Issue 49

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. One of the first expert systems was medical. In article the review in a historical retrospective show of this

class of expert systems contains, and some prospects of their application and the subsequent development in applied medicine are defined.

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(ABEL); 2) (MYCIN, HEME, -

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3) (AI/MM, DIALYSIS THERAPY ADVISOR, EEG ANALYSIS SYSTEM);

4) (AI/RHEUM, ARAMIS);

5) (ANGY, ANNA, DIAGNOSER, DIGITALIS ADVISOR, GALEN, HEART IMAGE IN-TERPRETER, HT-ATTENDING, MECS-AI, MI, );

6) (MECS-AI, THYROID MODEL);

7) (BLUE BOX, HEADMED, NEUREX);

8) (CAS-NET/GLAUCOMA, MEDICO, OCULAR HERPES MODEL, PEC);

9) (CENTAUR, PUFF, WHEEZE, );

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11) (MDX, PATREC, RADEX).

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2. ., . : , 4- .: . . – .: " . ", 2007. – 1152 .

3. . .: . .: . . – .: ", 2001. – 624 .

4. ., . : . . – .: , 1993. – 608 . 5. . ., . .

// " " (24-25 2008 .). – .: , 2008. – . 108-109.

6. . ., . . / . . , . . . – .: +, 1998. – 320 .

7. . . – / . . , . , . . // . – 1995. – 1. – . 30-32.

8. . . ATM Express: ( ): - / . ., . ., . . – .: , 2007. – 27 .

004.93(015.7)

This paper presents investigation of nonlinear Boolean transformations inverse for which are ambiguous and

its application in cryptographical algorithms. A new method for designing such class of Boolean trans-formations is suggested. The method deals with the procedure form representation of Boolean transformations. It allowed to buil Boolean transformatiom from hundreds Boolean variables. Boolean transformation on such class can be use for accelerate of user identification based on “zero-knoledge” conseption. The relationship between transformation building time and procedure form parameters is established.

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2. ., ., , ., SAC- . //

”K I”. , .–1998.– 31.– .131-140.

3. Seberry J., Zhang X., Zheng Y. Nonlinearity and propagation characteristics of balanced Boolean func-tions.//Information and Computation Academic Press. 1995.–Vol. 119, 1 –P.1-13.

681.3.07

, , . Investigation of neural network use for medical diagnostic problem on cancer detection example in

gynaecology is conducted in this work. Investigation of Takagi-Sugeno-Kang’s fuzzy neural network opportunities for this problem solving is conducted. Furthermore, posibility of test’s (which are necessary for diagnostics process) number decrease is conducted in this work.

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50:50 60:40 RI RQ RMSE train RMSE test RQ RMSE train RMSE test 0,1 64 1,8315 10-17 0,0332 64 1,5072 10-17 0,0414 0,2 64 2,6832 10-17 0,0312 64 3,1898 10-17 0,0389 0,3 64 3,2746 10-17 0,0297 64 3,7296 10-17 0,0371 0,4 64 5,9620 10-17 0,0282 64 4,5869 10-17 0,0351 0,5 62 6,0560 10-17 0,0277 61 5,4587 10-17 0,036 0,6 55 2,3649 10-16 0,0623 57 3,6669 10-16 0,0387 0,7 47 2,1801 10-16 0,0552 47 1,6546 10-16 0,047 0,8 32 4,3250 10-16 0,0376 33 2,2483 10-16 0,0637 0,9 21 2,7109 10-16 0,0504 27 4,2174 10-16 0,0776 1 19 6,3821 10-16 0,0549 20 7,7099 10-16 0,0912

2 70:30 80:20

RI RQ RMSE train RMSE test RQ RMSE train RMSE test 0,1 70 1,4754 10-17 0,0435 89 1,2581 10-17 0,0331 0,2 70 2,1681 10-17 0,0418 89 2,0689 10-17 0,0302 0,3 70 3,3746 10-17 0,0388 89 2,9218 10-17 0,0293 0,4 69 4,7760 10-17 0,0365 88 4,1680 10-17 0,0286 0,5 68 5,2207 10-17 0,0358 85 5,0317 10-17 0,0269 0,6 64 5,6841 10-17 0,0386 74 8,3585 10-17 0,0290 0,7 51 2,0565 10-16 0,0534 57 2,3107 10-16 0,047 0,8 37 4,0681 10-16 0,0967 34 6,5636 10-16 0,0956 0,9 28 4,5956 10-15 0,1212 25 9,5112 10-16 0,0865 1 6 1,2788 10-15 0,1691 5 2,3033 10-15 0,1637

