Guest Editorial of the Special Section on Neural Computing-Driven Artificial Intelligence for Consumer Electronics

作     者：Zhang, Haijun Gao, Xiao-Zhi Wang, Zenghui Wang, Guanghui 

作者机构：Harbin Inst Technol Dept Comp Sci Shenzhen 518055 Peoples R China Univ Eastern Finland Sch Comp Kuopio 70210 Finland Univ South Africa Dept Elect & Min Engn ZA-1709 Florida South Africa Toronto Metropolitan Univ Dept Comp Sci Toronto ON M5B 2K3 Canada 

出 版 物：《IEEE TRANSACTIONS ON CONSUMER ELECTRONICS》 (IEEE Trans Consum Electron)

年 卷 期：2024年第70卷第1期

页      面：3517-3520页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 

主　　题：Special issues and sections Neural engineering Artificial intelligence Smart manufacturing Autonomous systems Decision making Smart devices Consumer electronics Internet of Things Fourth Industrial Revolution Fault diagnosis 

摘      要：Recent advances in artificial intelligence (AI) technologies have driven the dramatic developments in key consumer applications, e.g., smart manufacturing, equipment conditions and fault diagnosis, quality inspection, autonomous decision-making, etc. In the Industry 4.0 era, AI has become the core technology to promote the revolution and development of consumer electronics intelligence. In practice, AI-driven consumer electronics integrate AI technologies and the domain knowledge of standard process and operations to achieve smart systems incorporated with techniques of the Internet of Things (IoT), neural computing, machine learning, and deep learning. However, many challenges are remained to implement AI-powered modes for consumer electronics by directly applying advanced neural computing techniques. Moreover, complex application context in consumer electronics environments and prior domain knowledge further make it challengeable to fulfill emerging intelligent consumer applications. On the other hand, recent years have witnessed the rapid development of neural computing in various AI tasks. In particular, deep neural networks have been widely applied in real-world application scenarios in consumer electronics manufacturing. Moreover, advanced techniques and approaches in data modeling and prediction, learning strategies, optimization and control theories are also incorporated and developed under various consumer application scenarios.