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Environmental assessment under uncertainty using Dempster–Shafer theory and Z-numbers

  • Bingyi KangEmail author
  • Pengdan Zhang
  • Zhenyu Gao
  • Gyan Chhipi-Shrestha
  • Kasun Hewage
  • Rehan Sadiq
Original Research

Abstract

Environmental assessment and decision making is complex leading to uncertainty due to multiple criteria involved with uncertain information. Uncertainty is an unavoidable and inevitable element of any environmental evaluation process. The published literatures rarely include the studies on uncertain data with variable fuzzy reliabilities. This research has proposed an environmental evaluation framework based on Dempster–Shafer theory and Z-numbers. Of which a new notion of the utility of fuzzy number is proposed to generate the basic probability assignment of Z-numbers. The framework can effectively aggregate uncertain data with different fuzzy reliabilities to obtain a comprehensive evaluation measure. The proposed model has been applied to two case studies to illustrate the proposed framework and show its effectiveness in environmental evaluations. Results show that the proposed framework can improve the previous methods with comparability considering the reliability of information using Z-numbers. The proposed method is more flexible comparing with previous work.

Keywords

Environmental assessment Environmental risk Dempster–Shafer theory Z-number Data fusion Fuzzy reliability 

Notes

Acknowledgements

The work is supported by a startup fund from Northwest A&F University (no. Z109021812).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

About the dataset used for model development

The data related to CDI were obtained from the Interior Health Authority (IHA), British Columbia. The database included the retrospective data of 22 hospitals spanned over 10 fiscal quarters from April 2012 to June 2014. The data were comprised of nursing staff to beds ratio (full-time); hand hygiene compliance; amount of Fluoroquinolone and Cephalosporins, and CDI cases. All cases of CDI were identified by Infection Prevention and Control, IHA. CDI incidence (output variable) was expressed in terms of number of CDI cases per 10,000 patient-days. For conveyance, the CDI related data of different hospitals of Quarter 2 of the fiscal year 2013 is given as an example in Table 12.