– 90%, – 10%. 3

RI RQ RMSE train RMSE test 0,1 96 1,1710 10-17 2,9216 10-18

0,2 96 2,1647 10-17 5,9195 10-17 0,3 96 2,6863 10-17 8,1233 10-17 0,4 96 3,3784 10-17 9,6381 10-17 0,5 92 4,4979 10-17 1,3867 10-16 0,6 80 1,6441 10-16 3,0583 10-16 0,7 61 2,2497 10-16 4,5655 10-16 0,8 44 5,5951 10-16 1,3725 10-15 0,9 31 1,0292 10-15 3,4575 10-15 1 5 1,7631 10-15 3,3432 10-15

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RI RQ RMSE train RMSE test RQ RMSE train RMSE test 0,1 64 2,3976 10-8 0,033091 64 1,7003 10-8 0,041256 0,2 64 2,3981 10-8 0,031066 64 1,7008 10-8 0,038731 0,3 64 2,411 10-8 0,029487 64 1,7095 10-8 0,036763 0,4 64 2,5551 10-8 0,027863 64 1,8073 10-8 0,034738 0,5 62 2,235 10-6 0,027419 61 1,6779 10-6 0,035122 0,6 55 5,51 10-6 0,028531 57 3,931 10-6 0,03407 0,7 47 8,112 10-6 0,030635 47 6,314 10-6 0,043796 0,8 32 8,5395 10-6 0,03862 33 7,1297 10-6 0,041018 0,9 21 1,9289 10-5 0,038845 27 1,0663 10-5 0,046861 1 19 3,6898 10-5 0,045584 20 2,8545 10-5 0,047587

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RI RQ RMSE train RMSE test RQ RMSE train RMSE test 0,1 70 1,4761 10-8 0,043158 89 1,4916 10-8 0,033651 0,2 70 1,4767 10-8 0,041357 89 1,4919 10-8 0,030443 0,3 70 1,4817 10-8 0,038156 89 1,4985 10-8 0,0293 0,4 69 5,7658 10-8 0,035841 88 5,5153 10-8 0,0288 0,5 68 1,3224 10-6 0,035571 85 6,0647 10-8 0,0291 0,6 64 3,4702 10-6 0,043127 74 3,7547 10-6 0,031 0,7 51 4,9418 10-6 0,047605 57 6,8552 10-6 0,0359 0,8 37 7,3412 10-6 0,048571 34 1,0559 10-5 0,0148 0,9 28 1,0643 10-5 0,046314 25 1,7685 10-5 0,0376 1 6 0,00022524 0,095813 5 6,9307 10-4 0,1178

– 90%, – 10%. 6

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1. Steve Saggese, Trevor Johnson, Ivy Basco, Yalew Tamrat. Automatic Segmentation of Uterine Cervix for in vivo localization and Identification of Cervical Intraepithelial Neoplasia.– Apogen Technologies 7545 Metropolitan Dr San Diego, CA 92108.

2. . . . – .: - « », 2004. – 352 .

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004.052.42

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. The paper is dedicated to solving the efficiency increasing problem for data transmission error detection in

spectrum modulation channel by properties such errors appearance accounted. For the guaranteed errors detec-tion in one and more channel symbols the approach based on Chinese Reminder Theorem has been proposed. In the course of the theoretical researches of proposed approach the guaranteed error detection has been proved for errors quantity less or equal to number of the check symbols in modular representation.

(EDGE, CDMA2000, 1xEV-DO), -

(DVB-T/H/C/S/S2, ATSC), -

(Wi-Fi, Wi-MAX, Bluetooth), - [1]. -

( -PSK, --QAM)

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, m : B={b1,b2,…,bm}, bl {0,1}, l=1,…,m

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« » , 49

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1. . . – .: .–2001.–153 . 2. . . .: . ., –1999.–340 . 3. . . . .:

”, 2004.– 1104 . 4. ., ., .

// -. : 3(14).– ., .– 2008.– .121-128

004.415.2.043

. : , .

: . . ,

, . -.

