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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Semi-Regular Mesh Extraction From Volumes, Z. J. Wood, M. Desbrun, P. Schroder, D. Breen
Semi-Regular Mesh Extraction From Volumes, Z. J. Wood, M. Desbrun, P. Schroder, D. Breen
Computer Science and Software Engineering
We present a novel method to extract iso-surfaces from distance volumes. It generates high quality semi-regular multiresolution meshes of arbitrary topology. Our technique proceeds in two stages. First, a very coarse mesh with guaranteed topology is extracted. Subsequently an iterative multi-scale force-based solver refines the initial mesh into a semi-regular mesh with geometrically adaptive sampling rate and good aspect ratio triangles. The coarse mesh extraction is performed using a new approach we call surface wavefront propagation. A set of discrete iso-distance ribbons are rapidly built and connected while respecting the topology of the iso-surface implied by the data. Subsequent multi-scale …
Robotic System Sensitivity To Neural Network Learning Rate: Theory, Simulation, And Experiments, Christopher M. Clark, James K. Mills
Robotic System Sensitivity To Neural Network Learning Rate: Theory, Simulation, And Experiments, Christopher M. Clark, James K. Mills
Computer Science and Software Engineering
Selection of neural network learning rates to obtain satisfactory performance from neural network controllers is a challenging problem. To assist in the selection of learning rates, this paper investigates robotic system sensitivity to neural network (NN) learning rate. The work reported here consists of experimental and simulation results. A neural network controller module, developed for the purpose of experimental evaluation of neural network controller performance of a CRS Robotics Corporation A460 robot, allows testing of NN controllers using real-time iterative learning. The A460 is equipped with a joint position proportional, integral, and derivative (PID) controller. The neural network module supplies …
Hybrid Probabilistic Programs, Alex Dekhtyar, V. S. Subrahmanian
Hybrid Probabilistic Programs, Alex Dekhtyar, V. S. Subrahmanian
Computer Science and Software Engineering
The precise probability of a compound event (e.g. e1 V e2,e1 Ʌ e2) depends upon the known relationships (e.g. independence, mutual exclusion, ignorance of any relationship, etc.) between the primitive events that constitute the compound event. To date, most research on probabilistic logic programming has assumed that we are ignorant of the relationship between primitive events. Likewise, most research in AI (e.g. Bayesian approaches) has assumed that primitive events are independent. In this paper, we propose a hybrid probabilistic logic programming language in which the user can explicitly associate, with any given probabilistic strategy, a conjunction and disjunction operator, and …
Monitoring Of Distributed Processes With Mobile Agents, Ryan P. Kennedy, Franz J. Kurfess
Monitoring Of Distributed Processes With Mobile Agents, Ryan P. Kennedy, Franz J. Kurfess
Computer Science and Software Engineering
The proliferation of networked and distributed systems presents a need for more tool development with regard to the monitoring and maintenance of distributed processes. The goal of this paper is to present a mechanism used to collect detailed process information from various remote Unix hosts on a network. The interface to this mechanism, a GUI applet, is accessible through a Java enabled browser such as Netscape Navigator. It presents the user with a menu of choices such as which host to view, and what process information to retrieve (I/O-bound processes, numbers of processes, individual/total process usage, etc.). The requested information …
Branch Transition Rate: A New Metric For Improved Branch Classification Analysis, Michael Haungs, Phil Sallee, Matthew Farrens
Branch Transition Rate: A New Metric For Improved Branch Classification Analysis, Michael Haungs, Phil Sallee, Matthew Farrens
Computer Science and Software Engineering
Recent studies have shown significantly improved branch prediction through the use of branch classification. By separating static branches into groups, or classes, with similar dynamic behavior, predictors may be selected that are best suited for each class. Previous methods have classified branches according to taken rate (or bias). We propose a new metric for branch classification: branch transition rate, which is defined as the number of times a branch changes direction between taken and not taken during execution. We show that transition rate is a more appropriate indicator of branch behavior than taken rate for determining predictor performance. When both …
Neural Networks And Structured Knowledge: Rule Extraction And Applications, Franz J. Kurfess
Neural Networks And Structured Knowledge: Rule Extraction And Applications, Franz J. Kurfess
Computer Science and Software Engineering
As the second part of a special issue on "Neural Networks and Structured Knowledge," the contributions collected here concentrate on the extraction of knowledge, particularly in the form of rules, from neural networks, and on applications relying on the representation and processing of structured knowledge by neural networks. The transformation of the low-level internal representation in a neural network into higher-level knowledge or information that can be interpreted more easily by humans and integrated with symbol-oriented mechanisms is the subject of the first group of papers. The second group of papers uses specific applications as starting point, and describes approaches …
Instructional Design Agents – An Integration Of Artificial Intelligence And Educational Technology, Erika Rogers, Carol Scheftic, Emilio Passi, Sharon Lanaghan
Instructional Design Agents – An Integration Of Artificial Intelligence And Educational Technology, Erika Rogers, Carol Scheftic, Emilio Passi, Sharon Lanaghan
Computer Science and Software Engineering
The purpose of this paper is to introduce a project whose goal is to design and develop a productivity tool which helps academic instructors in their course preparation. This tool will be composed of a number of “instructional design agents”, which combine techniques in human-computer interaction, artificial intelligence and educational theory. The first of these agents is based on Bloom’s Taxonomy, and a brief overview of this work in progress is presented.