Performance comparisons of bonding box-based contact detection algorithms and a new improvement technique based on parallelization

2016 ◽  
Vol 33 (1) ◽  
pp. 7-27
Author(s):  
Mahmoud Yazdani ◽  
Hamidreza Paseh ◽  
Mostafa Sharifzadeh
Keyword(s):  
Large Scale ◽  
Distinct Element ◽  
Detection Algorithm ◽  
Computational Technique ◽  
Contact Detection ◽  
Content Type ◽  
Running Time ◽  
Spatial Sorting ◽  
Bounding Boxes ◽  
Contact Detection Algorithm

Purpose – The purpose of this paper is to find a convenient contact detection algorithm in order to apply in distinct element simulation. Design/methodology/approach – Taking the most computation effort, the performance of the contact detection algorithm highly affects the running time. The algorithms investigated in this study consist of Incremental Sort-and-Update (ISU) and Double-Ended Spatial Sorting (DESS). These algorithms are based on bounding boxes, which makes the algorithm independent of blocks shapes. ISU and DESS algorithms contain sorting and updating phases. To compare the algorithms, they were implemented in identical examples of rock engineering problems with varying parameters. Findings – The results show that the ISU algorithm gives lower running time and shows better performance when blocks are unevenly distributed in both axes. The conventional ISU merges the sorting and updating phases in its naïve implementation. In this paper, a new computational technique is proposed based on parallelization in order to effectively improve the ISU algorithm and decrease the running time of numerical analysis in large-scale rock mass projects. Originality/value – In this approach, the sorting and updating phases are separated by minor changes in the algorithm. This tends to a minimal overhead of running time and a little extra memory usage and then the parallelization of phases can be applied. On the other hand, the time consumed by the updating phase of ISU algorithm is about 30 percent of the total time, which makes the parallelization justifiable. Here, according to the results for the large-scale problems, this improved technique can increase the performance of the ISU algorithm up to 20 percent.

scholarly journals A contact detection algorithm for superellipsoids based on the common‐normal concept

2008 ◽  
Vol 25 (5) ◽  
pp. 432-442 ◽  
Author(s):  
Christian Wellmann ◽  
Claudia Lillie ◽  
Peter Wriggers
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Problem ◽  
Detection Algorithm ◽  
Contact Detection ◽  
Smooth Convex ◽  
Content Type ◽  
Contact Points ◽  
Unconstrained Optimization Problem ◽  
The Common ◽  
Contact Detection Algorithm

PurposeThe paper aims to introduce an efficient contact detection algorithm for smooth convex particles.Design/methodology/approachThe contact points of adjacent particles are defined according to the common‐normal concept. The problem of contact detection is formulated as 2D unconstrained optimization problem that is solved by a combination of Newton's method and a Levenberg‐Marquardt method.FindingsThe contact detection algorithm is efficient in terms of the number of iterations required to reach a high accuracy. In the case of non‐penetrating particles, a penetration can be ruled out in the course of the iterative solution before convergence is reached.Research limitations/implicationsThe algorithm is only applicable to smooth convex particles, where a bijective relation between the surface points and the surface normals exists.Originality/valueBy a new kind of formulation, the problem of contact detection between 3D particles can be reduced to a 2D unconstrained optimization problem. This formulation enables fast contact exclusions in the case of non‐penetrating particles.

Construction of poly-ellipsoidal grain shapes from SMT imaging on sand, and the development of a new DEM contact detection algorithm

2018 ◽  
Vol 35 (2) ◽  
pp. 733-771 ◽  
Author(s):  
Boning Zhang ◽  
Richard Regueiro ◽  
Andrew Druckrey ◽  
Khalid Alshibli
Keyword(s):  
Particle Shape ◽  
Detection Algorithm ◽  
Contact Detection ◽  
Computationally Efficient ◽  
Content Type ◽  
Contact Algorithm ◽  
Shape Approximation ◽  
Speed Up ◽  
Contact Detection Algorithm ◽  
Particle Shapes

Purpose This paper aims to construct smooth poly-ellipsoid shapes from synchrotron microcomputed tomography (SMT) images on sand and to develop a new discrete element method (DEM) contact detection algorithm. Design/methodology/approach Voxelated images generated by SMT on Colorado Mason sand are processed to construct smooth poly-ellipsoidal particle approximations. For DEM contact detection, cuboidal shape approximations to the poly-ellipsoids are used to speed up contact detection. Findings The poly-ellipsoid particle shape approximation to Colorado Mason sand grains is better than a simpler ellipsoidal approximation. The new DEM contact algorithm leads to significant speedup and accuracy is maintained. Research limitations/implications The paper limits particle shape approximation to smooth poly-ellipsoids. Practical implications Poly-ellipsoids provide asymmetry of particle shapes as compared to ellipsoids, thus allowing closer representation of real sand grain shapes that may be angular and unsymmetric. When incorporated in a DEM for computation, the poly-ellipsoids allow better representation of particle rolling, sliding and interlocking phenomena. Originality/value Method to construct poly-ellipsoid particle shapes from SMT data on real sands and computationally efficient DEM contact detection algorithm for poly-ellipsoids.

Self-adaptive scale pedestrian detection algorithm based on deep residual network

2019 ◽  
Vol 12 (3) ◽  
pp. 318-332
Author(s):  
Shuang-Shuang Liu
Keyword(s):  
Large Scale ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Small Scale ◽  
Data Sets ◽  
Residual Network ◽  
Content Type ◽  
Detection Algorithms ◽  
Bounding Boxes ◽  
Self Adaptive

Purpose The conventional pedestrian detection algorithms lack in scale sensitivity. The purpose of this paper is to propose a novel algorithm of self-adaptive scale pedestrian detection, based on deep residual network (DRN), to address such lacks. Design/methodology/approach First, the “Edge boxes” algorithm is introduced to extract region of interests from pedestrian images. Then, the extracted bounding boxes are incorporated to different DRNs, one is a large-scale DRN and the other one is the small-scale DRN. The height of the bounding boxes is used to classify the results of pedestrians and to regress the bounding boxes to the entity of the pedestrian. At last, a weighted self-adaptive scale function, which combines the large-scale results and small-scale results, is designed for the final pedestrian detection. Findings To validate the effectiveness and feasibility of the proposed algorithm, some comparison experiments have been done on the common pedestrian detection data sets: Caltech, INRIA, ETH and KITTI. Experimental results show that the proposed algorithm is adapted for the various scales of the pedestrians. For the hard detected small-scale pedestrians, the proposed algorithm has improved the accuracy and robustness of detections. Originality/value By applying different models to deal with different scales of pedestrians, the proposed algorithm with the weighted calculation function has improved the accuracy and robustness for different scales of pedestrians.

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