The traditional analysis of program language operators if with laying out of them on separate lexemes as equal

in rights units of language complicates enough the syntactic analysis. The natural languages of intercourse are had at their perception of separation to the subject, to the predicate and other parts of language for the awareness of sense of suggestion. Something similar is offered on the stage of lexical analysis of program language operators. All lexemes are here divided into three groups: lexemes-objects, lexemes of action at al. A feature as programming if consists in the obligatory presence of pair of lexemes: lexeme-object and lexeme of action. Thus from this pair all operators begin and in this pair it is always the lexeme of action is a mandatory member. Therefore, if a lexeme-object appear at the lexical analysis, the simultaneous search of the proper lexeme of action is appropriate. Such approach allows considerably simplify the syntactic analysis of operators of language and accelelerate his imple-mentation.

– , LL- LR-

[1, 2]. -

. , , -

G = f(T, N, P, Ns), Ns – - ( ),

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4. . . . – ; , 2000. – 366 .

004.074.32

. . . .

In article the new hash-searching organization such that keys may storing in one of dual hash-address has been

proposed. It is allowed to limit the searching time by dual memory access. The procedure of recursion recording keys into the hash-memory has been developed. The analytical evaluation of collision probability has been obtained. The possibilities of proposed organization for hash-searching in static and dynamic arrays of data has been analyzed

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h1(X) h2(X) X

« » , 49

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« » , 49

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1. . .- .: ,1980.- 198 . 2. ., , . -

// ”. , .- 1998,- 31,- C.14-23.

3. . -. // ”K I”. ,

.- 2003.- 40.- .131-140. 4. Czech Z.J., Havas G., Majevski B.S. An Optimal algorithm for generating minimal perfect hash func-

tions.//Information processing letters. – 1997. – Vol.43.- 5. - P.257-264.

681.3.06

( ) , , , . ,

The approach to the complex task decision for analytical support of software analysis and synthesis is

considered. Key elements of such approach use representation formats for specification headers contents of task decision resources (TDR) and knowledge base for analogues search, a choice of best components, use of transformation rules and decomposition mechanisms for TDR. It is shown, that application of such knowledge bases allows using the most of the previous software, and making values of target semantic variables extremely close to restriction requirements and criteria of processing speed.

( ) ( )

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; ak1,…,aknmax – .

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42 rtk, rrk, rkk, rlk, rmk, adk, atk, aek, ark, ack, avk -

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« » , 49 45

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, [4].

1. A., ., . : , , : . . – .: , 2001. – 768 .

2. ., . . Oracle8. : – .: - ” ”. – 2000. – 800 .

3. ., . . . . .: , 1989. 424 .

4. . - // “ ”. , – .: « +».

2007, 47, . 269-279. 5. Hehner E.C.R. Practical theory of programming. Springer-Verlag, New York, 1993 – 243 p. 6. Metzger R.C., Zhaofang W. Automatic algorithm recognition and replacement: a new approach to program

optimization / The MIT Press, Cambridge, 2000. 219 p. 7. Woodcock J., Davies J. Using Z. Specification, Refinement, and Proof. C.A.R. Hoare Series editor, 1995 –

390 p.

004.94

OPENGPSS GPSS/PC

OpenGPSS GPSS/PC, .

The report deals with questions of computing experiment in distributed discrete-event simulation systems

OpenGPSS and GPSS/PC, their high-quality and quantitative job performances are compared. The problems of experiment distribution by independent part, deployment this part on cluster node, parallel execution and result assembles and here does not influence on the rightness of end-point also were laboured in the report.

[1, 2].

, -, SPEEDES [3], PARASOL [4] Triad.Net

[5]. -, -

GPSS [6]. -

GPSS . -

OpenGPSS [7],

GPSS. -.

GPSS/PC OpenGPSS. -

GPSS/PC - 2.0 Minuteman Software

(http://www.minitemansoftware.com/),

- GPSS, -

) ( . 1).

. 1. GPSS- GPSS

100 GENERATE 10,5 110 QUEUE QUE1 120 SEIZE PRIB1 130 DEPART QUE1 140 ADVANCE 15,5 150 RELEASE PRIB1 160 TERMINATE 170 GENERATE 5000 400 TERMINATE 1 500 START 1

GPSS- .

- GPSS/PC, -

. OpenGPSS

, [8] -

). OpenGPSS -

. . 2 -.

« » , 49 48

. 2. GPSS/PC 2.0 OpenGPSS

) 5000 5000

PRIB1 ENTRIES 338 334 UTIL (%) 0,997 0,997 AVE.TIME 14,75 15,0

QUE1 MAX. 169 161 CONTENT 168 161 ENTRIES 506 495 ENTR (0)

AVE.CON 80,85 81,0 AVE.TIME

798,89 816,0

AVE.(0) -

802,06 821,0

OpenGPSS - GPSS- . -

, OpenGPSS , -

GPSS- – -

GPSS- ( . 3).

