A Data-Augmented CGO-GAM Prediction Method for Plasma Hydrophilic Modification

Wenjie Xu, Wenhao Zhou, Feng Liu, Tingting Li, Zhi Fang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Polymer materials often require surface modification to address inherent structural limitations and enhance performance. This study presents a data-augmented chaotic game optimization- generalized additive model (CGO-GAM) designed to predict plasma hydrophilic modification, achieving high accuracy even with a limited number of data samples. The data augmentation method utilizing Generative Adversarial Networks (GAN) enhances the balance of modified data, particularly by supplementing hydrophilicity data in instances where treatment effects are suboptimal and data distribution is limited. The adaptive GAM algorithm effectively manages the nonlinear relationships among discharge parameters, gas gaps, gas compositions, and the effects of nanosecond-pulse dielectric barrier discharge (DBD) surface modifications. CGO-generated random perturbations, refined through mutation and crossover operations, enhance population diversity, effectively mitigating issues of early convergence and overfitting while improving the balance between global and local search strategies. The proposed model achieves an accuracy of 90% in predicting hydrophilic modification performance, demonstrating superiority over traditional methods such as Support Vector Machines (SVM) and General Regression Neural Networks (GRNN).

Original languageEnglish
Title of host publicationProceedings of the 1st Electrical Artificial Intelligence Conference, Volume 3 - EAIC 2024
EditorsRonghai Qu, Zhengxiang Song, Zhiming Ding, Gang Mu, Rui Xiong, Li Han
PublisherSpringer Science and Business Media Deutschland GmbH
Pages350-359
Number of pages10
ISBN (Print)9789819640669
DOIs
StatePublished - 2025
Event1st Electrical Artificial Intelligence Conference, EAIC 2024 - Nanjing, China
Duration: 6 Dec 20248 Dec 2024

Publication series

NameLecture Notes in Electrical Engineering
Volume1396 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference1st Electrical Artificial Intelligence Conference, EAIC 2024
Country/TerritoryChina
CityNanjing
Period6/12/248/12/24

Keywords

  • chaotic game optimization (CGO)
  • data augmentation
  • generalized additive model (GAM)
  • plasma hydrophilic modification

Fingerprint

Dive into the research topics of 'A Data-Augmented CGO-GAM Prediction Method for Plasma Hydrophilic Modification'. Together they form a unique fingerprint.

Cite this