Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges...Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models.Inspired by the success of diffusion models in addressing visual domain sample imbalances,this paper introduces a new approach that utilizes the Markov Transfer Field(MTF)method for time series data visualization.This visualization,when combined with the Denoising Diffusion Probabilistic Model(DDPM),effectively enhances sample data and mitigates noise within the original dataset.Additionally,a transformer-based model tailored for time series visualization and air target intent recognition is developed.Comprehensive experimental results,encompassing comparative,ablation,and denoising validations,reveal that the proposed method achieves a notable 98.86%accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities.This approach represents a promising avenue for advancing air target intent recognition.展开更多
Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives.This work proposes and demonstrates a customizer bas...Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives.This work proposes and demonstrates a customizer based on a classifier-free,conditional denoising diffusion probability model(cDDPM)to efficiently create the layouts of digital materials meeting the design goal of multiple mechanical properties all together.A case study has been conducted based on a micro mechanical resonator with four pre-assigned resonant frequencies.Using 29,430 samples generated via finite element analysis(FEA),the cDDPM is trained to simultaneously customize up to four vibrational modes,achieving over 95%prediction accuracy.Furthermore,the cDDPM approach also shows superior performances in the single-target customization for up to 99%in prediction accuracy when compared with traditional conditional generative adversarial networks(cGANs).As such,the proposed design framework provides a highly customizable and robust methodology for the design of complicated digital materials.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.61806219,61876189 and 61703426)the Young Talent Fund of University Association for Science and Technology in Shaanxi,China(Nos.20190108 and 20220106)the Innvation Talent Supporting Project of Shaanxi,China(No.2020KJXX-065)。
文摘Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models.Inspired by the success of diffusion models in addressing visual domain sample imbalances,this paper introduces a new approach that utilizes the Markov Transfer Field(MTF)method for time series data visualization.This visualization,when combined with the Denoising Diffusion Probabilistic Model(DDPM),effectively enhances sample data and mitigates noise within the original dataset.Additionally,a transformer-based model tailored for time series visualization and air target intent recognition is developed.Comprehensive experimental results,encompassing comparative,ablation,and denoising validations,reveal that the proposed method achieves a notable 98.86%accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities.This approach represents a promising avenue for advancing air target intent recognition.
文摘Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives.This work proposes and demonstrates a customizer based on a classifier-free,conditional denoising diffusion probability model(cDDPM)to efficiently create the layouts of digital materials meeting the design goal of multiple mechanical properties all together.A case study has been conducted based on a micro mechanical resonator with four pre-assigned resonant frequencies.Using 29,430 samples generated via finite element analysis(FEA),the cDDPM is trained to simultaneously customize up to four vibrational modes,achieving over 95%prediction accuracy.Furthermore,the cDDPM approach also shows superior performances in the single-target customization for up to 99%in prediction accuracy when compared with traditional conditional generative adversarial networks(cGANs).As such,the proposed design framework provides a highly customizable and robust methodology for the design of complicated digital materials.
