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Abstract

Pattern design has a wide range of applications in daily life, such as decoration design, web design, and so on. Common design methods include artificial design and intelligent design, and design methods based on intelligent technology tend to have higher efficiency. In order to make the generation of plane pattern intelligent design more convenient and fast, a pattern design generation method based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm was proposed, and the correlation was carried out. At the same time, the same type of genetic algorithm (GA) and ant colony algorithm (AC) were introduced for performance comparison and verification. The research results show that the maximum pattern sampling accuracy of the RJMCMC algorithm is about 0.972, and the AUC value corresponding to the ROC curve is about 0.936, which are higher than other algorithms. In the algorithm iteration process, the iterative efficiency of the RJMCMC algorithm is also higher than the other two algorithms. At the same time, in the same pattern sampling processing time, the sampling number of RJMCMC algorithm is significantly higher than the other two algorithms. In the comparison of the loss value curve, the RJMCMC algorithm shows a better fitting effect, and its loss value is lower than the other two algorithms after iteration. In addition, after iteration, the error of RJMCMC algorithm is also smaller than the other two algorithms. Comprehensive analysis shows that the RJMCMC algorithm has excellent performance in pattern sampling, which can provide great help for the design and generation of plane patterns.

Keywords

iterative efficiency pattern design RJMCMC algorithm sampling quantity GA algorithm AC algorithm

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References

Lin

. A computer-aided coloring method for virtual agents based on personality impression, color harmony, and designer preference. Int J Ind Ergon 2018; 68(1): 327–336.

Przyby

Radosz

Januszko

. Colour management system: Monte Carlo implementation for camouflage pattern generation. Color Technol 2020; 136(5): 407–416.

Aleardi

Salusti

. Application of reversible jump Markov chain Monte Carlo algorithms to elastic and petrophysical amplitude-versus-angle inversions. Pure Appl Geophys 2020; 177(7): 3335–3359.

Chapple

Peak

Hemal

. A novel Bayesian continuous piecewise linear log‐hazard model, with estimation and inference via reversible jump Markov chain Monte Carlo. Stat Med 2020; 39(12): 1766–1780.

Ali

Bouguila

. Multimodal action recognition using variational-based Beta-Liouville hidden Markov models. IET Image Process 2020; 14(17): 4785–4794.

Marrelec

Bernard

, et al. Lorentzian-model-based bayesian analysis for automated estimation of attenuated resonance spectrum. IEEE T. Signal Proces 2019; 67(1): 4–16.

Michels

Berh

Jiang

. An RJMCMC-Based method for tracking and resolving collisions of drosophila larvae. IEEE ACM T Comput Bi 2019; 16(2): 465–474.

Zirr

Dachsbacher

. Path differential-informed stratified MCMC and adaptive forward path sampling. ACM T Graphic 2020; 39(6): 1–19.

Mack

Rist

Herzog

, et al. Constraint-based automated reconstruction of grape bunches from 3D range data for high-throughput phenotyping. Biosyst Eng 2020; 197(1): 285–305.

10.

Karakuş

Kuruoğlu

Altınkaya

. Generalized Bayesian model selection for speckle on remote sensing images. IEEE T Image Process 2019; 28(4): 1748–1758.

11.

Gagnon

Bedard

Desgagne

. Weak convergence and optimal tuning of the reversible jump algorithm. Math Comput Simul 2019; 161(SI): 32–51.

12.

Jin

Yuan

Goncalves

. A full Bayesian approach to sparse network inference using heterogeneous datasets. IEEE T Automat Contr 2020; 66(7): 3282–3288.

13.

Livingston

Nur

. Bayesian estimation and model selection of a multivariate smooth transition autoregressive model. Environmetrics 2020; 31(6): 2615–2630.

14.

Robbe

Nuyens

Vandewalle

. Enhanced multi-index Monte Carlo by means of multiple semicoarsened multigrid for anisotropic diffusion problems. Numer Linear Algebr 2021; 28(3): 1–15.

15.

You

, et al. Retrieval of canopy gap fraction from terrestrial laser scanning data based on the Monte Carlo method. IEEE Geosci Remote S 2021; 19(99): 1–5.

16.

Zhang

Cheng

, et al. A general approach for the machining quality evaluation of S-shaped specimen based on POS-SQP algorithm and Monte Carlo method. J Manuf Syst 2021; 60(1): 553–568.

17.

Wang

Zhang

, et al. Cross-Lingual image caption generation based on visual attention model. IEEE Access 2020; 8: 104543–104554.

18.

Zhang

Chen

, et al. VSAM-Based visual keyword generation for image caption. IEEE Access 2021; 9(1): 27638–27649.

19.

Franzke

Kosko

. Noise can speed Markov chain Monte Carlo estimation and quantum annealing. Phys Rev E 2019; 100(5): 53309.

20.

Kim

Aguilar

Zhao

, et al. Narrow gap detection in microscope images using marked point process modeling. IEEE T Image Process 2019; 28(10): 5064–5076.

21.

Tan

. Detection of multiple Change-Points in the scale parameter of a gamma distributed sequence based on reversible jump MCMC. J Korean Stat Soc 2021; 50(1): 25–43.

22.

Lin

Zhang

Wei

. Velocity prediction using Markov chain combined with driving pattern recognition and applied to Dual-Motor Electric Vehicle energy consumption evaluation. Appl Soft Comput 2021; 101(1): 106998.

Intelligent design generation of plane pattern based on RJMCMC algorithm

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