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.     

Knowledge extraction for production management

A procedure and underlying algorithm for extracting knowledge from production and inventory databases to support engineering management activities is described. The process searches for, detects and isolates behaviour patterns inherent in the data. It relates these patterns to production irregularities, suggests connections with specific causes and helps propose possible corrective or preventive actions. The approach is based on a four-phase procedure: (1) the decision-maker focuses on the subject or difficulty at issue, represented by a target concept; (2) the KEDB algorithm, based on a machine learning approach, processes the relevant database and provides knowledge characterizing and classifying the target concept; (3) the output is interpreted in Pareto fashion as a series of possible circumstances explaining the target concept behaviour; and (4) based on these causes, the decision-maker decides on possible corrective actions to improve the situation, or preventive actions to forestall unfavourable conditions. A case study based on an actual quality control database is detailed.     

Distinguished Scientific Awards for an Early Career Contribution to Psychology.

The Early Career Awards recognize the large number of excellent young psychologists. Recipients of this award may not have held a PhD for more than nine years. The areas considered in 1991 were animal learning and behavior, developmental, and personality. The winners for 1991 were Kent C. Berridge, Susan A. Gelman, and Randy J. Larsen. For each winner a citation, biography, and selected bibliography of their works are presented. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Neural Network Feedback Control: Work at UTA’s Automation and Robotics Research Institute

This is an outline of research in neural networks for feedback control done since the mid 1990s at the Automation and Robotics Research Institute (ARRI) of The University of Texas at Arlington (UTA). It shows how the developments of Intelligent Control Systems based on neural networks have followed three main generations. This statement provides a short, broad-brush perspective on the development of intelligent neural feedback controllers.     

Feedback Specificity, Learning Opportunities, and Learning.

Although increasing feedback specificity is generally beneficial for immediate performance, it can undermine certain aspects of the learning needed for later, more independent performance. The results of the present transfer experiment demonstrate that the effects of increasing feedback specificity on learning depended on what was to be learned, and these effects were partially mediated through the opportunities to learn how to respond to different task conditions during practice. More specific feedback was beneficial for learning how to respond to good performance and detrimental for learning how to respond to poor performance. The former relationship was partially mediated by feedback specificity's effect on learning opportunities during practice. The results have implications for designing feedback interventions and training to maximize the learning of various aspects of a task. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Genetic Dissociation of Learning and Recall in Caenorhabditis elegans.

A learning event can be dissociated into 3 components: acquisition, storage, and recall. When the laboratory wild-type strain of Caenorhabditis elegans (N2 strain) is exposed to benzaldehyde in the absence of food, the worms display a reduction of their attractive response to this volatile odorant. This results from the association between benzaldehyde and a nutrient-deficient environment. Another wild-type isolate, the CB4856 strain, fails to display this decreased response to benzaldehyde after exposure to benzaldehyde in the absence of food. However, like the N2 strain, when tested to isoamyl alcohol after benzaldehyde conditioning, the CB4856 strain displays a decreased isoamyl alcohol response. Therefore, the CB4856 strain does not have an acquisition deficit, but it suffers from a recall deficit specific to benzaldehyde. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Pure perceptual-based sequence learning.

Learning a sequence of target locations when the sequence is uncorrelated with a sequence of responses and target location is not the response dimension (pure perceptual-based sequence learning) was examined. Using probabilistic sequences of target locations, the author shows that such learning can be implicit, is unaffected by distance between target locations, and is mostly limited to first-order transition probabilities. Moreover, the mechanism underlying learning affords processing of information at anticipated target locations and appears to be attention based. Implications for hypotheses of implicit sequence learning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Adoption of Technology-Mediated Distance Education: A longitudinal analysis

We investigated the factors that facilitated the fast adoption and utilization of Technology-Mediated Distance Education (TMDE) among higher education institutions. Our analysis was based on a rich data set on the utilization of TMDE between the 1997–1998 and 2000–2001 academic years. The analysis showed that size, public/private status, and location significantly predicted its actual adoption. Being in an urban location negatively affected enrollment in the courses at the undergraduate but not at the graduate level. While the intent to adopt TMDE correlated significantly with actual adoption, many schools that were not interested in TMDE in 1997–1998 adopted it by 2000–2001. Interestingly, late adopters utilized certain technologies as frequently as early adopters, such as synchronous Internet-based instruction and the use of CD-ROMs.     

A visual tool for computer supported learning: The robot motion planning example

We introduce an effective computer aided learning visual tool (CALVT) to teach graph-based applications. We present the robot motion planning problem as an example of such applications. The proposed tool can be used to simulate and/or further to implement practical systems in different areas of computer science such as graphics, computational geometry, robotics and networking. In the robot motion planning example, CALVT enables users to setup the working environment by creating obstacles and a robot of different shapes, specifying starting and goal positions, and setting other path or environment parameters from a user-friendly interface. The path planning system involves several phases. Each of these modules is complex and therefore we provide the possibility of visualizing graphically the output of each phase. Based on our experience, this tool has been an effective one in classroom teaching. It not only cuts down, significantly, on the instructor’s time and effort but also motivates senior/graduate students to pursue work in this specific area of research.     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.