Notice of Retraction: Research of Ontology-Based Model Representation Method

Author(s):  
Dai Chao-fan ◽  
Feng Yang-he ◽  
Zhang Peng-cheng
2010 ◽  
Vol 47 (4) ◽  
pp. 208-218 ◽  
Author(s):  
Robert M. Fuller ◽  
Uday Murthy ◽  
Brad A. Schafer

Author(s):  
Germánico González Badillo ◽  
Hugo I. Medellín Castillo ◽  
Theodore Lim ◽  
Víctor E. Espinoza López

Virtual environments (VE) are becoming a popular way to interact with virtual objects in several applications such as design, training, planning, etc. Physics simulation engines (PSE) used in games development can be used to increase the realism in virtual environments (VE) by enabling the virtual objects with dynamic behavior and collision detection. There exist several PSE available to be integrated with VE, each PSE uses different model representation methods to create the collision shape and compute virtual object dynamic behavior. The performance of physics based VEs is directly related to the PSE ability and its method to represent virtual objects. This paper analyzes different freely available PSEs — Bullet and the two latest versions of PhysX (v2.8 and 3.1) — based on their model representation algorithms, and evaluates them by performing various assembly tasks with different geometry complexity. The evaluation is based on the collision detection performance and their influence on haptic-virtual assembly process. The results have allowed the identification of the strengths and weaknesses of each PSE according to its representation method.


2019 ◽  
Vol 37 (1) ◽  
pp. 120-143
Author(s):  
Payam Asadi ◽  
Hosein Sourani

Purpose In the absence of random variables, random variables are generated by the Monte Carlo (MC) simulation method. There are some methods for generating fragility curves with fewer nonlinear analyses. However, the accuracy of these methods is not suitable for all performance levels and peak ground acceleration (PGA) range. This paper aims to present a method through the seismic improvement of the high-dimensional model representation method for generating fragility curves while taking advantage of fewer analyses by choosing the right border points. Design/methodology/approach In this method, the values of uncertain variables are selected based on the results of the initial analyses, the damage limit of each performance level or according to acceptable limits in the design code. In particular, PGAs are selected based on the general shape of the fragility curve for each performance limit. Also, polynomial response functions are estimated for each accelerogram. To evaluate the accuracy, fragility curves are estimated by different methods for a single degree of freedom system and a reinforced concrete frame. Findings The results indicated that the proposed method can not only reduce the computational cost but also has a higher accuracy than the other methods, compared with the MC baseline method. Originality/value The proposed response functions are more consistent with the actual values and are also congruent with each performance level to increase the accuracy of the fragility curves.


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