Yijun Zhang – författare

This volume reviews the latest global research results in computer applications. The book contains a selection of papers presented at the Fifth International Conference on Computer Applications in Production and Engineering, arranged by the International Federation for Information Processing and held in Beijing, China in May 1995.

This volume reviews the latest global research results in computer applications. The book contains a selection of papers presented at the Fifth International Conference on Computer Applications in Production and Engineering, arranged by the International Federation for Information processing and held in Beijing, China in May 1995. Contributions are worldwide, with a number of papers coming from the Asia-Pacific Rim area. The papers in the book aim to bridge the gap between academic research and the industrial implementation of information technology. They cover a wide range of applications, including basic theory, architecture, methodology, implementation and case studies.Specific topics discussed include: modelling technology; computational geometry and its applications; reverse engineering; application of CAD technology; theory and methodology of CAPM; one-of-a-kind production; production management techniques; theory and methodology of CIM; CIM systems; modelling and simulation in CAM; techniques and methods for computer applications; concurrent engineering; neural network applications; AI applications; computer application techniques; robotics; manufacturing systems and technology. All those working in computer applications should find this book of interest, especially engineers, researchers and developers and EDP managers. This book should be of interest to engineers, reasearchers and developers working in computer applications and EDP managers.

This book deals with hyperspectral image classification using graph neural network methods, focusing on classification model designing, graph information dissemination, and graph construction. In the book, various graph neural network based classifiers have been proposed for hyperspectral image classification to improve the classification accuracy. This book has promoted the application of graph neural network in hyperspectral image classification, providing reference for remote sensing image processing. It will be a useful reference for researchers in remote sensing image processing and image neural network design.