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.     

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.     

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.     

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.     

Multicolor Photo‐Crosslinkable AIEgens toward Compact Nanodots for Subcellular Imaging and STED Nanoscopy

Aggregation induced emission (AIE) has attracted considerable interest for the development of fluorescence probes. However, controlling the bioconjugation and cellular labeling of AIE dots is a challenging problem. Here, this study reports a general approach for preparing small and bioconjugated AIE dots for specific labeling of cellular targets. The strategy is based on the synthesis of oxetane‐substituted AIEgens to generate compact and ultrastable AIE dots via photo‐crosslinking. A small amount of polymer enriched with oxetane groups is cocondensed with most of the AIEgens to functionalize the nanodot surface for subsequent streptavidin bioconjugation. Due to their small sizes, good stability, and surface functionalization, the cell‐surface markers and subcellular structures are specifically labeled by the AIE dot bioconjugates. Remarkably, stimulated emission depletion imaging with AIE dots is achieved for the first time, and the spatial resolution is significantly enhanced to ≈95 nm. This study provides a general approach for small functional molecules for preparing small sized and ultrastable nanodots.     

Comparison of three-dimensional surface scanning techniques for capturing the external ear

Congenital facial anomalies, such as microtia (malformation of the external ear), lead to significant psychosocial effects starting from early childhood. Three-dimensional (3D) scanning and advanced manufacturing are being investigated as a cheaper and more personalised method of fabricating reconstructive treatments for patients compared to traditional approaches. To date, most case studies have used expensive 3D scanners, yet, there is potential for low-cost devices to provide comparable results. This study aimed to investigate these different approaches. Both ears of 16 adult participants were scanned with three devices: Artec Spider (Artec Group), Intel^® RealSense? (Intel), and the Apple iPhone^® 7 (Apple Inc.) combined with photogrammetry using 90, 60 and 30 photographs. The scanning time, processing time, accuracy, completeness, resolution and repeatability of each technique were assessed using the Artec Spider as a reference scanner. Our results show that the iPhone had the longest processing time however, this decreased nine-fold when reducing the number of photos from 90 to 30. There was no significant difference in the accuracy, completeness or repeatability of the iPhone scans with 90 photographs (1.4?±?0.6?mm, 79.9%, 1.0?±?0.1?mm), 60 photographs (1.2?±?0.2, 79.3%, 0.9?±?0.2?mm) or 30 photographs (1.2?±?0.3?mm, 74.3%, 1.0?±?0.2?mm). The Intel RealSesne performed significantly worse in each parameter (1.8?±?03?mm; 46.6%, 1.4?±?0.5). Additionally, the RealSense had significantly lower resolution with not enough detail captured for the application. These results demonstrate that the ear can be accurately 3D scanned using iPhone photographs. We would recommend capturing between 30 and 60 photographs of the ear to create a scan that is accurate but without the downfall of long processing time. Using these methods we aim to provide a more comfortable setting for the patient and a lower-cost and more personalised ear prosthesis.     

Privacy in geo-social networks: proximity notification with untrusted service providers and curious buddies

A major feature of the emerging geo-social networks is the ability to notify a user when any of his friends (also called buddies) happens to be geographically in proximity. This proximity service is usually offered by the network itself or by a third party service provider (SP) using location data acquired from the users. This paper provides a rigorous theoretical and experimental analysis of the existing solutions for the location privacy problem in proximity services. This is a serious problem for users who do not trust the SP to handle their location data and would only like to release their location information in a generalized form to participating buddies. The paper presents two new protocols providing complete privacy with respect to the SP and controllable privacy with respect to the buddies. The analytical and experimental analysis of the protocols takes into account privacy, service precision, and computation and communication costs, showing the superiority of the new protocols compared to those appeared in the literature to date. The proposed protocols have also been tested in a full system implementation of the proximity service.