Measurement of enterprise knowledge by state characterization

Abstract: Enterprise production is becoming more and more knowledge oriented nowadays and knowledge management is playing a key role in making commercial profit and maintaining enterprise competence. Knowledge characterization and measurement are the bases of knowledge management, and this paper aims to develop a method for characterizing and measuring knowledge in a comprehensive and practical way. Knowledge in its static state is modelled as a vector, whose values vary with the situation, and changes in state are modelled as a finite state machine.     

A taxonomy of argumentation models used for knowledge representation

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.     

Fuel efficient power management strategy for fuel cell hybrid powertrains

A real time control strategy for fuel cell hybrid vehicles is proposed. The objective is to reduce the hydrogen consumption by using an efficient power sharing strategy between the fuel cell system (FCS) and the energy buffer (EB). The energy buffer (battery or supercapacitor) is charge-sustained (no plug-in capabilities). The real time control strategy is derived from a non-causal optimization algorithm based on optimal control theory. The strategy is validated experimentally with a hardware-in-the-loop (HiL) test bench based on a 600 W fuel cell system.     

A visual digital library approach for time-oriented scientific primary data

Digital Library support for textual and certain types of non-textual documents has significantly advanced over the last years. While Digital Library support implies many aspects along the whole library workflow model, interactive and visual retrieval allowing effective query formulation and result presentation are important functions. Recently, new kinds of non-textual documents which merit Digital Library support, but yet cannot be fully accommodated by existing Digital Library technology, have come into focus. Scientific data, as produced for example, by scientific experimentation, simulation or observation, is such a document type. In this article we report on a concept and first implementation of Digital Library functionality for supporting visual retrieval and exploration in a specific important class of scientific primary data, namely, time-oriented research data. The approach is developed in an interdisciplinary effort by experts from the library, natural sciences, and visual analytics communities. In addition to presenting the concept and to discussing relevant challenges, we present results from a first implementation of our approach as applied on a real-world scientific primary data set. We also report from initial user feedback obtained during discussions with domain experts from the earth observation sciences, indicating the usefulness of our approach.     

On the composition of time Petri nets

Complex systems are often designed and built from smaller pieces, called components. Components are open sub-systems meant to be combined (or composed) to form other components or closed systems. It is well known that Petri nets allow such a component based modeling, relying on parallel composition and transition synchronization. However, synchronizing transitions that carry temporal constraints does not yield a compositional method for assembling components, a highly desirable property. The paper addresses this particular problem: how to build complex systems in a compositional manner from components specified by Time Petri nets (TPN). A first solution is proposed, adequate for a particular subclass of Time Petri nets but significantly increasing the complexity of components. Then an improved solution is developed, relying on an extension of Time Petri nets with two relations added on transitions. This latter solution requires a much simpler transformation of nets, does not significantly increase their complexity, and is applicable to a larger class of TPN.     

A Simplified Design for Strict Lyapunov Functions Under Matrosov Conditions

We construct strict Lyapunov functions for broad classes of nonlinear systems satisfying Matrosov type conditions. Our new constructions are simpler than the designs available in the literature. We illustrate the practical interest of our designs using a globally asymptotically stable biological model.     

Bayesian Clustering of Fuzzy Feature Vectors Using a Quasi-Likelihood Approach

Bayesian model-based classifiers, both unsupervised and supervised, have been studied extensively and their value and versatility have been demonstrated on a wide spectrum of applications within science and engineering. A majority of the classifiers are built on the assumption of intrinsic discreteness of the considered data features or on the discretization of them prior to the modeling. On the other hand, Gaussian mixture classifiers have also been utilized to a large extent for continuous features in the Bayesian framework. Often the primary reason for discretization in the classification context is the simplification of the analytical and numerical properties of the models. However, the discretization can be problematic due to its textit{ad hoc} nature and the decreased statistical power to detect the correct classes in the resulting procedure. We introduce an unsupervised classification approach for fuzzy feature vectors that utilizes a discrete model structure while preserving the continuous characteristics of data. This is achieved by replacing the ordinary likelihood by a binomial quasi-likelihood to yield an analytical expression for the posterior probability of a given clustering solution. The resulting model can be justified from an information-theoretic perspective. Our method is shown to yield highly accurate clusterings for challenging synthetic and empirical data sets.     

A new representation of systems with frictional unilateral constraints and its Baumgarte-like relaxation

This paper proposes a new representation of multibody mechanical systems involving three-dimensional frictional unilateral constraints. The new representation is of the form of a differential algebraic inclusion (DAI) employing a normal cone with a non-Euclidean, singular norm metric. It can be seen as a generalization of a differential algebraic equation (DAE) using Lagrange multipliers, which has been used to represent mechanical systems with equality constraints. The paper also presents an approach to approximate the aforementioned DAI by another form of DAI, which can be equivalently converted into an ordinary differential equation (ODE). The approach can be seen as a generalization of the Baumgarte stabilization, which was originally developed for DAEs. The new DAI representation and its ODE approximation are illustrated with some simple examples.     

Interval observers for linear time-invariant systems with disturbances

It is shown that, for any time-invariant exponentially stable linear system with additive disturbances, time-varying exponentially stable interval observers can be constructed. The technique of construction relies on the Jordan canonical form that any real matrix admits and on time-varying changes of coordinates for elementary Jordan blocks which lead to cooperative linear systems. The approach is applied to detectable linear systems.     

On the ERA ranking representability of pairwise bipartite ranking functions

In domains like decision theory and social choice theory it is known for a long time that stochastic transitivity properties yield necessary and sufficient conditions for the ranking or utility representability of reciprocal preference relations. In this article we extend these results for reciprocal preference relations originating from the pairwise comparison of random vectors in a machine learning context. More specifically, the expected ranking accuracy (ERA) is such a reciprocal relation that occurs in multi-class classification problems, when ranking or utility functions are fitted to the data in a pairwise manner. We establish necessary and sufficient conditions for which these pairwise bipartite ranking functions can be simplified to a single ranking function such that the pairwise expected ranking accuracies of both models coincide. Similarly as for more common reciprocal preference relations, cycle transitivity plays a crucial role in this new setting. We first consider the finite sample case, for which expected ranking accuracy can be estimated by means of the area under the ROC curve (AUC), and subsequently, we further generalize these results to the underlying distributions. It turns out that the ranking representability of pairwisely compared random vectors can be expressed elegantly in a distribution-independent way by means of a specific type of cycle transitivity, defined by a conjunctor that is closely related to the algebraic product.