References

  1. Abiyev RH, Akkaya N, Gunsel I (2018) Control of omnidirectional robot using z-number-based fuzzy system. IEEE Trans Syst Man Cybern Syst 49(1):238–252.  https://doi.org/10.1109/TSMC.2018.2834728 CrossRefGoogle Scholar
  2. Aboutorab H, Saberi M, Asadabadi MR, Hussain O, Chang E (2018) Zbwm: the z-number extension of best worst method and its application for supplier development. Expert Syst Appl 107:115–125.  https://doi.org/10.1016/j.eswa.2018.04.015 CrossRefGoogle Scholar
  3. Aliev R, Memmedova K (2015) Application of z-number based modeling in psychological research. Comput Intell Neurosci 2015:1–7.  https://doi.org/10.1155/2015/760403 CrossRefGoogle Scholar
  4. Aliev RA, Alizadeh AV, Huseynov OH (2015a) The arithmetic of discrete z-numbers. Inf Sci 290:134–155.  https://doi.org/10.1016/j.ins.2014.08.024 MathSciNetzbMATHCrossRefGoogle Scholar
  5. Aliev RA, Alizadeh AV, Huseynov OH, Jabbarova K (2015b) Z-number-based linear programming. Int J Intell Syst 30(5):563–589.  https://doi.org/10.1002/int.21709 CrossRefGoogle Scholar
  6. Aliev R, Huseynov O, Zeinalova L (2016) The arithmetic of continuous z-numbers. Inf Sci 373:441–460.  https://doi.org/10.1016/j.ins.2016.08.078 CrossRefGoogle Scholar
  7. Aliev R, Pedrycz W, Huseynov O (2018) Hukuhara difference of z-numbers. Inf Sci 466:13–24.  https://doi.org/10.1016/j.ins.2014.08.024 MathSciNetCrossRefGoogle Scholar
  8. Azadeh A, Kokabi R (2016) Z-number dea: a new possibilistic dea in the context of z-numbers. Adv Eng Inf 30(3):604–617.  https://doi.org/10.1016/j.aei.2016.07.005 CrossRefGoogle Scholar
  9. Bozdag E, Asan U, Soyer A, Serdarasan S (2015) Risk prioritization in failure mode and effects analysis using interval type-2 fuzzy sets. Expert Syst Appl 42(8):4000–4015.  https://doi.org/10.1016/j.eswa.2015.01.015 CrossRefGoogle Scholar
  10. Chen SM (1996) New methods for subjective mental workload assessment and fuzzy risk analysis. Cybern Syst 27(5):449–472.  https://doi.org/10.1080/019697296126417 zbMATHCrossRefGoogle Scholar
  11. Chen TY (2014) A promethee-based outranking method for multiple criteria decision analysis with interval type-2 fuzzy sets. Soft Comput 18(5):923–940.  https://doi.org/10.1007/s00500-013-1109-4 zbMATHCrossRefGoogle Scholar
  12. Chen SJ, Chen SM (2003) Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE Trans Fuzzy Syst 11(1):45–56.  https://doi.org/10.1109/TFUZZ.2002.806316 CrossRefGoogle Scholar
  13. Chen L, Deng X (2018a) A modified method for evaluating sustainable transport solutions based on AHP and Dempster Shafer evidence theory. Appl Sci 8(4):563.  https://doi.org/10.3390/app8040563 CrossRefGoogle Scholar
  14. Chen L, Deng Y (2018b) A new failure mode and effects analysis model using Dempster–Shafer evidence theory and grey relational projection method. Eng Appl Artif Intell 76:13–20.  https://doi.org/10.1016/j.engappai.2018.08.010 CrossRefGoogle Scholar
  15. Chen TY, Ku TC (2008) Importance-assessing method with fuzzy number-valued fuzzy measures and discussions on TFNS and TRFNS. Int J Fuzzy Syst 10(2):92–103.  https://doi.org/10.30000/IJFS.200806.0003 MathSciNetCrossRefGoogle Scholar
  16. Chhipi-Shrestha G, Mori J, Hewage K, Sadiq R (2016) Clostridium difficile infection incidence prediction in hospitals (cdiiph): a predictive model based on decision tree and fuzzy techniques. Stoch Environ Res Risk Assess 31(2):417–430.  https://doi.org/10.1007/s00477-016-1227-5 CrossRefGoogle Scholar
  17. Chou CC (2014) A new similarity measure of fuzzy numbers. J Intell Fuzzy Syst 26(1):287–294.  https://doi.org/10.3233/IFS-120737 MathSciNetzbMATHCrossRefGoogle Scholar
  18. Dempster AP (1967) Upper and lower probabilities induced by a multi-valued mapping. Ann Math Stat 38:325–339.  https://doi.org/10.1214/aoms/1177698950 zbMATHCrossRefGoogle Scholar
