Wuyang Fan, Shisheng Zhong^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2525-2555, 2024, DOI:10.32604/cmes.2023.046951 - 11 March 2024

Abstract The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment. In dynamic balance debugging, reliance on rudimentary counterweight empirical formulas persists, resulting in suboptimal debugging accuracy and an increased repetition rate. To mitigate this challenge, we present a multi-head residual graph attention network (ResGAT) model, designed to predict dynamic balance counterweights with high precision. In this research, we employ graph neural networks for interaction feature extraction from assembly graph data. An SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for More >

Hongqiao Wang^1,*, Yanning Cai²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 187-203, 2022, DOI:10.32604/cmes.2022.018000 - 29 November 2021

Abstract Inertial system platforms are a kind of important precision devices, which have the characteristics of difficult acquisition for state data and small sample scale. Focusing on the model optimization for data-driven fault state prediction and quantitative degree measurement, a fast small-sample supersphere one-class SVM modeling method using support vectors pre-selection is systematically studied in this paper. By theorem-proving the irrelevance between the model's learning result and the non-support vectors (NSVs), the distribution characters of the support vectors are analyzed. On this basis, a modeling method with selected samples having specific geometry character from the training More >