START, CLEAR RMULT.

PRIB1 QUE1 RES.TXT RESULT.

. 3. GPSS- GPSS

100 GENERATE 10,5 110 QUEUE QUE1 120 SEIZE PRIB1 130 DEPART QUE1 140 ADVANCE 15,5 150 RELEASE PRIB1 160 TERMINATE 170 GENERATE 5000 180 SAVEVALUE MSV01,FR$PRIB1 190 SAVEVALUE MSV02,FC$PRIB1 200 SAVEVALUE MSV03,FT$PRIB1 210 SAVEVALUE MSV04,Q$QUE1 220 SAVEVALUE MSV05,QA$QUE1 230 SAVEVALUE MSV06,QM$QUE1

240 SAVEVALUE MSV07,QC$QUE1 250 SAVEVALUE MSV08,QZ$QUE1 260 SAVEVALUE MSV09,QT$QUE1 270 SAVEVALUE MSV10,QX$QUE1 400 TERMINATE 1 410 CLEAR 420 RMULT 1,2,8,5,8,2,8 500 START 1 510 RESULT RES.TXT,MSV01,01;FR$PRIB1 520 RESULT RES.TXT,MSV02,02;FC$PRIB1 530 RESULT RES.TXT,MSV03,03;FT$PRIB1 540 RESULT RES.TXT,MSV04,04;Q$QUE1 550 RESULT RES.TXT,MSV05,05;QA$QUE1 560 RESULT RES.TXT,MSV06,06;QM$QUE1 570 RESULT RES.TXT,MSV07,07;QC$QUE1 580 RESULT RES.TXT,MSV08,08;QZ$QUE1 590 RESULT RES.TXT,MSV09,09;QT$QUE1 600 RESULT RES.TXT,MSV10,10;QX$QUE1 610 CLEAR 620 RMULT 5,1,6,6,7,1,1 630 START 1 640 RESULT RES.TXT,MSV01,01;FR$PRIB1 650 RESULT RES.TXT,MSV02,02;FC$PRIB1 660 RESULT RES.TXT,MSV03,03;FT$PRIB1 670 RESULT RES.TXT,MSV04,04;Q$QUE1 680 RESULT RES.TXT,MSV05,05;QA$QUE1 690 RESULT RES.TXT,MSV06,06;QM$QUE1 700 RESULT RES.TXT,MSV07,07;QC$QUE1 710 RESULT RES.TXT,MSV08,08;QZ$QUE1 720 RESULT RES.TXT,MSV09,09;QT$QUE1 730 RESULT RES.TXT,MSV10,10;QX$QUE1 740 CLEAR 750 RMULT 2,2,5,2,8,9,3 760 START 1 770 RESULT RES.TXT,MSV01,01;FR$PRIB1 780 RESULT RES.TXT,MSV02,02;FC$PRIB1 790 RESULT RES.TXT,MSV03,03;FT$PRIB1 800 RESULT RES.TXT,MSV04,04;Q$QUE1

49 OPENGPSS GPSS/PC

810 RESULT RES.TXT,MSV05,05;QA$QUE1 820 RESULT RES.TXT,MSV06,06;QM$QUE1 830 RESULT RES.TXT,MSV07,07;QC$QUE1 840 RESULT RES.TXT,MSV08,08;QZ$QUE1 850 RESULT RES.TXT,MSV09,09;QT$QUE1 860 RESULT RES.TXT,MSV10,10;QX$QUE1

RMULT -

. START -. GPSS-

- START, -

. CLEAR

. CLEAR , ,

. RESULT

- ( ) – -

, CLEAR RMULT, CLEAR, RMULT, START -

START RESULT. -

( ) , RMULT

CLEAR. --

( ) – , -

CLEAR, RMULT, START. CLEAR, RMULT “ -

” , .

- ( ) –

, , -, -

. , ijS – j ,

i , ni ,1 , jinj ,1 ,

jin – i . -

i , },..,,{ 21 t

iiniii tttT .

CLEAR, RMULT, START,

, },..,,{ 21 u

iijnijijij uuuU .

, – },..,,{ 21 ciijnijijij cccC .

},..,,{ 21 uiijnijijij uuuU .

