Special Issue No. – 8, April, 2020

Abstract:

High-rise construction requires scientific and technical support, which the authors of the paper suggest to comprehend as a “Man-Technology-Environment” system. With reference to this system, it is expedient to adapt the models, methods and means of anthropo technics management. It can serve as an effective means of improving the quality of innovative process-organizational and technological solutions for high-rise construction, taking into account some specific conditions of the Russian Federation environment. One of the examples examines the problems and possibilities of arrangement (design, erection, reorganization, and in particular - building reconstruction) of different roof types. In the Russian Federation, there are significant amounts of construction of high-rise buildings with different number of floors, on the roofs of which it is possible to create architectural and landscape objects with lawns and greenery. Such roofs are called exploitable; they are also implemented as “green roofs” or “hanging gardens”. With the use of traditional building and roofing materials, traditional technologies and organizational solutions for waterproofing (so-called “screeds”), it is difficult, and sometimes practically impossible to prevent leaks and provide sustainable protection from the root systems of green plantations.A horizontal screed made of cement and sand, vertical screeds made of bricks, concrete blocks or flat asbestos-cement sheets are very laborious; when using them, “wet” processes are necessary, and asbestos-cement sheets fixing also reduces the reliability and water-resisting properties of the hydro insulation. An innovative solution to these problems is the TEFOND hydro-insulation (waterproofing) system based on a flexible polyethylene membrane. The system exhibits high density, strength, ductility and flexibility at negative temperatures, low water absorption and resistance to plant roots, reliability of roofing carpet underlay during service.

Keywords:

Scientific and technical support ,erection and reconstruction ,exploitable roofs ,screeds ,TEFOND waterproofing system,

Refference:

I. A. A. Potovalov, S. V. Tabakov. Technology and mechanization of the erection of buildings and structures. Lecture notes. [Tehnologii i mehanizaciyavozvedeniyazdaniy I struktur] [Electronic source]. – https://studfiles.net/preview/5198268/ page:12.- (Ref. Date: 3.12.2017).

II. Green roof. Green Roof Membrane. – http://sesli-zero.net/2017/32689/green-roof-membrane.- (Ref. Date: 9.12.2017).

III. Green roofs in Russia: problems and prospects. – “Green buildings”. Journal of Ecological Technologies http://green-buildings.ru/ru/zelenye-krovli-v-rossii-problemy-i-perspektivy.-

IV. Green roofs of Norway. – https://pikabu.ru/story/zelenyie_kryishi_norvegii_1882496.-(Ref. Date: 9.12.2017).

V. Green roofs of Scandinavia. – http://hyggelife.ru/zelenye-kryshi-skandinavii.html.- (Ref. Date: 9.12.2017).

VI. Hanging Gardens of Babylon – the second wonder of the world. https://www.syl.ru/article/75032/visyachie-sadyi-semiramidyi-vtoroe-chudo-sveta.- (Ref. Date: 9.12.2017).

VII. Husnu M. Kalkanoglu. Multilayer roofing sheet with mechanical interlock laminate structure. United States Patent US20080248241 dated March 28, 2008. – (Ref. Date: 3.12.2017).

VIII. John A. D’Annunzio. Technical Details: Waterproofing of Rooftop Gardens (2008) BNP Media. – https://www.roofingcontractor.com/articles/85808-technical-details-waterproofing-of-rooftop-gardens.- (Ref. Date: 3.12.2017).

IX. Johnston, J., and J. Newton. Building Green: A Guide to Using Plants on Roofs, Walls, and Pavement. London: The Ecology Unit, 1992, p. 49.

X. Konopacki, S., and Akbari, H. Measured Energy Savings and Demand Reduction from a Reflective Roof Membrane on a Large Retail Store in Austin. Op. cit., 2001.

XI. Laberge, K.M., P.E., Chicago Department of Environment. “Urban Oasis: Chicago’s City Hall Green Roof.” Presented at “Greening Rooftops for Sustainable Communities,” the First North American Green Roofs Infrastructure Conference, Awards, and Trade Show. Chicago, IL May 29-30, 2003.

XII. Living roof. Innovation technologies. http://stroy-magazin.ru/pdf/Green_roof. pdf. – (Ref. Date: 12.1.2017).

XIII. Manufacture and use of building materials, products and systems. – Volume 1: Finishing materials, products and systems. “Infographic foundations of functional system” series” (IOFS) / Ed. V.O. Chulkov, 2nd Edition. – M.: SVR-ARGUS, 2009. 296 p.

XIV. Materials for roof waterproofing. http://homedecorrs.com/en/pages/735785.- (Ref. Date: 12.01.2018).

XV. Omnova Specialty Chemicals. Nonwovens (2007). http://www.omnova.com/ products/chemicals/documents/SpecChemNonWovens_07June.pdf.- (Ref. Date: Dec 03, 2017 г.)

XVI. Overview of waterproofing membrane of different manufacturers. – http://maddyyoung. com/11123. – (Ref. Date: 3.12.2017).

XVII. Peck Steven, Chris Callaghan, Kuhn Monica, Bass Brad. Greenbacks from Green Roofs: Forging a New Industry in Canada. Toronto: Canada Mortgage and Housing Corporation, March 1999. www.greenroofs.ca/grhcc/Greenbacks.pdf.- (Ref. Date: 12.1.2017).

XVIII. Peck Steven, Kuhn Monica E. Design Guidelines for Green Roofs. Toronto, Ontario, Canada: Ontario Association of Architects, with the Canada Mortgage and Housing Corporation SCHL. Accessed in April 2004. www.greenroofs.com/Greenroofs101/how-tos.htm.- (Ref. Date: 12.1.2017).

XIX. Polimerosadhesivos y derivadoss.a. de c.v. Water-based impermeabilization composition for coating diverse substrates. United States Patent US20110293839 dated July 8, 2009. – (Ref. Date: 3.12.2017).

XX. Polimerosadhesivos y derivadoss.a. de c.v. Water-based impermeabilization composition for coating diverse substrates. World Intellectual Property Organization (WIPO) Patent WO2010030154A1 dated July 8, 2009. – (Ref. Date: 3.12.2017).

XXI. Robert Bartek. Highly reflective and emissive asphalt-based roofing membrane. – United States Patent US20050252137 dated June 3, 2005 (Ref. Date: 3.12.2017).

XXII. Roger L. Souther. Nonwoven polymeric fiber mat composites and method. United States Patent US20060228963 dated March 15, 2006. (Ref. Date: 3.12.2017).

XXIII. Roofing materials on the European market. – http://survincity.com/2013/06/overview-of-roofing-materials-in-europe. – (Ref. Date: 3.12.2017).

XXIV. Scholz-Barth, Katrin. Green Roofs: Storm-water Management from the Top Down. Environmental Design and Construction, January/February 2001: pp. 63-69. www.edcmag.com/edc/cda/articleinformation/features/bnp_features_item/0,4120,18769,00.html.- (Ref. Date: 12.1.2017).

XXV. Scientific and technical support manual for monitoring of buildings and structures under construction, including unique, large-span and high-rise (MRDS 02-08), First edition. – Moscow, 2008. [Electronic source].-http://files.stroyinf.ru/ data2/1/4293834/4293834435.htm.- (Ref. Date: 3.12.2017).

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Abstract:

The article considers the possibility to simulate, using a differential equation, the behavior of the crowd in extreme situations. The author demonstrates the very possibility to develop a mathematical model that describes the changes in the main parameters of the crowd at each time moment. This study is conducted to predict the behavior of the crowd in a particular room, for a more efficient location of escape routes there. The simulation results show that the force of internal friction of the crowd decreases as the speed of the crowd moves from the center to the outskirts. That is, the probability that a person suffers from excessive crowd pressure is higher in the center than on the periphery. This study will be useful in such areas of human activities as building design, engineering, etc. The data obtained by the calculations can be used to arrange emergency exits in buildings to avoid human casualties in case of an emergency.

Keywords:

Mathematical modeling,differential equation,crowd linear density,speed,discharge capacity,

Refference:

I. Agafonov S.A. and MuratovaT.V. Ordinary differential equations [Obyknovennyyedifferentsial’nyyeuravneniya]. Moscow: Academy, 2008.

II. AnosovD.V. Differential equations: solved and drawn [Differentsial’nyyeuravneniya: to reshayem, to risuyem]. Moscow: MTSNMO, 2010.
III. Antidemidovich: BoyarchukA.K.,GolovachG.P. Vol.5. Part 2: Higher order differential equations, systems of differential equations, partial differential equations of the first order [Differentsial’nyyeuravneniyavysshikhporyadkov. Sistemydifferentsial’nykhuravneniy. Uravneniya v chastnykhproizvodnykhpervogoporyadka]. Moscow: LKI, 2014.

IV. Chen, Z., Wu, B., &Xu, Y. Multilevel augmentation methods for differential equations. Advances in Computational Mathematics. 2006. 24(1-4). Pp. 213–238.

