Hybrid evolutionary algorithm for the Capacitated Centered Clustering Problem

The Capacitated Centered Clustering Problem (CCCP) consists of defining a set of p groups with minimum dissimilarity on a network with n points. Demand values are associated with each point and each group has a demand capacity. The problem is well known to be NP-hard and has many practical applications. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the CCCP. This method identifies promising regions of the search space by generating solutions with a metaheuristic, such as Genetic Algorithm, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of CS. (C) 2010 Elsevier Ltd. All rights reserved.

Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems

One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

New Insight into the Mechanism of Action of Wasp Mastoparan Peptides: Lytic Activity and Clustering Observed with Giant Vesicles

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Quark clustering and chiral symmetry breaking in nuclear matter

Chiral symmetry breaking at finite baryon density is usually discussed in the context of quark matter, i.e. a system of deconfined quarks. Many systems like stable nuclei and neutron stars however have quarks confined within nucleons. In this paper we construct a Fermi sea of three-quark nucleon clusters and investigate the change of the quark condensate as a function of baryon density. We study the effect of quark clustering on the in-medium quark condensate and compare results with the traditional approach of modeling hadronic matter in terms of a Fermi sea of deconfined quarks.

EVOLUTION OF ADENINE CLUSTERING IN 5S RIBOSOMAL-RNA

The frequency of adenine mononucleotides (A), dinucleotides (AA) and clusters, and the positions of clusters, were studied in 502 molecules of the 5S rRNA.All frequencies were reduced in the evolutive lines of vertebrates, plants and fungi, in parallel with increasing organismic complexity. No change was observed in invertebrates. All frequencies were increased in mitochondria, plastids and mycoplasmas. The presumed relatives to the ancestors of the organelles, Rhodobacteria alfa and Cyanobacteria, showed intermediate values, relative to the eubacterial averages. Firmibacterid showed very high number of cluster sites.Clusters were more frequent in single-stranded regions in all organisms. The routes of organelles and mycoplasmas accummulated clusters at faster rates in double-stranded regions. Rates of change were higher for AA and clusters than for A in plants, vertebrates and organeltes, higher for cluster sites and A in mycoplasmas, and higher for AA and A in fungi. These data indicated that selection pressures acted more strongly on adenine clustering than on adenine frequency.It is proposed that AA and clusters, as sites of lower informational content. have the property of tolerating positional variation in the sites of other molecules (or other regions of the same molecule) that interact with the adenines. This reasoning was consistent with the degrees of genic polymorphism. low in plants and vertebrates and high in invertebrates. In the eubacteria endosymbiontic or parasitic to eukaryotes, the more tolerant RNA would be better adapted to interactions with the homologous nucleus-derived ribosomal proteins: the intermediate values observed in their precursors were interpreted as preadaptive.Among other groups, only the Deinococcus-Thermus eubacteria showed excessive AA and cluster contents, possibly related to their peculiar tolerance to mutagens, and the Ciliates showed excessive AA contents, indicative of retention of primitive characters.

Clustering of rare earth in glasses, aluminum effect: experiments and modeling

Luminescent spectra of Eu3+-doped sol-gel glasses have been analyzed during the densification process and compared according to the presence or not of aluminum as a codoping ion. A transition temperature from hydrated to dehydroxyled environments has been found different for doped and codoped samples. However, only slight modifications have been displayed from luminescence measurements beyond this transition. To support the experimental analysis, molecular dynamics simulations have been performed to model the doped and codoped glass structures. Despite no evidence of rare earth clustering reduction due to aluminum has been found, the modeled structures have shown that the luminescent ions are mainly located in aluminum-rich domains. The synthesis of both experimental and numerical analyses has lead us to interpret the aluminum effect as responsible for differences in structure of the luminescent sites rather than for an effective dispersion of the rare earth ions. (C) 2004 Elsevier B.V. All rights reserved.

Structure, mobility and clustering of interstitial O in La2CuO4+delta in the limit of small delta

Anelastic spectra (elastic energy absorption as a function of temperature) are reported which provide evidence that excess O in La2CuO4+delta starts forming two different types of defects already at very low concentrations, where no phase separation or changes in the type of O intercalation are believed to occur. The absorption peak with the lowest activation enthalpy, H/k(B) = 5600 K, is visible at lowest values of delta and is attributed to the hopping of single interstitial O2- ions. The second process, with a slightly slower dynamics, appears at higher values of delta and soon becomes preponderant over the former process. The latter process is proposed to be due to stable pairs of O atoms and is put in connection with the formation of partially covalent bonds between interstitial and apical oxygen; such bonds would reduce the doping efficiency of excess O at increasing delta. The geometry of the interstitial O defect is discussed. O 1998 Published by Elsevier B.V. B.V. All rights reserved.

Comparative phylogeography of Trypanosoma cruzi TCIIc: New hosts, association with terrestrial ecotopes, and spatial clustering

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Insulator-to-metal transition driven by clustering of solitons in polyacetylene

It's believed that the simple Su-Schrieffer-Heeger Hamiltonian can not predict the insulator to metal transition of transpolyacetylene (t-PA). The soliton lattice configuration at a doping level y=6% still has a semiconductor gap. Disordered distributions of solitons close the gap, but the electronic states around the Fermi energy are localized. However, within the same framework, it is possible to show that a cluster of solitons can produce dramatic changes in the electronic structure, allowing an insulator-to-metal transition.

Land use image classification through optimum-path forest clustering

Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.

Spatial clustering applied to health area

The significant volume of work accidents in the cities causes an expressive loss to society. The development of Spatial Data Mining technologies presents a new perspective for the extraction of knowledge from the correlation between conventional and spatial attributes. One of the most important techniques of the Spatial Data Mining is the Spatial Clustering, which clusters similar spatial objects to find a distribution of patterns, taking into account the geographical position of the objects. Applying this technique to the health area, will provide information that can contribute towards the planning of more adequate strategies for the prevention of work accidents. The original contribution of this work is to present an application of tools developed for Spatial Clustering which supply a set of graphic resources that have helped to discover knowledge and support for management in the work accidents area. © 2011 IEEE.

Post-processing association rules with clustering and objective measures

The post-processing of association rules is a difficult task, since a large number of patterns can be obtained. Many approaches have been developed to overcome this problem, as objective measures and clustering, which are respectively used to: (i) highlight the potentially interesting knowledge in domain; (ii) structure the domain, organizing the rules in groups that contain, somehow, similar knowledge. However, objective measures don't reduce nor organize the collection of rules, making the understanding of the domain difficult. On the other hand, clustering doesn't reduce the exploration space nor direct the user to find interesting knowledge, making the search for relevant knowledge not so easy. This work proposes the PAR-COM (Post-processing Association Rules with Clustering and Objective Measures) methodology that, combining clustering and objective measures, reduces the association rule exploration space directing the user to what is potentially interesting. Thereby, PAR-COM minimizes the user's effort during the post-processing process.

Characterization of the temperature, load and damage effects using piezoelectric transducer patches based on fuzzy clustering

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in a structure. One of the critical challenges for practical implementation of SHM system is the ability to detect damage under changing environmental conditions. This paper aims to characterize the temperature, load and damage effects in the sensor measurements obtained with piezoelectric transducer (PZT) patches. Data sets are collected on thin aluminum specimens under different environmental conditions and artificially induced damage states. The fuzzy clustering algorithm is used to organize the sensor measurements into a set of clusters, which can attribute the variation in sensor data due to temperature, load or any induced damage.

Electrical consumers data clustering through optimum-path forest

Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.