, , -:

.,1,,1,,1},__

,|{}:|{ui

jiimif

ijififijijmijmijm

nmnjniuc

CccUumuK

. ijK – i

-ijR :

nij RR

iA = { AgSimiA , pAg

iA Re , AgSpliA , AgSnc

iA , AgTrf

iA , AgPwriA , AgUsr

iA , AgGbriA } – -

i , ni ,1 , n – --

[9]. izB nn ,

',1ziizB ,

ni ,1 , nz ,1 .

« » , 49 50

i ),,,( iiiiii R,KTSAP ,

– ),,,(1

iiiiii

nR,KTSAPP .

OpenGPSS , -

. j GPSS- AgUsr

iA , [10] -

GPSS- -:

.}){},{(),(: ijiijiiiiiAgUsr

i kKsSPKSPA AgRep [11] GPSS-

,1:,),,(),(:Re

izizizz

iiipAg

ziKKSSPKSPA

. -: -

– –

[12] - (

). AgSim -

}),,..,,..,,({

}),..,,..,,({:`

inijiii

inijiiiAgSimi

ttttPA

`ijij tt .

AgRep, PL/SQL [13, 14],

REPORT REPORT_DETAIL :

.1:,),()(:Reizziizz

pAgi ziRRPRPA B

AgRep – -

. - GPSS- . 3 , -

, 4. 1 – ,

; 2 3 – ,

CLEAR, RMULT, ” .

. 4. GPSS- GPSS -

… 1 ( ) 1 410 CLEAR 420 RMULT 1,2,8,5,8,2,8 500 START 1 510 RESULT RES.TXT,MSV01,01;FR$PRIB1 520 RESULT RES.TXT,MSV02,02;FC$PRIB1 530 RESULT RES.TXT,MSV03,03;FT$PRIB1 540 RESULT RES.TXT,MSV04,04;Q$QUE1 550 RESULT RES.TXT,MSV05,05;QA$QUE1 560 RESULT RES.TXT,MSV06,06;QM$QUE1 570 RESULT RES.TXT,MSV07,07;QC$QUE1 580 RESULT RES.TXT,MSV08,08;QZ$QUE1 590 RESULT RES.TXT,MSV09,09;QT$QUE1 600 RESULT RES.TXT,MSV10,10;QX$QUE1 610 CLEAR 2 ( ) 2

51 OPENGPSS GPSS/PC

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1. Richard M. Fujimoto. Parallel and Distributed Simulation Systems. Wiley, 2000. 2. . : . – : ,

2007. – 119 . 3. SPEEDES. http://www.speedes.com. 4. Mascarenhas E., Knop F., Vernon R. ParaSol: A multithreaded system for parallel simulation based on mobile

threads. Winter Simulation Conference, 1995. 5. . , . , . . -

. Proceedings of XXII International Conference “Knowledge-Dialogue-Solution”.– FOI-COMMERCE, Sofia, 2006, pp. 280-287.

6. . GPSS. – .: , 1980. – 593 . 7. . http://www.simulation.kiev.ua. 8. . -

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cation//Information Technologies and Knowledge. – 2008. – Vol.2. – P.139-145. 4. Vassilis G. Kaburlasos Unified Analysis and Design of ART/SOM Neural Networks. Heidelberg: -

Springer Berlin, Volume 4507, 2007.-p 80-93 5. MacQueen J. Some methods for classification and analysis of multivariate observations // Proceedings

of the 5th Berkley Symposium on Mathematical Statistic and Probability, University of California Press, 1967, Vol.1, p. 281-297.

6. Peter Grabusts A Study of Clustering Algorithm Application In RBF Neural Networks. //Information technology and management science. - Riga, - 2001. - 5.serija., p.50-57.

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The way to increase cluster system efficiency using virtualization is considered. The short review of virtual-

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2007 Conference, Boston, MA. June 2007. 2. NAKADA, H., YOKOI, T., EBARA, T., TANIMURA, Y., OGAWA, H., AND SEKIGUCHI, S. The

design and implementation of a virtual cluster management system. In Proceedings of the first IEEE/IFIP International Workshop on End-to-end Virtualization and Grid Management, 2007.

3. Foster, I., T. Freeman, K. Keahey, D. Scheftner, B. Sotomayor, X. Zhang. Virtual Clusters for Grid Communities, CCGRID 2006, Singapore. May 2006.

4. Overhead Matters: A Model for Virtual Resource Management, Sotomayor, B., K. Keahey, I. Foster. VTDC 2006, Tampa, FL. November 2006.

