Disjoint essential sets of implicates of a CQ Horn function

In this paper we study a class of CQ Horn functions introduced in Boros et al. (Ann Math Artif Intell 57(3–4):249–291, 2010). We prove that given a CQ Horn function f, the maximal number of pairwise disjoint essential sets of implicates of f equals the minimum number of clauses in a CNF representing f. In other words, we prove that the maximum number of pairwise disjoint essential sets of implicates of f constitutes a tight lower bound on the size (the number of clauses) of any CNF representation of f.     

Numerical solutions of unsteady flows with low inlet Mach numbers

This study deals with a numerical solution of a 2D unsteady flow of a compressible viscous fluid in a channel for low inlet airflow velocity. The unsteadiness of the flow is caused by a prescribed periodic motion of a part of the channel wall with large amplitudes, nearly closing the channel during oscillations. The channel is a simplified model of the glottal space in the human vocal tract and the flow can represent a model of airflow coming from the trachea, through the glottal region with periodically vibrating vocal folds, and to the human vocal tract.     

Adaptive content-based music retrieval system

This paper presents a tunable content-based music retrieval (CBMR) system suitable the for retrieval of music audio clips. The audio clips are represented as extracted feature vectors. The CBMR system is expert-tunable by altering the feature space. The feature space is tuned according to the expert-specified similarity criteria expressed in terms of clusters of similar audio clips. The main goal of tuning the feature space is to improve retrieval performance, since some features may have more impact on perceived similarity than others. The tuning process utilizes our genetic algorithm. The R-tree index for efficient retrieval of audio clips is based on the clustering of feature vectors. For each cluster a minimal bounding rectangle (MBR) is formed, thus providing objects for indexing. Inserting new nodes into the R-tree is efficiently performed because of the chosen Quadratic Split algorithm. Our CBMR system implements the point query and the n-nearest neighbors query with the O(logn) time complexity. Different objective functions based on cluster similarity and dissimilarity measures are used for the genetic algorithm. We have found that all of them have similar impact on the retrieval performance in terms of precision and recall. The paper includes experimental results in measuring retrieval performance, reporting significant improvement over the untuned feature space.     

Look-ahead heuristics for the dynamic traveling purchaser problem

Given a set of products each with positive discrete demand, and a set of markets selling products at given prices, the traveling purchaser problem (TPP) looks for a tour visiting a subset of markets such that products demand is satisfied at minimum purchasing and traveling costs. In this paper we analyze a dynamic variant of the problem, where quantities may decrease as time goes on. Complete information is assumed on current state of the world, i.e. decision maker knows quantities available for each product in each market at present time and is informed about any consumption event when it occurs. Nevertheless, planner does not have any information on future events. Two groups of heuristics are described and compared. The first group consists of simplified approaches deciding which market to visit next on the basis of some greedy criteria considering only one of the two objective costs. The second one includes heuristics based on a look-ahead approach taking into account both traveling and purchasing costs and inserting some future prediction. Heuristics behavior has been tested on a large set of randomly generated instances under different levels of dynamism.     

An elitist non-dominated sorting genetic algorithm enhanced with a neural network applied to the multi-objective optimization of a polysiloxane synthesis process

This paper presents an original software implementation of the elitist non-dominated sorting genetic algorithm (NSGA-II) applied and adapted to the multi-objective optimization of a polysiloxane synthesis process. An optimized feed-forward neural network, modeling the variation in time of the main parameters of the process, was used to calculate the vectorial objective function of NSGA-II, as an enhancement to the multi-objective optimization procedure. An original technique was utilized in order to find the most appropriate parameters for maximizing the performance of NSGA-II. The algorithm provided the optimum reaction conditions (reaction temperature, reaction time, amount of catalyst, and amount of co-catalyst), which maximize the reaction conversion and minimize the difference between the obtained viscometric molecular weight and the desired molecular weight. The algorithm has proven to be able to find the entire non-dominated Pareto front and to quickly evolve optimal solutions as an acceptable compromise between objectives competing with each other. The use of the neural network makes it also suitable to the multi-objective optimization of processes for which the amount of knowledge is limited.     

Shakedown analysis of circular plates using a yield criterion of the mean

Structural optimization based on the shakedown theory is a powerful and promising technique. However, due to the nonlinearities of physical materials and the number of variable loads in real structures, it is computationally complex and time-consuming. To simplify the occurring non-linear, non-convex optimization problems, the paper suggests reducing the number of yield conditions. The so-called a yield criterion of the mean (integral yield condition) is analysed and explained in detail, which allows taking into account one yield condition for the entire finite element instead of multiple point-wise conditions. This approach shows promising results in numerical application to the optimization of a circular plate, considering a possibility of employing the yield criteria of the mean or pointwise yield conditions in different areas of the plate in particular. The methods applied are based on the assumptions of perfect plasticity and small deformations.     

A synergistic model-driven approach for persistence modeling with UML

The Model Driven Development (MDD) approach proposes that models (and model-to-model transformations) play the main role on system development. However, there is not a consensual notation to model persistence based upon object-relational mapping frameworks: while UML lacks specific resources for persistence modeling, the entity-relationship model does not make reference to the dynamic concepts existing in UML.This paper proposes MD-JPA, a UML profile for persistence modeling based on the well-known Java Persistence API 2 (JPA) standard for object-relational mapping, pursuing the modeling of transient and persistent elements in a more coherent and synergistic way. This paper describes the main characteristics of MD-JPA as well as the way that models that adopt such profile can them be used to generate a Java implementation by the application of the proposed model transformations on a MDD approach. Finally, an open source tool was developed to make the results of this work available to the community.