Results 11 to 20 of about 471,556 (267)

BioSentinel: Validating Sensitivity of Yeast Biosensors to Deep Space Relevant Radiation [PDF]

Astrobiology, 2023
Sofia Massaro Tieze, Michael R Padgen, Sharmila Bhattacharya   +2 more
exaly   +2 more sources

BioSentinel: A Biological CubeSat for Deep Space Exploration [PDF]

Astrobiology, 2023
Sofia Massaro Tieze, Sérgio R Santa Maria, Sharmila Bhattacharya   +2 more
exaly   +2 more sources

Numerical Simulation of the Lunar Polar Environment: Implications for Rover Exploration Challenge

Aerospace, 2023
The lunar polar regions are key areas for future exploration due to the long-term continuous illumination and persistently shadowed regions that can cold trap abundant water and other volatiles.
Hong Gan, Chengxuan Zhao, Guangfei Wei, Xiongyao Li, Guojun Xia, Xiao Zhang, Jingjing Shi   +6 more
doaj   +1 more source

Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis

Science Progress, 2022
The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can safely operate.
Yuqing Li, Mingjia Lei, Yao Cheng, Rixin Wang, Minqiang Xu   +4 more
doaj   +1 more source

Experimental study on electrostatic migration of different mineral particles composing lunar dust under electron irradiation

Frontiers in Astronomy and Space Sciences, 2023
Dust electrostatic migration is one of the primary causes of dust events on the surface of airless celestial bodies, which can result in multiple dust phenomena, including lunar horizon glow and dust pools, and also offer crucial insights into the ...
Hong Gan, Guangfei Wei, Guangfei Wei, Xiao Zhang, Xiao Zhang, Guojun Xia, Guojun Xia, Jingjing Shi   +7 more
doaj   +1 more source

Learn Generalization Feature via Convolutional Neural Network: A Fault Diagnosis Scheme Toward Unseen Operating Conditions

IEEE Access, 2020
In recent years, Convolutional neural networks (CNNs) have achieved start-of-art performance in the fault diagnosis field. If there is no available information on the unseen operating conditions, the model trained on the seen operating condition cannot ...
Yuantao Yang, Jiancheng Yin, Huailiang Zheng, Yuqing Li, Minqiang Xu, Yushu Chen   +5 more
doaj   +1 more source

Mission analysis of lunar scientific research station based on MBSE

Hangkong gongcheng jinzhan, 2023
After the completion of the three steps of "circling back", the lunar exploration has changed from the short-term single point exploration to the long-term exploration of lunar scientific research stations with the con-struction of lunar surface ...
GUAN Feng, GE Ping, SHAO Yanli, YUAN Wenqiang, XU Shuai, KANG Yan, PEI Zhaoyu   +6 more
doaj   +1 more source

Deep Learning in Target Space

J. Mach. Learn. Res., 2020
Deep learning uses neural networks which are parameterised by their weights. The neural networks are usually trained by tuning the weights to directly minimise a given loss function. In this paper we propose to re-parameterise the weights into targets for the firing strengths of the individual nodes in the network.
Fairbank, Michael, Samothrakis, Spyridon, Citi, Luca   +2 more
openaire   +4 more sources

Spectral interpretation of late-stage mare basalt mineralogy unveiled by Chang’E-5 samples

Nature Communications, 2022
Laboratory spectroscopic and X-ray diffraction analyses on the Chang’E-5 lunar soil samples have demonstrated that the spectral features of lunar late-stage mare basalts are representative of iron-rich high-Ca pyroxene rather than olivine.
Dawei Liu, Xing Wang, Jianjun Liu, Bin Liu, Xin Ren, Yuan Chen, Zhaopeng Chen, Hongbo Zhang, Guangliang Zhang, Qin Zhou, Zhoubin Zhang, Qiang Fu, Chunlai Li   +12 more
doaj   +1 more source

Deep state-space Gaussian processes [PDF]

Statistics and Computing, 2021
AbstractThis paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed Gaussian process (GP) priors on the length scales and magnitudes of the next level of Gaussian processes in the hierarchy. The idea of the state-space approach is to represent the DGP as a non-
Emzir Muhammad, Särkkä Simo, Zhao Zheng   +2 more
openaire   +3 more sources