Article

Article title SCHEMES MULTIPARAMETER IDENTIFICATION OF THE STATE OF THE TECHNICAL OBJECT BASED SYSTEMS MEDIA LINES FORMING LOCAL AREA CONDITIONS
Authors S.I. Klevtsov, A.B. Klevtsova
Section SECTION IV. INFORMATION TECHNOLOGY APPLIED INFORMATION SYSTEMS AND NETWORKS
Month, Year 11, 2014 @en
Index UDC 681.3.062
DOI
Abstract The task of classifying the states of objects based on an analysis of several parameters has many applications in the control of various dangerous situations. In solving the problem of classification in real-time is necessary to include the existing fixed at a particular moment or period of time set of parameters subject to certain classes. Data may be represented in various ways. However, the most widely used is a method where a set of parameters is represented as a vector. The signals from the sensors are processed by the microcontroller information system. Due to the fact that the evaluation is performed in real time, and computational capabilities of the microcontroller can be quite modest, or dissipated part of the computing power of the microcontroller is not very significant, by the assessment model based on the classification are requirements on the complexity of the algorithm, namely, the requirement of low complexity of the algorithm. An approach was formulated and the generalized scheme of multi-parameter identification of the technical object states on the basis the introduction of the median line, forming localized areas of the states is presented. Suitable forms a platform for the development of models, such as neural network models aimed at identifying the technical objects based on sensor data of physical quantities. It is shown that for the identification of the state is necessary to develop models and algorithms for determining the integrated proximity of the current settings to their median values in the various configurations and states are ranked according to the degree of integrated configuration vicinity.

Download PDF

Keywords Identification; condition; evaluation; technical object; parameter; microcontroller; classification.
References 1. Klevtsova A.B. Parametricheskaya zonnaya otsenka sostoyaniya tekhnicheskogo ob"ekta s ispol'zovaniem rezhimnoy karty [Parametrical band model of the estimation condition for technical object with use of the regime card], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 5 (106), pp. 107-111.
2. Klevtsova A.B., Klevtsov G.S. Modeli parametricheskoy ekspress-otsenki sostoyaniya tekhnicheskogo ob"ekta [Models parametrical the express train – estimations of a condition the technical object], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2008, No. 11 (88), pp. 15-19.
3. Klevtsov S.I. Modelirovanie algoritma kratkosrochnogo prognozirovaniya izmeneniya bystromenyayushcheysya fizicheskoy velichiny v real'nom vremeni [The simulation algorithm of short-term forecasting changes rapidly changing physical quantities in real time], Inzhenernyy vestnik Dona [Engineering journal of Don], 2012, No. 3 (21), pp. 199-205.
4. Matuszewski J. Application of clustering methods for recognition of technical objects, Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 2010
International Conference, 2010, pp. 39-40.
5. Klevtsov S.I. Prognozirovanie izmeneniya sostoyaniya sovokupnosti parametrov tekhnicheskogo ob"ekta s pomoshch'yu intellektual'nogo mikroprotsessornogo modulya [Predicting
changes in the status of the set of parameters of a technical object with intelligent microprocessor module], Vserossiyskaya nauchno-tekhnicheskaya konferentsiya «Problemy razrabotki perspektivnykh mikro- i nanoelektronnykh sistem (MES)»: Sb. trudov [All-Russian scientific-technical conference "problems of development of perspective micro- and nanoelectronic systems (MES): proceedings], 2010, No. 1, pp. 619-623.
6. Klevtsov S.I. Predvaritel'naya otsenka sostoyaniya sovokupnosti parametrov tekhnicheskogo ob"ekta s ispol'zovaniem intellektual'nogo mikroprotsessornogo modulya [The simplified estimation of the condition for set of parameters of technical object with use of the intellectual microprocessor module ], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 5 (106), pp. 43-48.
7. Lihua Sun, Yingjun Guo, Haichao Ran. A New Method of Early Real-Time Fault Diagnosis for Technical Process, Electrical and Control Engineering (ICECE), 2010 International Conference, 2010. Wuhan, China, pp. 4912-4915.
8. Klevtsova A.B. Integral'naya otsenka sostoyaniya ob"ekta monitoringa [Integrated assessment monitoring], Izvestiya TRTU [Izvestiya TSURE], 2004, No. 2 (37), pp. 58-65.
9. Klevtsova A.B. Algoritm otsenki i prognozirovaniya povedeniya peremennoy sostoyaniya ob"ekta [The algorithm for estimating and forecasting the behavior of a state variable of the object], Izvestiya TRTU [Izvestiya TSURE], 2006, No. 5 (60), pp. 133-139.
10. Klevtsov S.I. Prognozirovanie izmeneniy fizicheskoy velichiny v real'nom vremeni s ispol'zovaniem lineynogo adaptivnogo fil'tra [Forecasting of changes of size physical in real time with use of the linear adaptive filter], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya
SFedU. Engineering Sciences], 2013, No. 5 (142), pp. 180-185.
11. Klevtsov S.I. Otslezhivanie izmeneniya sostoyaniya dinamicheskogo ob"ekta v real'nom vremeni s ispol'zovaniem mikroprotsessornogo modulya [Tracking state changes of the dy-
namic object in real time using a microprocessor module], Vserossiyskaya nauchno-tekhnicheskaya konferentsiya «Problemy razrabotki perspektivnykh mikro- i nanoelektronnykh sistem (MES)»: Sb. trudov [All-Russian scientific-technical conference "problems of
development of perspective micro- and nanoelectronic systems (MES): proceedings], 2012, No. 1, pp. 684-687.
12. Lipman R. An introdaction to computing with neural nets, IEEE Acoustic, Speech and Signal Processing Magazine, 1987, No. 2, pp. 4-22.
13. Borisov E.S. Klassifikator na osnove neyronnoy seti Khemminga [The classifier on the basis of the Hamming neural network]. Available at: http://mechanoid.kiev.ua/neural-net-hamming-
classifier.html.
14. Raus, M., Ameling W. A layered information processing model for neural classification modules, Intelligent Systems Engineering, Second International Conference. 1994. Hamburg-Harburg, IET, pp. 144-153.
15. Gartner, K.-P., Holzhausen, K.-P., Kruger, W., Pitrella, F.D., Wolf H. Identification of field objects in reduced quality TV pictures transmitted from telerobots to a remote control station, Intelligent Robots and Systems '93, IROS '93. Proceedings of the 1993 IEEE/RSJ nternational
Conference, 1993, Vol. 3, pp. 1479-1486.

Comments are closed.