Learning Membership Functions in a Function-Based Object Recognition System

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.Comment: See http://www.jair.org/ for any accompanying file

Projectification of the Firm : the Renault Case

Many industrial firms are implementing fundamental changes in their organizations to increase the efficiency of their product development processes. Here we focus on the relations between project management models and the permanent organization and processes of the firm. The case of the French firm Renault is being studied. This firm implemented a transition, from a classical funtional organization in the 1960's to project coordination in the 1970's and autonomous and powerful project teams since 1989. Such advanced project management has deep and destabilising effects on the other permanent logics of the firm (task definitions, hierarchic regulations, carrier management, functions and suppliers relationship). Therefore a phase of "projectification" is now under way to adapt these permanent processes to the new context.project management, organization, organizational learning, automobile industry.

Projectification of the Firm : the Renault Case

Many industrial firms are implementing fundamental changes in their organizations to increase the efficiency of their product development processes. Here we focus on the relations between project management models and the permanent organization and processes of the firm. The case of the French firm Renault is being studied. This firm implemented a transition, from a classical funtional organization in the 1960's to project coordination in the 1970's and autonomous and powerful project teams since 1989. Such advanced project management has deep and destabilising effects on the other permanent logics of the firm (task definitions, hierarchic regulations, carrier management, functions and suppliers relationship). Therefore a phase of "projectification" is now under way to adapt these permanent processes to the new context

A knowledge-based approach for the extraction of machining features from solid models

Computer understanding of machining features such as holes and pockets is essential for bridging the communication gap between Computer Aided Design and Computer Aided Manufacture. This thesis describes a prototype machining feature extraction system that is implemented by integrating the VAX-OPS5 rule-based artificial intelligence environment with the PADL-2 solid modeller. Specification of original stock and finished part geometry within the solid modeller is followed by determination of the nominal surface boundary of the corresponding cavity volume model by means of Boolean subtraction and boundary evaluation. The boundary model of the cavity volume is managed by using winged-edge and frame-based data structures. Machining features are extracted using two methods : (1) automatic feature recognition, and (2) machine learning of features for subsequent recognition. [Continues.