5. Enabling Cost-Effective Resource Leases with Virtual Machines, Sotomayor, B., K. Keahey, I. Foster, T. Freeman. HPDC 2007 Hot Topics session, Monterey Bay, CA. June 2007

6. Combining Batch Execution and Leasing Using Virtual Machines, Sotomayor, B., K. Keahey, I. Foster. HPDC 2008, Boston. June 2008.

7. Jones T. An overview of virtualization methods, architectures, and implementations, IBM, 2006, http://www.ibm.com/developerworks/linux/library/l-linuxvirt.

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method of designing pipelined datapaths which are configured in FPGA is proposed. The method

provides the hardware minimization due to the wide utilization of the shift register components. The method is proven at the example of the zigzag scan reordering buffer design.

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6. Richardson I.E.G. H.264 and MPEG-4 Video Compression. Video Coding for Next-generation Multimedia. –Wiley. –2003. –281p.

519.854.2

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network imagination of technological processes and the limited resources (NITPLR) is given. On the basis of the scheme the complexes of sequential interconnected mathematical models which are compatible with the hierarchy of decisions that made on every planning level, and the systems of new highly effective intercon-nected algorithms for the planning tasks solution in current conditions were created. This has let in the first time to solve the task of planning by different optimality criteria in complex.

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The subject of the article is an application of the method of prediction with certainty coefficient to a task of

developement of FOREX market mechanical trading system. An example of usage of certainty coefficient is demonstrated and formal procedure for definition of its relevance scale is provided. Quantitative results of effec-tiveness of certainty coefficient usage are provided with regard to prediction of the currency pair trend.

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This article is dedicated to the problems of increasing of efficiency of fault search and elimination in IT-

systems. Methods of threshold values determination for the three-threshold scheme are proposed and analyzed. Method of fault localization in IT-systems that incorporates passive symptom gathering and active probing is proposed. Fault management system which uses these methods was developed.

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. —2006. — 45. — . 112—126.

« » , 49

2. . -// . .

. . — 2007. — 2 (20). — . 73—82. 3. ., ., .

// « ». , . — 2006. — 44. — . 234—239.

4. ., ., ., . // . .

. — 2006. — 3 (37). — C. 33—43. 5. ., . -

// « ». , . — 2007. — 47. — . 113—124.

6. ., . -// « ». ,

. — 2008. — 48. — . 113—120. 7. Cormode G., Muthukrishnan S., Yi K. Algorithms for Distributed Functional Monitoring// Proceedings of

the nineteenth annual ACM-SIAM symposium on Discrete algorithms. — San Francisco, California. — 2008. — P. 1076—1085.

8. Wuhib F., Dam M., Stadler R., Clemm A. Decentralized computation of threshold crossing alerts// Proc. 16th IEEE/IFIP International Workshop on Distributed Systems. — Barcelona, Spain. — 2005. — Vol. 3775. — P. 220—232.

9. Wuhib F., Stadler R., Clemm C. Decentralized service-level monitoring using network threshold crossing alerts// IEEE Communications Magazine. — 2006. — Vol. 44. — 10. — P. 70—76.

10. Dilman M., Raz D. Efficient reactive monitoring// IEEE JSAC. — 2002.— Vol. 20, 4. — P. 668—676.

11. Stallings W. SNMP, SNMPv2, SNMPv3, RMON1 and 2. — 3rd edition. AdisonWesley. — 1998. — 640 p.

12. Steinder M., Sethi A. S. Probabilistic Fault Diagnosis in Communication Systems Through Incremental Hypothesis Updating// Computer Networks.— July 2004.— vol. 45.— no. 4.— pp. 537—562.

13. Appleby K., Goldszmidt G., Steinder M. Yemanja – A Layered Event Correlation Engine for Multi-domain Server Farms// Integrated Network Management Proceedings, 2001 IEEE/IFIP International Symposium on. — 2001.— pp. 329—344.

14. Rish I., Brodie M., Odintsova N., Ma S., Grabarnik G. Real-time Problem Determination in Distributed Systems using Active Probing// Network Operations and Management Symposium. NOMS 2004. IEEE/IFIP.— April 2004.— Vol. 1.— pp. 133—146.

15. Guo J., Kar G., Kermani P. Approaches to Building Self Healing System using Dependency Analysis// Network Operations and Management Symposium. NOMS 2004. IEEE/IFIP.— April 2004.— Vol. 1.— pp. 119—132.

16. ., ., ., . // . .

