Flux-line cutting in hard superconductors

The effect of flux-line cutting upon the magnetic behavior of hard superconductors subjected to a dc bias magnetic field H_z and a transverse field H_y undergoing half-wave oscillations of large amplitude is investigated theoretically. We have applied both the generalized double critical-state model and the elliptic flux-line-cutting one to interpret available experimental results for VTi ribbons. The predictions of these models are compared and discussed.     

Anomaly Detection Using Real-Valued Negative Selection

This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.     

VPM: A visual,precise and multilevel metamodeling framework for describing mathematical domains and UML (The Mathematics of Metamodeling is Metamodeling Mathematics)

As UML 2.0 is evolving into a family of languages with individually specified semantics, there is an increasing need for automated and provenly correct model transformations that (i) assure the integration of local views (different diagrams) of the system into a consistent global view, and, (ii) provide a well-founded mapping from UML models to different semantic domains (Petri nets, Kripke automaton, process algebras, etc.) for formal analysis purposes as foreseen, for instance, in submissions for the OMG RFP for Schedulability, Performance and Time. However, such transformations into different semantic domains typically require the deep understanding of the underlying mathematics, which hinders the use of formal specification techniques in industrial applications. In the paper, we propose a multilevel metamodeling technique with precise static and dynamic semantics (based on a refinement calculus and graph transformation) where the structure and operational semantics of mathematical models can be defined in a UML notation without cumbersome mathematical formulae.     

Finding multiple solutions in job shop scheduling by niching genetic algorithms

The interest in multimodal optimization methods is increasing in the last years. The objective is to find multiple solutions that allow the expert to choose the solution that better adapts to the actual conditions. Niching methods extend genetic algorithms to domains that require the identification of multiple solutions. There are different niching genetic algorithms: sharing, clearing, crowding and sequential, etc. The aim of this study is to study the applicability and the behavior of several niching genetic algorithms in solving job shop scheduling problems, by establishing a criterion in the use of different methods according to the needs of the expert. We will experiment with different instances of this problem, analyzing the behavior of the algorithms from the efficacy and diversity points of view.     

Topic Identification in Dynamical Text by Complexity Pursuit

The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearing in Internet chat lines. In this Letter a recently introduced source separation method, termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do correspond to meaningful topics inherent in the dynamical text data, and also suggest the applicability of the method to query-based retrieval from a temporally changing text stream. This revised version was published online in June 2006 with corrections to the Cover Date.     

Selection of Strategies for Collision-free Motion in Multi-manipulator Systems

Strategy-based path planning was introduced by the authors as a way to give robotic manipulators some sort of reactive behaviour in dynamic and unpredictable environments. This approach is based on making robots react to moving obstacles using a restricted subset of the configuration space, and on using an off-line pre-planning stage to choose the restricted subset. In this paper, strategy-based path planning is applied and explored, focusing our attention on the off-line stage. It is applied to the joint motion of two and three robots that perform pick and place tasks sharing common working areas.     

Basic fuzzy logic and <Emphasis Type="Italic">BL</Emphasis>-algebras II

 Three new (easy) results about the computational complexity of basic propositional fuzzy logic BL are presented. An important formula of predicate logic is shown 1-true in all interpretations over saturated BL-chains but is not a BL-1-tautology, i.e. is not 1-true in a safe interpretation over a non-saturated BL-algebra. Partial support of the grant No. A1030004/00 of the Grant Agency of the Academy of Science of the Czech Republic is acknowledged.     

Using Self-Similarity to Cluster Large Data Sets

Clustering is a widely used knowledge discovery technique. It helps uncovering structures in data that were not previously known. The clustering of large data sets has received a lot of attention in recent years, however, clustering is a still a challenging task since many published algorithms fail to do well in scaling with the size of the data set and the number of dimensions that describe the points, or in finding arbitrary shapes of clusters, or dealing effectively with the presence of noise. In this paper, we present a new clustering algorithm, based in self-similarity properties of the data sets. Self-similarity is the property of being invariant with respect to the scale used to look at the data set. While fractals are self-similar at every scale used to look at them, many data sets exhibit self-similarity over a range of scales. Self-similarity can be measured using the fractal dimension. The new algorithm which we call Fractal Clustering (FC) places points incrementally in the cluster for which the change in the fractal dimension after adding the point is the least. This is a very natural way of clustering points, since points in the same cluster have a great degree of self-similarity among them (and much less self-similarity with respect to points in other clusters). FC requires one scan of the data, is suspendable at will, providing the best answer possible at that point, and is incremental. We show via experiments that FC effectively deals with large data sets, high-dimensionality and noise and is capable of recognizing clusters of arbitrary shape.     

Declarative Formalization of Reasoning Strategies: A Case Study on Heuristic Nonlinear Planning

We study the declarative formalization of reasoning strategies by presenting declarative formalizations of: (1) the SNLP algorithm for nonlinear planning, and (2) a particular algorithm for blocks world nonlinear planning proposed in this paper. The formal models of a heuristic forward chaining planner, which can take advantage of declarative formalizations of action selection strategies, and of a reasoning strategy based planner, which can use declarative formalizations of reasoning strategies, are proposed. The effectiveness of these systems is studied from formal and empirical points of view. Empirical results showing how the use of declarative formalizations of reasoning strategies can reduce the amount of search required for solving planning problems (with respect to state of the art planning systems) are presented.