  19. Deng X, Deng Y (2018) D-AHP method with different credibility of information. Soft Comput 23(2):683–691.  https://doi.org/10.1007/s00500-017-2993-9 CrossRefGoogle Scholar
  20. Deng Y, Shi W, Du F, Liu Q (2004) A new similarity measure of generalized fuzzy numbers and its application to pattern recognition. Pattern Recognit Lett 25(8):875–883.  https://doi.org/10.1016/j.patrec.2004.01.019 CrossRefGoogle Scholar
  21. Deng Y, Jiang W, Sadiq R (2011) Modeling contaminant intrusion in water distribution networks: a new similarity-based dst method. Expert Syst Appl 38(1):571–578.  https://doi.org/10.1016/j.eswa.2010.07.004 CrossRefGoogle Scholar
  22. Dubberke ER, Yan Y, Reske KA, Butler AM, Doherty J, Pham V, Fraser VJ (2011) Development and validation of a Clostridium difficile infection risk prediction model. Infect Control Hosp Epidemiol 32(04):360–366.  https://doi.org/10.1086/658944 CrossRefGoogle Scholar
  23. Dubois D, Fusco G, Prade H, Tettamanzi AG (2017a) Uncertain logical gates in possibilistic networks: theory and application to human geography. Int J Approx Reason 82:101–118.  https://doi.org/10.1016/j.ijar.2016.11.009 MathSciNetzbMATHCrossRefGoogle Scholar
  24. Dubois D, Prade H, Rico A, Teheux B (2017b) Generalized qualitative sugeno integrals. Inf Sci 415:429–445.  https://doi.org/10.1016/j.ins.2017.05.037 CrossRefGoogle Scholar
  25. Dubois D, Prade H, Schockaert S (2017c) Generalized possibilistic logic: foundations and applications to qualitative reasoning about uncertainty. Artif Intell 252:139–174.  https://doi.org/10.1016/j.artint.2017.08.001 MathSciNetzbMATHCrossRefGoogle Scholar
  26. Ezadi S, Allahviranloo T, Mohammadi S (2018) Two new methods for ranking of z-numbers based on sigmoid function and sign method. Int J Intell Syst 33(7):1476–1487.  https://doi.org/10.1002/int.21987 CrossRefGoogle Scholar
  27. Fei L, Deng Y (2018) A new divergence measure for basic probability assignment and its applications in extremely uncertain environments. Int J Intell Syst.  https://doi.org/10.1002/int.22066 CrossRefGoogle Scholar
  28. Fei L, Deng Y, Hu Y (2018a) DS-VIKOR: a new multi-criteria decision-making method for supplier selection. Int J Fuzzy Syst.  https://doi.org/10.1007/s40815-018-0543-y CrossRefGoogle Scholar
  29. Fei L, Wang H, Chen L, Deng Y (2018b) A new vector valued similarity measure for intuitionistic fuzzy sets based on owa operators. Iran J Fuzzy Syst.  https://doi.org/10.22111/ijfs.2018.4302 CrossRefGoogle Scholar
  30. Han Y, Deng Y (2018) An enhanced fuzzy evidential DEMATEL method with its application to identify critical success factors. Soft Comput 22(15):5073–5090.  https://doi.org/10.1007/s00500-018-3311-x CrossRefGoogle Scholar
  31. Han Y, Deng Y (2018a) An evidential fractal ahp target recognition method. Def Sci J 68(4):367–373.  https://doi.org/10.14429/dsj.68.11737 CrossRefGoogle Scholar
  32. Han Y, Deng Y (2018b) A hybrid intelligent model for assessment of critical success factors in high risk emergency system. J Ambient Intell Humaniz Comput 9(6):1933–1953.  https://doi.org/10.1007/s12652-018-0882-4 CrossRefGoogle Scholar
  33. Han Y, Deng Y (2018c) A novel matrix game with payoffs of maxitive belief structure. Int J Intell Syst.  https://doi.org/10.1002/int.22072 CrossRefGoogle Scholar
  34. Hg Peng, Jq Wang (2018) A multicriteria group decision-making method based on the normal cloud model with zadeh’s z-numbers. IEEE Trans Fuzzy Syst 26(6):3246–3260.  https://doi.org/10.1109/TFUZZ.2018.2816909 CrossRefGoogle Scholar
  35. Huang Y, Huang G, Hu Q (2012) A fuzzy-parameterised stochastic modelling system for predicting multiphase subsurface transport under dual uncertainties. Civ Eng Environ Syst 29(2):91–105.  https://doi.org/10.1080/10286608.2012.663355 CrossRefGoogle Scholar
