Integrating working and enrichment approach learning: a document

Integrating working and learning is seen as a desirable alternative to traditional training regimes. An integrated approach to working and learning is more gradual, contextual, problem oriented and cost effective. Knowledge technology aims to catalyze workplace learning, but requires the right organisational culture and brings additional costs regarding the articulation, representation and transmission of knowledge. Our approach reduces these costs by making articulation a natural part of collaborative working, designing for both informal and formal knowledge, and facilitating the transition of socially situated knowledge through enriched documents. To be successful, our approach has certain prequesites concerning organisational culture, and the nature of shared documents, organisational knowledge and work activities.     

Building ensemble classifiers using belief functions and OWA operators

A pervasive task in many forms of human activity is classification. Recent interest in the classification process has focused on ensemble classifier systems. These types of systems are based on a paradigm of combining the outputs of a number of individual classifiers. In this paper we propose a new approach for obtaining the final output of ensemble classifiers. The method presented here uses the Dempster–Shafer concept of belief functions to represent the confidence in the outputs of the individual classifiers. The combing of the outputs of the individual classifiers is based on an aggregation process which can be seen as a fusion of the Dempster rule of combination with a generalized form of OWA operator. The use of the OWA operator provides an added degree of flexibility in expressing the way the aggregation of the individual classifiers is performed.     

First-order syntactic characterizations of minimal entailment,domain-minimal entailment,and Herbrand entailment

This paper investigates a consistent versioncwa _s of Reiter's closed-world assumptioncwa. It provides a syntactic characterization of minimal entailment ? _min : for every ∨-sentence ? and for every ∨-theory Σ, $$\varphi \varepsilon cwa_S \left( \sum \right)iff\sum \vdash _{min} \varphi .$$ A version of this characterization remains valid if not all relations are subject to minimization. These two characterizations do not use the domain-closure axiom nor the unique-names assumptionuna, although they may be easily modified to ones that incorporateuna. A similar result for Herbrand entailment ? _Her , by means of generalized domain-closure axiomdca _s , is provided: for every ∨-sentence ? and every ∨-theory Σ, $$\varphi \varepsilon dca_S \left( \sum \right)iff\sum \vdash _{Her} \varphi .$$ Finally, a syntactic characterization of domain-minimal entailment ? _dom in terms of a versionmda _s of minimal-domain assumption is shown: for every ∨-sentence ? and for every ∨-theory Σ, $$\varphi \varepsilon mda_S \left( \sum \right)iff\sum \vdash _{dom} \varphi .$$ The proving power of these entailments is then evaluated. In particular, it is shown that (1) neither ? _min nor its versions are strong enough to derive positive sentences from Σ unless they are first-order provable from Σ however, a double application of ? _min has enough power to derive such positive sentences; (2) ? _Her has the strength of infinitary rule of inference but cannot derive existential nor quantifier-free sentences from Σ unless they are first-order provable from Σ (3) ? _Her and ? _dom can derive from Σ certain positive facts about = which are otherwise unprovable from Σ and (4) ? _dom cannot derive from Σ sentences without positive occurrences of = unless they are first-order provable from Σ. Moreover, the paper relatescwa _s to Reiter'scwa and to Minker's generalized closed-world assumptionGCWA and its extension.     

Combining classifiers under probabilistic models: experimental comparative analysis of methods

This work will present a review of the concept of classifier combination based on the combined discriminant function. We will present a Bayesian approach, in which the discriminant function assumes the role of the posterior probability. We will propose a probabilistic interpretation of expert rules and conditions of knowledge consistency for expert rules and learning sets. We will suggest how to measure the quality of learning materials and we will use the measure mentioned above for an algorithm that eliminates contradictions in the rule set. In this work several recognition algorithms will be described, based on either: (i) pure rules, or; (ii) rules together with learning sets. Furthermore, the original concept of information unification, which enables the formation of rules on the basis of learning set or learning set on the basis of rules will be proposed. The obtained conclusions will serve as a spring‐board for the formulation of new project guidelines for this type of decision‐making system. At the end, experimental results of the proposed algorithms will be presented, both from computer generated data and for a real problem from the medical diagnostics field.     

