### Refine

#### Year of publication

- 1994 (40) (remove)

#### Document Type

- Preprint (40) (remove)

#### Keywords

- Boltzmann Equation (2)
- Numerical Simulation (2)
- CPLD (1)
- Case-Based Classification Algorithms (1)
- Case-Based Planning (1)
- Case-Based Representability (1)
- Domain Decomposition (1)
- Fallbasierte Planung (1)
- PABS-Methode (1)
- Particle Methods (1)

#### Faculty / Organisational entity

A Case Study on Specifikation,Detection and Resolution of IN Feature Interactions with Estelle
(1994)

We present an approach for the treatment of Feature Interactions in Intelligent Networks. The approach is based on the formal description technique Estelle and consists of three steps. For the first step, a specification style supporting the integration of additional features into a basic service is introduced . As a result, feature integration is achieved by adding specification text, i.e . on a purely syntactical level. The second step is the detection of feature interactions resulting from the integration of additional features. A formal criterion is given that can be used for the automatic detection of a particular class of feature interactions. In the third step, previously detected feature interactions are resolved. An algorithm has been devised that allows the automatical incorporation of high-level design decisions into the formal specification. The presented approach is applied to the Basic Call Service and several supplementary interacting features.

Lernen von Abstraktionshierarchien zur Optimierung der Auswahl von maschinell abstrahierten Plänen
(1994)

Mit Hilfe von "Multistrategy" Ansätzen, die erklärungsbasiertes und induktives Lernen integrieren, ist es möglich, die Performanz von Planungssystemen signifikant zu verbessern. Dabei können gelöste Planungsprobleme zunächst mit einem wissensintensiven Verfahren abstrahiert und generalisiert werden. Durch den in diesem Beitrag im Vordergrund stehenden induktiven inkrementellen Lernalgorithmus ist es dann weiterhin möglich, die Gesamtheit des deduktiv generierten Wissens in einer Abstraktionshierarchie anzuordnen. Dabei wird die, im allgemeinen unentscheidbare, "spezieller-als-Relation" zwischen generalisierten Plänen, induktiv aus den gegebenen Planungsfällen gelernt. Diese Abstraktionshierarchie dient dann zur Klassifikation neuer Problemstellungen und damit zur Bestimmung einer speziellsten anwendbaren abstrakten Problemlösung.

Within the present paper we investigate case-based representability as well as case-based learnability of indexed families of uniformly recursive languages. Since we are mainly interested in case-based learning with respect to an arbitrary fixed similarity measure, case-based learnability of an indexed family requires its representability, first. We show that every indexed family is case- based representable by positive and negative cases. If only positive cases are allowed the class of representable families is comparatively small. Furthermore, we present results that provide some bounds concerning the necessary size of case bases. We study, in detail, how the choice of a case selection strategy influences the learning capabilities of a case-based learner. We define different case selection strategies and compare their learning power to one another. Furthermore, we elaborate the relations to Gold-style language learning from positive and both positive and negative examples.

This paper presents the systematic synthesis of a fairly complex digitalcircuit and its CPLD implementation as an assemblage of communicatingasynchronous sequential circuits. The example, a VMEbus controller, waschosen because it has to control concurrent processes and to arbitrateconflicting requests.

We present a convenient notation for positive/negativeADconditional equations. Theidea is to merge rules specifying the same function by using caseAD, ifAD, matchAD, and letADexpressions.Based on the presented macroADruleADconstruct, positive/negativeADconditional equational specifiADcations can be written on a higher level. A rewrite system translates the macroADruleADconstructsinto positive/negativeADconditional equations.

Ohne auf wesentliche Aspekte der in [Bergstra&al.89] vorgestellten alge-braischen Spezifikationssprache ASF zu verzichten, haben wir ASF um die folgenden Konzepteerweitert: Während in ASF einmal exportierte Namen bis zur Spitze der Modulhierarchie sichtbarbleiben müssen, ermöglicht ASF + ein differenziertes Verdecken von Signaturnamen. Das fehlerhafteVermischen unterschiedlicher Strukturen, welches in ASF beim Import verschiedener Aktualisie-rungen desselben parametrisierten Moduls auftritt, wird in ASF + durch eine adäquatere Form derParameterbindung vermieden. Das neue Namensraum_Konzept von ASF + erlaubt es dem Spe-zifizierer, einerseits die Herkunft verdeckter Namen direkt zu identifizieren und anderseits beimImport eines Moduls auszudrücken, ob dieses Modul nur benutzt oder in seinen wesentlichen Ei-genschaften verändert werden soll. Im ersten Fall kann er auf eine einzige global zur Verfügungstehende Version zugreifen; im zweiten Fall muß er eine Kopie des Moduls importieren. Schließlicherlaubt ASF + semantische Bedingungen an Parameter und die Angabe von Beweiszielen.