V. Crowd simulation [Electronic resource]. URL: http://tm.spbstu.ru/(accessed on December 8, 2018).

VI. De la Hoz, F., &Vadillo, F. Numerical simulations of time-dependent partial differential equations. Journal of Computational and Applied Mathematics. 2016. No. 295. Pp. 175–184.
VII. Elizarov, I.A., System modeling [Modelirovaniye system]. Moscow: TNT, 2013.
VIII. Hoefkens, J., Berz, M., & Makino, K. Computing Validated Solutions of Implicit Differential Equations.Advances in Computational Mathematics. 2003. Vol. 19. Issue 1-3. Pp. 231–253.
IX. Hon, Y. C., &Schaback, R. Solvability of partial differential equations by meshless kernel methods. Advances in Computational Mathematics. 2006. 28(3). Pp. 283–299.
X. Logos M., Introduction to mathematical modeling. –2015.

XI. Mora, H., Mora-Pascual, J., García-Chamizo, J. M., &Signes-Pont, M. T. 2017. Mathematical model and implementation of rational processing. Journal of Computational and Applied Mathematics. Vol. 309. Pp. 575–586.
XII. PTC companies [Electronic resource], URL: https://www.ptc.com/en/products/mathcad. (accessed on December 8, 2018).
XIII. ReizlinV.I. Mathematical modeling [Matematicheskoyemodelirovaniye]. Moscow: Yurayt, 2016.
XIV. RoytmanM.Ya. Forced evacuation of people from buildings [Vynuzhdennayaevakuatsiyalyudeyizzdaniy]. Moscow: Stroiizdat, 2017..

XV. Sokolowski John A., Banks Catherine M. Handbook of Real-World Applications in Modeling and Simulation. Hoboken, NJ: John Wiley and Sons, 2012.

XVI. Scherer, G. On the numerical modelling of the transitional flow in rarefied gases. Journal of Computational and Applied Mathematics. 1999. 103(1). Pp. 165–173.
XVII. Synthetic CDOs. Cambridge: Cambridge University Press, 2008.

XVIII. Taneja, L., &Bolia, N. B. Network redesign for efficient crowd flow and evacuation. Applied Mathematical Modelling. 2018. Vol. 53. Pp. 251–266.
XIX. Twarogowska, M., Goatin, P., &Duvigneau, R. Macroscopic modeling and simulations of room evacuation. Applied Mathematical Modelling. 2014. Vol. 38. Issue 24. Pp. 5781–5795.
XX. Wang, J., Sun, J., & Lo, S. Randomness in the evacuation route selection of large-scale crowds under emergencies. Applied Mathematical Modelling. 2015. Vol. 39. Issue 18. Pp. 5693–5706.

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Abstract:

The urgency of the issue under consideration is stipulated by the fact that the technologies of casing string cementing traditionally applied while developing oil fields do not always provide the necessary long-term impermeability of casing support.  Therefore, this article is aimed at solving the mentioned key technological problem of well drilling. The Multi-technololgy Complex of the Wellbore Hydromechanical Hardening (KMGUS) is proposed as a basic research method.  The methodological basis for the research is the synchronization (simultaneity) of well drilling and the hydromechanical hardening of wellbores by jetting out a drilling fluid at wellbore walls. The following particular problems have been solved: maintaining stability of the uncased wellbore and hydraulic drilling conditions, ensuring maintaining of natural reservoir properties of productive formations, forming a long-term tightness of casing support and wellbore screen. The proposed method has been partially introduced into production. The results of industrial pilot tests have proved the high efficiency of KMGUS, which prevents the appearance of most problems when constructing oil and gas wells: absorption; gas, oil, and water shows, hydraulic fractures and instability of the exposed rock formations, kicks and blowouts. In addition, during the well operation, the complex provides the prevention of annular fluid shows and cross flows. As a result, the production rates for wells drilled with use of KMGUS are 2 and more times higher compared with traditional well construction technologies, water cut is lower by more than 2 times, and as a positive effect is a non-linear increase in the oil recovery factor. The article can be useful for specialists in the field of construction of oil and gas wells, scientists and graduate students studying the casing strings cementing problems

Keywords:

Oil and gas wells,control and management method,technological processes,production string cementing,wellbore hydromechanical hardening,synchronization of drilling processes,

Refference:

I. Akhmetov R.T., Andreev A.V., Mukhametshin V.V. Metodikaprognozaostatochnoyneftenasyshchennosti i koeffitsientavytesneniyapodannymgeofizicheskikhissledovaniydlyaotsenkieffektivnostiprimeneniyananotekhnologiy [Residual oil saturation and the displacement factor prediction methodology based on geophysical studies data to evaluate efficiency of nanotechnologies application]. Nanotehnologii v stroitel’stve = Nanotechnologies in Construction, 2017. Vol. 9, № 5. P. 116-133. DOI: dx.doi.org/10.15828/2075-8545-2017-9-5-116-133. (In Russian).
II. Akhmetov R.T., Mukhametshin V.V., Andreev A.V., SultanovSh.Kh. Some Testing Results of Productive Strata Wettability Index Forecasting Technique // SOCAR Procеedings. – 2017. – № 4. – P. 83-87. DOI: 10.5510/OGP20170400334
III. Andreev A.V., MukhametshinV.Sh.,KotenevYu.A. Deposit Productivity Forecast in Carbonate Reservoirs with Hard to Recover Reserves. SOCAR Proceedings, 2016. № 3. P. 40-45. DOI: dx.doi.org/10.5510/OGP20160300287. (In Russian).
IV. Ashrafyan M.O. Tekhnologiyarazobshcheniyaplastov v oslozhnennykhusloviyakh [Technology of layers segregation in complicated conditions]. M.: Nedra, 1989. 228 p. (In Russian).
V. Dmitriev A. Yu. Osnovytekhnologiibureniyaskvazhin: uchebnoyeposobiye [Fundamentals of drilling technology: A textbook]. Tomsk, TPU Publ., 2008, 216 P.

