Ontology-based feature mapping and verification between CAD systems

Data interoperability between computer-aided design (CAD) systems remains a major obstacle in the information integration and exchange in a collaborative engineering environment. The use of CAD data exchange standards causes the loss of design intent such as construction history, features, parameters, and constraints, whereas existing research on feature-based data exchange only focuses on class-level feature definitions and does not support instance-level verification, which causes ambiguity in data exchange. In this paper, a hybrid ontology approach is proposed to allow for the full exchange of both feature definition semantics and geometric construction data. A shared base ontology is used to convey the most fundamental elements of CAD systems for geometry and topology, which is to both maximize flexibility and minimize information loss. A three-branch hybrid CAD feature model that includes feature operation information at the boundary representation level is constructed. Instance-level feature information in the form of the base ontology is then translated to local ontologies of individual CAD systems during the rule-based mapping and verification process. A combination of the Ontology Web Language (OWL) and Semantic Web Rule Language (SWRL) is used to represent feature classes and properties and automatically classify them by a reasoner in the target system, which requires no knowledge about the source system.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

Approximate factorization of multivariate polynomials using singular value decomposition

We describe the design, implementation and experimental evaluation of new algorithms for computing the approximate factorization of multivariate polynomials with complex coefficients that contain numerical noise. Our algorithms are based on a generalization of the differential forms introduced by W. Ruppert and S. Gao to many variables, and use singular value decomposition or structured total least squares approximation and Gauss–Newton optimization to numerically compute the approximate multivariate factors. We demonstrate on a large set of benchmark polynomials that our algorithms efficiently yield approximate factorizations within the coefficient noise even when the relative error in the input is substantial (10⁻³).     

A dynamic workflow framework for mass customization using web service and autonomous agent techniques

Custom software development and maintenance is one of the key expenses associated with developing automated systems for mass customization. This paper presents a method for reducing the risk associated with this expense by developing a flexible environment for determining and executing dynamic workflow paths. Strategies for developing an autonomous agent-based framework and for identifying and creating web services for specific process tasks are presented. The proposed methods are outlined in two different case studies to illustrate the approach for both a generic process with complex workflow paths and a more specific sequential engineering process.     

The movement patterns and the experiential components of virtual environments

Human movement in virtual environments (VEs) is a largely unstudied area, and there are no well-established methods of measuring it in VEs. Consequently, it is unclear how movement affects the experiential side of VEs. We introduce a novel method of measuring and modelling human movement. A specific information entropy-based modelling method enabled us to identify different movement patterns and analyse the experiential components related to them. The data was collected by registering the movement patterns of 68 participants who were in a virtual house doing a search task. The experiential side of the VE was measured with the Experimental Virtual Environment Questionnaire (EVEQ). Four movement patterns were identified. In addition, fluent movement in VEs was related to a high sense of presence. Moreover, the participants who moved fluently in the environment assessed their skills high. The results show how movement is related the way in which people experience the VE. The movement analysis method introduced here is applicable to other related research areas as well.     

ICCD: Interactive Continuous Collision Detection between Deformable Models Using Connectivity-Based Culling

We present an interactive algorithm for continuous collision detection between deformable models. We introduce multiple techniques to improve the culling efficiency and the overall performance of continuous collision detection. First, we present a novel formulation for continuous normal cones and use these normal cones to efficiently cull large regions of the mesh as part of self-collision tests. Second, we introduce the concept of “procedural representative triangles” to remove all redundant elementary tests between nonadjacent triangles. Finally, we exploit the mesh connectivity and introduce the concept of “orphan sets” to eliminate redundant elementary tests between adjacent triangle primitives. In practice, we can reduce the number of elementary tests by two orders of magnitude. These culling techniques have been combined with bounding volume hierarchies and can result in one order of magnitude performance improvement as compared to prior collision detection algorithms for deformable models. We highlight the performance of our algorithm on several benchmarks, including cloth simulations, N-body simulations, and breaking objects.     

