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Lianyan Li
School of Civil Engineering, Wuhan University, Wuhan 430072, China

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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.

Conference paper
Published: 12 January 2020 in Advances in Intelligent Systems and Computing
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Studying the spatial distribution of real estate price can not only help consumers to choose the suitable real estate, but also help urban planners to make better decisions. Under the background big data, a new perspective of research on urban real estate prices is produced. Based on the real estate parameters of ordinary residential buildings in Xining city, ArcGIS software is used to conduct the nearest neighbor distance analysis, and it is found that the residential buildings in the area presented significant agglomeration. Then, Moran’s I index is selected for spatial autocorrelation analysis, which proves the positive spatial correlation of real estate prices in Xining city. Finally, the spatial distribution map of real estate prices is obtained by fitting the real estate prices in the research area with the statistical analysis of land, which finds the area of Chengxi is the center of local housing price, and the price is decreasing gradually around other areas. In addition, the main factors affecting housing prices in four areas can be obtained by analyzing the distribution figure, which gives a significant reference for residents and policy planners.

ACS Style

Hongzhang Zhu; Lianyan Li; Xiaobin Ren; Yangting Fan; Xiaoliang Sui. Real Estate Spatial Price Distribution in Xining from the Perspective of Big Data. Advances in Intelligent Systems and Computing 2020, 91 -99.

AMA Style

Hongzhang Zhu, Lianyan Li, Xiaobin Ren, Yangting Fan, Xiaoliang Sui. Real Estate Spatial Price Distribution in Xining from the Perspective of Big Data. Advances in Intelligent Systems and Computing. 2020; ():91-99.

Chicago/Turabian Style

Hongzhang Zhu; Lianyan Li; Xiaobin Ren; Yangting Fan; Xiaoliang Sui. 2020. "Real Estate Spatial Price Distribution in Xining from the Perspective of Big Data." Advances in Intelligent Systems and Computing , no. : 91-99.

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.