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Research on intelligent classification of multi-attribute safety information and determination of operating environment

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Abstract

To analyze the system safety through the field system operation data, especially the system operation information provided by the operators, and to obtain the environment scope of the safe operation of the system, the multi-attribute safety information intelligent mining method is adopted. The new definitions and methods of attribute circle classification are proposed. The improvement is using inner attribute polygon and outer attribute polygon to construct the object attribute area. Monte Carlo method is used to calculate the overlap area of the object attribute area, and define overlap and transform with similarity, which is calculated by the overlap degree and the distance between two points in multi-dimensional space. The object classification reasoning method and system adaptive environment analysis method is established. The improved similarity analysis method is more suitable for computer implementation and intelligent analysis. Based on the previous study, this paper also analyzes the similarity of operation safety evaluation given by 30 operators of an electrical system, and obtains the pair wise similarity. The results show that the algorithm classification results have good correspondence with the original data decision level, and the range of the system safe operation environment is obtained, which can lay a foundation for further study of intelligent classification and system safety operation environment optimization.

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References

  • Afraei S, Shahriar K, Madani SH (2019a) Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, section 2: designing classifiers. Tunn Undergr Space Technol 84:522–537

    Article  Google Scholar 

  • Afraei S, Shahriar K, Madani SH (2019b) Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, section 1: literature review and data preprocessing procedure. Tunn Undergr Space Technol 83:324–353

    Article  Google Scholar 

  • Ahmad A, Cuomo S, Wu W et al (2019) Intelligent algorithms and standards for interoperability in internet of things. Fut Generat Comput Syst 92:1187–1191

    Article  Google Scholar 

  • Akpinar P, Khashman A (2017) Intelligent classification system for concrete compressive strength. Procedia Comput Sci 120:712–718

    Article  Google Scholar 

  • Ali Z, Hossain MS, Muhammad G et al (2018) An intelligent healthcare system for detection and classification to discriminate vocal fold disorders. Fut Generat Comput Syst 85:19–28

    Article  Google Scholar 

  • Bergamasco LCC, Nunes FLS (2019) Intelligent retrieval and classification in three-dimensional biomedical images—a systematic mapping. Comput Sci Rev 31:19–38

    Article  Google Scholar 

  • Chaitanya BK, Yadav A (2018) An intelligent fault detection and classification scheme for distribution lines integrated with distributed generators. Comput Electr Eng 69:28–40

    Article  Google Scholar 

  • Chang BG, Zang HY (2018) Time series classifier design based on piecewise dimensionality reduction and updated dynamic time warping. Coden Jyiidu 38(07):1910–1915

    Google Scholar 

  • Chi YX, Zhao SL (2018) Text feature selection based on inclusion degree and frequent pattern. J Chin Inf Proc 32(08):91–102

    Google Scholar 

  • Cui TJ, Li SS (2017) Study on the relationship between system reliability and influencing factors under big data and multi-factors. Cluster Comput. https://doi.org/10.1007/s10586-017-1278-5

    Article  Google Scholar 

  • Cui TJ, Li SS (2018a) Deep learning of system reliability under multi-factor influence based on space fault tree. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3416-2

    Article  Google Scholar 

  • Cui TJ, Li SS (2018b) Study on the construction and application of discrete space fault tree modified by fuzzy structured element. Cluster Comput. https://doi.org/10.1007/s10586-018-2342-5

    Article  Google Scholar 

  • Cui TJ, Li SS (2019a) Research on basic theory of space fault network and system fault evolution process. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04247-0

    Article  Google Scholar 

  • Cui TJ, Li SS (2019b) Research on complex structures in space fault network for fault data mining in system fault evolution process. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2937641

    Article  Google Scholar 

  • Cui TJ, Ma YD (2014) Definition and understand on size set domain and cut set domain based on multi-dimensional space fault tree. China Saf Sci J 24(4):27–32

    Google Scholar 

  • Cui TJ, Ma YD (2015a) Definition of the attribute circle in factors space and its application in object classification. Comput Eng Sci 37(11):2170–2174

    Google Scholar 

  • Cui TJ, Ma YD (2015b) Research on the similarity of object classification of attribute circular and application based on factors space. Fuzzy Syst Math 29(6):56–64

    MATH  Google Scholar 

  • Cui TJ, Ma YD (2015c) The definition and cognition of the factor importance distribution in continuous space fault tree. China Saf Sci J 25(3):24–28

    Google Scholar 

  • Cui TJ, Ma YD (2016) The construction of fuzzy structured element characteristic function and the significance of structure elemented in DSFT. Fuzzy Syst Math 30(2):144–152

    MATH  Google Scholar 

  • Cui TJ, Wang PZ, Li SS (2017) The function structure analysis theory based on the factor space and space fault tree. Cluster Comput 20(2):1387–1398

    Article  Google Scholar 

  • Gao XT (2018) Modeling and optimization of gene microarray date classification based on intelligent optimization. Zhejiang University, Hangzhou

