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Keping Li
State Key Laboratory of Rail Traffic Control and Safety, Beijing JiaoTong University, Beijing 100044, China

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Journal article
Published: 03 June 2021 in Applied Sciences
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Link prediction to optimize network performance is of great significance in network evolution. Because of the complexity of network systems and the uncertainty of network evolution, it faces many challenges. This paper proposes a new link prediction method based on neural networks trained on scale-free networks as input data, and optimized networks trained by link prediction models as output data. In order to solve the influence of the generalization of the neural network on the experiments, a greedy link pruning strategy is applied. We consider network efficiency and the proposed global network structure reliability as objectives to comprehensively evaluate link prediction performance and the advantages of the neural network method. The experimental results demonstrate that the neural network method generates the optimized networks with better network efficiency and global network structure reliability than the traditional link prediction models.

ACS Style

Keping Li; Shuang Gu; Dongyang Yan. A Link Prediction Method Based on Neural Networks. Applied Sciences 2021, 11, 5186 .

AMA Style

Keping Li, Shuang Gu, Dongyang Yan. A Link Prediction Method Based on Neural Networks. Applied Sciences. 2021; 11 (11):5186.

Chicago/Turabian Style

Keping Li; Shuang Gu; Dongyang Yan. 2021. "A Link Prediction Method Based on Neural Networks." Applied Sciences 11, no. 11: 5186.

Journal article
Published: 22 January 2021 in Applied Sciences
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In recent years, transportation system safety analysis has become increasingly challenging and highly demanding. Unstructured data contain sufficient information from which inherent interactions can be extracted. Determining how to process and fuse a large amount of unstructured data is a challenging task. In this paper, we propose a text-based Bayesian network (TBN) method to establish a Bayesian network (BN) based on text records, where the BN’s arcs are obtained from barrier relationships identified by a graphical model and its prior probabilities stem from fault trees. The comparative experimental results illustrate that the text-based method in TBN is efficient. The precision, recall and F-measure of TBN are 8.64%, 10.70% and 9.84% higher, respectively, than the most frequent (MF) result. Moreover, compared to the traditional BN, whose prior probabilities are frequently acquired from experts, the prior probabilities of the proposed text-based BN (TBN) have a high confidence. The experimental results of a train derailment accident case study show that with changes in the train derailment probabilities and the safety potentials of the barriers, the TBN generates quantitative results and reveals the critical risks of derailment accidents. Additionally, this work demonstrates relevant nonlinear relationships to improve the assessment results. Therefore, based on text-based data, this study reveals that barrier safety analysis has the potential to identify high-risk barriers, which can guide managers to enhance these barriers.

ACS Style

Liu Yang; Keping Li; Guozheng Song; Faisal Khan. Dynamic Railway Derailment Risk Analysis with Text-Data-Based Bayesian Network. Applied Sciences 2021, 11, 994 .

AMA Style

Liu Yang, Keping Li, Guozheng Song, Faisal Khan. Dynamic Railway Derailment Risk Analysis with Text-Data-Based Bayesian Network. Applied Sciences. 2021; 11 (3):994.

Chicago/Turabian Style

Liu Yang; Keping Li; Guozheng Song; Faisal Khan. 2021. "Dynamic Railway Derailment Risk Analysis with Text-Data-Based Bayesian Network." Applied Sciences 11, no. 3: 994.

Journal article
Published: 13 June 2019 in Physica A: Statistical Mechanics and its Applications
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Correlation of words in the text is of great importance in text analysis like text retrieval, keywords extraction, and text clustering. For short text, because of the limited information of text content, it is difficult to catch the correlation well among words. In this paper, we propose an algorithm based on the complex network to calculate the correlation of words in short texts. A new variable Edge-degree is proposed and used in studying the network model of texts. By using fluctuation analysis, we give the condition that Edge-degree correlation between words exists beyond nearest neighbors. Further analysis shows that numerical results of the fluctuation function of Edge-degree act a power law distribution and that the scaling exponent diverges at a long distance under the finite size effect and varies in different texts. The fluctuation function separates the words in a text into different clusters, and this property is used to measure inner-correlation of different words. Hub nodes act a significant influence on the long-range Edge-degree correlation through changing the linear trend of the fluctuation function in a log-log plot.

ACS Style

Dongyang Yan; Keping Li; Jingjing Ye. Correlation analysis of short text based on network model. Physica A: Statistical Mechanics and its Applications 2019, 531, 121728 .

AMA Style

Dongyang Yan, Keping Li, Jingjing Ye. Correlation analysis of short text based on network model. Physica A: Statistical Mechanics and its Applications. 2019; 531 ():121728.

