Engineering, Technology & Applied Science Research

269-272

Authors: K. Theofilatos, S. Likothanassis, A. Karathanasopoulos

The present paper aims in investigating the performance of state-of-the-art machine learning techniques in trading with the EUR/USD exchange rate at the ECB fixing. For this purpose, five supervised learning classification techniques (K-Nearest Neighbors algorithm, Naïve Bayesian Classifier, Artificial Neural Networks, Support Vector Machines and Random Forests) were applied in the problem of the one day ahead movement prediction of the EUR/USD exchange rate with only autoregressive terms as inputs. For comparison reasons, the performance of all machine learning techniques was benchmarked by two traditional techniques (Naïve Strategy and moving average convergence/divergence model). Trading strategies produced by the machine learning techniques of Support Vector Machines and Random Forests clearly outperformed all other strategies in terms of annualized return and sharp ratio. To the best of our knowledge, this is the first application of Random Forests in the problem of trading with the EUR/USD exchange rate providing extremely satisfactory results.

278-280

Authors: K. C. Selvam, S. Latha

A novel analog divider is described in this paper. The circuit enables the division of a dc voltage with another dc voltage. The constant of the division is dependent upon a third dc voltage and a pair of resistors. Employing a precision source for the third dc voltage and matched resistors, an acceptable level of accuracy can be obtained.

285-290

Authors: E. Ilhan, T. E. Motoasca, J. J. H. Paulides, E. A. Lomonova

Induction and synchronous machines have traditionally been the first choice of automotive manufacturers for electric/hybrid vehicles. However, these conventional machines are not able anymore to meet the increasing demands for a higher energy density due to space limitation in cars. Flux-switching PM (FSPM) machines with their high energy density are very suitable to answer this demand. In this paper, the energy conversion loop technique is implemented on FSPM for the first time. The energy conversion technique is a powerful tool for the visualization of machine characteristics, both linear and nonlinear. Further, the technique provides insight into the torque production mechanism. A stepwise explanation is given on how to create these loops for FSPM along with the machine operation.