VI. KhayredinovN.Sh., Popov A.M., MukhametshinV.Sh. Povyshenieeffektivnostizavodneniyanizkoproduktivnyhzalezhejnefti v karbonatnyhkollektorah [Increasing the flooding efficiency of poor-producing oil deposits in carbonate collectors]. Neftyanoekhozyaystvo = Oil industry, 1992. № 9. P. 18-20. (In Russian).
VII. MirzadzhanzadeA.Kh., Karaev A.K., S.A. Shirinzade. Gidravlika v burenii i tsementirovaniineftyanykh i gazovykhskvazhin [Hydraulics in oil and gas wells drilling and cementing]. M.: Nedra, 1977. 230 p. (In Russian).
VIII. MukhametshinV.Sh. Zavisimost’ nefteizvlecheniyaotplotnostisetkiskvazhinprirazrabotkenizkoproduktivnyhkarbonatnyhzalezhej [Dependence of crude-oil recovery on the well spacing density during development of low-producing carbonate deposits]. Neftyanoekhozyaystvo = Oil industry, 1989. № 12. P. 26-29. (In Russian).
IX. MukhametshinV.Sh., ZeigmanYu.V., Andreev A.V. Ekspress-ocenkapotencialadobyvnyhvozmozhnostejzalezhejdlyaopredeleniyaeffektivnostiprimeneniyananotekhnologij i neobhodimostistimulirovaniyavvodaih v razrabotku [Rapid assessment of deposit production capacity for determination of nanotechnologies application efficiency and necessity to stimulate their development]. Nanotehnologii v stroitel’stve = Nanotechnologies in Construction, 2017. Vol. 9. № 3. P. 20-34. DOI: dx.doi.org/10.15828/2075-8545-2017-9-3-20-34. (In Russian).
X. Mukhametshin V.V. Ustranenieneopredelennosteypriresheniizadachvozdeystviyanaprizaboynuyuzonuskvazhin [Eliminating uncertainties in solving bottom hole zone stimulation tasks]. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2017. Vol. 328, № 7. P. 40-50. (In Russian).
XI. Mukhametshin V.V., Andreev V.E. Search and Argumentation of Decisions Aimed at Increasing the Efficiency of Bottom-Hole Zone Stimulation in Oil Accumulations with Challenged Reserves. SPE Russian Petroleum Technology Conference. Moscow, Russia, 2017. P. 1-23. DOI: dx.doi.org/10.2118/187785-MS.
XII. Mukhametshin V.V., Andreev V.E., Dubinsky G.S., SultanovSh.Kh.,Akhmetov R.T. The Usage of Principles of System Geological-Technological Forecasting in the Justification of the Recovery Methods. SOCAR Proceedings, 2016. № 3. P. 46-51. DOI: dx.doi.org/10.5510/OGP20160300288. (In Russian).
XIII. Mukhametshin V.V., Kadyrov R.R. Vliyanienanodobavoknamekhanicheskie i vodoizoliruyushchiesvoystvasostavovnaosnovetsementa [Influence of nanoadditives on mechanical and isolating properties of cement-based compositions]. Nanotehnologii v stroitel’stve = Nanotechnologies in Construction, 2017. Vol. 9, № 6. P. 18–36. DOI: dx.doi.org/10.15828/2075-8545-2017-9-6-18-36. (In Russian).
XIV. Polyakov V.N. O vliyaniiprotsessovdestruktsii i degradatsiinatekhnologiyubureniya i zakanchivaniyaskvazhin [On the destruction and degradation processes impact of on the wells drilling and completion technology]. II open scientific-practical conference “Actual problems of oil and gas wells on land and the continental shelf of the Russian Federation construction and repair “: doc. coll. LLC “NPO Bentotechnologies”, 2013. (In Russian).
XV. Polyakov V.N. Trebovaniya, predyavlyaemye k germetichnosti i prochnostistvolaprizakanchivaniiskvazhinmestorozhdeniyBashkirii [Requirements for wellbore integrity and strength in well completion process in Bashkortostan oil fields]. Neftyanoekhozyaystvo = Oil industry, 1983. № 5. P. 27-28. (In Russian).
XVI. Polyakov V.N., Ishkaev R.K., Lukmanov R.R. Tekhnologiyazakanchivaniyaneftyanykh i gazovykhskvazhin [Oil and gas wells completion technique]. Ufa: TAU, 1999. 408 p. (In Russian).
XVII. Polyakov V.N., KuznetsovYu.S., Sagidullin I.A., ShulginaN.Yu., Dubrovsky V.S., Khusainov V.M., Khaminov N.I., Akhmetzyanov R.G., Vildanov A.A., Starov O.E. Reshenie problem zakanchivaniya i ekspluatatsiiskvazhin v anomal’nykhtermodinamicheskikhusloviyakh [The solving of problems of tailing-in and wells operation in abnormal thermodynamic conditions]. Neftyanoekhozyaystvo = Oil industry, 2005. № 5. P. 104-110. (In Russian).
XVIII. Polyakov V.N., Lukmanov R.R., Sharipov A.U. et al. Povyshenieeffektivnostirazobshcheniya i izolyatsiiproduktivnykhplastovpriikhrazburivanii [Productive layers segregation and isolation efficiency increasing in their drilling process]. II Drilling: scientific-technical abstract journal. M.: VNIIOENG, 1979. № 9. P. 8-12. (In Russian).
XIX. ZeigmanYu.V., MukhametshinV.Sh.,Khafizov A.R., Kharina S.B. Prospects of Application of Multi-Functional Well Killing Fluids in Carbonate Reservoirs. SOCAR Proceedings, 2016. № 3. P. 33-39. DOI: dx.doi.org/10.5510/OGP20160300286. (In Russian).
XX. ZeigmanYu.V., MukhametshinV.Sh., Sergeev V.V., Kinzyabaev F.S. Eksperimental’noeissledovanievyazkostnykhsvoystvemul’sionnykhsistem s soderzhaniemnanochastits SiO2 [Experimental study of viscosity properties of emulsion system with SiO2 nanoparticles]. Nanotehnologii v stroitelʼstve = Nanotehnologies in Construction, 2017. Vol. 9, № 2. P. 16-38. – DOI: dx.doi.org/10.15828/2075-8545-2017-9-2-16-38. (In Russian).

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Abstract:

Nowadays, large-scale dairy units, where milking machines are completed with automatic controls for milking mode control, are built alongside with farms with tie-up housing of small groups of cows. In these farms it is expedient to use multi-functional milking machines that allow performing such major process operations as increasing the milk yield of newly-calved cows in the maternity barn and milking the core herd. The goal of this study is to make the machine-based milking of cows more efficient by developing a multi-functional milking machine with substantiated operation modes. The research objectives are to determine the area of upgrades and develop the design and process layout of a multi-functional milking machine; identify the operating parameters of the milking machine’s upgraded vibration pulser; identify the physiological parameters of the milking machine’s effect on the mammary gland in various milking modes and substantiate the operating parameters of the multi-functional milking machine. To stimulatea full milk flow reflex at the beginning and the end of the milking process, the developed design and process layout allows massaging the udder by microscale vibrations of the teatrubber of 1 to 2 mm in amplitude and affecting the udder nipples with a low vacuum pressure of 33 to 38 kPa, taking account of the animals’ physiological features. To identify the operating parameters of the milking machine and determine the physiological parameters of its effect on the mammary gland, laboratory plants with a Pulso Test Comfort vacuum and pulsingmeter and an Artificial-Udder test bench were used. Characteristic curves were derived to show the vacuum pressure-time relation in the interstitial and subte at areas of the teat cups; in addition, these relations were summated to derive the characteristic curve of differential pressure in the chambers. The physiological parameters of the milking excitants’ effect on the mammary glands were calculated. The operating modes of the test machine (single-phase low-vacuum mode with continuous stimulation and three-phase mode with controlled stimulation) and their parameters were set.

Keywords:

Subteat and interstitial chambers, milking mode,stimulating,capacity,milking machine,massage,

Refference:

I. Brand S. Ma S. Stimulation by the milking machine. Proceeding Symposium on machine milking, Reading (England). 1998. Pp.119-130.
II. В.А. Zakharov, V.F. Nekrashevich, V.M. Ulyanov, V.V. Utolin. The patent of the Russian Federation No. 2115304 RU, A01J 5/04 (1995.01). Milking machine. No. 97108417. Declared on 20.05.1997; Publ. July 20, 1998, bul. No. 20.

III. E. A. Andrianov, V. P. Shatsky, A. A. Andrianov, and S. A. Borodin. Modelling of lactation. Asian journal of microbiology, biotechnology and environmental sciences. 2017. Vol. 19. No. 3. Pp. 594-597.

IV. E.A. Andrianov, S.A. Borodin, A.A. Andrianov, T.N. Tertychnaya. Justification of the regime parameters of the multifunctional stimulating milking unit [Teoreticheskoye obosnovaniye konstruktivnykh parametrov ustroystva upravleniya rezhimom doyeniya]// Engineering and equipment for the village. 2018. No. 4. Pp. 18-23.
V. H. J. Schuiling. Teat cleaning and stimulating, in: A.H. Ipema et al. Pro-ceedings of the international symposium on prospects for automatic milking, EAAP publication 65. 1992. Wageningen: Wageningen Pers. The Netherlands. Pp. 164-168.
VI. I. Ohnstad, R. Blowey, N. Frame., R. Laven, A. Norton, A. White. Assessment of milking systems. Clinical Forum UK Vet. 2006. Vol. 11. No. 1. Рp. 28-34.
VII. J. A. Hoekstra. A not on a partial adjustment model to beseribe lactation curves. Anim. Product. 1986.
VIII. M. D. Rasmussen. Influence of switch level of automatic cluster removers on milking performance and udder health. Journal of Dairy Research. 1993. Vol. 60. No. 3. Рp. 287-297.
IX. M. Grossman, A. L. Kuck, H. W. Norton Lactation curves of purebred and gross-bred dairy cattle. Doiry Sc. 1986.
X. M. Shou Entwicklungstendenzen des maschinellen Milkhentzuges. Bayer Landw. 1987. Vol. 64. No. 4
XI. O.V. Uzhik. Development and theoretical justification of technologies and technical means for dairy cattle breeding: thesis for the degree of Doctor of Engineering Sciences. Michurinsk. Russia. 2015. 384 p.
XII. S.A. Borodin, E.A. Andrianov, V.P. Shatsky, A.A. Andrianov. Approximation of the milk yield curve [Approksimatsiya krivoy molokootdachi]. Rural mechanic. 2017. No. 11. Pp. 24-25.
XIII. S.А. Borodin, E.A. Andrianov, V.P. Shatsky, A.A. Andrianov. Theoretical substantiation of design parameters of the milking control device [Teoreticheskoye obosnovaniye konstruktivnykh parametrov ustroystva upravleniya rezhimom doyeniya]. Bulletin of the Voronezh State Agrarian University. 2017. No. 2 (53). Pp. 105-112.
XIV. S. I. Shchukin. Justification of the parameters of the actuating mechanisms of the milking machine of a pairwise action: thesis for the degree of Candidate of Engineering Sciences. Moscow, 2006. 146 p.
XV. V. F. Uzhik, A. I. Teteryadchenko, O. V. Uzhik, D. O. Kutovoy. Patent No. 2621015 RU, IPC A01J 5/04 (2006.01). Milking machine / – №2015150676. Declared on November 25, 2015; Publ. 05/30/2017. Bul. №16.
XVI. V. M. Ulyanov. Improvement of the technology of machine milking of cows by developing stimulating-adapted milking machines and manipulators: thesis for the degree of Doctor of Engineering Sciences. Ryazan: Ryazan State Agricultural Academy. 2008. 300 p.
XVII. V.F. Uzhik, A.I. Teteryadchenko, O.V. Kitaeva. Substantiation of the constructive-regime parameters of the hydraulic circuit of a gyrostabilized pulser of an adaptive milking machine [Obosnovaniye konstruktivno-rezhimnykh parametrov gidravlicheskogo kontura gidrostabilizirovannogo pul’satora adaptivnogo doil’nogo apparata]. Bulletin of the Voronezh State Agrarian University. 2017. No. 2 (53). Pp. 112-120.
XVIII. V.M. Ulyanov, V.A. Khripin. Physiologically adapted milking machine [Fiziologicheski adaptirovannyy doil’nyy apparat]. Rural mechanic. 2007. No. 1. Pp. 12-13.
XIX. V.M. Ulyanov. Improving the milking of cows of tie-up housing [Sovershenstvovaniye doyeniya korov pri privyaznom soderzhanii]. Engineering in agriculture. 2008. No. 3. Pp. 12-14.
XX. Y.A. Tsoi. “Nurlat” milking machine [Doil’nyy apparat «Nurlat»]. Rural mechanic. 2006. No. 1. Pp. 28-29.

