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Tsinghua Science and Technology 2022, 27(1): 207-222 https://doi.org/10.26599/TST.2021.9010034
Open Access | Issue | Published: 17 August 2021

GGC: Gray-Granger Causality Method for Sensor Correlation Network Structure Mining on High-Speed Train

Show Author's Information Jie Man,  Honghui Dong( ),  Limin Jia,  Yong Qin
State Key Laboratory of Rail Traffic Control and Safety, and Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China
Keywords:
high-speed train, vehicle information network, structure mining, gray theory, Granger causality theory, complex network theory
Cite this article:
Man J, Dong H, Jia L, et al. GGC: Gray-Granger Causality Method for Sensor Correlation Network Structure Mining on High-Speed Train. Tsinghua Science and Technology, 2022, 27(1): 207-222. https://doi.org/10.26599/TST.2021.9010034
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Abstract Full text About this article

Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally, but also predict the train’s future operating status. How to obtain valuable information from massive vehicle data is a difficult point. First, we divide the vehicle data of a high-speed train into 13 subsystem datasets, according to the functions of the collection components. Then, according to the gray theory and the Granger causality test, we propose the Gray-Granger Causality (GGC) model, which can construct a vehicle information network on the basis of the correlation between the collection components. By using the complex network theory to mine vehicle information and its subsystem networks, we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network. In addition, the vehicle information network is weak against attacks, but the subsystem network is closely connected and strong against attacks.


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About this article

GGC: Gray-Granger Causality Method for Sensor Correlation Network Structure Mining on High-Speed Train

Show Author's information Jie Man, Honghui Dong( ), Limin Jia, Yong Qin
State Key Laboratory of Rail Traffic Control and Safety, and Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China

Abstract

Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally, but also predict the train’s future operating status. How to obtain valuable information from massive vehicle data is a difficult point. First, we divide the vehicle data of a high-speed train into 13 subsystem datasets, according to the functions of the collection components. Then, according to the gray theory and the Granger causality test, we propose the Gray-Granger Causality (GGC) model, which can construct a vehicle information network on the basis of the correlation between the collection components. By using the complex network theory to mine vehicle information and its subsystem networks, we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network. In addition, the vehicle information network is weak against attacks, but the subsystem network is closely connected and strong against attacks.

Keywords: high-speed train, vehicle information network, structure mining, gray theory, Granger causality theory, complex network theory

References(32)