    Google Scholar 

  • Guo ZR (2018) Research of time series classification model based on bp neural network and semi-supervised learning. Taiyuan University of Technology, Taiyuan

    Google Scholar 

  • Hen WB, Zeng LL (2019) Intelligent face recognition algorithm based on spectral feature extraction and linear regression classification. Appl Res Comput. http://kns.cnki.net/kcms/detail/51.1196.TP.20180811.1326.038.html. Accessed 1 May 2019

  • Herbert D, Kang BH (2018) Intelligent conversation system using multiple classification ripple down rules and conversational context. Expert Syst Appl 112:342–352

    Article  Google Scholar 

  • Isa NAM, Sani ZM, Al-Batah MS (2011) Automated Intelligent real-time system for aggregate classification. Int J Miner Process 100:1–2

    Article  Google Scholar 

  • Jusman Y, Isa NAM, Adnan R et al (2012) Intelligent classification of cervical pre-cancerous cells based on the FTIR spectra. Ain Shams Eng J 3(1):61–70

    Article  Google Scholar 

  • Khaleghian S, Taheri S (2017) Terrain classification using intelligent tire. J Terrramech 71:15–24

    Article  Google Scholar 

  • Legashev LV, Letuta TV, Polezhaev PN et al (2019) Monitoring, certification and verification of autonomous robots and intelligent systems: technical and legal approaches. Procedia Comput Sci 150:544–551

    Article  Google Scholar 

  • Li SS, Cui TJ, Liu J (2017) Study on the construction and application of cloudization space fault tree. Cluster Comput. https://doi.org/10.1007/s10586-017-1398-y

    Article  Google Scholar 

  • Li JX, Zhao SL, An L et al (2018a) Scaling-up algorithm of multi-scale classification based on fractal theory. Comput Sci 45(S1):453–459

    Google Scholar 

  • Li SS, Cui TJ, Liu J (2018b) Research on the clustering analysis and similarity in factor space. Int J Comput Syst Sci Eng 33(5):397–404

    Google Scholar 

  • Peng JX (2018) Data detection based on intelligent classification algorithm. J Univ Jinan (Sci Technol) 32(05):412–416

    Google Scholar 

  • Seera M, Lim CP (2014) A hybrid intelligent system for medical data classification. Expert Syst Appl 41(5):2239–2249

    Article  Google Scholar 

  • Seera M, Wong MLD, Nandi AK (2017) Classification of ball bearing faults using a hybrid intelligent model. Appl Soft Comput 57:427–435

    Article  Google Scholar 

  • Shen YL (2018) Research on SVW feature selection and parameter optimization based on improved fireworks algorithm. Anhui University, Hefei

    Google Scholar 

  • Shi LY, Zhou RJ, Zhang WJ, et al (2018) Load classification method using deep learning and multi-dimensional fuzzy C-means clustering. In: proceedings of the CSU-EPSA, October 3. https://doi.org/10.19635/j.cnki.csu-epsa.000089

  • Xie HH (2018) The classification algorithm based on unbalanced data set and its application in communication intelligent operation. Beijing University Of Posts And Telecommunications, Beijing

    Google Scholar 

  • Zeng JS, Rao YB (2018) Intelligent classification of massive information based on conflict game algorith. Comput Sci 45(08):208–212

    Google Scholar 

  • Zhang RC, Du YB, Xue L et al (2018) A hybrid large sample credit evaluation model based on combining similar samples. J Manag Sci China 21(07):77–90

    Article  Google Scholar 

  • Zhao Y, Wang HW (2018) Intelligent classification of railway communication equipment manufactures information based on cluster analysis. Railw Comput Appl 27(07):75–79

    Google Scholar 

  • Zhi HX, Yu HT, Shaomei LI, et al (2018) A deep metric learning based video classification method. J Electron Inf Technol. October 3, 2018, http://kns.cnki.net/kcms/detail/11.4494.TN.20180822.1023.004.html. Accessed 1 May 2019

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Acknowledgements

The authors wish to thank all his friends for their valuable critics, comments and assistances on this paper. This study was partially supported by the grants (Grant Nos. 51704141, 71771111, 2017YFC1503102) from the Natural Science Foundation of China.

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Authors and Affiliations

  1. College of Business Administration, Liaoning Technical University, Huludao, 125105, Liaoning, China

    Xiaofeng Qi, Liangshan Shao & Yuyan Xing

  2. College of Safety Science and Engineering, Liaoning Technical University, Fuxin, 123000, Liaoning, China

    Tiejun Cui

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  1. Xiaofeng Qi
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Correspondence to Tiejun Cui.

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Qi, X., Cui, T., Shao, L. et al. Research on intelligent classification of multi-attribute safety information and determination of operating environment. J Ambient Intell Human Comput 11, 3509–3520 (2020). https://doi.org/10.1007/s12652-019-01474-0

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  • DOI: https://doi.org/10.1007/s12652-019-01474-0

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