Yongzhong Li – författare

Taking classifiers as its foundation, this book explores the cognitive typological similarities and differences between Chinese and English classifier-noun structures. As a primarily theoretical work, it diverges from traditional grammatical analysis and pedagogical grammar research.The book is structured in two parts. The first part provides an ontological and cognitive analysis of Chinese classifier-noun structures, focusing on prototypical and non-prototypical forms, as well as conventional and unconventional classifier-noun collocations. The analysis draws on frameworks such as prototype theory, image schemas, conceptual metaphor, conceptual metonymy, and construction grammar. The second part offers a cognitive typological comparison of Chinese and English classifier-noun structures, examining non-prototypical forms, unconventional collocations, syntactic behaviours, and dialectical relationships. By integrating cognitive linguistics and linguistic typology, the book highlights correlations between typological features and cognitive thinking patterns, contributing to the development of cognitive typology as a discipline.This book will appeal to linguists and students interested in classifier-noun structures, cognitive linguistics, and Chinese-English comparative studies. It also serves as a valuable guide for Chinese language learners seeking insights into typical expressions and the cognitive aspects of Chinese-English cultural comparison.

This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios. Studies on EM scattering problems have attracted researchers in various fields, such as antenna design, geophysical exploration and remote sensing. Pursuing a holistic perspective, the book introduces the whole workflow in utilizing the DL framework to solve the scattering problems. To achieve precise approximation, medium-scale data sets are sufficient in training the proposed model. As a result, the fully trained framework can realize three orders of magnitude faster than the conventional FDFD solver. It is worth noting that the 2D and 3D scatterers in the scheme can be either lossless medium or metal, allowing the model to be more applicable. This book is intended for graduate students who are interested in deep learning with computational electromagnetics, professional practitioners working on EM scattering, or other corresponding researchers.

This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios. Studies on EM scattering problems have attracted researchers in various fields, such as antenna design, geophysical exploration and remote sensing. Pursuing a holistic perspective, the book introduces the whole workflow in utilizing the DL framework to solve the scattering problems. To achieve precise approximation, medium-scale data sets are sufficient in training the proposed model. As a result, the fully trained framework can realize three orders of magnitude faster than the conventional FDFD solver. It is worth noting that the 2D and 3D scatterers in the scheme can be either lossless medium or metal, allowing the model to be more applicable. This book is intended for graduate students who are interested in deep learning with computational electromagnetics, professional practitioners working on EM scattering, or other corresponding researchers.