Session Program


  • 10 July 2017
  • 08:00AM - 10:00AM
  • Room: Sveva
  • Chairs: Luciano Sánchez and José Ranilla

Soft Computing Methods for Eco-Efficiency

Abstract - The main objective of this work is to reduce power consumption and energy of a datacenter. There are various power saving techniques. A fuzzy system-based scheduler has been used, comparing its results with other well-known algorithms. The methods used in this paper are based on a combination of the DVFS algorithm and the development of a rule-based expert system to provide power-based planners for task planning domains. The parameters considered in the system are explained in detail and the results obtained are analyzed.
Abstract - Eco-efficiency in hearing aids is an important issue, related to the maximization of the battery life. In order to minimize the power consumption, the embedded digital signal processor works at very low clock rates, constraining the implementation of signal processing techniques. The implemented algorithms can only use a small number of instructions per second and a small amount of memory. One of the main algorithms implemented in nowadays hearing aids is the voice activity detection algorithm, useful for several noise reduction and speech enhancement algorithms. The objective of this paper is the study of the implementation of voice activity detection algorithms in hearing aids using tailored fuzzy features, taking into account the optimization of the available resources.
Abstract - Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. The large number of computing nodes, resources heterogeneity and diversity of application requirements are factors that turn the scheduling into an NP-Hard problem. Evolutionary algorithms have proved to be effective for scheduling applications. In this paper, we present a novel approach combining particle swarm optimization and a genetic algorithm to solving the resource matching and scheduling of parallel applications in Federated cluster environments. The proposed hybrid meta-heuristic, referred to as MPSO-FGA, not only minimizes the overall energy consumption but also the makespan for a whole workload. The experimental results show the superiority of evolutionary algorithms over basic heuristics. The hybrid meta-heuristic is able to obtain similar results to a genetic algorithm in terms of energy consumption and makespan but reducing the time for scheduling decisions by two orders of magnitude.
Abstract - A fuzzy rule-based classifier is proposed in this paper where the number of rules in the knowledge base that are fired when an object is classified is anti-monotone with respect to the prior probability of its class. This classifier is intended to secure an equilibrium between accuracy and energy consumption, which is critical in battery operated embedded devices. The method is compared to legacy multi- criteria evolutionary algorithms, where a group of classifiers with different balances between accuracy and consumption are evolved, and the most accurate classifier is selected among those individuals in the Pareto front whose use of the battery does not exceed a given threshold. A significant increase in the battery life is reported without a degradation in the quality of service.
Abstract - The power shortages and/or outages in the distribution system enables the need of load shedding or curtailment, where the important loads are given high priority. This paper presents a novel method to solve the load point prioritization problem. The load points in the distribution system contains different types of loads and each load type has certain importance over other load type. The inclusion of load types in the load point ranking, increases the complexity in the problem. The proposed ranking method based on the fuzzy theory resolves this complexity and evaluates the weights of load points and thereupon ranking. The proposed method is validated on a test system considering a natural disaster situation.
Abstract - HVDC technology is a promising option to cope with regional power imbalances introduced by the increased generation from renewable energies. In particular, dc grids can be a technically and economically feasible solution for offshore and overlay applications. For dc grids, a concept for active power balancing has to be developed. This is usually done using piece-wise linear proportional control. This paper proposes a methodology for the design of nonlinear control characteristics using a fuzzy logic based P-controller. The controller is able to achieve a control characteristic with linear regions and smooth transitions, thus avoiding possible oscillations around the transition points. The controller is also able to handle security constraints using additional input variables.