Wear and mileage performance are the foremost performances for truck bus radial (TBR) tires. There are a lot of researches about the tire wear performance as well as the contact patch phenomenon by using finite elemen...Wear and mileage performance are the foremost performances for truck bus radial (TBR) tires. There are a lot of researches about the tire wear performance as well as the contact patch phenomenon by using finite element analysis (FEA) method or testing. But there is little published data on the correlations between the footprint geometry and the tread wear performance of tires. In this paper, an experiment on tire-ground performance of TBR tires is carried out by using Tekscan. The real-time changes of contact-area pressure distribution that occurred during the process of continuous load and unload are recorded. Three types of tires that act differently in behavior under normal usage are analyzed. A new method of researching in tire tread wear, which focuses on the geometrical characters of the footprint, is put forward. The experimental results of the three tires are described by using footprint geometrical characters. On the basis of studying the changing laws of footprint geometrical characters during the loading process and considering consumer survey and factory feedback information, the correlations between the geometrical character of footprints and tread destruction form are built. The analyzed results show that a greater contact area coefficient and a steady coefficient of contact result in a better wear performance for TBR tires. The footprint-shape coefficient changing laws in the process of loading are found to have a very good coincidence with the tread wear of the three types of tires. Tires with a smaller footprint-shape coefficient are likely to have an average tread wear while avoiding the shoulder wear first. The proposed research provides a new solution to predict tire-ground performance at the point of footprint and several useful references for improving tire design.展开更多
对汽车转向系统进行受力分析,建立了不同工况下的转向盘阻力矩模型。提出了一种驾驶员理想转向盘力矩参数化特性模型,并在此基础之上对EPS(electric power steering system)助力特性曲线的设计机理进行研究。提出了以驾驶员理想转向盘...对汽车转向系统进行受力分析,建立了不同工况下的转向盘阻力矩模型。提出了一种驾驶员理想转向盘力矩参数化特性模型,并在此基础之上对EPS(electric power steering system)助力特性曲线的设计机理进行研究。提出了以驾驶员理想转向盘力矩与车速、转向盘转角、侧向加速度的关系为基础将助力特性曲线按照高速和低速分别进行设计的观点,进而基于此观点探讨了EPS助力特性曲线的产生过程,并对EPS助力特性曲线的几何特征进行了论证。展开更多
The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)o...The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.展开更多
基金supported by Jiangsu Provincial Innovation Program of Graduate Student of China (Grant No. CXZZ11_0551 )
文摘Wear and mileage performance are the foremost performances for truck bus radial (TBR) tires. There are a lot of researches about the tire wear performance as well as the contact patch phenomenon by using finite element analysis (FEA) method or testing. But there is little published data on the correlations between the footprint geometry and the tread wear performance of tires. In this paper, an experiment on tire-ground performance of TBR tires is carried out by using Tekscan. The real-time changes of contact-area pressure distribution that occurred during the process of continuous load and unload are recorded. Three types of tires that act differently in behavior under normal usage are analyzed. A new method of researching in tire tread wear, which focuses on the geometrical characters of the footprint, is put forward. The experimental results of the three tires are described by using footprint geometrical characters. On the basis of studying the changing laws of footprint geometrical characters during the loading process and considering consumer survey and factory feedback information, the correlations between the geometrical character of footprints and tread destruction form are built. The analyzed results show that a greater contact area coefficient and a steady coefficient of contact result in a better wear performance for TBR tires. The footprint-shape coefficient changing laws in the process of loading are found to have a very good coincidence with the tread wear of the three types of tires. Tires with a smaller footprint-shape coefficient are likely to have an average tread wear while avoiding the shoulder wear first. The proposed research provides a new solution to predict tire-ground performance at the point of footprint and several useful references for improving tire design.
文摘对汽车转向系统进行受力分析,建立了不同工况下的转向盘阻力矩模型。提出了一种驾驶员理想转向盘力矩参数化特性模型,并在此基础之上对EPS(electric power steering system)助力特性曲线的设计机理进行研究。提出了以驾驶员理想转向盘力矩与车速、转向盘转角、侧向加速度的关系为基础将助力特性曲线按照高速和低速分别进行设计的观点,进而基于此观点探讨了EPS助力特性曲线的产生过程,并对EPS助力特性曲线的几何特征进行了论证。
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korean government(MSIT)(NRF-2023R1A2C1005950).
文摘The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.
