Abstract
Electric power systems have undergone major changes in recent years. Electricity markets are one of the sectors that has been most affected by these changes. Electricity market design is being updated in order to support efficient operation and investments incentives. However, the development of efficient rules is neither easy nor guaranteed. This paper addresses the simulation of multi-participation in electric energy markets. The purpose of this simulation is to offer solutions to electricity market players, in order to support their decisions on future participation situations. For this, artificial intelligence techniques will be used, namely for forecasting and optimization processes. In specific, an optimization approach based on Evolutionary Particle Swarm Optimization (EPSO) is proposed. The achieved results are compared to those of a deterministic resolution method, and of the classical Particle Swarm Optimization (PSO). Results show that the proposed approach is able to achieve higher mean and maximum objective function results than the classical PSO, with a smaller standard deviation. The execution time is higher than using PSO, but still very fast when compared the deterministic method. The case study is based on real data from the Iberian electricity market. © 2018 IEEE.

Abstract
With the recent development of weblogs and social networks, many supplier industries share their data on different websites and weblogs. Even the Small-to-Medium sized enterprises (SMEs) in the manufacturing sector (as well as non-manufacturing sector) are rapidly strengthening their web presence in order to improve their visibility, customer reachability and remain competitive in the global market. Our study aims to classify data into various groups so that users can identify the most appropriate content based on their choice at any given time. To classify and characterize manufacturing suppliers in supply chain through their capability narratives and textual portfolios obtained from websites of such suppliers online source portals for testing and Naive Bayes and support vector machine (SVM) Classification method at term-level for classification has been used. The performance of the proposed classifier was tested experimentally based on the standard metrics such as precision, recall, and F-measure.

Abstract

Abstract
Using Fourier methods to reconstruct the phase measured by a wavefront sensor (WFS) can significantly re- duce the number of computations required, as well as easily enable predictive reconstruction methods based on knowledge of the adaptive optics system, atmospheric turbulence and wind profile. Previous work on Fourier re- construction has focused on the Shack-Hartmann WFS. With increasing interest in the highly sensitive Pyramid WFS we present the development of Fourier reconstruction tools tailored to the Pyramid sensor. We include the development of the Fourier model, it's use for formulating error budgets and a laboratory demonstration of Fourier reconstruction with a Pyramid WFS.

Abstract
Algorithm selection methods can be speeded-up substantially by incorporating multi-objective measures that give preference to algorithms that are both promising and fast to evaluate. In this paper, we introduce such a measure, A3R, and incorporate it into two algorithm selection techniques: average ranking and active testing. Average ranking combines algorithm rankings observed on prior datasets to identify the best algorithms for a new dataset. The aim of the second method is to iteratively select algorithms to be tested on the new dataset, learning from each new evaluation to intelligently select the next best candidate. We show how both methods can be upgraded to incorporate a multi-objective measure A3R that combines accuracy and runtime. It is necessary to establish the correct balance between accuracy and runtime, as otherwise time will be wasted by conducting less informative tests. The correct balance can be set by an appropriate parameter setting within function A3R that trades off accuracy and runtime. Our results demonstrate that the upgraded versions of Average Ranking and Active Testing lead to much better mean interval loss values than their accuracy-based counterparts.

Abstract
Power systems are becoming more complex and the need for increased awareness at the lower voltage levels of the distribution grid requires new tools that provide a reliable and accurate estimation of the system state. This paper describes an innovative state estimation method for low voltage (LV) grids that analyses similarities between a real-time snapshot comprising only a subset of smart meters with real-time communications and fully observed system states present in historical data. Real-time estimates of voltage magnitudes are obtained by smoothing the most similar past snapshots with a data-driven methodology that does not relies on full knowledge of the grid topology and electrical characteristics. Moreover, the output of the LV state estimator is a conditional probability distribution obtained with kernel density estimation. The results show highly accurate estimation of voltage magnitude, even in a scenario characterized by a strong integration of photovoltaic (PV) microgeneration.