Chicago/Turabian Style

Dongyang Yan; Keping Li; Jingjing Ye. 2019. "Correlation analysis of short text based on network model." Physica A: Statistical Mechanics and its Applications 531, no. : 121728.

Journal article
Published: 18 May 2019 in Energies
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Root cause identification is an important task in providing prompt assistance for diagnosis, security monitoring and guidance for specific routine maintenance measures in the field of railway transportation. However, most of the methods addressing rail faults are based on state detection, which involves structured data. Manual cause identification from railway equipment maintenance and management text records is undoubtedly a time-consuming and laborious task. To quickly obtain the root cause text from unstructured data, this paper proposes an approach for root cause factor identification by using a root cause identification-new word sentence (RCI-NWS) keyword extraction method. The experimental results demonstrate that the extraction of railway fault text data can be performed using the keyword extraction method and the highest values are obtained using RCI-NWS.

ACS Style

Liu Yang; Keping Li; Dan Zhao; Shuang Gu; Dongyang Yan. A Network Method for Identifying the Root Cause of High-Speed Rail Faults Based on Text Data. Energies 2019, 12, 1908 .

AMA Style

Liu Yang, Keping Li, Dan Zhao, Shuang Gu, Dongyang Yan. A Network Method for Identifying the Root Cause of High-Speed Rail Faults Based on Text Data. Energies. 2019; 12 (10):1908.

Chicago/Turabian Style

Liu Yang; Keping Li; Dan Zhao; Shuang Gu; Dongyang Yan. 2019. "A Network Method for Identifying the Root Cause of High-Speed Rail Faults Based on Text Data." Energies 12, no. 10: 1908.

Journal article
Published: 17 September 2018 in Computers & Industrial Engineering
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Aimed to increase usage of regenerative energy and stabilize voltage variation of traction supply grid, an energy-saving model with on-board energy storage devices is proposed by jointly optimizing the running time and recommended speed profile of trains over the whole urban rail transit line. Regenerative energy, generated by the braking train, is considered to store into its individual on-board energy storage devices and provided for the follow-up traction operations. Some parameters, including the comfort criterion and increased train mass due to the installation of energy storage devices, are all taken into account in the energy consumption calculation. In the optimization process, the total running time is only required to be adjusted in a predetermined range to guarantee transportation capacity and meet passenger travel demands. A dynamic programming based heuristic is designed with operational experiences and a series of numerical experiments are implemented to demonstrate the effectiveness, corresponding to Beijing metro Yizhuang line.

ACS Style

Yeran Huang; Lixing Yang; Tao Tang; Ziyou Gao; Fang Cao; Keping Li. Train speed profile optimization with on-board energy storage devices: A dynamic programming based approach. Computers & Industrial Engineering 2018, 126, 149 -164.

AMA Style

Yeran Huang, Lixing Yang, Tao Tang, Ziyou Gao, Fang Cao, Keping Li. Train speed profile optimization with on-board energy storage devices: A dynamic programming based approach. Computers & Industrial Engineering. 2018; 126 ():149-164.

Chicago/Turabian Style

Yeran Huang; Lixing Yang; Tao Tang; Ziyou Gao; Fang Cao; Keping Li. 2018. "Train speed profile optimization with on-board energy storage devices: A dynamic programming based approach." Computers & Industrial Engineering 126, no. : 149-164.

Journal article
Published: 01 October 2016 in Communications in Theoretical Physics
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It is an important issue to identify important influencing factors in railway accident analysis. In this paper, employing the good measure of dependence for two-variable relationships, the maximal information coefficient (MIC), which can capture a wide range of associations, a complex network model for railway accident analysis is designed in which nodes denote factors of railway accidents and edges are generated between two factors of which MIC values are larger than or equal to the dependent criterion. The variety of network structure is studied. As the increasing of the dependent criterion, the network becomes to an approximate scale-free network. Moreover, employing the proposed network, important influencing factors are identified. And we find that the annual track density-gross tonnage factor is an important factor which is a cut vertex when the dependent criterion is equal to 0.3. From the network, it is found that the railway development is unbalanced for different states which is consistent with the fact.

ACS Style

Fu-Bo Shao; Ke-Ping Li. A Complex Network Model for Analyzing Railway Accidents Based on the Maximal Information Coefficient. Communications in Theoretical Physics 2016, 66, 459 -466.

AMA Style

Fu-Bo Shao, Ke-Ping Li. A Complex Network Model for Analyzing Railway Accidents Based on the Maximal Information Coefficient. Communications in Theoretical Physics. 2016; 66 (4):459-466.

Chicago/Turabian Style

Fu-Bo Shao; Ke-Ping Li. 2016. "A Complex Network Model for Analyzing Railway Accidents Based on the Maximal Information Coefficient." Communications in Theoretical Physics 66, no. 4: 459-466.