Method of statistical spline functions for solving problems of data approximation and prediction of objects state

CEUR Workshop Proceedings

Volumn 2353, Issue , 2019, Pages 810-821

  Babak, Serhiy ^a   Babak, Vitaliy ^b   Zaporozhets, Artur ^b   Sverdlova, Anastasia ^b  

^a University of New Technologies   (Ukraine)

^b Department of Modeling of Heat Transfer Processes in Energy and Heat Engineering Facilities   (Ukraine)

Author keywords

Diagnostics; Mathematical model; Overhead power lines; Prediction; Spline functions; Technical state

Indexed keywords

FORECASTING; FUNCTIONS; HEATING; INTELLIGENT SYSTEMS; INTERPOLATION; MATHEMATICAL MODELS; PLASMA DIAGNOSTICS;

CHOICE OF PARAMETERS; DATA APPROXIMATION; OVERHEAD POWER LINES; SHORT TERM; SPLINE FUNCTIONS; TECHNICAL OBJECTS; TECHNICAL STATE;

SPLINES;

EID: 85065482200     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Velimirovic, L., Peric, Z., Stankovic, M.: Approximation of the optimal compressor function combining different spline functions in segments. 2013 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS), 16-19 October, 2013, Nis, Serbia. doi: 10.1109/TELSKS.2013.6704436
2. Kabore, P., Wang, H.: A B-spline neural network based actuator fault diagnosis in nonlinear systems. Proceedings of the 2001 American Control Conference. (Cat No.01CH37148) 25-27 June, 2001, Arlington, VA, USA. doi: 10.1109/ACC.2001.945873
3. Zaporozhets, A.A., Eremenko, V.S., Serhiienko, R.V., Ivanov, S.A.: Development of an Intelligent System for Diagnosing the Technical Condition of the Heat Power Equipment. 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), 11-14 September, 2018, Lviv, Ukraine. doi: 10.1109/STC-CSIT.2018.8526742
4. Zaporozhets, A., Redko, O., Babak, V., Eremenko, V., Mokiychuk, V.: Method of indirect measurement of oxygen concentration in the air. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2018, No.5, pp. 105-114. doi: 10.29202/nvngu/2018-5/14
5. Yao, L., Guan, Y.: Minimum entropy fault diagnosis and fault tolerant control for the non- Gaussian stochastic system. 2016 American Control Conference (ACC). 6-8 July, 2016, Boston, MA, USA. doi: 10.1109/ACC.2016.7526753
6. Peng, X., Qian, G., Li, X., Gao, F.: Intelligent trip-out fault diagnosis of overhead transmission line. High Voltage Engineering, 2012, Vol. 38, Issue 8, pp. 1965-1972.
7. Li, P., Li, Q., Cao, M., Gao, S., Huang, H.: Time series prediction for icing process of overhead power transmissions line based on BP neural networks. Proceedings of the 30th Chinese Control Conference. 22-24 July, 2011, Yantai, China.
8. Kuts, Y., Eremenko, V., Monchenko, E., Protasov, A.: Ultrasound method of multi-layer material thickness measurement. AIP Conference Proceedings 1096, 2009, Vol. 1115. doi: 10.1063/1.3114079
9. Zaporozhets, A., Eremenko, V., Serhiienko, R., Ivanov, S.: Methods and Hardware for Dianosing Thermal Power Equipment Based on Smart Grid Technology, Advances in Intelligent Systems and Computing III, 2019, Vol. 871, pp. 476-492. doi: 10.1007/978-3-030-01069-0-34
10. Babak., V., Mokiychuk, V., Zaporozhets, A., Redko, O.: Improving the efficiency of fuel combustion with regard to the uncertainty of measuring oxygen concentration. Eastern- European Journal of Enterprise Technologies, 2016, Vol. 6, No.8, pp. 54-59. doi: 10.15587/1729-4061.2016.85408
11. Klevtsov, S.: Identification of the state of technical objects based on analyzing a limited set of parameters. 2016 International Siberian Conference on Control and Communications (SIBCON), 12-14 May, 2016, Moscow, Russia. doi: 10.1109/SIBCON.2016.7491752
12. Manusov, V.Z., Orlov, D.V., Frolova, V.V.: Diagnostic of Technical State of Modern Transformer Equipment Using the Analytical Hierarchy Process. 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 12-15 June, 2018, Palermo, Italy. doi: 10.1109/EEEIC.2018.8493904
13. Ming, J., Zhang, L., Sun, J., Zhang, Y.: Analysis models of technical and economic data of mining enterprises based on big data analysis. 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 20-22 April, 2018, Chengdu, China. doi: 10.1109/ICCCBDA.2018.8386516
14. Dmitriev, S., Manusov, V., Ahyoev, J.: Diagnosing of the current technical condition of electric equipment on the basis of expert models with fuzzy logic. 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 13-14 October, 2016, Riga, Latvia. doi: 10.1109/RTUCON.2016.7763126