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Abstract:

This work makes use of Navier-Stokes equations to describe an analytical method of finding the motion speed of a flexible inextensional shell falling down to the ground from a preset height and determines the duration of this fall. The soft shell in question is a fabric body of aerodynamic shape or an item of clothes, an airborne vehicle element, etc. Analytical relations are presented for the speed at which the shell moves in the air, taking account of the air resistance and the shell fall duration. The boundary problem of the soft shell vertically falling in the air is solved.

Keywords:

Flexible inextensional (soft) shell,Navier-Stokes equations,analytical calculation method,shell gravity force,motion resistance forces,

Refference:

I. A. Lin, A. Milshteyn, G. Herman, M. Garcia, C. Liu, K. Rad, D. Guillaume, H. Boussalis. Virtual reality head-tracking observation system for mobile robot. 3rd Mediterranean Conference on Embedded Computing (MECO). 2014. Pp. 152–157. DOI: 10.1109/MECO.2014.6862681

II. E. Perl. Review of Airport Surface Movement Radar Technology. IEEE Aerospace and Electronic Systems Magazine. 2006. Vol. 21. Issue 10. Pp. 24–27. DOI: 10.1109/MAES.2006.275302.

III. J. A. Paivanas, J. K. Hassan. Attraction Force Characteristics Engendered by Bounded, Radially Diverging Air Flow. IBM Journal of Research and Development. 1981. Vol. 25. Issue 3. Pp. 176–186. DOI: 10.1147/rd.252.0176

IV. J. S. Rocha, C.A.B.O. Lira, E.S.G. Maciel. Numerical Techniques For Future Applications In Termo-Fluid-Dinamic Projects Of VHTGR Reactors: Viscous Case. IEEE Latin America Transactions. 2018. Vol. 16. Issue 4. Pp. 1263–1268. DOI: 10.1109/TLA.2018.8362166

V. J. T. Bingham, J. Lee, R. N. Haksar, J. Ueda, C. K. Liu. Orienting in mid-air through configuration changes to achieve a rolling landing for reducing impact after a fall. IEEE/RSJ International Conference on Intelligent Robots and Systems. 2014. Pp. 3610–3617. DOI: 10.1109/IROS.2014.6943068

VI. L. G. Loytsyanskiy, Fluid and Gas Mechanics [Mekhanikazhidkosti i gaza]. 5th Ed. – Мoscow: Nauka, 1978. – 736 pp.

VII. L. Kerhuel, S. Viollet, N. Franceschini. A sighted aerial robot with fast gaze and heading stabilization. IEEE/RSJ International Conference on Intelligent Robots and Systems. 2007. Pp. 2634 – 2641. DOI: 10.1109/IROS.2007.4399497

VIII. L.A. Bekmurzaev,,M. F. Mitsik, M.V. Byrdina,, G.B. Grigoryeva. Conditions of Stability of Vertical Cylindrical Soft Shell. Proceedings of 2018 IEEE East-West Design and Test Symposium, EWDTS 2018. DOI: 10.1109/EWDTS.2018.8524774

IX. M. F. Mitsik,,M.V. Byrdina, L.A. Bekmurzaev. Modeling of developable surfaces of three-dimensional geometric objects. Proceedings of 2017 IEEE East-West Design and Test Symposium, EWDTS 2017. DOI: 10.1109/EWDTS.2017.8110086

X. M. Indenbirken,T. Schneider, V. Siepmann, K. Strauss.A new model for the propagation of jets in dilute gas‐solid crossflows. Canadian Journal of Chemical Engineering. 2000. 78(3), 2000. Pp. 468-477. DOI: 10.1002/cjce.5450780305

XI. M.V. Byrdina, L.A. Bekmurzaev, M. F. Mitsik. Three-dimensional visualization of garments in the Embarcadero Rad Studio environment. Fundamental Research. 2017. Issue 8-1. Pp. 27-31.

XII. S. M. Targ. A Concise Course on Engineering Mechanics [Kratkiykursteoreticheskoymekhaniki]. Moscow: Vysshaya shkola, 1986. – 416 pp.

XIII. V. N. Kocharenko, M. F. Mitsik, O. A. Aleynikova. Modeling of two-dimensional supercritical flow. Global Journal of Pure and Applied Mathematics, 2016. Vol. 12. Issue 1. Pp. 617-642.

XIV. Z. Zhang, H. Liu, Z. Yu, X. Chen, Q. Huang, Q. Zhou, Z. Cai, X. Guo, W. Zhang. Biomimetic upper limb mechanism of humanoid robot for shock resistance based on viscoelasticity. IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids). 2017. Pp. 637–642. DOI: 10.1109/HUMANOIDS.2017.8246939

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Abstract:

Due to depletion of such natural non-renewable energy sources, as oil and gas, the problem of using renewable resources is especially acute nowadays. Utilising river flows as a source of energy is one of the directions for solving the problem. The paper reflects the objectives and problems, that can be resolved through a sustainable use of the river flow energy. An impact of various factors on interaction between energy and extraction systems has been shown. The essence of the new approach to making hydroelectric units using the quantity-related component of the flow, and the prospects for creating hydroelectric units in this direction are represented. The presented analysis of some developments incorporates the specific features of the utilised working organs of free-flow micro HPSs in their interacting with the river flow. It serves a basis for suggesting a new trend in their developing and the use of a quantity-related flow component, i.e. the momentum, in particular.

Keywords:

Systems,hydraulic flow,momentum, energy extraction,hydraulic gradient,generator,polymers,

Refference:

I. Balkibekov C.E. The Development of non-traditional renewable energy sources (RES) and malingas in the Kyrgyz Republic. CRT, proceedings of the int. scientific-technical. Conf. April 22-24, 2011, S. 3.
II. BezrukikhР., Vissarionov V. Basic characteristics of Energy Renewed Resources and Prospects of their Utilization in Fuel and Energetical Balance of Russia Perspective of Renewable energy sources utilization in Karelian Fuel Energy Balance: Proceed, of the First Intern, seminar Eds P. Pelkonen, G. Sidorenko. 1993, Vol. LP.11-21.
III. Chugaev R.R. Hydraulic Engineering. Rev.3, M. – L.: “Energy”, 1975/ Чугаев Р.Р. Гидравлика. Изд.3., М.- Л.: «Энергия», 1975 г.
IV. http://abratsev.ru/hydrosphere/mechanizm.html – River flow mechanism (according to L.K. Davydov)/ Механизм течения рек (по Л. К. Давыдову)
V. http://miniges.com/variant – Mini HPS advantages and disadvantages/ Преимущества и недостатки мини ГЭС.
VI. http://www.inset.ru/r/predm.htm – Equipment for mini HPSs /Оборудование для мини ГЭС.
VII. http://www.solarhome.ru/ru/hydro/index.htm – Energy sources for small hydroelectric engineering/ Источники энергии для малой гидроэнергетики.
VIII. International workshop ISTC – expert Meeting on renewable energy Alternative energy and energy security concerns, Issyk-Kul, Kyrgyz Republic, 21-24.04.2013.
IX. Krasnov V.G. Free-flow hydropower installations [Text] V.G. Krasnov// Innovations and Investments. – 2015. – №4. – Pp.128–131 /Краснов В.Г. Свободнопоточные гидросиловые установки [Текст] / В. Г. Краснов // Инновации и инвестиции. – 2015. – № 4. – С. 128–131.
X. Krasnov V.G. Power engineering facilities for micro HPSs [Text] / V.G. Krasnov, U.K. Zhenishbek // Proceedings of the All-Russian applied research conference of students, postgraduates, and scientists. – Tyumen: TIU, 2017. – Pp. 211–216. /Краснов В. Г. Энергетическое обеспечение микроГЭС [Текст] / В. Г. Краснов, У. К. Женишбек // Материалы Всероссийской научно-практич. конференции обучающихся, аспирантов и учёных. – Тюмень: ТИУ, 2017. – С. 211–216.
XI. Krasnov V.G. Renewable energy sources for micro HPSs: [Text]: monography/ Krasnov V.G. – Cheboksary: NOU DPO “Expert-methodological centre”, 2017. – 56p. /Краснов В.Г. Возобновляемые источники энергии микроГЭС: [Текст]: монография / В.Г. Краснов. – Чебоксары: НОУ ДПО «Экспертно-методический центр», 2017. – 56 с.
XII. Markeev A.P. Theoretical mechanics: Textbook for universities. – Izhevsk: R&D Centre “Regular and chaotic dynamics”, 2001, p.59 /Маркеев А.П. Теоретическая механика: Учебник для университетов. – Ижевск: НИЦ «Регулярная и хаотическая динамика», 2001, 59.
XIII. Revue general nuclear, 1977, 1. 123. O. Eckstein. Water Resource Development. The Economics of Projects Evaluation. Harvard University Press, 1958.
XIV. Rusakova, T.I. and Karpliuk, V.I. (2002), “Numerical research of features of a flow with a separation of the varying cylinder”, Visnyk Dnipropetrovskoho universytetu, vol. 2, issue. 6, pp. 115-123.
XV. Sadovnichiy V.A., Samsonov V.A. (Moscow). On dynamics of fireballs// IX All-Russian conference on theoretical and applied mechanics. Abstract of papers. V.I (Nizhny Novgorod-2006) Nizhny Novgorod: Publ. house of N.I. Lobachevsky State University of Nizhny Novgorod, 2006. p.103/ Садовничий В.А., СамсоновВ.А. (Москва) К вопросу о динамике болидов// IX Всероссийский съезд по теоретической и прикладной механике. Аннотация докладов. т. I (Нижний Новгород-2006) Нижний Новгород: Изд-во Нижегородского госуниверситета им. Н. И. Лобачевского, 2006. С. 103