  36. Jiang W, Xie C, Zhuang M, Shou Y, Tang Y (2016) Sensor data fusion with z-numbers and its application in fault diagnosis. Sensors 16(9):1509.  https://doi.org/10.3390/s16091509 CrossRefGoogle Scholar
  37. Jiang W, Wei B, Liu X, Li X, Zheng H (2018) Intuitionistic fuzzy power aggregation operator based on entropy and its application in decision making. Int J Intell Syst 33(1):49–67.  https://doi.org/10.1002/int.21939 CrossRefGoogle Scholar
  38. Kang B, Hu Y, Deng Y, Zhou D (2016) A new methodology of multicriteria decision-making in supplier selection based on z-numbers. Math Probl Eng 8475:987.  https://doi.org/10.1155/2016/8475987 MathSciNetzbMATHCrossRefGoogle Scholar
  39. Kang B, Deng Y, Hewage K, Sadiq R (2018d) A method of measuring uncertainty for z-number. IEEE Trans Fuzzy Syst.  https://doi.org/10.1109/TFUZZ.2018.2868496 CrossRefGoogle Scholar
  40. Kang B, Chhipi-Shrestha G, Deng Y, Hewage K, Sadiq R (2018a) Stable strategies analysis based on the utility of Z-number in the evolutionary games. Appl Math Comput 324:202–217.  https://doi.org/10.1016/j.amc.2017.12.006 MathSciNetCrossRefGoogle Scholar
  41. Kang B, Chhipi-Shrestha G, Deng Y, Mori J, Hewage K, Sadiq R (2018b) Development of a predictive model for Clostridium difficile infection incidence in hospitals using gaussian mixture model and Dempster–Shafer theory. Stoch Environ Res Risk Assess 32(6):1743–1758.  https://doi.org/10.1007/s00477-017-1459-z CrossRefGoogle Scholar
  42. Kang B, Deng Y, Hewage K, Sadiq R (2018c) Generating Z-number based on OWA weights using maximum entropy. Int J Intell Syst 33(8):1745–1755.  https://doi.org/10.1002/int.21995 CrossRefGoogle Scholar
  43. Kang B, Deng Y, Sadiq R (2018e) Total utility of z-number. Appl Intell 48(3):703–729.  https://doi.org/10.1007/s10489-017-1001-5 CrossRefGoogle Scholar
  44. Katagiri H, Uno T, Kato K, Tsuda H, Tsubaki H (2013) Random fuzzy multi-objective linear programming: optimization of possibilistic value at risk (pvar). Expert Syst Appl 40(2):563–574.  https://doi.org/10.1016/j.eswa.2012.07.064 CrossRefGoogle Scholar
  45. Kentel E, Aral MM (2007) Risk tolerance measure for decision-making in fuzzy analysis: a health risk assessment perspective. Stoch Environ Res Risk Assess 21(4):405–417.  https://doi.org/10.1007/s00477-006-0073-2 MathSciNetzbMATHCrossRefGoogle Scholar
  46. Kirmeyer GJ, Martel K (2001) Pathogen intrusion into the distribution system. American Water Works Association, Washington, DCGoogle Scholar
  47. Lee HS (2002) Optimal consensus of fuzzy opinions under group decision making environment. Fuzzy Set Syst 132(3):303–315.  https://doi.org/10.1016/S0165-0114(02)00056-8 MathSciNetzbMATHCrossRefGoogle Scholar
  48. Li Z, Chen L (2019) A novel evidential FMEA method by integrating fuzzy belief structure and grey relational projection method. Eng Appl Artif Intell 77:136–147.  https://doi.org/10.1016/j.engappai.2018.10.005 CrossRefGoogle Scholar
  49. Li Y, Deng Y (2018) Generalized ordered propositions fusion based on belief entropy. Int J Comput Commun Control 13(5):792–807.  https://doi.org/10.15837/ijccc.2018.5.3244 CrossRefGoogle Scholar
  50. Li Y, Huang G (2011) Integrated modeling for optimal municipal solid waste management strategies under uncertainty. J Environ Eng 137(9):842–853.  https://doi.org/10.1061/(ASCE)EE.1943-7870.0000393 CrossRefGoogle Scholar
  51. Li HL, Huang GH, Zou Y (2008) An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution. Stoch Environ Res Risk Assess 22(6):789–803.  https://doi.org/10.1007/s00477-007-0187-1 MathSciNetCrossRefGoogle Scholar
  52. Li M, Zhang Q, Deng Y (2018) Evidential identification of influential nodes in network of networks. Chaos Solition Fract 117:283–296.  https://doi.org/10.1016/j.chaos.2018.04.033 MathSciNetCrossRefGoogle Scholar
  53. Lindley TR (2001) A framework to protect water distribution systems against potential intrusions. Ph.D. thesis, University of CincinnatiGoogle Scholar
