Construction of set of standards to improve the accuracy of expert assessments

Authors

  • Николай Борисович Копытчук Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044, Ukraine
  • Петр Метталинович Тишин Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044, Ukraine
  • Игорь Николаевич Копытчук Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044, Ukraine
  • Игорь Генрикович Милейко Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044, Ukraine

DOI:

https://doi.org/10.15587/2313-8416.2015.41579

Keywords:

time series, fuzzy logic, knowledge base, classification of anomalies, tensometry

Abstract

The aim of this work is to develop an apparatus for constructing representative set of standards to improve the accuracy of detection of abnormal data sets. For this purpose in this paper we introduce the stage of constructing of set of standards of the strain-measuring signals which allow to carry out the diagnosis of time series (TS) formed the diagnosing of processes occurring in the weighing process

Author Biographies

Николай Борисович Копытчук, Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044

Doctor of Technical Sciences, Professor, pensioner

Department of Computer intellectual systems and networks

Петр Метталинович Тишин, Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044

Candidate of Physical and Mathematical Sciences, Associate Professor

Department of Computer intellectual systems and networks

Игорь Николаевич Копытчук, Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044

Senior Lecturer

Department of Computer intellectual systems and networks

Игорь Генрикович Милейко, Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044

Technical candidate Sciences, Associate Professor

Department of Computer intellectual systems and networks

References

Batyrshin, I., Nedosekin, A., Stetsko, A. et. al. (2007). Fuzzy hybrid systems. Theory and practice. Moscow: FIZMATLIT, 208.

Kopytchuk, I., Kopytchuk, M., Silence, P., Mileiko, I. (2014). Building approximating fuzzy relations for determining the parameters of classification anomalies strain signals. Proc. Odessa. Polytechnic Univ. Odessa, 68–69.

Kopytchuk, M., Shendrik, E. (1999). Using the least squares method to estimate the parameters of the signal with a periodic nuisance with limited observation time. Proc. Odessa. Polytechnic Univ. Odessa, 3 (9), 167–169.

Kopytchuk, M., Shendrik, E. (2001). Increasing the accuracy of the least squares method by introducing a weighting function. Proc. Odes. Politekh Unt., 2 (14), 110–112.

Kopytchuk M, Shendrik E Study of the effectiveness of the algorithm method of least squares with a preliminary study of data transformation // Pratsі UNDІRT. Odes, 2001. - № 3 (27). - S. 72 - 74.

Kopytchuk, M., Tishyn, P., Botnari, K. (2011). Solving optimization problems for queuing systems with failures in the face of uncertainty. "The problem programuvannya". Singapore: Іnstitut software systems NAS of Ukraine, 4, 108–117.

Kopytchuk, M., Tishyn, P., Tsyurupa, M. (2013). Analysis of computer networks using a multi-level ontology of risk assessment using the methodology CORAS. "Electrical and Computer Systems", 10 (86), 120–126.

Shtovba, S. (2007). Design of fuzzy systems by means of MATLAB. Telecom, 288.

Afanasyev, T., Yarushkina, N. (2009). Fuzzy modeling of time series analysis and fuzzy trends. Ulyanovsk: UlSTU, 299.

Chandola, V. (2009). Anomaly Detection: A Survey. The University Of Minnesota, 72. Available at: http://cucis.ece.northwestern.edu/projects/DMS/publications/AnomalyDetection.pdf (Last accessed: 04.19.2014)

Published

2015-04-27

Issue

Section

Technical Sciences