XVI. Samsonov V.I. Studying mechanics of arterial vessels // IX All-Russian conference on theoretical and applied mechanics. Abstract of papers. V.I (Nizhny Novgorod-2006) Nizhny Novgorod: Publ. house of N.I. Lobachevsky State University of Nizhny Novgorod, 2006. p.165 /Самсонов В. И. Исследование механики артериальных сосудов // IX Всерос сийский съезд по теоретической и прикладной механике. Аннотация докла дов. т. I (Нижний Новгород-2006) Нижний Новгород: Изд-во Нижегородского госуниверситета им. Н. И. Лобачевского, 2006. С. 165.
XVII. Satybaldyev A.B., Matisakov T.K., Attokurov A.K. Determination of optimal angle of water wheel paddle //International Journal of applied and fundamental research. – 2015. – Pp.413-416; URL: http://applied-research.ru/ru/article/view?id=6915 (date of reference: 17.08.2019)./ Сатыбалдыев А.Б., Матисаков Т.К., Аттокуров А.К. определение оптимального угла лопасти водяного колеса // Международный журнал прикладных и фундаментальных исследований. – 2015. – № 6-3. – С. 413-416; URL: http://applied-research.ru/ru/article/view?id=6915 (дата обращения: 17.08.2019).
XVIII. Survey of Energy Resources. WEC/1992 16. The European Renewable Energy Study. Commission of the European Communities. DGXVII. Annex 2. 1
XIX. V.G. Krasnov, A.D. Obozov, O.R. Nurislamov. Analysis of efficiency of use of the longitudinal-flow hydropower plant unit of a micro hydropower station without a dam for small rivers. Journal of Environmental Management and Tourism 9(3):439-451pp., July 2018
XX. Victor G. Krasnov, Petr M. Kosyanov and Nikolai P. Dmitriev, 2016. To the Study of the Motion of a Cylinder with Variable Mass in Flow: The Dynamics of a Free-Flow Micro Hydropower Plant. Journal of Engineering and Applied Sciences, 11: 3136-3141

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Abstract:

The paper summarizes the author’s research of topologies of parallel computation systems and the tasks solved with them, including the relevant tools of their modeling. The original topological model of such systems is presented, based on the modified Amdahl’s law. It allowed formalizing the dependence of the necessary number of processors and the maximal distance between informational adjacent nodes in the graph on directive values of acceleration or efficiency. The dependences of these values on the topology of the system interconnection and on the informational graph of the parallel task are also formalized. The tools for comparative evaluation of these dependences, the topological criteria and the functions of scaling and fault-tolerant functioning of parallel systems are based on the author’s technique of projective description of graph and the algorithms using it.

Keywords:

Interconnect topology,parallel computation systems,projective description of graphs,topological scalability ,fault-tolerance functions,

Refference:

I. Xian-He Sun, Yong Chen. Reevaluating Amdahl’s law in the multicore era, Journal of Parallel and Distributed Computing, Volume 70, Issue 2 (2010) 183–188. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2009.05.002 (accessed 20 June 2019).
II. Abd-El-Barr, M., Gebali, F. Reliability analysis and fault tolerance for hypercube multi-computer networks, Information Sciences, Volume 276 (2014) 295–318. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2013.10.031 (accessed 20 June 2019).
III. Caselli, S., Conte, G., Malavolta, U. Topology and process interaction in concurrent architectures: A GSPN modeling approach, Journal of Parallel and Distributed Computing, Volume 15, Issue 3 (1992) 270–281. ISSN 0743-7315. https://doi.org/10.1016/0743-7315(92)90008-B (accessed 20 June 2019).
IV. Chechina, N., Huiqing Li, Ghaffari, A., Thompson, S., Trinder, Ph. Improving the network scalability of Erlang, Journal of Parallel and Distributed Computing, Volumes 90–91 (2016). 22–34. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2016.01.002 (accessed 20 June 2019).
V. Das, Ch. R., Bhuyan, L. N. Dependability evaluation of interconnection networks, Information Sciences, Volume 43, Issues 1–2 (1987) 107–138. ISSN 0020-0255. https://doi.org/10.1016/0020-0255(87)90033-8 (accessed 20 June 2019).
VI. Dutt, Sh., Hayes, J. P. Designing fault-tolerant systems using automorphisms, Journal of Parallel and Distributed Computing, Volume 12, Issue 3 (1991) 249–268. ISSN 0743-7315. https://doi.org/10.1016/0743-7315(91)90129-W (accessed 20 June 2019).
VII. Emmert-Streib, F., Dehmer, M.,Yongtang Shi. Fifty years of graph matching, network alignment and network comparison, Information Sciences, Volumes 346–347 (2016) 180–197. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2016.01.074 (accessed 20 June 2019).
VIII. Grout, V. M., Sanders, P. W. Communication network optimization, Computer Communications, Volume 11, Issue 5 (1988) 281–287. ISSN 0140-3664. https://doi.org/10.1016/0140-3664(88)90039-4 (accessed 20 June 2019).
IX. HaoChe, Minh Nguyen. Amdahl’s law for multithreaded multicore processors, Journal of Parallel and Distributed Computing, Volume 74, Issue 10 (2014) 3056–3069. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2014.06.012 (accessed 20 June 2019).
X. Hayter, Th., Brookes, G. R. Approach to the simulation of various LAN topologies, Computer Communications, Volume 12, Issue 4 (1989) 204–212.
ISSN 0140-3664, https://doi.org/10.1016/0140-3664(89)90197-7 (accessed 20 June 2019).
XI. Hutchison, D., Sterbenz, J. P.G. Architecture and design for resilient networked systems, Computer Communications, Volume 131 (2018) 13–21. ISSN 0140-3664. https://doi.org/10.1016/j.comcom.2018.07.028 (accessed 20 June 2019).

