Y
Yuming Bo
Researcher at Nanjing University of Science and Technology
Publications - 25
Citations - 536
Yuming Bo is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: GNSS applications & Navigation system. The author has an hindex of 10, co-authored 25 publications receiving 268 citations. Previous affiliations of Yuming Bo include Finnish Geodetic Institute.
Papers
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UWB NLOS/LOS Classification Using Deep Learning Method
TL;DR: Deep learning method CNN-LSTM was employed in the UWB NLOS/LOS signal classification and obtained stat e-of-art classification performance.
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A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN).
TL;DR: An Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMs gyroscope outputs, in which the signals were treated as time series.
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Point Cloud Based Relative Pose Estimation of a Satellite in Close Range
TL;DR: This paper aims to estimate the pose of a target satellite in close range on the basis of its known model by using point cloud data generated by a flash LIDAR sensor using a novel model based pose estimation method.
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A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing
TL;DR: A deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neuralnetwork for noise modeling of a micromechanics system gyroscope, and results supported a positive conclusion on the performance of designed method.
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Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR
Yuwei Chen,Changhui Jiang,Juha Hyyppä,Shi Qiu,Zheng Wang,Tian Mi,Wei Li,Eetu Puttonen,Hui Zhou,Ziyi Feng,Yuming Bo,Zhijie Wen +11 more
TL;DR: This letter investigates the feasibility of ore classification applications with hyperspectral LiDAR (HSL), and builds a spectral band selection criterion based on the feature contribution degree (FCD), which is calculated using the normalized variance of the reflectance values for different ore samples at each wavelength.