A Nonlinear Ray Theory
(1994)

A proof of the famous Huygens" method of wavefront construction is reviewed and it is shown that the method is embedded in the geometrical optics theory for the calculation of the intensity of the wave based on high frequency approximation. It is then shown that Huygens" method can be extended in a natural way to the construction of a weakly nonlinear wavefront. This is an elegant nonlinear ray theory based on an approximation published by the author in 1975 which was inspired by the work of Gubkin. In this theory, the wave amplitude correction is incorporated in the eikonal equation itself and this leads to a sytem of ray equations coupled to the transport equation. The theory shows that the nonlinear rays stretch due to the wave amplitude, as in the work of Choquet-Bruhat (1969), followed by Hunter, Majda, Keller and Rosales, but in addition the wavefront rotates due to a non-uniform distribution of the amplitude on the wavefront. Thus the amplitude of the wave modifies the rays and the wavefront geometry, which in turn affects the growth and decay of the amplitude. Our theory also shows that a compression nonlinear wavefront may develop a kink but an expansion one always remains smooth. In the end, an exact solution showing the resolution of a linear caustic due to nonlinearity has been presented. The theory incorporates all features of Whitham" s geometrical shock dynamics.

Based on normalized coprime factorizations with respect to indefinite metrics and the construction of suitable characteristic functions, the Ober balanced canonical forms for the classes of bounded real and positive real are derived. This uses a matrix representation of the shift realization with respect to a basis related to sets of orthogonal polynomials.

The edge enhancement property of a nonlinear diffusion equation with a suitable expression for the diffusivity is an important feature for image processing. We present an algorithm to solve this equation in a wavelet basis and discuss its one dimensional version in some detail. Sample calculations demonstrate principle effects and treat in particular the case of highly noise perturbed signals. The results are discussed with respect to performance, efficiency, choice of parameters and are illustrated by a large number of figures. Finally, a comparison with a Fourier method and a finite volume method is performed.

Whenever new parts of a car have been developed, the manufacturer needs an estimation of the lifetime of this new part. On one hand the construction must not be too weak, so that the part holds long enough to satisfy the customer, but on the other hand, if the construction is too excessive, the part gets too heavy.; One is interested in methods that only need few measured data from the specimen itself, but use data about the material, because constructing and testing of specimen is expensive.

Monte-Carlo methods are widely used numerical tools in various fields of application, like rarefied gas dynamics, vacuum technology, stellar dynamics or nuclear physics. A central part in all applications is the generation of random variates according to a given probability law. Fundamental techniques to generate non-uniform random variates are the inversion principle or the acceptance-rejection method. Both procedures can be quite time-consuming if the given probability law has a complicated structure.; In this paper we consider probability laws depending on a small parameter and investigate the use of asmptotic expansions to generate random variates. The results given in the paper are restrictedto first order expansions. We show error estimates for the discrepancy as well as for the bounded Lipschitz distance of the asymptotic expansion. Furthermore the integration error for some special classes of functions is given. The efficiency of the method is proved by a numerical example from rarefied gas flows.

In spite of its lack of theoretical justification, nonlinear diffusion filtering has become a powerful image enhancement tool in the recent years. The goal of the present paper is to provide a mathematical foundation for nonlinear diffusion filtering as a scale-space transformation which is flexible enough to simplify images without loosing the capability of enhancing edges. By stuying the Lyapunow functional, it is shown that nonlinear diffusion reduces Lp norms and central moments and increases the entropy of images. The proposed anisotropic class utilizes a diffusion tensor which may be adapted to the image structure. It permits existence, uniqueness and regularity results, the solution depends continuously on the initial image, and it fulfills an extremum principle. All considerations include linear and certain nonlinear isotropic models and apply to m-dimensional vector-valued images. The results are juxtaposed to linear and morphological scale-spaces.

In this paper we deal with the problem of computing the stresses in stationary loaded bearings. A method to obtain the pressure in the lubrication fluid, which is given as a solution of Reynolds" differential equation, is presented. Furthermore, using the theory of plain stress, the stresses in the bearing shell are described by derivatives of biharmonic functions. A spline interpolation method for computing these functions is developed and an estimate for the error on the boundaries is presented. Finally the described methods are tested theoretically as well as with real examples.

Particle methods to simulate rarefied gas flows have found an increasing interest in Computational Fluid Dynamics during the last decade, see for example [1], [2], [3] and [4]. The general goal is to develop numerical schemes which are reliable enough to substitute real windtunnel experiments, needed for example in space research, by computer experiments. In order to achieve this goal one needs numerical methods solving the Boltzmann equation including all important physical effects. In general this means 3D computations for a chemically reacting rarefied gas. With codes of this kind at hand, Boltzmann simulation becomes a powerful tool in studying rarefied gas phenomena.

ALICE
(1994)

Based on the idea of using topologic feature-mapsinstead of geometric environment maps in practical mobile robot tasks, we show an applicable way tonavigate on such topologic maps. The main features regarding this kind of navigation are: handling of very inaccurate position (and orientation) information as well as implicit modelling of complex kinematics during an adaptation phase. Due to the lack of proper a-priori knowledge, a re-inforcement based model is used for the translation of navigator commands to motor actions. Instead of employing a backpropagation network for the cen-tral associative memory module (attaching actionprobabilities to sensor situations resp. navigatorcommands) a much faster dynamic cell structure system based on dynamic feature maps is shown. Standard graph-search heuristics like A* are applied in the planning phase.

The problem to be discussed here, is the usage of neural network clustering techniques on a mobile robot, in order to build qualitative topologic environment maps. This has to be done in realtime, i.e. the internal world model has to be adapted by the flow of sensor- samples without the possibility to stop this data-flow.Our experiments are done in a simulation environment as well as on a robot, called ALICE.