XII. Jianxi Fan, XiaohuaJia, Xin Liu, Shukui Zhang, Jia Yu. Efficient unicast in bijective connection networks with the restricted faulty node set, Information Sciences, Volume 181, Issue 11 (2011) 2303–2315. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2010.12.011 (accessed 20 June 2019).
XIII. Jianxi Fan, XiaohuaJia. Edge-pancyclicity and path-embeddability of bijective connection graphs, Information Sciences, Volume 178, Issue 2 (2008) 340–351. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2007.08.012 (accessed 20 June 2019).
XIV. Ke Huang, Jie Wu. Fault-tolerant resource placement in balanced hypercubes, Information Sciences, Volume 99, Issues 3–4 (1997) 159–172. ISSN 0020-0255. https://doi.org/10.1016/S0020-0255(96)00270-8 (accessed 20 June 2019).
XV. Kornushk, V. F., Bogunova, I. V., Flid, A. A., Nikolaeva, O. M., Grebenshchikov, A. A. Information-algorithmic support for development of solid pharmaceutical form. Fine Chemical Technologies, 13(5) (2018) 73–81. https://doi.org/10.32362/2410-6593-2018-13-5-73-81 (accessed 20 June 2019).
XVI. Levi, G., Luccio, F. A technique for graph embedding with constraints on node and arc correspondences, Information Sciences, Volume 5 (1973) 1-24. ISSN 0020-0255. https://doi.org/10.1016/0020-0255(73)90001-7 (accessed 20 June 2019).
XVII. Lloret, J., Palau, C., Boronat, F., Tomas, J. Improving networks using group-based topologies, Computer Communications, Volume 31, Issue 14 (2008) 3438–3450. ISSN 0140-3664. https://doi.org/10.1016/j.comcom.2008.05.030 (accessed 20 June 2019).
XVIII. Melent’ev ,V.A., Shubin, V.I., Zadorozhny, A.F. Topological scalability of hypercubic parallel systems and tasks. ISJ Theoretical & Applied Science 11 (31) (2015) 122–129. Doi: http://dx.doi.org/10.15863/TAS.2015.11.31.19 (accessed 20 June 2019).
XIX. Melent’ev, V. A. An analytical approach to the synthesis of regular graphs with preset values of the order, degree and girth, Prikl. Diskr. Mat., 2(8), (2010) 74–86. http://mi.mathnet.ru/eng/pdm178 (accessed 20 June 2019).
XX. Melent’ev, V. A. Bracket form of the graph description and its use in the structural investigations of enduring computer systems, Avtometriya, 4 (2000) 36–51. https://elibrary.ru/item.asp?id=14954075 (accessed 20 June 2019).
XXI. Melent’ev, V. A. Embedding of subsystems limiting length and number of paths between vertexes of computing system graph, UBS, 47 (2014) 212–246. http://mi.mathnet.ru/eng/ubs749 (accessed 20 June 2019).
XXII. Melent’ev, V. A. Fault-tolerance of hypercubic and compact topology of computing systems. ISJ Theoretical & Applied Science, 12 (44) (2016) 98–105. Doi: http://dx.doi.org/10.15863/TAS.2016.12.44.20 (accessed 20 June 2019).
XXIII. Melent’ev, V. A. On approach to the configuring of fault-tolerant subsystems in case of scarce topological fault-tolerance of the computing system. ISJ Theoretical & Applied Science, 10 (54) (2017) 101–105. Doi: https://dx.doi.org/10.15863/TAS.2017.10.54.20 (accessed 20 June 2019).
XXIV. Melent’ev, V. A. On topological fault-tolerance of scalable computing systems, UBS, 70 (2017) 58–86. URL: https://doi.org/10.25728/ubs.2017.70.3 (accessed 20 June 2019).
XXV. Melent’ev, V. A. On topological scalability of computing systems, UBS, 58 (2015) 115–143. http://mi.mathnet.ru/eng/ubs844 (accessed 20 June 2019).
XXVI. Melent’ev, V. A. The metric, cyclomatic and synthesis of topology of systems and networks (2012). https://elibrary.ru/download/elibrary_22249411_42044045.pdf (accessed 20 June 2019).
XXVII. Melent’ev, V. A. Use of Melentiev’s graph representation method for identification and enumeration of circuits of the given length. ISJ Theoretical & Applied Science, 11 (67) (2018) 85–91. Doi: https://dx.doi.org/10.15863/TAS.2018.11.67.16 (accessed 20 June 2019).
XXVIII. Melent’ev, V. A. Use of Melentiev’s graph representation method for detection of cliques and the analysis of topologies of computing systems. ISJ Theoretical & Applied Science, 12 (68), (2018) 201–211. Doi: https://dx.doi.org/10.15863/TAS.2018.12.68.28 (accessed 20 June 2019).
XXIX. Melentiev, V. A. Formalnyeosnovyskobochnyhobrazov v teoriigrafov [Formal bases of bracket images in graph theory]. 2nd International Conference “Parallel computations andy management tasks” PACO’2004: In-t problem upravleniya RAN im. V.A. Trapeznikova, (2004) 694–706.
XXX. Melentiev, V. A. Formalnyypodkhod k issledovaniyustrukturvychislitelnykh system [Formal approach to researching the structures of computation systems]. VestnikTomskogogosudarstvennogo universiteta,14 (2005) 167–172.
XXXI. Melentiev, V. A. The bracket pattern of a graph. 6th International Conference on Pattern Recognition and Image Analysis: New Information Technologies, PRIA-6-2002, October 21-26 (2002) Proceedings, Velikiy Novgorod, Russian Federation, 57–61.
XXXII. Melentiev, V. A. The limiting paralleling in the computing system with a hypercube topology under length restriction of interprocess connections. https://elibrary.ru/item.asp?id=23316608 (accessed 20 June 2019).
XXXIII. Melentiev, V. A., Limit configuring of subsystems in hypercubic computing systems, Journal of Information Technologies and Computing Systems, 2 (2015) 20–30. http://www.jitcs.ru/images/documents/2015-02/20_30.pdf (accessed 20 June 2019).
XXXIV. Melentiev, V. Edge scaling of computing systems, UBS, 64 (2016) 81–11. http://mi.mathnet.ru/eng/ubs898 (accessed 20 June 2019).
XXXV. Nutaro, J., Zeigler, B. How to apply Amdahl’s law to multithreaded multicore processors, Journal of Parallel and Distributed Computing, Volume 107 (2017) 1–2. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2017.03.006 (accessed 20 June 2019).
XXXVI. Subharthi, P., Jianli Pan, Raj Jain. Architectures for the future networks and the next generation Internet: A survey, Computer Communications, Volume 34, Issue 1 (2011) 2–42. ISSN 0140-3664. https://doi.org/10.1016/j.comcom.2010.08.001 (accessed 20 June 2019).
XXXVII. Volkova, A. A Technical Translation of Melentiev’s Graph Representation Method with Commentary. University Honors Theses. Paper 503 (2018). http://dx.doi.org/10.15760/honors.507 (accessed 20 June 2019).
XXXVIII. Wu A. Y. Embedding of tree networks into hypercubes, Journal of Parallel and Distributed Computing, Volume 2, Issue 3 (1985) 238–249. ISSN 0743-7315, https://doi.org/10.1016/0743-7315(85)90026-7 (accessed 20 June 2019).
XXXIX. Xian-He Sun, Yong Chen. Reevaluating Amdahl’s law in the multicore era, Journal of Parallel and Distributed Computing, Volume 70, Issue 2 (2010) 183–188. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2009.05.002 (accessed 20 June 2019).
XL. Yavits, L., Morad, A., Ginosar, R. The effect of communication and synchronization on Amdahl’s law in multicore systems, Parallel Computing, Volume 40, Issue 1 (2014) 1–16. ISSN 0167-8191. https://doi.org/10.1016/j.parco.2013.11.001 (accessed 20 June 2019).

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Abstract:

The article explores symbolic systems as a special type of complex systems. The relationship between the concepts of “sign” and “symbol” and symbolic means are analyzed. Four research methods of analyzing a language as a symbolically symbolic system are investigated: linguistic, semiotic, systemic and informational. The article describes the symbolic system using the information approach and demonstrates the presence of emergence in sign-symbolic systems. The article shows that a sign-symbolic system is best described as an informational construction. The main functions of sign-symbolic systems are the following: representation, communication, information and externalization of implicit knowledge.

Keywords:

Complex systems,sign,symbol,symbolically symbolic systems,linguistics,information approach,

Refference:

I. Amaglobeli G. Semantic triangle and linguistic sign //Journal in Humanities. – 2012. – Т. 1. – №. 1. – С. 37-40.

II. Chang H. H., Ying Z. A global information approach to computerized adaptive testing //Applied Psychological Measurement. – 1996. – Т. 20. – №. 3. – С. 213-229.

III. Chowdhury M. F. Interpretivism in aiding our understanding of the contemporary social world //Open Journal of Philosophy. – 2014. – Т. 4. – №. 03. – С. 432.

IV. De Man P. Sign and Symbol in Hegel’s Aesthetics //Critical Inquiry. – 1982. – Т. 8. – №. 4. – С. 761-775

V. Fischer K. Linguistic methods for investigating concepts in use //Methodologie in der Linguistik. – 2003. – С. 39-62.

VI. Folke C. Resilience: The emergence of a perspective for social–ecological systems analyses //Global environmental change. – 2006. – V. 16. – №. 3. – р. 253-267.

VII. Giffin M. et al. Change propagation analysis in complex technical systems //Journal of Mechanical Design. – 2009. – Т. 131. – №. 8. – С. 081001.

VIII. Goldstein J. Emergence as a construct: History and issues //Emergence. – 1999. – V. 1. – №. 1. – р. 49-72.
IX. I. N. Rozenberg. Information Construction and Information Units in the Management of Transport Systems // European Journal of Technology and Design, 2016, 2(12), pp. 54-62, DOI: 10.13187/ejtd.2016.12.54 www.ejournal4.com

X. Koppenjan J., Groenewegen J. Institutional design for complex technological systems //International Journal of Technology, Policy and Management. – 2005. – Т. 5. – №. 3. – С. 240-257.

XI. Leontiev A. A. Sign and activity //Journal of Russian & East European Psychology. – 2006. – Т. 44. – №. 3. – С. 17-29.

