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Data acquisition and transmission of laboratory local area network based on fuzzy DEMATEL algorithm

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Abstract

The traditional laboratory LAN technology has the problem that the transmission efficiency is lower than the error rate. In this paper, a data acquisition and transmission method based on fuzzy DEMATEL algorithm for laboratory LAN is proposed. By laboratory LAN Internet data acquisition model, the laboratory LAN statistical characteristics of the original data, extract the laboratory LAN of higher order spectral characteristics of sampled data, the fuzzy c-means clustering model applied in the laboratory LAN data information fusion processing, and use the wireless sensor network detection technology, to achieve optimum laboratory LAN data transmission. The simulation results show that the method can accurately realize the prediction and evaluation of the laboratory LAN data, the data transmission accuracy is high, the characteristics of the laboratory LAN transmission information can be accurately calibrated, the prediction and evaluation of the laboratory LAN transmission information has a good application value in the laboratory LAN transmission control.

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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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References

  1. Rong, C. T., Lin, C. B., Silva, Y., et al. (2017). Fast and scalable distributed set similarity joins for big data analytics. in Proceedings of the 2017 IEEE 33rd international conference on data engineering (1–12). Piscataway, NJ: IEEE.

  2. Kimmett, B., Srinivasan, V., & Thomo, A. (2015). Fuzzy joins in MapReduce: An experimental study. Proceedings of the VLDB Endowment, 8(12), 1514–1517.

    Article  Google Scholar 

  3. Zhao, Q. Q., & Huang, T. M. (2018). Multi-objective decision making based on entropy weighted-vague sets. Journal of Computer Applications, 38(5), 1250–1253.

    Google Scholar 

  4. Li, A. N., Zhang, X., Zhang, B. Y., Liu, C. Y., & Zhao, X. N. (2017). Research on performance evaluation method of public cloud storage system. Journal of Computer Applications, 37(5), 1229–1235.

    Google Scholar 

  5. Lin, J. M., Ban, W. J., Wang, J. Y., et al. (2016). Query optimization for distributed database based on parallel genetic algorithm and max-min ant system. Journal of Computer Applications, 36(3), 675–680.

    Google Scholar 

  6. Zhou, X. P., Zhang, X. F., & Zhao, X. N. (2014). Cloud storage performance evaluation research. Computer Science, 41(4), 190–194.

    Google Scholar 

  7. Zhang, H. L., Li, X. F., Yang, S. B., et al. (2019). Dual closed-loop fuzzy PID depth control for deep-sea self-holding intelligent buoy. Information and control, 48(2), 202–208, 216.

    Google Scholar 

  8. Chen, L., Pan, B. B., Cao, Z. L., et al. (2017). Research status and prospects of automatic profiling floats. Journal of Ocean Technology, 36(2), 1–9.

    Google Scholar 

  9. Chu, Z., Xiang, X., Zhu, D., et al. (2017). Adaptive fuzzy sliding mode diving control for autonomous underwater vehicle with input constraint. International Journal of Fuzzy Systems, 8, 1–10.

    Google Scholar 

  10. Jiang, C., Wan, L., & Sun, Y. (2017). Design of novel S-plane controller of autonomous underwater vehicle established on sliding mode control. Journal of Harbin Institute of Technology, 24(2), 58–64.

    Google Scholar 

  11. Fan, C. L., Song, Y. F., Lei, L., et al. (2018). Evidence reasoning for temporal uncertain information based on relative reliability evaluation. Expert Systems with Applications, 113, 264–276.

    Article  Google Scholar 

  12. Gu, Q., Yuan, L., Ning, B., et al. (2012). A noval classification algorithm for imbalanced datasets based on hybrid resampling strategy. Computer Engineering and Science, 34(10), 128–134.

    Google Scholar 

  13. Sun, B., Wang, J. D., Chen, H. Y., et al. (2014). Diversity measures in ensemble learning. Control and Decision, 29(3), 385–395.