. — 2006.— 5 (34).— C. 117—124. 17. Tang Y., Al-Shaer E. S. Boutaba R. Active Integrated Fault Localization in Communication Networks//

Integrated Network Management Proceedings. IM’2005. IEEE/IFIP International Symposium on.— May 2005.— pp. 543—556.

18. ., . . — .: « ».— 1989. — 526 .

The table and algorithmic method of calculation of polynomials based on preliminary coefficient processing

is offered. Possibility of acceleration of calculation of polynomials in comparison with realization of the well-known table and algorithmic methods is shown.

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3. . : 3- ., . 2. – .: , 1977. – 723 . 4. ., . //

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1981. – 360 .

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puter words in the systems, guided the flow of data is offered. The structure of the system, allowing the simul-taneous forming and execution of a few instructions, is considered. Possibility of automatic identification of words of actors and dates on the basis of graph of task is shown.

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2. Dennis J. B., Missunas D. P. A preliminary architecture for basic data flow processor// Proc. 2nd Annual Symp. Comput. Stockholm, May 1975. N. Y. IEEE. – 1975. – P. 126 – 132.

3. Silva J.G.D., Wood J.V. Design of processing subsystems for Manchester data flow computer // IEEE Proc. N.Y. – 1981. – Vol. 128, N 5. – P. 218 – 224.

4. Watson R., Guard J. A practical data flow computer // Computer. – 1982. – Vol. 15, N 2.– P. 51 – 57. 5. Hogenauer E.B., Newbold R.F. Inn Y.T. DDSP – a data flow computer for signal processing/ Proc. Int.

Conf. Parall. Process. Ohio, August 1982. N.Y. // IEEE. – 1982. – P. 126 – 133. 6. Johnson D. Data flow machines threaten the program counter// Electronic Design. – 1980. – N 22. – P.

255 – 258. 7. / ., ., ., -

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1. Hyun-Yeop Lee, Kyung-Il Moon. Fuzzy Logic and Neural Networks Using MATLAB. 2008. 2. Jeff Heaton. Using Joone for Artificial Intelligence Programming. Developer.com. 2008. – Vol.

2. – P.40-46. 3. . -

.// « -», , 2007.

4. . -.// « », « , -

», 46. – 2007. – . 135-149.

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. Virtualization is a key technology which helps to unite applications on various platforms and hardware of the

previous generations with use of smaller number of modern, more powerful servers with low energy consumption. Now, the opportunities offered by this technology can be essentially expanded by its using for satisfaction of re-quirements of a multilevel data storage (storage virtualization), and also for so-called client place virtualization which provides to the user access to working materials from any terminal, including territorially remote. In the article it is considered virtualization of all levels of IT infrastructure: servers, systems of storage, workplaces of a client, an infrastructure of a data processing centre.

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1,05 .

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http://www.udec.ntu-kpi.kiev.ua 2. . . – .: «Knowledge», 1999. – 320 . 3. http://top500.org 4. http://openvpn.net 5. OpenNET. — http://www.opennet.ru/docs 6. FreeBSD manual. — http://www.freebsd.org/docs.html 7. The Linux documentation project. — http://tldp.org 8. . – http://paral-

lel.ru 9. ., . . – .: « », 2005. – 226 . 10. " " http://kpi.ua/ru/edu-

cation

004.057.6

In article the approaches for optimization of results of three-dimension scanning processing has been proposed.

Those approaches are based on procedure of processing of sets of three-dimension points to different forms of object representations. Proposed approaches has been realizing on system for computer designing in mechanical engineering.

(CAD -Computer Aided Design)

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CAD. -

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: VS: S

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1. , . , „ . ”, 2008, , .

2. , . . 3D . Interna-tional Scientific Conference “Uniteh 07”, Volume I, 23-24 2007, , .

3. http://www.cse.ohio-state.edu/~tamaldey/ paper/tcocone/tcocone.pdf 4. http://www.cs.utexas.edu/users/amenta/ pubs/sm.pdf 5. http://interviso.openfmi.net/index.php 6. http://web.mit.edu/manoli/www/ publications/Amenta _Siggraph_98.pdf

519.85

For the construction of fuzzy expert system the calculation method of membership functions of fuzzy regres-

sion, calculated on results measuring of fuzzy controlled parameters is offered.

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)k()()k(

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« » , 49 141

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« » , 49 143

nx,...,x,xX 21 –

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2121, (14)

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21210, (15)

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uw , uw .

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p,...,,k 21 .