XII. Luhmann N., Baecker D., Gilgen P. Introduction to systems theory. – Cambridge : Polity, 2013.

XIII. Markus M. L., Majchrzak A., Gasser L. A design theory for systems that support emergent knowledge processes //MIS quarterly. – 2002. – С. 179-212.

XIV. Mesarovic M. D., Takahara Y. Abstract systems theory. – 1989.

XV. Plummer K. A world in the making: Symbolic interactionism in the twentieth century //A Companion to Social Theory. 2nd ed.[Web document], Blackwell [Cited 15.12. 2011]. – 2000. – С. 193-222.

XVI. Po-An Hsieh J. J., Wang W. Explaining employees’ extended use of complex information systems. – 2007.

XVII. Reis D. S. et al. Applicability of the semiotic inspection method: A systematic literature review // Proceedings of the 10th Brazilian Symposium on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction. – Brazilian Computer Society, 2011. – С. 177-186.

XVIII. Toren C. Sign into symbol, symbol as sign: Cognitive aspects of a social process //Cognitive aspects of religious symbolism. – 1993. – С. 147-264.

XIX. Tsvetkov V. Ya. Information Units as the Elements of Complex Models // Nanotechnology Research and Practice. – 2014, № 1(1), р.57-64.

XX. Tsvetkov V. Yа. Dichotomic Assessment of Information Situations and Information Superiority // European Researcher.. 2014, № 11-1 (86), pp. 1901-1909. DOI: 10.13187/er.2014.86.1901
XXI. Tsvetkov V. Yа. Information interaction // European researcher. 2013. № 11-1 (62). С. 2573-2577

XXII. TsvetkovV.Ya. Information field // Life Science Journal. – 2014 – Т.11. №5. -с.551-554.

XXIII. V. Ya. Tsvetkov System Information // European Journal of Economic Studies, 2018, 6(1): 18-22. DOI: 10.13187/ejtd.2018.1.18

XXIV. V. Ya. Tsvetkov. Information Relations // Modeling of Artificial Intelligence. 2015. № 4(8). – р.252-260/

XXV. Williams M. Interpretivism and generalisation //Sociology. – 2000. – Т. 34. – №. 2. – С. 209-224

XXVI. Цветков В.Я. Теория систем: Монография. – М.: МАКС Пресс, 2018. – 88 с. ISBN 978-5-317-05718-3

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Abstract:

The implementation of ‘Smart Objects’ is an important part of the development of adaptive Smart Grid structures. For this class of objects, poorly for malizable factors, such as microclimate parameters, environmental indicators, and consumer load, acquire a significant influence. To solve this, PID controllers are usually used in Smart Objects; however, their accuracy is limited. Fuzzy neural controllers are an alternative solution for the integrated optimization of Smart Objects. This article proposes a scalable model of Smart Object equilibrium by the example of basic utility systems (heating, air conditioning/ventilation and illumination). It was found that the use of fuzzy neural controllers in such systems makes it possible to improve their efficiency by increasing the accuracy of energy consumption forecasts. Control systems based on PID controllers and fuzzy neural controllers in Smart Object were comparted only to find that the latter have a higher accuracy.

Keywords:

Smart Objects, distributed objects,PID controllers,fuzzy neural network,fuzzy neural controller,mathematical modeling,

Refference:

I. Alanis, A. Y., Ricalde, L. J., Simetti, C., Odone, F. (2013). Neural model with particle swarm optimization kalman learning for forecasting in smart grids. Mathematical Problems in Engineering, 2013, 1–9.

II. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L. (2016). Realizatsiyaprogrammnogokompleksaprognozirovaniyaurovnyaregionalnogoenergopotrebleniya [Sales program complex: Prediction of the regional level of energy consumption]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 12(3), 41–47.

III. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L., Shukur, O.M. (2016). Realizatsiyaelementovprogrammnogokompleksaprognozirovaniyaregionalnogoenergopotrebleniyanabazeneyronnoyseti [Implementation of elements of program complex for prediction of the regional level of energy consumption based on neural network]. Proceedings of the 4th International Conference on modern methods of applied mathematics, control theory and computer technology (PMUKT-2016), Voronezh, Russia, 59–63.
IV. Çevik, H.H., Çunkaş, M. (2015). Short-term load forecasting using fuzzy logic and ANFIS. Neural Computing and Applications, 26(6), 1355–1367.
V. Cheng, Z., Juncheng, T. (2015). Adaptive combination forecasting model for china’s logistics freight volume based on an improved PSO-BP neural network. Kybernetes, 44(4), 646.
VI. Danilov, A.D., Shukur, O.M., Rutskov, A.L. (2016). Analizprimeneniyanechotkikhneyronnykhseteydlyaprognozirovaniyaenergopotrebleniyapromyshlennykhpredpriyatiy [Analysis of the use of fuzzy neural networks for predicting power consumption of industrial enterprises]. Aktualnyyenauchnyyeissledovaniya XXI veka: teoriya i praktika = Topical scientific research of the 21st century: Theory and practice, 4, 6(26), 59–63.

VII. Gusev, K.Yu.,Burkovsky, V.L. (2012). Neyrosetevoyemodelirovaniyedinamikinelineynykhsistem [Neural network modeling of nonlinear dynamics]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 8(12.1), 51–56.

VIII. Hopfield, J.J. (1982). Neural networks and physical systems with emergent computations abilities. Proceedings of the National Academy of Sciences, 79, 2544–2558.

IX. Jiang, X., Ling, H., Yan, J., Li, B., & Li, Z. (2013). Forecasting electrical energy consumption of equipment maintenance using neural network and particle swarm optimization. Mathematical Problems in Engineering, 2013.
X. Komartsova, L.G. (2004). Neyrokompyutery [Neurocomputers], 2nd ed. Moscow, Bauman Moscow State Technical University, 400 p.
XI. Krysanov, V.N., Gamburg, K.S., Rutskov, A.L. (2014). Problemyprognozirovaniyapotrebleniyaelektroenergiinapredpriyatiyakh s odnostavochnymtarifom [Problems of forecasting electricity consumption in enterprises having straight-line rate]. Proceedings of International Scientific and Technical Conference “Power Supply Industry and Electrical Engineering”, International Institute of Computers Technology, May 15–21, Voronezh, Russia.
XII. Krysanov, V.N., Rutskov, A.L. (2013). Matematicheskoyemodelirovaniyesistemupravleniyaraspredelonnoynasosnoynagruzki s primeneniyemiskusstvennykhneyronnykhsetey [Mathematical modeling of distributed pump load control systems using artificial neural networks]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, May 14–15, Voronezh, Russia.
XIII. Krysanov, V.N., Rutskov, A.L. (2014). Primeneniyemetodovneyronnykh i neyro-nechotkikhsetey v system akhupravleniyastaticheskimipreobrazovatelyami v elektroprivode [Application of methods of neural and neuro-fuzzy networks in static converter control systems in electric drive]. Proceedings of the International (19th All-Russian) Conference on Automated Electric Drive (AEP-2014), Saransk, Russia.

XIV. Krysanov, V.N., Rutskov, A.L. (2014). Prognozirovaniyepotrebleniyaelektroenergiipromyshlennymipredpriyatiyami s ispolzovaniyemmetodoviskusstvennykhneyronnykh i neyro-nechotkikhsetey [Forecasting power consumption by industrial enterprises using artificial neural and neuro-fuzzy networks]. Proceedings of the International (19th All-Russian) Conference on Automated Electric Drive (AEP-2014), Saransk, Russia.

XV. Krysanov, V.N., Rutskov, A.L., Sharapov, Yu.V. (2015). Prostranstvennyy 3-d interpolyator s ispolzovaniyemnechotkoylogiki [Spatial 3-d interpolator with fuzzy logic]. Proceedings of the 15th International Seminar “Physical and Mathematical Modeling of Systems” (FMMS-15), November 27–28, Voronezh, Russia.

XVI. Krysanov, V.N., Rutskov, A.L., Shukur, O., Shukur M. (2015). Prognozirovaniyepotrebleniyaelektroenergii v razvivayushcheysyaregionalnoysistemeelektrosnabzheniya [Forecasting of electricity consumption in a developing regional power supply system]. Proceedings of the 15th International Seminar “Physical and Mathematical Modeling of Systems” (FMMS-15), November 27–28, Voronezh, Russia.

XVII. Krysanov, V.N., Shukur, O., Shukur M., Rutskov, A.L. (2015). Otsenkaeffektivnostiprimeneniyaiskusstvennykhneyronnykh i neyro-nechotkikhseteydlyakontseptsii Smart Grid v elementakhtransporta i potrebleniyaelektroenergii [Evaluation of the effectiveness of use of artificial neural and neuro-fuzzy networks for the Smart Grid concept in the elements of transport and electricity consumption]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, October 25–28, Voronezh, Russia.
XVIII. Li, P., Li, Y., Xiong, Q., Chai, Y., Zhang, Y. (2014). Application of a hybrid quantized elman neural network in short-term load forecasting. International Journal of Electrical Power & Energy Systems, 55, 749–759.
XIX. Mamdani, E.H. (1977). Advances in the linguistic synthesis of fuzzy controllers. IEEE Trans. on Computer, C-26, 1182–1191.

