Classification of signals by means of Genetic Programming

This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.     

Best practices in measuring algorithm performance for dynamic optimization problems

Dynamic optimization problems (DOPs) have attracted considerable attention due to the wide range of problems they can be applied to. Lots of efforts have been expended in modeling dynamic situations, proposing algorithms, and analyzing the results (too often in a visual way). Numeric performance measurements and their statistical validation have been however barely used in the literature. Most of works in DOPs report only the best-of-generation fitness, due to its simplicity of computation. Although this measure indicates the best algorithm in terms of fitness, it does not provide any details about the actual strength and weakness of each algorithm. In this article, we conduct a comparative study among algorithms of different search modes via several performance measures to demonstrate their relative advantages. We discuss the role of using different performance measures in drawing balanced conclusions about algorithms for DOPs.     

Dealing with hardware heterogeneity: a new parallel search model

In this article we present Ethane, a parallel heterogeneous metaheuristic model specifically designed for its execution on heterogeneous hardware environments. With Ethane we propose a hybrid parallel search algorithm inspired in the structure of the chemical compound of the same name, implementing a heterogeneous island model based in the structure of the chemical bonds of the ethane compound. Here we also shape a schema for describing a complete family of parallel heterogeneous metaheuristics inspired by the structure of hydrocarbons in nature, HydroCM (HydroCarbon inspired Metaheuristics), establishing a resemblance between atoms and computers, and between chemical bonds and communication links. Our goal is to gracefully match computers of different computing power to algorithms of different behavior (genetic algorithm and simulated annealing in this study), all them collaborating to solve the same problem. In addition to the nice natural metaphor we will show that Ethane, though simple, can solve search problems in a faster and more robust way than well-known panmictic and distributed algorithms very popular in the literature, as well as can achieve a better exploration/exploitation balance during the search process.     

A percentile system optimization approach with and without path enumeration

In this paper we deal with the travel time reliability PUE (probabilistic user equilibrium) problem studied by Lo et al. (2006) [12] and Nie (2011) [15] and we propose an alternative model that assumes a location-scale family for the path travel times, whose means and variances are evaluated in terms of link travel times. This avoids the use of the central limit theorem and convolutions providing a flexible and simple alternative. Contrary to the most existing models that require path enumeration or an iterative method to add paths sequentially, we present a percentile system optimization in its two versions: with and without path enumeration. Two examples of applications, one of them real, are used to illustrate the power of the proposed method. The cpu times required to solve the problem seem reasonable. In addition, we answer an open question raised by Nie (2011) [15] about the permutability of percentiles and partial derivatives of route travel times with respect to route flows. A family of counterexamples is given to demonstrate that the two operations: (a) obtain percentiles and (b) partial derivation of route travel times do not commute. Finally, to reproduce the trial-and-error sequence followed by users when selecting paths, we also present an algorithm that simulates this iterative process and shows that the final long-term user behavior coincides with PUE (probabilistic user equilibrium) problem resulting from some existing models.     

The influence of oxygen and nitrogen contamination on the densification behavior of cryomilled copper powders during spark plasma sintering

It has been found difficult to fully densify some mechanically milled pure metal powders by spark plasma sintering (SPS). In this study, the densification behavior of cryomilled, nanostructured (NS) Cu powders during SPS was related to changes to the chemistry of the powders. The results showed that the presence of very small amounts of O and N in the powders, which were introduced during cryomilling and handling, significantly influenced the densification response. Moreover, reduction/removal of O/N via thermal annealing of the powders before SPS led to complete densification of the powders during subsequent SPS. The mechanisms responsible for this behavior were ascertained: O and N existed in the cryomilled powders in the form of thermally unstable compounds, and the subsequent thermal decomposition of these compounds during SPS generated the gaseous species, leading to porosity formation and incomplete densification; annealing of the powders before SPS removed the gases which resulted from thermal decomposition, thereby facilitating complete consolidation during subsequent SPS.     

The effect of ultra-thin graphite on the morphology and physical properties of thermoplastic polyurethane elastomer composites

Composites of thermoplastic polyurethane (TPU) and ultra-thin graphite (UTG) with concentrations ranging from 0.5 wt.% to 3 wt.% were prepared using a solution compounding strategy. Substantial reinforcing effects with increased loadings are achieved. Compared to neat TPU, values for storage modulus and shear viscosity are enhanced by 300% and 150%, respectively, for UTG concentrations of 3 wt.%. Additionally, an enhancement of thermal properties is accomplished. The crystallization temperature and thermal stability increased by 30 °C and 10 °C, respectively, compared to neat TPU. Furthermore, the use of oxidized UTG (UTGO) with its added functional oxygen groups suggests the presence of chemical interactions between UTG and TPU, which additionally impact on the thermal properties of the corresponding composites. Controlling the oxidation degree, thus offers further possibilities to obtain composites with tailored properties. The presented approach is straightforward, leads to homogeneous TPU-UTG composites with improved materials properties and is especially suitable for commercial UTG materials and further up-scaled production.     

Maximum power point tracker based on maximum power point resistance modeling

This paper presents a new, simple, accurate, and inexpensive practical methodology and experimental solution for the modeling of conventional (domestic and commercial facilities) photovoltaic generators (PVG), so that they can work at their maximum power point (MPP). The PVG may be a panel, an array of panels, or a photovoltaic field. As a starting restriction (actual) it has to be assumed that it is not possible to isolate the PVG variables dependence (mainly current, voltage, and hence power) with solar radiation and temperature, because they are highly correlated. This methodology proposes the modeling facility on its MPP by its MPP resistance (R_MPP = V_MPP/I_MPP), being V_MPP and I_MPP the voltage and current of the PVG, respectively, at its MPP. The analysis shows, by simulation first and then experimentally, that R_MPP does not present significant temperature dependencies, at least in the usual range. This important result allows us to model the PVG only in terms of solar radiation. From a set of experimental data, different models to estimate R_MPP are proposed. The obtained results are very accurate. These models allow an immediate practical application that it is also developed in the paper: MPP tracker (MPPT) design by the calculation of the DC/DC converter duty cycle which places the PVG at its MPP directly and continuously. This new methodology and experimental system has been registered in the Spanish Patent and Trademark Office with the number P201530352. Copyright © 2015 John Wiley & Sons, Ltd.     

Short-run statistical process control: Q-Chart enhancements and alternative methods

The first two tables in Reference 1 are incorrect. Corrected tables are provided below. The errors do not affect the conclusions or the discussion in that paper.