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An Improved Generative Design Approach based on Graph Grammar for Pattern Drawing

Liu Yufeng, Zhou Yangchen, Yang Fan, Li Song, Wu Jun

Machine Graphics and Vision

2024

Vol. 33, No. 1

3--20

Generative design is used to efficiently generate design solutions with powerful computational methods. Generative design based on shape grammar is currently the most commonly used approach, but it is difficult for shape grammar to formally analyze the generated pattern. Graph grammar derived from one-dimensional character grammar is mainly used for generating and analyzing abstract models of visual languages. However, there is a significant gap between the generated node-edge graphs and the representation of shape appearance. To address these problems, we propose an improved generative design approach based on virtual-node based continuous Coordinate Graph Grammar (vcCGG). This approach defines a new type of grammatical rule named node transformation rules to convert nodes into shapes with node transformation applications. By combining node transformation applications and L-applications in vcCGG, we can generate a node-edge graph as the structure of the pattern through L-applications, and then draw the shape outline, next adjust the positions of these shapes, thus relating abstract structures and the physical layouts of visual languages. At the end of the paper, we provide an example application of this approach: generating an illustration from Emma Talbot using a combination of node transformation applications and L-applications.

Event-triggered adaptive neural network trajectory tracking control for underactuated ships under uncertain disturbance

Su Wenxue, Zhang Qiang, Liu Yufeng

Polish Maritime Research

2023

nr 3

119--131

An adaptive neural network (NN) event-triggered trajectory tracking control scheme based on finite time convergence is proposed to address the problem of trajectory tracking control of underdriven surface ships. In this scheme, both NNs and minimum learning parameters (MLPS) are applied. The internal and external uncertainties are approximated by NNs. To reduce the computational complexity, MLPs are used in the proposed controller. An event-triggered technique is then incorporated into the control design to synthesise an adaptive NN-based event-triggered controller with finite-time convergence. Lyapunov theory is applied to prove that all signals are bounded in the tracking system of underactuated vessels, and to show that Zeno behavior can be avoided. The validity of this control scheme is determined based on simulation results, and comparisons with some alternative schemes are presented.