XX. Monteleoni, C., Schmidt, G.A., Saroha, S., Asplund, E. (2011). Tracking climate models. Statistical Analysis and Data Mining, 4(4), 372–392.
XXI. Nedellec, R., Cugliari, J., Goude, Y. (2014). Gefcom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting, 30(2), 375–381.
XXII. Panklib, K., Prakasvudhisarn, C., Khummongkol, D. (2015). Electricity consumption forecasting in Thailand using an artificial neural network and multiple linear regression. Energy Sources, Part B: Economics, Planning, and Policy, 10(4), 427–434.

XXIII. Pierrot, A., Goude, Y. (2011). Short-term electricity load forecasting with generalized additive models. Proceedings of ISAP Power, 593–600.

XXIV. Rutskov, A.L., Gagarinov, N.V., Romanov, A.V. (2015). Analizeffektivnostiupravleniyarezhimamisetey 220 kV [Efficiency analysis of mode control in 220 kV networks]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, October 25–28, Voronezh, Russia.

XXV. Rutskov, A.L., Myazin, D.S., Romanov, A.V. (2015). Povysheniyetekhnologicheskoy i energeticheskoyeffektivnostinaklonnykhdiffuzionnykhustanovokputomoptimizatsiiparametrov s primeneniyemneyro-nechotkikhprintsipov [Improving the technological and energy efficiency of inclined diffusion plants by optimizing their parameters using neuro-fuzzy principles]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, October 25–28, Voronezh, Russia.

XXVI. Shi B., Yu-Xia L I., Xin-Hua Y.U. (2009). Short-term load forecast based on modified particle swarm optimizer and back propagation neural network model. Journal of Computer Applications, 29(4), 1036–1039.

XXVII. Zadeh, L.A. (1974). Outline to a new approach to the analysis complex systems and decision processes. IEEE Trans. on Systems, Man, and Cybernetics, 3, 28–44.

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Abstract:

The article addresses optimization of power supply systems by using fuzzy neural networks to increase the accuracy of operational forecasts and implementactive control systems in the power supply grids. As a practical example, the article considers the optimization of parameters of the 220 kV Yuzhnaya Substation operated by the Regional Dispatching Office of the Voronezh Region Electric Power System (Voronezh, Russia). The obtained results indicate an increase in the energy efficiency of the studied equipment by 4.38% (in terms of real power loss),as compared to the existing control mode, through the use of fuzzy neural controllers that improve the accuracy of forecasts of the relevant technological parameters. The developed solutions can be used in electrical power systems and load nodes as a part of control modules. The economic effect is achieved by taking into account the poorly for malizablefactors and compensating for their impact on real power loss in the transformer equipment.

Keywords:

Optimization of power supply systems,energy efficiency,energy efficiency,distributed objects,fuzzy neural networks,adaptive control systems,

Refference:

I. Aiolfi, M., Capistran C., Timmermann, A. (2010). Forecast combinations. Working Papers 2010-04, Banco de México.

II. Al Rashidi, M. R., El-Hawary, M. E. (2009). A survey of particle swarm optimization applications in electric power systems. IEEE Transactions on Evolutionary Computation, 13(4), 913–918.

III. Antoniadis, A., Brossat, X., Cugliari, J., Poggi, J. (2013). Clustering functional data using wavelets. International Journal of Wavelets, Multiresolution and Information Processing, 11(01).

IV. Burkovsky, V.L., Gusev, K.Yu. (2010). Neyrosetevaya model prognozirovaniyadinamikiekonomicheskikhpokazateley [Neural network simulation for forecasting the dynamics of economic indicators]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 6(4), 80–82.

V. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L. (2014). Prognozirovaniyepotrebleniyaelektroenergiipromyshlennymipredpriyatiyami s ispolzovaniyemmetodoviskusstvennykhneyronnykh i neyro-nechotkikhsetey [Forecasting power consumption by industrial enterprises using artificial neural and neuro-fuzzy networks]. Proceeding of the International (19th All-Russian) Conference on Automated Electric Drive (AEP-2014), Saransk, Russia.

VI. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L. (2016). Realizatsiyaprogrammnogokompleksaprognozirovaniyaurovnyaregionalnogoenergopotrebleniya [Sales program complex: Prediction of the regional level of energy consumption]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 12(3), 41–47.

VII. Çevik, H.H., Çunkaş, M. (2015). Short-term load forecasting using fuzzy logic and ANFIS. Neural Computing and Applications, 26(6), 1355–1367.

VIII. Cheng, Z., Juncheng, T. (2015). Adaptive combination forecasting model for china’s logistics freight volume based on an improved PSO-BP neural network. Kybernetes, 44(4), 646.

IX. Cho, H., Goude, Y., Brossat, X., Yao, Q. (2013). Modeling and forecasting daily electricity load curves: a hybrid approach. Journal of the American Statistical Association, 108, 7–21.

X. Danilov, A.D., Shukur, O.M., Rutskov, A.L. (2016). Analizprimeneniyanechotkikhneyronnykhseteydlyaprognozirovaniyaenergopotrebleniyapromyshlennykhpredpriyatiy [Analysis of the use of fuzzy neural networks for predicting power consumption of industrial enterprises]. Aktualnyyenauchnyyeissledovaniya XXI veka: teoriya i praktika = Topical scientific research of the 21st century: Theory and practice, 4, 6(26), 59–63.

XI. Devaine, M., Gaillard, P., Goude, Y., Stoltz, G. (2013). Forecasting electricity consumption by aggregating specialized experts. Machine Learning, 90(2), 231–260.

XII. Devaine, M., Gaillard, P., Goude, Y., Stoltz, G. (2013). Forecasting electricity consumption by aggregating specialized experts. Machine Learning, 90(2), 231–260.

XIII. Eban, E., Birnbaum, A., Shalev-Shwartz, S., Globerson, A. (2012). Learning the experts for online sequence prediction. Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK.

XIV. Gamm, A.Z., Gerasimov, L.N., Golub, I.I., et al. (1983). Otsenivaniyesostoyaniya v elektroenergetike [State estimation in power generation industry]. Moscow, Nauka.

XV. Krysanov, V.N., Rutskov, A.L., Myazin, D.S. (2015). Optimizatsiyaparametrovtsikladiffuziisveklosakharnogoproizvodstva s primeneniyemneyro-nechotkikhprintsipov [Optimization of diffusion cycle parameters in beet-sugar production using neuro-fuzzy principles]. Elektrotekhnicheskiyekompleksy i sistemyupravleniya = Electrotechnical Complexes and Control Systems, 2, 65–70.

XVI. Li, P., Li, Y., Xiong, Q., Chai, Y., Zhang, Y. (2014). Application of a hybrid quantized elman neural network in short-term load forecasting. International Journal of Electrical Power & Energy Systems, 55, 749–759.

XVII. Monteleoni, C., Schmidt, G.A., Saroha, S., Asplund, E. (2011). Tracking climate models. Statistical Analysis and Data Mining, 4(4), 372–392.

XVIII. Nedellec, R., Cugliari, J., Goude, Y. (2014). Gefcom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting, 30(2), 375–381.

XIX. Order of the Ministry of Industry and Energy of the Russian Federation No. 380 of June 23, 2015 “On the procedure for calculating the ratio of the consumption of real and reactive power for certain power receiver (groups of power receivers) of electrical energy consumers.” Available at: https://normativ.kontur.ru/document?moduleId=1&documentId=256534

XX. Rosenblatt, R. (1961). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Book, Washington D.C.

XXI. Russian National Standard GOST 32144-2013 (2013). Electric energy. Electromagnetic compatibility of technical equipment. Power quality limits in the public power supply systems.

XXII. Shi, B., Yu-Xia, L.I., Xin-Hua, Y.U. (2009). Short-term load forecast based on modified particle swarm optimizer and back propagation neural network model. Journal of Computer Applications, 29(4), 1036–1039.

XXIII. Taylor, J. (2003). Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing. Journal of Operational Research Society, 54, 799–805.

XXIV. Vorotnitsky, V.E., Zaslonov, S.V., Kalinkina, M.A., Parinov, I.A., Turkina, O.V. (2006). Metody i sredstvarascheta, analiza i snizheniyapoterelektricheskoyenergiipriyeyeperedachepoelektricheskimsetyam [Methods and means of calculating, analyzing and reducing electric power losses when it is transmitted through electrical networks]. Moscow.

XXV. Zadeh, L.A. (1974). Outline to a new approach to the analysis complex systems and decision processes. IEEE Trans. on Systems, Man, and Cybernetics, 3, 28–44.

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