  54. Lu H, Huang G, Zeng G, Maqsood I, He L (2008) An inexact two-stage fuzzy-stochastic programming model for water resource management. Water Resour Manag 22(8):991–1016.  https://doi.org/10.1007/s11269-007-9206-8 CrossRefGoogle Scholar
  55. Mo H, Deng Y (2018) A new MADA methodology based on D numbers. Int J Fuzzy Syst 20(8):2458–2469.  https://doi.org/10.1007/s40815-018-0514-3 MathSciNetCrossRefGoogle Scholar
  56. Mohsen O, Fereshteh N (2017) An extended vikor method based on entropy measure for the failure modes risk assessment—a case study of the geothermal power plant (gpp). Saf Sci 92:160–172.  https://doi.org/10.1016/j.ssci.2016.10.006 CrossRefGoogle Scholar
  57. Pal SK, Banerjee R, Dutta S, Sarma SS (2013) An insight into the z-number approach to CWW. Fundam Inform 124(1–2):197–229.  https://doi.org/10.3233/FI-2013-831 MathSciNetCrossRefGoogle Scholar
  58. Pan L, Deng Y (2018) A new belief entropy to measure uncertainty of basic probability assignments based on belief function and plausibility function. Entropy 20(11):842.  https://doi.org/10.3390/e20110842 CrossRefGoogle Scholar
  59. Patel P, Khorasani ES, Rahimi S (2016) Modeling and implementation of z-number. Soft Comput 20(4):1341–1364.  https://doi.org/10.1007/s00500-015-1591-y CrossRefGoogle Scholar
  60. Sabahi F (2018) Introducing validity into self-organizing fuzzy neural network applied to impedance force control. Fuzzy Set Syst 337:113–127.  https://doi.org/10.1016/j.fss.2017.09.007 MathSciNetzbMATHCrossRefGoogle Scholar
  61. Sadiq R, Tesfamariam S (2009) Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (if-AHP). Stoch Environ Res Risk Assess 23(1):75–91.  https://doi.org/10.1007/s00477-007-0197-z MathSciNetCrossRefzbMATHGoogle Scholar
  62. Sadiq R, Kleiner Y, Rajani B (2004) Aggregative risk analysis for water quality failure in distribution networks. J Water Supply Res Technol 53(4):241–261.  https://doi.org/10.2166/aqua.2004.0020 CrossRefGoogle Scholar
  63. Sadiq R, Kleiner Y, Rajani B (2006) Estimating risk of contaminant intrusion in water distribution networks using Dempster–Shafer theory of evidence. Civ Eng Environ Syst 23(3):129–141.  https://doi.org/10.1080/10286600600789276 CrossRefGoogle Scholar
  64. Sahrom NA, Dom RM (2015) A z-number extension of the hybrid analytic hierarchy process-fuzzy data envelopment analysis for risk assessment. In: IEEE 2015 international conference on research and education in mathematics (ICREM7), pp 19–24.  https://doi.org/10.1109/ICREM.2015.7357019
  65. Shafer G (1976) A mathematical theory of evidence. Princeton University Press, PrincetonzbMATHGoogle Scholar
  66. Shen KW, Wang JQ, Wang TL (2018) The arithmetic of multidimensional z-number. J Intell Fuzzy Syst.  https://doi.org/10.3233/JIFS-18927 CrossRefGoogle Scholar
  67. Simor AE, Williams V, McGeer A, Raboud J, Larios O, Weiss K, Hirji Z, Laing F, Moore C, Gravel D (2013) Prevalence of colonization and infection with methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus and of Clostridium difficile infection in canadian hospitals. Prevalence 34(7):687–693.  https://doi.org/10.1086/670998 CrossRefGoogle Scholar
  68. Siuta D, Markowski AS, Mannan MS (2013) Uncertainty techniques in liquefied natural gas (LNG) dispersion calculations. J Loss Prev Proc Process Ind 26(3):418–426.  https://doi.org/10.1016/j.jlp.2012.07.020 CrossRefGoogle Scholar
  69. Smets P (2000) Data fusion in the transferable belief model. In: IEEE proceedings of the third international conference on information fusion, 2000. FUSION 2000, vol 1, pp PS21–PS33.  https://doi.org/10.1109/IFIC.2000.862713
  70. Subagadis YH, Schütze N, Grundmann J (2016) A fuzzy-stochastic modeling approach for multiple criteria decision analysis of coupled groundwater-agricultural systems. Water Resour Manag 30(6):2075–2095.  https://doi.org/10.1007/s11269-016-1270-5 CrossRefGoogle Scholar
