Abstract

Abstract

Abstract

Abstract

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
Human gait analysis provides valuable information regarding the way of walking of a given subject. Low-cost RGB-D cameras, such as the Microsoft Kinect, are able to estimate the 3-D position of several body joints without requiring the use of markers. This 3-D information can be used to perform objective gait analysis in an affordable. portable, and non-intrusive way. In this contribution, we present a system for fully automatic gait analysis using a single RGB-D camera, namely the second version of the Kinect. Our system does not require any manual intervention (except for starting/stopping the data acquisition), since it firstly recognizes whether the subject is walking or not, and identifies the different gait cycles only when walking is detected. For each gait cycle, it then computes several gait parameters, which can provide useful information in various contexts, such as sports, healthcare, and biometric identification. The activity recognition is performed by a predictive model that distinguishes between three activities (walking, standing and marching), and between two postures of the subject (facing the sensor, and facing away from it). The model was built using a multilayer perceptron algorithm and several measures extracted from 3-D joint data, achieving an overall accuracy and F-1 score of 98%. For gait cycle detection, we implemented an algorithm that estimates the instants corresponding to left and right heel strikes, relying on the distance between ankles, and the velocity of left and right ankles. The algorithm achieved errors for heel strike instant and stride duration estimation of 15 +/- 25 ms and 1 +/- 29 ms (walking towards the sensor), and 12 +/- 23 ms and 2 +/- 24 ms (walking away from the sensor ) Our gait cycle detection solution can be used with any other RGB-D camera that provides the 3-D position of the main body joints.