[1]
P. Y. Pan, H. T. Hu, X. W. Yang, F. Blaabjerg, X. F. Wang, and Z. Y. He, Impedance measurement of traction network and electric train for stability analysis in high-speed railways, IEEE Trans. Power Electron., vol. 33, no. 12, pp. 10086-10100, 2018.
Google Scholar
[2]
M. E. J. Newman, Networks: An Introduction. Oxford, UK: Oxford University Press, 2010.
[3]
R. Albert and A. L. Barabási, Statistical mechanics of complex networks, Rev. Mod. Phys., vol. 74, pp. 47-97, 2002.
Google Scholar
[4]
G. Caldarelli, Scale-Free Networks: Complex Webs in Nature and Technology. Oxford, UK: Oxford University Press, 2007.
[5]
M. E. J. Newman, Models of the small world, J. Stat. Phys., vol. 101, no. 3, pp. 819-841, 2000.
Google Scholar
[6]
B. Hayes, Computing science: Graph theory in practice: Part 1, Am. Sci., vol. 88, no. 1, pp. 9-13, 2000.
Google Scholar
[7]
G. P. Liu, Y. Q. Xia, J. Chen, D. Rees, and W. S. Hu, Networked predictive control of systems with random network delays in both forward and feedback channels, IEEE Trans. Ind. Electron., vol. 54, no. 3, pp. 1282-1297, 2007.
Google Scholar
[8]
W. B. Du, M. Y. Zhang, Y. Zhang, X. B. Cao, and J. Zhang, Delay causality network in air transport systems, Transp. Res. Part E: Logist. Transp. Rev., vol. 118, pp. 466-476, 2018.
Google Scholar
[9]
J. Du, J. Song, Y. Ren, and J. T. Wang, Convergence of broadband and broadcast/multicast in maritime information networks, Tsinghua Science and Technolology, vol. 26, no. 5, pp. 592-607, 2021.
Google Scholar
[10]
A. D. F. Santos, D. Valério, J. A. T. Machado, and A. M. Lopes, A fractional perspective to the modelling of Lisbon’s public transportation network, Transportation, vol. 46, no. 5, pp. 1893-1913, 2019.
Google Scholar
[11]
F. T. S. Chan and H. J. Qi, An innovative performance measurement method for supply chain management, Supply Chain Manage., vol. 8, no. 3, pp. 209-223, 2003.
Google Scholar
[12]
Y. Ma, G. Q. Cheng, Z. Liu, and X. X. Liang, Clustering-based link prediction in scientific coauthorship networks, Int. J. Mod. Phys. C, vol. 28, no. 6, p. 1750082, 2017.
Google Scholar
[13]
A. S. Khanna, P. Schumm, and J. A. Schneider, Facebook network structure and awareness of preexposure prophylaxis among young men who have sex with men, Ann. Epidemiol., vol. 27, no. 3, pp. 176-180, 2017.
Google Scholar
[14]
X. Fei, Y. H. Zhang, and W. M. Zheng, XB-SIM: A simulation framework for modeling and exploration of ReRAM-based CNN acceleration design, Tsinghua Science and Technolology, vol. 26, no. 3, pp. 322-334, 2021.
Google Scholar
[15]
B. de Chassey, V. Navratil, L. Tafforeau, M. S. Hiet, A. Aublin-Gex, S. Agaugué, G. Meiffren, F. Pradezynski, B. F. Faria, and T. Chantier, et al., Hepatitis C virus infection protein network, Mol. Syst. Biol., vol. 4, p. 230, 2008.
Google Scholar
[16]
F. Pelisch, T. Tammsalu, B. Wang, E. G. Jaffray, A. Gartner, and R. T. Hay, A SUMO-dependent protein network regulates chromosome congression during oocyte meiosis, Mol. Cell, vol. 65, no. 1, pp. 66-77, 2017.
Google Scholar
[17]
K. Osman, J. H. Yang, E. Roitinger, C. Lambing, S. Heckmann, E. Howell, M. Cuacos, R. Imre, G. Dürnberger, K. Mechtler, et al., Affinity proteomics reveals extensive phosphorylation of the Brassica chromosome axis protein ASY1 and a network of associated proteins at prophase I of meiosis, Plant J., vol. 93, no. 1, pp. 17-33, 2018.
Google Scholar
[18]
P. H. Reingen, B. L. Foster, J. J. Brown, and S. B. Seidman, Brand congruence in interpersonal relations: A social network analysis, J. Consum. Res., vol. 11, no. 3, pp. 771-783, 1984.
Google Scholar
[19]
W. M. Bowler and D. J. Brass, Relational correlates of interpersonal citizenship behavior: A social network perspective, J. Appl. Psychol., vol. 91, no. 1, pp. 70-82, 2006.
Google Scholar
[20]
S. Pillai, K. Szekeres, N. J. Lawrence, S. P. Chellappan, and G. Blanck, Regulation of interlocking gene regulatory network subcircuits by a small molecule inhibitor of retinoblastoma protein (RB) phosphorylation: Cancer cell expression of HLA-DR, Gene, vol. 512, no. 2, pp. 403-407, 2013.
Google Scholar
[21]
M. Shibata and K. Sugimoto, A gene regulatory network for root hair development, J. Plant Res., vo. 132, no. 3, pp. 301-309, 2019.
Google Scholar
[22]
C. G. Toenhake, S. A. K. Fraschkaet, M. S. Vijayabaskar, D. R. Westhead, S. J. van Heeringen, and R. Bártfai, Chromatin accessibility-based characterization of the gene regulatory network underlying Plasmodium falciparum blood-stage development, Cell Host Microbe, vol. 23, no. 4, pp. 557-569, 2018.
Google Scholar
[23]
P. C. Ma, B. Jiang, Z. G. Lu, N. Li, and Z. W. Jiang, Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields, Tsinghua Science and Technolology, vol. 26, no. 3, pp. 259-265, 2021.
Google Scholar
[24]
D. Garlaschelli and M. I. Loffredo, Patterns of link reciprocity in directed networks, Phys. Rev. Lett., vol. 93, no. 26, p. 268701, 2004.
Google Scholar
[25]
A. A. Tsonis and K. L. Swanson, Topology and predictability of El Niño and La Niña networks, Phys. Rev. Lett., vol. 100, no. 22, p. 228502, 2008.
Google Scholar
[26]
J. F. Donges, Y. Zou, N. Marwan, and J. Kurths, The backbone of the climate network, Europhys. Lett., vol. 87, no. 4, p. 48007, 2009.
Google Scholar
[27]
J. F. Donges, Y. Zou, N. Marwan, and J. Kurths, Complex networks in climate dynamics, Eur. Phys. J. Spec. Top., vol. 174, no. 1, pp. 157-179, 2009.
Google Scholar
[28]
S. van den Elzen, D. Holten, J. Blaas, and J. J. van Wijk, Dynamic network visualization with extended massive sequence views, IEEE Trans. Vis. Comput. Graph., vol. 20, no. 8, pp. 1087-1099, 2014.
Google Scholar
[29]
Y. H. Sun, W. C. Duan, Y. Q. Li, and H. Xie, A construction method of spatial network of ANP-BOCR for complex systems, (in Chinese), Chin. J. Manage. Sci., vol. 24, no. 2, pp. 144-152, 2016.
Google Scholar
[30]
Q. Zhang and Y. Ma, High-speed train service network features based on complex network, (in Chinese), J. Railway Sci. Eng., vol. 15, no. 3, pp. 559-566, 2018.
Google Scholar
[31]
N. Wang, Futures time series characteristics of complex network and portfolio strategy research, (in Chinese), PhD dissertation, China University of Geosciences (Beijing), Beijing, China, 2016.
[32]
R. Lambiotte, J. C. Delvenne, and M. Barahona, Laplacian dynamics and multiscale modular structure in networks, Physics, vol. 10, pp. 812-841, 2009.
Google Scholar
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Publication history

Received: 01 April 2021
Revised: 26 April 2021
Accepted: 28 April 2021
Published: 17 August 2021
Issue date: February 2022

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© The author(s) 2022

Acknowledgements

This work was supported by the Graduate Innovation Project of Beijing Jiaotong University (No. 2020YJS098).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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