Abstract
The correct estimation of the friction coefficient in automotive applications is of paramount importance in the design of effective vehicle safety systems. In this article a new parametrization for estimating the peak friction coefficient, in the tire-road interface, is presented. The proposed parametrization is based on a feedforward neural network (FFNN), trained by the Extreme Learning Machine (ELM) method. Unlike traditional learning techniques for FFNN, typically based on backpropagation and inappropriate for real time implementation, the ELM provides a learning process based on random assignment in the weights between input and the hidden layer. With this approach, the network training becomes much faster, and the unknown parameters can be identified through simple and robust regression methods, such as the Recursive Least Squares. Simulation results, obtained with the CarSim program, demonstrate a good performance of the proposed parametrization; compared with previous methods described in the literature, the proposed method reduces the estimation errors using a model with a lower number of parameters.

Abstract
This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn. Crown Copyright

Abstract
This paper describes an improved micro-power electric generator where energy harvested from human movements is used as an everlasting mechanical energy source to suffice smart hip implant electronics power needs. Its architecture is designed so that the mechanical energy promotes the movement of a combination of magnets and a spring embedded inside a Teflon tube, used to reduce friction. The changing magnetic field induces current in two coils so that the output of the generator is the sum of their signals. The end result is like a double generator in one casing. Produced electrical energy is stored in an energy reservoir handed over to a power management module. Experimental results shows that energy harvested from human walking can be used as an effective power source for hip prosthesis implants. (C) 2010 Published by Elsevier Ltd.

Abstract
Recent developed button heat pulse probes (BHPP) demonstrated a great potential for soil water content measurements. This new probe compared to conventional heat pulse probes (HPP), does not use needles, and measurement accuracy is significantly improved. This new design, with the possibility to assembly the probe and electronics in the same package, with low-cost, and with less power consumption compared to conventional HPP, make it suitable to be connected to wireless data acquisition systems in precision agriculture. The probe was tested in agar to demonstrate the potential advantages of the button heat pulse sensor for soil water content measurements. It was possible to have an 0.5 °C temperature rise with only 156mW of power consumption, a ten times power reduction in heat-pulse soil water content measurements. These tests showed the potential use of the button heat pulse sensor for the determination of soil water content. © 2010 IEEE.

Abstract
In this paper we present a software tool to be used in residential/household generic power circuitry analysis and simulation, under non-linear loads. This tool can both be used by electrical engineers and by students of electrical engineering. It has an easy-to-use, friendly interface, and can be used to teach design techniques or as a laboratory tool to study the applicability of known methods to real world practical situations. Also, the users may supply their own data. The simulated results are very close to the measured ones.

Abstract
The main aim of this paper is to analyze the behavior of the three-phase squirrel cage induction motor under different voltage fluctuation levels. To achieve this goal several simulations were performed using the EMTP/ATP tool. Here we present how mechanical torque, speed and efficiency parameters varied with different levels of voltage fluctuation and modeling frequencies. As it was expected, the induction motor is sensitive to voltage fluctuations within certain amplitude levels and frequencies. Also, the speed is more affected by low frequencies and high amplitudes of voltage fluctuations, while the torque and efficiency are more affected by middle and high frequencies and amplitudes. ©2010 IEEE.