Crack and flaw identification in elastodynamics using Kalman filter techniques

Filter-driven optimization based on the extended Kalman filter concept is used here for the numerical solution of crack and flaw identification problems in elastodynamics. The mechanical modeling of the studied two-dimensional problem, which includes the effect of unilateral contact along the sides of the crack, is done with the help of the boundary element method. The effect of various dynamical test loads and the applicability of this method for crack and defect identification in disks are investigated. The work has been supported by the German Research Foundation (DFG). Partial support has been provided by a Greek-German Research Cooperation Grant (IKYDA2001). This support is greatfully acknowledged.     

Defect identification in 3-D elastostatics using a genetic algorithm

An inverse problem in engineering mechanics is considered where the position and the geometry of three-dimensional, ellipsoidal defects are identified by using measurements of the mechanical response under static loading on the external surface of the structure. The problem is solved by appropriate combination of genetic optimization (GO) and boundary element method (BEM) and following previously published two-dimensional problems. The three-dimensional case presents some additional difficulties. Furthermore, the function of several genetic operators and the effect of the parameters of genetic optimization on the efficiency of the solution has been numerically examined.     

Direct Segmentation of Algebraic Models for Reverse Engineering

In Reverse Engineering a physical object is digitally reconstructed from a set of boundary points. In the segmentation phase these points are grouped into subsets to facilitate consecutive steps as surface fitting. In this paper we present a segmentation method with subsequent classification of simple algebraic surfaces. Our method is direct in the sense that it operates directly on the point set in contrast to other approaches that are based on a triangulation of the data set. The segmentation process involves a fast algorithm for k-nearest neighbors search and an estimation of first and second order surface properties. The first order segmentation, that is based on normal vectors, provides an initial subdivision of the surface and detects sharp edges as well as flat or highly curved areas. One of the main features of our method is to proceed by alternating the steps of segmentation and normal vector estimation. The second order segmentation subdivides the surface according to principal curvatures and provides a sufficient foundation for the classification of simple algebraic surfaces. If the boundary of the original object contains such surfaces the segmentation is optimized based on the result of a surface fitting procedure.     

Wave propagation in delaminated beam

The wave propagation model investigated herein is based on the known fact that material discontinuities affect the propagation of elastic waves in solids. The change in certain material characteristics, such as a local change in stiffness or inertia caused by a crack or the presence of material damage, will affect the propagation of transmitted elastic waves and will modify the received signal. Wave frequencies associated with the highest detection sensitivity depend, among other things, on the type of structure, the type of material, and the type of damage. This paper presents a method of wave propagation, which can be further used to detect small delaminations in beam-like structures. The considered beam is modelled by spectral finite elements.     

Constraint Lingo: towards high‐level constraint programming

Logic programming requires that the programmer convert a problem into a set of constraints based on predicates. Choosing the predicates and introducing appropriate constraints can be intricate and error prone. If the problem domain is structured enough, we can let the programmer express the problem in terms of more abstract, higher‐level constraints. A compiler can then convert the higher‐level program into a logic‐programming formalism. The compiler writer can experiment with alternative low‐level representations of the higher‐level constraints in order to achieve a high‐quality translation. The programmer can then take advantage of both a reduction in complexity and an improvement in runtime speed for all problems within the domain. We apply this analysis to the domain of tabular constraint‐satisfaction problems. Examples of such problems include logic puzzles solvable on a hatch grid and combinatorial problems such as graph coloring and independent sets. The proper abstractions for these problems are rows, columns, entries, and their interactions. We present a higher‐level language, Constraint Lingo, dedicated to problems in this domain. We also describe how we translate programs from Constraint Lingo into lower‐level logic formalisms such as the logic of propositional schemata. These translations require that we choose among competing lower‐level representations in order to produce efficient results. The overall effectiveness of our approach depends on the appropriateness of Constraint Lingo, our ability to translate Constraint Lingo programs into high‐quality representations in logic formalisms, and the efficiency with which logic engines can compute answer sets. We comment on our computational experience with these tools in solving both graph problems and logic puzzles. Copyright © 2004 John Wiley & Sons, Ltd.