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.     

Migration Processes II: The Discrete Case

Following the study of migration processes in the continuous domain in Part I of this paper, we reformulate the concept of migration in the discrete domain (Z^m) and define Discrete Migration Processes (DMP). We demonstrate that this model is a natural discrete representation of the continuous model and maintains the model's features in a qualitative sense. We show that under discrete migration any discrete set shrinks to a limit in finitely many iterations. The discrete representation provides an advantageous basis for digitally implementing the MP model. Using this implementation we illustrate the discrete migration of various types of sets under various types of constraints.     

Dynamics of coupled and uncoupled two-phase flows in a slab mold

Two-phase flows in a mold of a slab caster are studied using water modeling, particle-image velocimetry (PIV), and computational fluid-dynamics techniques. Two-way coupled flows are observed in liquidgas systems, because both phases influence each other’s momentum transfer. In addition to this concept, PIV measurements indicate the existence of structurally coupled flows, where the velocity vectors of both phases observe similar orientations. When the drag forces of the liquid, exerted on the bubbles, exceed a certain value of the inertial forces of the liquid phase, at high mass loads of gas (ratio of mass flow rates of the gas phase and the liquid phase), the flow becomes structurally coupled. These types of flows promote large oscillations of the meniscus level. Two jets, liquid and bubble, were identified; the latter always reported larger angles than the first, independent of the gas load. Thus, a gas-rich jet is located closer to the lower edge of the submerged entry nozzle (SEN) port, and the liquid-rich jet is found above this position. The liquid-jet angle approaches that of the SEN port when the flow becomes structurally coupled. Structurally uncoupled flows report gas jets that follow torrent-type patterns which are well explained using a multiphase fluid-dynamics model. Structurally coupled flows yield gas jets with a continuous pattern.     

Evolution of secondary phases in Cr-V low-alloy steels during aging

The influence of both bulk vanadium content and aging conditions on the evolution of secondary phases in Cr-V low-alloy steels was studied. Three 0.1C-0.9Cr-V steels with different vanadium contents (0, 0.258, and 0.512 wt pct) were aged for 100 to 5,000 hours at 773, 853, 953, and 993 K. In the investigation, a limited experimental program (transmission electron microscopy (TEM)) was combined with credible thermodynamic predictions (ThermoCalc). Going out from the good agreement between the predicted and experimental results, behavior of the iron-rich M₇C₃ carbide in time-temperature scale was characterized. The influence of bulk vanadium content was determined on appearance of the M₃C carbide in equilibrium, temperature of the M₇C₃ carbide precipitation, metal compositions of M₃C or M₇C₃ carbides, and vanadium portion in the metallic part of the MX phase.     

Analysis and optimum design of fibre-reinforced composite structures

The optimal design of a carbon-fibre-reinforced plastic (CFRP) sandwich-like structure with aluminium (Al) webs is addressed. The material parameters are determined using tensile tests, whereafter the results of an analytical model, a numerical model and an experimental setup are compared. The analytical and numerical approximations are then used to optimize the structure in a multi-algorithm approach for minimum cost and maximum stiffness. The selected algorithm and approximation are motivated by their accuracy and computational efficiency. The CFRP plates are optimized with respect to ply arrangement, while the complete sandwich-like structure is optimized with respect to the combination of manufacturing and material cost. Design constraints on maximum deflection of the total structure, buckling of the CFRP composite plates, buckling of the Al webs, stress in the composite plates and stress in the Al stiffeners are included in the formulation. For the different phases in the optimization process, we use the recently proposed particle swarm optimization algorithm, a dynamic search technique and a continuous-discrete optimization technique .     

Quattor: Tools and Techniques for the Configuration,Installation and Management of Large-Scale Grid Computing Fabrics

This paper describes the quattor tool suite, a new system for the installation, configuration, and management of operating systems and application software for computing fabrics. At present Unix derivatives such as Linux and Solaris are supported. Quattor is a powerful, portable and modular open source solution that has been shown to scale to thousands of computing nodes and offers a significant reduction in management costs for large computing fabrics. The quattor tool suite includes innovations compared to existing solutions which make it very useful for computing fabrics integrated into grid environments. Evaluations of the tool suite in current large scale computing environments are presented.     

Outlier Detection and Data Cleaning in Multivariate Non-Normal Samples: The PAELLA Algorithm

A new method of outlier detection and data cleaning for both normal and non-normal multivariate data sets is proposed. It is based on an iterated local fit without a priori metric assumptions. We propose a new approach supported by finite mixture clustering which provides good results with large data sets. A multi-step structure, consisting of three phases, is developed. The importance of outlier detection in industrial modeling for open-loop control prediction is also described. The described algorithm gives good results both in simulations runs with artificial data sets and with experimental data sets recorded in a rubber factory. Finally, some discussion about this methodology is exposed.