Hongmei Bai

Chengdu University of Technology — China

Physics and Astronomy · Physical Sciences

9h-index222citations19works0.02yr avg
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Research Topics

Ionosphere and magnetosphere dynamics(8), Remote Sensing in Agriculture(6), GNSS positioning and interference(5), Earthquake Detection and Analysis(5), Ecology and Vegetation Dynamics Studies(3)

Publications19 total

Analysis of Crop Planting Strategies Based on Linear Programming
Highlights in Science Engineering and Technology·2025Open Access
Radio Signal Recognition Using Two-Stage Spatiotemporal Network with Bispectral Analysis
Sensors·2025Open Access
Study on the Optimized Production Scheduling of Polymetallic Mines Considering the Vehicle Routing Problem
IEEE Access·2025Open Access
A Forecasting Model of Ionospheric foF2 Using the LSTM Network Based on ICEEMDAN Decomposition
IEEE Transactions on Geoscience and Remote Sensing·2023· 19 cited
Large Signal Modeling for Microwave Power Devices Based on LSTM
2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)·2022· 3 cited
Room-temperature CMOS 4.2THz Thermal Detector Comprising a Metamaterial Absorber and a PTAT Sensor
2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)·2022· 2 cited
Multi‐month time‐lag effects of regional vegetation responses to precipitation in arid and semi‐arid grassland: A case study of Hulunbuir, Inner Mongolia
Natural Resource Modeling·2022· 8 citedOpen Access
A Combination Prediction Model of Long-Term Ionospheric foF2 Based on Entropy Weight Method
Entropy·2020· 30 citedOpen Access
A regional model for the prediction of M(3000)F2 over East Asia
Advances in Space Research·2020· 11 cited
A spatio-temporal prediction of NDVI based on precipitation: an application for grazing management in the arid and semi-arid grasslands
International Journal of Remote Sensing·2019· 22 cited
A new temporal prediction method of grazing pressure based on normalized difference vegetation index and precipitation using nonlinear autoregressive with exogenous input networks
Grassland Science·2019· 1 cited
Modeling M(3000)F2 based on extreme learning machine
Advances in Space Research·2019· 6 cited
Advanced Method to Capture the Time-Lag Effects between Annual NDVI and Precipitation Variation Using RNN in the Arid and Semi-Arid Grasslands
Water·2019· 28 citedOpen Access
Simplified Regional Prediction Model of Long-Term Trend for Critical Frequency of Ionospheric F2 Region over East Asia
Applied Sciences·2019· 19 citedOpen Access
Modeling of the Ionospheric Critical Frequency of the <i>F</i><sub>2</sub> layer over Asia based on Modified Temporal‐Spatial Reconstruction
Radio Science·2019· 17 cited
Nonlinear dependence study of ionospheric F2 layer critical frequency with respect to the solar activity indices using the mutual information method
Advances in Space Research·2019· 4 cited
A new approach to predict normalized difference vegetation index using time-delay neural network in the arid and semi-arid grassland
International Journal of Remote Sensing·2019· 24 cited
Prediction of the livestock carrying capacity using neural network in the meadow steppe
The Rangeland Journal·2019· 5 cited
A Prediction Model of Ionospheric <i>f</i><sub><i>o</i></sub><i>F</i><sub>2</sub> Based on Extreme Learning Machine
Radio Science·2018· 23 cited

Frequent Co-authors

Taosuo Wu(13), Feng Feng(9), Jian Wang(8), Qian Lin(5), Jianguo Ma(4), Haipeng Fu(3), Qiang Chen(3), Xiangdong Huang(2), Yue-Bin Cao(2), Kaixue Ma(1), Yafei Shi(1), Cheng Yang(1), Zhigang Zhang(1), Fanyi Meng(1), Hua‐ye Cheng(1), G. Jin(1), Hongbao Wu(1), Xiaozheng Wang(1), Xu Wang(1), Qian Lin(1)