Driver identification methods in electric vehicles, a review
D Zhao, J Hou, Y Zhong, W He, Z Fu… - World Electric Vehicle …, 2022 - mdpi.com
Driver identification is very important to realizing customized service for drivers and road
traffic safety for electric vehicles and has become a research hotspot in the field of modern …
traffic safety for electric vehicles and has become a research hotspot in the field of modern …
Temporal early exiting with confidence calibration for driver identification based on driving sensing data
Driver identification systems that use deep-neural-network-based sequential models have
been studied for personalized intelligent vehicles. After a vehicle starts moving for a trip, the …
been studied for personalized intelligent vehicles. After a vehicle starts moving for a trip, the …
[HTML][HTML] 胎儿心电信号的无创提取： 基于时间卷积编解码网络
曹石， 巩高， 肖慧， 方威扬， 阙与清… - Journal of Southern …, 2022 - ncbi.nlm.nih.gov
胎儿心电信号的无创提取：基于时间卷积编解码网络- PMC Back to Top Skip to main content NIH
NLM Logo Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation …
NLM Logo Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation …
A Comprehensive Review: Analysis of Machine Learning, Deep Learning, and Large Language Model Techniques for Revolutionizing Driver Identification
Driver identification is crucial for various applications including automotive security, law
enforcement, and the ride-sharing industry, as well as for advanced driver assistance …
enforcement, and the ride-sharing industry, as well as for advanced driver assistance …
[引用][C] 胎儿心电信号的无创提取胎儿心电信号的无创提取: 基于时间卷积编解码网基于时间卷积编解码网络
曹石， 巩高， 肖慧， 方威扬， 阙与清， 陈超敏 - europepmc.org
目的实现从孕妇腹壁混合心电信号中提取微弱的胎儿心电信号, 为准确估计胎儿心率,
分析胎儿心电波形等提供基础. 方法利用深度卷积网络(deep CNN) 优越的非线性映射能力 …
分析胎儿心电波形等提供基础. 方法利用深度卷积网络(deep CNN) 优越的非线性映射能力 …