these three approaches based on its requirements.

Abstract
This paper reports on results for the well-known resource-constrained project scheduling problem. A branch-and-bound procedure is developed that takes into account all best performing components from literature, varying branching schemes and search strategies, using the best performing dominance rules and assembling these components into a unified search algorithm. A composite lower bound strategy that statically and dynamically selects the best performing bounds from literature is used to find optimal solutions within reasonable times. An extensive computational experiment is set up to determine the best combination of the various components used in the procedure, in order to benchmark the current existing knowledge on four different datasets from the literature. By varying the network topology, resource scarceness and the size of the projects, the computational experiments are carried out on a diverse set of projects. The procedure was able to find some new lower bounds and optimal solutions for the PSPLIB instances. Moreover, new best known results are reported for other, more diverse datasets that can be used in future research studies. The experiments revealed that even project instances with 30 activities cannot be solved to optimality when the topological structure is varied.

Abstract

Abstract
The motion of cloud over photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power, so the tracking of cloud motion is very crucial. In this study, Block-matching algorithm, Optical Flow algorithm and feature matching algorithm are three prevailing methods. However, as a rigid registration method, Block-matching cannot obtain the parameters of cloud deformation or rotation. The accuracy of the optical flow, which is based on the assumption that the image grayscale is not changed, is easily disturbed by noise. When the image texture information is not rich enough, the accuracy of the feature matching will also be reduced. That is, in order to improve their robustness, they must be combined through a certain strategy. Therefore, a pattern classification and PSO optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed in this paper. The method consists of two parts. Firstly, we use k-means clustering method and texture features based on Gray-Level Co-occurrence Matrix (GLCM) to classify the clouds. Because texture can adequately reflect image information, compared with other image features, it can better take into account both the macro nature and the fine structure of images. Secondly, for different cloud classes, we build the corresponding combined calculation modeling to obtain cloud motion speed. The Particle Swarm Optimization algorithm is used to give different weights to different methods to adapt to different clouds. The performances of the method are investigated using real data recorded at Yunnan Electric Power Research Institute. Under the measurement of common precision index, the comparisons with various benchmark methods show the effectiveness of the proposed approaches over cloud tracing. © 2018 IEEE