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3D Simulation of Warp Knitted Structures with a New Algorithm Based on NURBS

Xu H Y, Chen N L, Jiang J H, Jin L X, Wang Z X

Fibres & Textiles in Eastern Europe

2015

Vol. 23, no. 1 (109)

57--60

In order to obtain a three-dimensional computer simulation of warp knitted structures with more flexibility and realism, a new algorithm using Matlab was developed by NURBS based on empirical geometrical loop models. With the principles of NURBS curves, once the values of data points are known, the control points with two coincidence points at the start and end points can be uncomplicatedly calculated by Matlab. Then the NURBS curve of a single typical stitch can be simulated flexibly by Matlab. A new typical stitch selected from two stitches simulated directly by the new method is redefined to improve the joint of neighboring stitches, and it is found that there are two types of redefined typical warp knitted stitches judged by whether the two under lap on the same side or not. Based on the redefined typical warp knitted stitch, two warp knitted structures are simulated regardless of the loop offset, and all the joints of stitches are smooth.

Przeprowadzono symulację komputerową struktur dzianin w wymiarze 3D, opracowując w tym celu nową procedurę opartą na algorytmie NURBS. Do tego celu wykorzystano oprogramowanie MATLAB. Symulację 3D przeprowadzono w oparciu o procedury numeryczne umożliwiające wykreślenie przestrzennych figur geometrycznych opisujących strukturę dzianin. Symulowano kształt oczek zamkniętych i otwartych podstawowych splotów kolumienkowych trykotu, sukna i aksamitu. W oparciu o 8 punktów referencyjnych zorientowano konfigurację pętli oczka i łącznika w postaci przestrzennej linii eliptycznej. Do zapisu struktury splotu kształtu oczek wykorzystywano aparat matematyczny w postaci macierzy.

Novel approach based on topological simplification algorithm optimized with particle swarm optimization

Wei Z. K., Wang Z. X., Kim H. Y., Kim Y. K., Kim J. H.

Machine Graphics and Vision

2014

Vol. 23, No. 1/2

115--132

The movement of people can be considered as the flow of liquid, so we can use the methods employed for the flow of liquid to understand the motion of a crowd. Based on this, we present a novel framework for abnormal behavior detection in crowded scenes. We extract a topological structure from the crowd with the topology simplification algorithm. However, a conventional topology simplification algorithm can not work well if we apply it to the crowd directly because there is too much noises produced by the random motion of the people in the original image. To overcome this, we make a step forward by optimizing this model using Particle Swarm Optimization (PSO) to perform the advection of particle population spread randomly over the image frames. Then we propose two new methods for analyzing the boundary point structure and extraction of a critical point from the particle motion field; both methods can be used to describe the global topological structure of the crowd motion. The advantage of our approach is that each kind of abnormal event can be described as a specific change in the topological structure, so we do not need construct a complex classifier, but can classify the crowd anomalies dynamically and directly. Moreover, the approach monitors the crowd motion macroscopically, making it insensitive to the motion of an individual, disregarding the global movement. The result of an experiment conducted on a common data set shows that our method is both precise and stable.