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Xiaobin Ren
GNSS Research Center, Wuhan University, Wuhan 430079, China

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Journal article
Published: 10 March 2021 in Remote Sensing
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The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AMLSTM NN) model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) landslide displacement prediction. The CEEMDAN method is implemented to ingest landslide Global Navigation Satellite System (GNSS) time series. The AMLSTM algorithm is then used to realize prediction work, jointly with multiple impact factors. The Baishuihe landslide is adopted to illustrate the capabilities of the model. The results show that the CEEMDAN-AMLSTM model achieves competitive accuracy and has significant potential for landslide displacement prediction.

ACS Style

Jing Wang; Guigen Nie; Shengjun Gao; Shuguang Wu; Haiyang Li; Xiaobing Ren. Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model. Remote Sensing 2021, 13, 1055 .

AMA Style

Jing Wang, Guigen Nie, Shengjun Gao, Shuguang Wu, Haiyang Li, Xiaobing Ren. Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model. Remote Sensing. 2021; 13 (6):1055.

Chicago/Turabian Style

Jing Wang; Guigen Nie; Shengjun Gao; Shuguang Wu; Haiyang Li; Xiaobing Ren. 2021. "Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model." Remote Sensing 13, no. 6: 1055.

Journal article
Published: 27 June 2020 in Symmetry
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Recently, X-ray pulsar-based navigation (XPNAV) as a significant navigation method has been widely used in deep space exploration. However, the accuracy of XPNAV is limited to the existence of the pulsar direction error. To improve the performance of XPNAV, we have proposed a novel algorithm named “the modified augmented state extended Kalman filter” (MASEKF). The algorithm considers the high-order terms of direction error and then adds a more precise direction error into state equation and measurement equation. In the simulation, by comparing the performance of MASEKF, EKF, and ASEKF at the same time, it is found that MASEKF has better performance in the accuracy and stability, and the results also demonstrate that MASEKF algorithm has faster convergence speed. This paper provides a strong reference for other improvements of algorithms towards direction error. The purpose of this study is to establish MASEKF and add the direction error into the measurement equation and the state equation, so as to realize the coordination and symmetry of the algorithm.

ACS Style

Xiaobin Ren; Guigen Nie; Lianyan Li. An Improved Augmented Algorithm for Direction Error in XPNAV. Symmetry 2020, 12, 1059 .

AMA Style

Xiaobin Ren, Guigen Nie, Lianyan Li. An Improved Augmented Algorithm for Direction Error in XPNAV. Symmetry. 2020; 12 (7):1059.

Chicago/Turabian Style

Xiaobin Ren; Guigen Nie; Lianyan Li. 2020. "An Improved Augmented Algorithm for Direction Error in XPNAV." Symmetry 12, no. 7: 1059.

Journal article
Published: 09 January 2020 in Symmetry
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The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 (“normal”), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada’s climate change. In 2025, the climate level of Canada will become “a little bad” based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change.

ACS Style

Xiaobin Ren; Lianyan Li; Yang Yu; Zhihua Xiong; Shunzhou Yang; Wei Du; Mengjia Ren. A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method. Symmetry 2020, 12, 139 .

AMA Style

Xiaobin Ren, Lianyan Li, Yang Yu, Zhihua Xiong, Shunzhou Yang, Wei Du, Mengjia Ren. A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method. Symmetry. 2020; 12 (1):139.

Chicago/Turabian Style

Xiaobin Ren; Lianyan Li; Yang Yu; Zhihua Xiong; Shunzhou Yang; Wei Du; Mengjia Ren. 2020. "A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method." Symmetry 12, no. 1: 139.

Journal article
Published: 03 November 2019 in Sustainability
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Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth.

ACS Style

Lianyan Li; Xiaobin Ren. A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network. Sustainability 2019, 11, 6125 .

AMA Style

Lianyan Li, Xiaobin Ren. A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network. Sustainability. 2019; 11 (21):6125.

Chicago/Turabian Style

Lianyan Li; Xiaobin Ren. 2019. "A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network." Sustainability 11, no. 21: 6125.

Journal article
Published: 11 June 2019 in Remote Sensing
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There is always a need to extract more accurate regional common mode component (CMC) series from coordinate time series of Global Positioning System (GPS) stations, which would be of great benefit to describe the deformation features of the Earth’s surface with more reliability. For this purpose, this paper combines all 11 International Global Navigation Satellite System (GNSS) Service (IGS) stations in China with over 70 stations selected from the Crustal Movement Observation Network of China (CMONOC) to compute CMC series of IGS stations by using a principal component analysis (PCA) method under cases of one whole region and eight sub-regions. The comparison results show that the percentage of first-order principal component (PC1) in North, East and Up components increase by 10.8%, 16.1% and 25.1%, respectively, after dividing the whole China region into eight sub-regions. Meanwhile, Root Mean Square (RMS) reduction rates of residual series that have removed CMC also improve obviously after partitioning. In addition, we compute displacements of these IGS stations caused by environmental loadings (including atmospheric pressure loading, non-tidal oceanic loading and hydrological loading) to analyze their contributions to the non-linear variation in GPS coordinate time series. The comparison result shows that the method we raise, PCA filtering in sub-regions, performs better than the environmental loading corrections (ELCs) in improving the signal-to-noise ratio (SNR) of GPS coordinate time series. This paper raises new criteria for selecting appropriate CMONOC stations around IGS stations when computing sub-regional CMC, involving three criteria of interstation distance, geology and self-condition of stations themselves. According to experiments, these criteria are implemental and effective in selecting suitable stations, by which to extract sub-regional CMC with higher accuracy.

ACS Style

Shuguang Wu; Guigen Nie; Jingnan Liu; Kezhi Wang; Changhu Xue; Jing Wang; Haiyang Li; Fengyou Peng; Xiaobin Ren. A Sub-Regional Extraction Method of Common Mode Components from IGS and CMONOC Stations in China. Remote Sensing 2019, 11, 1389 .

AMA Style

Shuguang Wu, Guigen Nie, Jingnan Liu, Kezhi Wang, Changhu Xue, Jing Wang, Haiyang Li, Fengyou Peng, Xiaobin Ren. A Sub-Regional Extraction Method of Common Mode Components from IGS and CMONOC Stations in China. Remote Sensing. 2019; 11 (11):1389.

Chicago/Turabian Style

Shuguang Wu; Guigen Nie; Jingnan Liu; Kezhi Wang; Changhu Xue; Jing Wang; Haiyang Li; Fengyou Peng; Xiaobin Ren. 2019. "A Sub-Regional Extraction Method of Common Mode Components from IGS and CMONOC Stations in China." Remote Sensing 11, no. 11: 1389.