  71. Tanner J, Khan D, Anthony D, Paton J (2009) Waterlow score to predict patients at risk of developing Clostridium difficile-associated disease. J Hosp Infect 71(3):239–244.  https://doi.org/10.1016/j.jhin.2008.11.017 CrossRefGoogle Scholar
  72. Wang S, Huang G, Baetz BW (2015) An inexact probabilistic–possibilistic optimization framework for flood management in a hybrid uncertain environment. IEEE Trans Fuzzy Syst 23(4):897–908.  https://doi.org/10.1109/TFUZZ.2014.2333094 CrossRefGoogle Scholar
  73. Wang C, Li Y, Huang G, Zhang J (2016a) A type-2 fuzzy interval programming approach for conjunctive use of surface water and groundwater under uncertainty. Inf Sci 340:209–227.  https://doi.org/10.1016/j.ins.2016.01.026 CrossRefGoogle Scholar
  74. Wang LE, Liu HC, Quan MY (2016b) Evaluating the risk of failure modes with a hybrid mcdm model under interval-valued intuitionistic fuzzy environments. Comput Ind Eng 102:175–185.  https://doi.org/10.1016/j.cie.2016.11.003 CrossRefGoogle Scholar
  75. Xiao F (2018a) A hybrid fuzzy soft sets decision making method in medical diagnosis. IEEE Access 6:25300–25312.  https://doi.org/10.1109/ACCESS.2018.2820099 CrossRefGoogle Scholar
  76. Xiao F (2018b) An improved method for combining conflicting evidences based on the similarity measure and belief function entropy. Int J Fuzzy Syst 20(4):1256–1266.  https://doi.org/10.1007/s40815-017-0436-5 MathSciNetCrossRefGoogle Scholar
  77. Xiao F (2018c) A novel multi-criteria decision making method for assessing health-care waste treatment technologies based on D numbers. Eng Appl Artif Intell 71(2018):216–225.  https://doi.org/10.1016/j.engappai.2018.03.002 CrossRefGoogle Scholar
  78. Xiao F (2019) Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy. Inf Fusion 46(2019):23–32.  https://doi.org/10.1016/j.inffus.2018.04.003 CrossRefGoogle Scholar
  79. Yaakob AM, Gegov A (2016) Interactive topsis based group decision making methodology using z-numbers. Int J Comput Intell Syst 9(2):311–324.  https://doi.org/10.1080/18756891.2016.1150003 CrossRefGoogle Scholar
  80. Yager RR (2012) On z-valuations using zadeh’s z-numbers. Int J Intell Syst 27(3):259–278.  https://doi.org/10.1002/int.21521 CrossRefGoogle Scholar
  81. Yang H, Deng Y, Jones J (2018) Network division method based on cellular growth and physarum-inspired network adaptation. Int J Unconv Comput 13(6):477–491Google Scholar
  82. Yin L, Deng Y (2018) Toward uncertainty of weighted networks: an entropy-based model. Phys A 508:176–186.  https://doi.org/10.1016/j.physa.2018.05.067 CrossRefGoogle Scholar
  83. Zadeh LA (2011) A note on z-numbers. Inf Sci 181(14):2923–2932.  https://doi.org/10.1016/j.ins.2011.02.022 zbMATHCrossRefGoogle Scholar
  84. Zhang X (2016) Multicriteria pythagorean fuzzy decision analysis: a hierarchical qualiflex approach with the closeness index-based ranking methods. Inf Sci 330:104–124.  https://doi.org/10.1016/j.ins.2015.10.012 CrossRefGoogle Scholar
  85. Zhang H, Deng Y (2018) Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory. Adv Mech Eng 10(11):1–10.  https://doi.org/10.1177/1687814018809184 CrossRefGoogle Scholar
  86. Zhang W, Deng Y (2018) Combining conflicting evidence using the DEMATEL method. Soft Comput.  https://doi.org/10.1007/s00500-018-3455-8 CrossRefGoogle Scholar
  87. Zhou X, Hu Y, Deng Y, Chan FTS, Ishizaka A (2018) A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP. Ann Oper Res 271(2):1045–1066.  https://doi.org/10.1007/s10479-018-2769-3 MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Information EngineeringNorthwest A&F UniversityYanglingChina
  2. 2.School of EngineeringUniversity of British Columbia OkanaganKelownaCanada

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