Prediction of urban stormwater quality using artificial neural networks

There are a vast number of complex, interrelated processes influencing urban stormwater quality. However, the lack of measured fundamental variables prevents the construction of process-based models. Furthermore, hybrid models such as the buildup-washoff models are generally crude simplifications of reality. This has created the need for statistical models, capable of making use of the readily accessible data. In this paper, artificial neural networks (ANN) were used to predict stormwater quality at urbanized catchments located throughout the United States. Five constituents were analysed: chemical oxygen demand (COD), lead (Pb), suspended solids (SS), total Kjeldhal nitrogen (TKN) and total phosphorus (TP). Multiple linear regression equations were initially constructed upon logarithmically transformed data. Input variables were primarily selected using a stepwise regression approach, combined with process knowledge. Variables found significant in the regression models were then used to construct ANN models. Other important network parameters such as learning rate, momentum and the number of hidden nodes were optimized using a trial and error approach. The final ANN models were then compared with the multiple linear regression models. In summary, ANN models were generally less accurate than the regression models and more time consuming to construct. This infers that ANN models are not more applicable than regression models when predicting urban stormwater quality.     

Postural support strategies of disabled drivers and the effectiveness of postural support aids

The paper discusses a series of driving trials that were conducted to investigate postural stability of disabled drivers and to assess the effectiveness of a representative sample of support aids. Twenty-three disabled drivers with varying levels of physical disability and seven non disabled drivers participated in the study. The test car was equipped with transducers to measure vehicle velocity and acceleration (longitudinal and lateral), steering wheel movement and torque, and the bracing forces exerted by the driver on the steering wheel. Video cameras were installed to record postural support strategies and displacement of the driver and to record deviation of the car from the specified path. Subjective data regarding driver attitudes and acceptance were also collected through the administration of questionnaires. Findings from the study showed that support aids significantly improved driving performance and reduced physical exertion to maintain an upright driving position for disabled drivers. However, ergonomics design aspects regarding the ease of use and acceptance of the support aids by the end users were identified as obstacles to their sustained use in everyday driving.     

Aniline hydrogenolysis on nickel: effects of surface hydrogen and surface structure

Fluorescence yield near-edge spectroscopy (FYNES) above the carbon K edge and temperature programmed reaction spectroscopy (TPRS) have been used as the methods for characterizing the reactivity and structure of adsorbed aniline and aniline derived species on the Ni(100) and Ni(111) surfaces over an extended range of temperatures and hydrogen pressures. The Ni(100) surface shows appreciably higher hydrogenolysis activity towards adsorbed aniline than the Ni(111) surface. Hydrogenolysis of aniline on the Ni(100) surface results in benzene formation at 470 K, both in reactive hydrogen atmospheres and in vacuum. External hydrogen significantly enhances the hydrogenolysis activity for aniline on the Ni(100) surface. Based on spectroscopic evidence, we believe that the dominant aniline hydrogenolysis reaction is preceded by partial hydrogenation of the aromatic ring of aniline in the presence of 0.001 Torr of external hydrogen on the (100) surface. In contrast, very little adsorbed aniline undergoes hydrogen induced C-N bond activation on the Ni(111) surface for hydrogen pressures as high as 10^–7 Torr below 500 K. Thermal dehydrogenation of aniline dominates with increasing temperature on the Ni(111) surface, resulting in the formation of a previously observed polymeric layer which is stable up to 820 K. Aniline is adsorbed at a smaller angle relative to the Ni(111) surface than the Ni(100) surface at temperatures below the hydrogenolysis temperature. We believe that the proximity and strong -interaction between the aromatic ring of the aniline and the surface is one major factor which controls the competition between dehydrogenation and hydrogen addition. In this case the result is a substantial enhancement of aniline dehydrogenation relative to hydrogenation on the Ni(111) surface.