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9 results on '"Xue, Yujingyang"'

5. MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results

Author

Changhui Jiang, Xue Yujingyang, Li Yuanjun, Lin Han, Guo Xuan, Yuwei Chen, and Zhu Kai

Subjects
0209 industrial biotechnology, odometer, Computer science, Real-time computing, Satellite system, 02 engineering and technology, lcsh:Chemical technology, Biochemistry, Odometer, Article, Analytical Chemistry, 020901 industrial engineering & automation, Inertial measurement unit, 0202 electrical engineering, electronic engineering, information engineering, lcsh:TP1-1185, Electrical and Electronic Engineering, Instrumentation, Inertial navigation system, GNSS, state constraints, Navigation system, 020206 networking & telecommunications, Atomic and Molecular Physics, and Optics, GNSS applications, Satellite, MIMU, Multipath propagation
Abstract

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included.

Published
2020

8. Human and organizational factors in Chinese hazardous chemical accidents: a case study of the ‘8.12’ Tianjin Port fire and explosion using the HFACS-HC

Author

Zhou, Lin, Fu, Gui, and Xue, Yujingyang

Abstract

Human and organizational factors have been proven to be the prime causes of Chinese hazardous chemical accidents (HCAs). A modified version of the Human Factors Analysis and Classification System (HFACS), namely the HFACS-Hazardous Chemicals (HC), was developed to identify the human factors involved in Chinese HCAs. The ‘8.12’ Tianjin Port fire and explosion, the costliest HCA in recent years, was reanalyzed using this framework, and the results were compared with the official accident inquiry report to determine their differences related to the identification of human and organizational factors. The study revealed that interacting human factors from different levels in Ruihai Company led to this catastrophe, and the inquiry report had limitations in the identification of human factors and the guidance for similar accident prevention. This study showed the applicability of the HFACS-HC in HCA analyses as well as the necessity to recommend this approach for future HCA investigations.

Published
2018
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9. Risk prediction and factors risk analysis based on 1F0A-GRNN and apriori algorithms: Application of artificial intelligence in accident prevention.

Author

Xie Xuecai, Fu Gui, Xue Yujingyang, Zhao Ziqi, Chen Ping, Lu Baojun, and Jiang Song

Subjects
*ACCIDENT prevention, *ARTIFICIAL intelligence in medicine, *APRIORI algorithm, *RISK assessment, *REGRESSION analysis, *ARTIFICIAL neural networks
Abstract

Risk prediction of disasters is one of the most effective ways to prevent accidents. To solve the problems in multi-factor complex disaster prediction, this paper proposes a new method for risk prediction and factorial risk analysis. Coal and gas outburst accidents w e re selected as research objects. First, a new coal and gas outburst prediction model was established that consists of 4 levels and 14 factors. Then, the Improved Fruit Fly Optimization Algorithm (IFOA) and the General Regression Neural Network (GRNN) algorithm w e re combined to establish the IFOA-GRNN prediction model. After that, the sensitivity analysis method was applied to the analysis of the sensitive factors of coal and gas outbursts. Finally, an apriori algorithm was used to mine the disaster information. The method proposed in this pap e r was applied to the Pingdingshan No. 8 Min. The application results show that the IFOA-GRNN algorithm proposed in this paper has an accuracy rate of 100% for the prediction of accident risk levels. Compared with the Back Propagation (BP), GRNN and FOA-GRNN algorithms, IFOA-GRNN has the characteristics of a smaller prediction error, higher stability and faster convergence. The sensitivity analysis method can judge the sensitive factors of coal and gas outbursts without knowing the mechanisms of the accident. The a priori algorithm can perform good data mining on the combination of high frequency factors leading to accidents and the relationships between the coal and gas outburst levels and factors. The data mining results are very helpful for the prevention and management of coal and gas outbursts. [ABSTRACT FROM AUTHOR]

Published
2019
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