    MATH  Google Scholar 

  14. Li, N., Yu, Y., Zhou, Z. H. (2012). Diversity regularized ensemble pruning. in Proceedings of the 2012 Joint European conference on machine learning and knowledge discovery in databases, LNCS 7523 (330–345). Berlin: Springer.

  15. Parvin, H., Mirnabibaboli, M., & Alinejad-Rokny, H. (2015). Proposing a classifier ensemble framework based on classifier selection and decision tree. Engineering Applications of Artificial Intelligence, 37(8), 34–42.

    Article  Google Scholar 

  16. Verhage, M. L., Schuengel, C., Madigan, S., et al. (2016). Narrowing the transmission gap: A synthesis of three decades of research on intergenerational transmission of attachment. Psychological Bulletin, 142(4), 337.

    Article  Google Scholar 

  17. Hassan, A. S., Pybus, O. G., Sanders, E. J., et al. (2017). Defining HIV-1 transmission clusters based on sequence data: A systematic review and perspectives. AIDS, 31(9), 1211.

    Article  Google Scholar 

  18. Colucci, G., Giabbani, E., Barizzi, G., et al. (2011). Laboratory-based ROTEM analysis: Implementing pneumatic tube transport and real-time graphic transmission. International Journal of Laboratory Hematology, 33(4), 441–446.

    Article  Google Scholar 

  19. Steckbeck, R., & Aronoff, R. D. (1990). Local area network improves catheterization laboratory productivity. Journal of the American College of Cardiology, 15(2), 269.

    Article  Google Scholar 

  20. Furse, C., Woodward, R. J., & Jensen, M. A. (2004). Laboratory project in wireless FSK receiver design. IEEE Transactions on Education, 47(1), 18–25.

    Article  Google Scholar 

  21. Abdullah, L., & Zulkifli, N. (2018). A new DEMATEL method based on interval type-2 fuzzy sets for developing causal relationship of knowledge management criteria. Neural Computing and Applications, 13(5), 1–17.

    Google Scholar 

  22. Sun, Y. H., Han, W., & Duan, W. C. (2017). Review on research progress of DEMATEL algorithm for complex systems. Control and Decision, 32(3), 385–392.

    MATH  Google Scholar 

  23. Asan, U., Kadaifci, C., Bozdag, E., et al. (2018). A new approach to DEMATEL based on interval-valued hesitant fuzzy sets. Applied Soft Computing, 66(5), 654–660.

    Google Scholar 

  24. He, L., Shao, F., Ren, L. (2020). Sustainability appraisal of desired contaminated groundwater remediation strategies: An information-entropy-based stochastic multi-criteria preference model. Environment, Development and Sustainability, 23, 1759–1779.

    Article  Google Scholar 

  25. Lv, Z., & Qiao, L. (2020). Analysis of healthcare big data. Future Generation Computer Systems, 2020(109), 103–110.

    Article  Google Scholar 

  26. Ruan, F., & Wan, B. (2018). Simulation of network data transmission to prevent attack security assessment. Computer Simulation, 35(7), 351–354, 413.

    Google Scholar 

  27. Keskin, G. A. (2018). Using integrated fuzzy DEMATEL and fuzzy C: Means algorithm for supplier evaluation and selection. International Journal of Production Research, 53(12), 3586–3602.

    Article  Google Scholar 

  28. Luthra, S., Govindan, K., Kharb, R. K., et al. (2016). Evaluating the enablers in solar power developments in the current scenario using fuzzy DEMATEL: An Indian perspective. Renewable and Sustainable Energy Reviews, 63, 379–397.

    Article  Google Scholar 

  29. Ni, T., Yao, Y., Chang, H., Lu, L., Liang, H., Yan, A., Huang, Z., & Wen, X. (2020). LCHR-TSV: Novel low cost and highly repairable honeycomb-based TSV redundancy architecture for clustered faults. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(10), 2938–2951.