« » , 49 145

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RL , -

1. . : . . / . – .: , 1989. –

338 . 2. . : . . / . . – .: -

, 1991. – 285 . 3. Zadeh L.A. The concept of linguistic variable and its application to approximate reasoning / L.A. Zadeh //

Information Sciences, 1975. – Vol.4. – pp 199-249. 4. . Fuzzy Technology:

. – .: , – , 2003. – 296 . 5. . , : . . / .

, . , . . – .: – , 2004. – 452 . 6. akagi T. Fuzzy identifications of systems and its application to modeling and control / T. akagi, M.

Cugeno // IEEE Trans. SMC, 1985. – pp 116-132. 7. . -

: . . : 05.13.06; 17.01.02; . 13.03.02 / ; « ». – ., 2001. – 251 .

8. . . : . . / . , . . – .: , 1990. – 286 .

9. . MATLAB fuzzy TECH. / . . – .: – , 2003. – 736 .

10. . . / . -. – .: , 1984. – 206 .

004.942

Educatory and learning processes were studied in psychological aspect in the article, which gives a possibility to

make a mathematical model. Analysis of methods and models of statistics educational theory was also made, as well as theory of stochastic processes, which are used during processing of results of control and education plan-ning, was studied.

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« » , 49 147

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, [1].

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ecbey ,

y – ; n – ; = a b; c – ( ); b – n .

bcncnay

)()( ,

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[3]. -

np , 0 1n 1 n .

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b – .

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nnn pxPqA 10,1 . , -

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« » , 49 149

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– [1/c]. /1 [c].

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( = 1).

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1i i -.

j , j = 1,2,... , -

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– 160 . 2. ., . . – .: , 1962. – 484 . 3. ., . . – : , 1988.

– 160 . 4. ., ., . : . . – .:

, 1969. – 486 . 5. . . – .: , 1980. – 542 . 6. . . . 2. -

. – ., 1974. – 152 . 7. . // -

: I . . . – , 1970. – .225-228.

004.942(045)

802.11. -

. - WI-FI.

. Case and distributing of traffic frames are built in the networks of standard of IEEE 802.11. The parameters of

models fully answer parameters works real of network. A management is modeled a traffic in the network stan-dards of WI-FI. The comparative analysis of algorithms of management turns is conducted from the point of view a management and distributing of traffic.

( WI-FI).

FIFO - -

FIFO -. -

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« » , 49 153

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802.11 ( ) -

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) G\G\1 -.

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: 1. WI-FI IEEE 802.11a

5 ; 54 ; 1; 2; “FIFO”.

2. WI-FI IEEE 802.11b 2,4 ; 11 ; 4; 2; “ -

”. 3. WI-FI IEEE 802.11g

2,4 ; 54 ; 4; 2. “

, -, -

: ,/1 f – -, f – -

. WI-FI:

0000000002,05000000000/1/1 aa f0000000004,02400000000/1/1 bb f 0000000004,02400000000/1/1 gg f

WI-FI -

: ./1 mb b – , m –

FI: 0000000185,054000000/1/1 aa mb0,0000000911000000/1/1 mb mb 0000000185,054000000/1/1 gg mb

, b, g, n - 4 , 802.11 a, g, n – -

. 0000000185,0 10,00000009 4. -

-. 802.11g

0,0000000046, 802.11b – 0,0000000227. .

1000000 -

1. : 802.11 – t = 0,000321 ; 802.11b: t ( 1) = 0,001652 ,

t ( 2) = 0,001619 , t ( 3) = 0,001619 , t ( 4) 0,001589 ;

802.11g: t ( 1) = 0,000328 , t ( 2) = 0,000326 , t ( 3) = 0,000329 , t ( 4) = 0,000329 .

1/1 ( – , t –

, k= ). -:

802.11 – a= 1 / 0,000321 = 3,115 ;

802.11b – Cb = 2,5 ;

802.11g – Cg = 12,18 M /c;

Wi-Fi 802.11a 3,115 300 Wi-Fi 802.11b 2,5 300 Wi-Fi 802.11g 12,18 M /c 300

802.11a ( - “FIFO”) : -

“FIFO”,

802.11b ( ) :

802.11a ( 4 ). -

2,5 - Bluetooth -

( ). :

« » , 49 155

. 802.11g :

, , -.

- WI-FI, -

; WI-FI -

1. . ., . . . – ., : 2008. – 957 . 2. . . GPSS WORLD. – ., : 2004. – 405 .

Issue 49

Documents