Artificial neural networks for coordination and control: The portability of experiential representations

It is time to locate connectionist representation theory in the new wave of robotics research. The utility of representations developed in artificial neural networks (ANNs) during learning has been demonstrated in cognitive science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a classifier net is trained with laser scan data to output transformationally invariant position classes; (ii) an arm net is trained for picking up objects; (iii) an inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalization of decentered viewpoints.     

Self-learning inductive inference machines

Self-knowledge is a concept that is present in several philosophies. In this article, we consider the issue of whether or not a learning algorithm can in some sense possess self-knowledge. The question is answered affirmatively. Self-learning inductive inference algorithms are taken to be those that learn programs for their own algorithms, in addition to other functions.
La connaissance de soi est un concept qui se retrouve dans plusieurs philosophies. Dans cet article, les auteurs s'interrogent à savoir si un algorithme d' apprentissage peut dans une certaine mesure posséder la connaissance de soi. lis apportent une reponse positive a cette question. Les algorithmes d'inference inductive autodidactes sont ceux qui font l'apprentissage de programmes pour leurs propres algorithmes, en plus d' autres fonctions.     

Significance of fluid regime in biological filtration

A dispersed flow model previously developed to study substrate utilization in unsaturated media was experimentally verified and its practical application was considered. For this purpose, measurements were made using tap water and a synthetic feed solution. The importance of the change in fluid regime as regards to the simultaneous transport and reaction within biological filters were demonstrated. The effect of drop formation and the breakage of liquid jets inside the filters on substrate utilization was also shown.     

Hierarchical intelligent control with flexible AC transmission systems application

A novel hierarchical intelligent controller configuration is proposed using an artificial neural network as a control-mode classifier in the supervisory level and a set of pre-designed controllers in the lower level. Controller outputs are modified nonlinearly by the classifying signals in a structure resembling one artificial neuron with adaptively changed weights. The lower-level local controllers are implemented using neural networks. An illustrative example of this approach is based on the transient stabilization of a single-machine infinite-bus system studied in Flexible AC Transmission Systems (FACTS) research.     

Effect of Channel Changes in the St. Clair River During the Present Century

Periodic man-made changes in the outlet of Lake Huron through the St. Clair River date back to the middle of the last century. These artificial channel changes have been well documented during the present century. They consist of dredging for commercial gravel removal in the upper river during 1908–25 and uncompensated navigation improvements for the 7.6-m (25-ft) and 8.2-m (27-ft) projects completed in 1933 and 1962, respectively. The total effect of these changes on the levels of Lakes Michigan and Huron (hydraulically one lake) and on the upper St. Clair River profile was determined with dynamic flow models. The ultimate effect of the above dredging was a permanent lowering of the Lake Michigan-Huron levels 0.27 m (0.89 ft), which represents a tremendous loss of freshwater resource [32 km³ (7.7 mi³)].     

Ecophysiology of ochratoxigenic Aspergillus ochraceus and Penicillium verrucosum isolates. Predictive models for fungal spoilage prevention - a review

Ochratoxin A (OTA) is a secondary metabolite produced by several species of Aspergillus and Penicillium; among them Aspergillus ochraceus and Penicillium verrucosum are two ochratoxigenic species capable of growing in different climates and thus contamination of food crops with OTA can occur worldwide. OTA can be found in a wide range of foods such as cereals, coffee, cocoa, spices, beer, wine, dried vine fruit, grapes and meat products. OTA is toxic to animals, it presents neurotoxic, immunotoxic and nephrotoxic effects. It has been implicated in a human kidney disorder known as Balkan Endemic Nephropathy. This review focuses on the ecophysiology of ochratoxin-producing Aspergillus ochraceus and Penicillium verrucosum, the effect of environmental factors on their germination, mycelial growth, and OTA production. Knowledge of environmental conditions required for sucessive stages of fungal development represent the first step towards preventing mycotoxin formation. Predictive models for different stages of fungal development are presented, which allow prediction of the time before spoilage as a function of the abiotic factors. Finally, the implications of these studies in management of barley, coffee and grapes are described. This can help to identify the critical control points in their production, storage and distribution processes.     

CONTEXTUALIZING LEARNING OBJECTS USING ONTOLOGIES

Educational research over the past three years has intensified such that the context of learning resources needs to be properly modeled. Many researchers have described and even mandated the use of ontologies in the research being conducted, yet the process of actually connecting one or more ontologies to a learning object has not been extensively discussed. This paper describes a practical model for associating multiple ontologies with learning objects while making full use of the IEEE LOM specification. The model categorizes these ontologies according to five major categories of context based on the most popular fields of study actively being pursued by the educational research community: Thematic context, Pedagogical context, Learner context, Organizational context, and Historical/Statistical context.