    Article  Google Scholar 

  30. Ren, J., Zhang, C., & Hao, Q. A. (2020). A theoretical method to evaluate honeynet potency. Future Generation Computer Systems, 116, 76–85.

    Article  Google Scholar 

  31. Marcelino, L. V., Pinto, A. L., & Marques, C. A. (2020). Scientific specialties in Green Chemistry. Iberoamerican Journal of Science Measurement and Communication, 1(1), 005.

    Article  Google Scholar 

  32. Zhu, J., Wang, X., Chen, M., et al. (2019). Integration of BIM and GIS: IFC geometry transformation to shapefile using enhanced open-source approach. Automation in Construction, 106, 102859.

    Article  Google Scholar 

  33. Zhu, J., Wang, X., Wang, P., et al. (2019). Integration of BIM and GIS: Geometry from IFC to shapefile using open-source technology. Automation in Construction, 2019(102), 105–119.

    Article  Google Scholar 

  34. Liou, J. J. H., Chuang, Y. C., & Tzeng, G. H. (2014). A fuzzy integral-based model for supplier evaluation and improvement. Information Sciences, 266, 199–217.

    Article  MathSciNet  Google Scholar 

  35. Sajedi-Hosseini, F., Choubin, B., Solaimani, K., et al. (2018). Spatial prediction of soil erosion susceptibility using FANP: Application of the fuzzy DEMATEL approach. Land Degradation and Development, 29(9), 3092–3103.

    Article  Google Scholar 

  36. Xiong, Z. G., Wu, Y., Ye, C. H., Zhang, X. M., & Xu, F. (2019). Color image chaos encryption algorithm combining CRC and nine palace map. Multimedia Tools and Applications, 22(78), 31035–31055.

    Article  Google Scholar 

  37. Spannenberg, J., Atangana, A., & Vermeulen, P. D. (2019). New approach to groundwater recharge on a regional scale: Uncertainty analysis and application of fractional differentiation. Arabian Journal of Geosciences, 12(16), 511.

    Article  Google Scholar 

  38. Shi, K., Tang, Y., Liu, X., & Zhong, S. (2017). Non-fragile sampled-data robust synchronization of uncertain delayed chaotic Lurie systems with randomly occurring controller gain fluctuation. Isa Transactions, 66, 185–199.

    Article  Google Scholar 

  39. An, Y., Li, Z., Wu, C., Hu, H., Shao, C., Li, B. (2020). Earth pressure field modeling for tunnel face stability evaluation of EPB shield machines based on optimization solution. Discrete & Continuous Dynamical Systems 13(6), 1721–1741.

    Article  MathSciNet  MATH  Google Scholar 

  40. Shi, K., Wang, J., Zhong, S., Tang, Y., & Cheng, J. (2019). Non-fragile memory filtering of T-S fuzzy delayed neural networks based on switched fuzzy sampled-data control. Fuzzy Sets and Systems, 394, 40–64.

    Article  MathSciNet  MATH  Google Scholar 

  41. Shi, K., et al. (2018). Nonfragile asynchronous control for uncertain chaotic Lurie network systems with Bernoulli stochastic process. International Journal of Robust and Nonlinear Control, 28(5), 1693–1714.

    Article  MathSciNet  MATH  Google Scholar 

  42. Wen, D., Zhang, X., Liu, X., & Lei, J. (2017). Evaluating the consistency of current mainstream wearable devices in health monitoring: A comparison under free-living conditions. Journal of Medical Internet Research, 19(3), e68.

    Article  Google Scholar 

  43. Xie, J., Wen, D., Liang, L., Jia, Y., Gao, L., & Lei, J. (2018). Evaluating the validity of current mainstream wearable devices in fitness tracking under various physical activities: Comparative study. Jmir Mhealth Uhealth, 6(4), e94.

    Article  Google Scholar 

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Yang, L. Data acquisition and transmission of laboratory local area network based on fuzzy DEMATEL algorithm. Wireless Netw 28, 2795–2804 (2022). https://doi.org/10.1007/s11276-021-02709-9

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