Threat Evaluation of Early Warning Detection Based on Incomplete Attribute Information TODIM Method Zhiyang Gao, Li Zhu, Zheng Li, Pengcheng Fan

Transcription

1 3rd Interntionl Conference on chiner, terils nd Informtion Technolog Applictions (ICITA 05) Thret Evlution of Erl Wrning Detection Bsed on Incomplete Attribute Informtion TODI ethod Zhing Go, Li Zhu, Zheng Li, Pengcheng Fn Electronic Engineer Institute.Hefei,Chin Keword: Incomplete ttribute informtion; TODI method; Erl wrning detection; Thret ssessment. Abstrct. The incomplete ttribute informtion TODI method is pplied to the erl wrning detection thret evlution to enhnce decision-mking scientific nd trgeted due to the limittions of trditionl multi-ttribute evlution methods. This pper introduces the incomplete ttribute informtion TODI method on the bsis of criticl thinking nd nlsis steps combining with the chrcteristics of erl wrning detection sstem, estblishes the evlution inde sstem of erl wrning detection sstem threts, epounds the process of thret ssessment nd verifies the vlidit nd rtionlit of this method ccording to cse simultion. Introduction Thret ssessment of erl wrning detection which is complicted ultiple Attribute Decision king (AD) problem, with series of uncertin fctors need to be solved scientificll nd properl. The method of TODI originted from Tomd de deciso intertiv e multicritévio in Portuguese mens interctive multiple ttribute decision mking. B mens of clculting the superiorit of certin trget thn others, this method reorders nd selects trgets ccording to the description of eperts eperience.its purpose is to provide nswers for decision mking. Regulr TODI method requires tht ttribute informtion should be epressed completel.however, eperts could hrdl provide complete ttribute informtion mong ll kinds of decision mking problems. The re more likel to provide incomplete ttribute informtion which cuses these kinds of problems hrdl to be solved. Incomplete ttribute informtion TODI method whose conclusions re more objective nd prcticl b defining incomplete ttribute mtrices, clculting superiorit nd reordering trget superiorit etends nd develops the method of AD. Therefore, the method is pplied into thret ssessment of erl wrning detection. Brief Introduction of Incomplete Attribute Informtion TODI ethod Incomplete ttribute informtion TODI method is used to solve AD problems which include two tpes: intervl number ttribution nd ttribution concluded b method Delphi. Incomplete ttribute informtion TODI method need eight steps:.defining incomplete ttribute informtion. This method need to define the scope of incomplete ttribute informtion which mens scope represents the concept of incomletence..stndrdizing decision mking mtrices. AD problems re usull divided into two tpes of benefit nd cost which is often stndrdized into benefit tpe for convenient. This pper onl reserches thret ssessment of erl wrning detection which should be clssified into benefit tpes. Therefore, the step of stndrdiztion could be omitted in it.3. Clculting relted ttribute weight. Relted ttribute weight mens the rtios between ech trget ttribute weight nd the mimum weight of ll the trget ttribute.4.clculting the superiorit mtrices of ech trget ttribute reltive to others.superiorit is etended s reltive thret degree.5.clculting superiorit mtrices in totl. In this pper, superiorit mtrices re etended s thret degree mtrices.6. Determining trget functions nd constrint condition models.7.solving the optiml model.8.reordering trgets ccording to thret degree in totl. 05. The uthors - Published b Atlntis Press 40

2 Thret Assessment of Erl Wrning Detection Inde Sstem The opertionl cpbilit of thret ssessment of erl wrning detection sstem is minl determined b functions, opertion missions, deploment nd bttlefield survivbilit. This pper defines tht ll the erl wrning detection sstems re the sme kind detecting our side, which mens tht the thret of erl wrning detection sstems re just determined b opertion cpbilit of detection sstem. According to opertion, detecting re, detecting precision, timeliness, numbers of detecting trgets nd nlzing trgets, detecting period nd econtermesure (EC ) cpbilit re selected s thret ssessment indices. Are. re is significnt inde of erl wrning detection sstem, which mens the lrgest re of detecting certin trget t certin detecting probbilit. The detecting res of different detection sstems re vrious due to their pltforms, missions nd opertionl cpcities. Generll speking, the lrger detecting re the more detiled informtion of our opertion movements nd trgets the will get to know, the more threten we will possess. Precision. precision mens the error rnge between the true vlues from trgets nd mesured vlues from detection sstems, which is the mimum mesured vlues of sttisticl verges. Generll, different detecting precision of the detection sstem is vrious, the detection precision is higher, the higher resolution precision of trget. Time Sensitiveness. Time sensitiveness is kind of inde of erl wrning detection sstems whose time from detection to cquision. The time of erl wrning detection sstems needs to be reduced s much s possible from detection to get ll the process of trget informtion nd the totl process of trnsport. Different erl wrning detection sstems, iming t different ppliction levels, lso hve its specific timeliness. A generl sense tht the stronger the timeliness, the higher the thret level. umbers of nd Anlzing. The numbers of detecting trgets nd nlzing trgets mens erl wrning detection sstems detect nd nlze the number of trgets t the sme time. The lrger the number is, the greter probbilit of detection will be nd the sme btch of trgets will be more likel destroed. Period. period mens the time from erl wrning detection sstems first time detecting trgets to second time identifing sme trgets. Generll speking, the second time identifing trgets mens the trgets lose their cpbilit of concelment. The shorter detecting period is, the higher thret will be during the opertion. Econtermesure (EC) Cpbilit. Econtermesure (EC) cpbilit is n importnt opertionl integrted inde when opertion forces re used in the EW environment, which is both technicl problem nd tcticl problem. The cpbilit is relted with the electronic mens of ttck, electronic ttck strteg, the performnce of erl wrning detection sstem nd opertionl chrcteristics. The higher EC cpbilit, the stronger nti electronic jmming nd nti entit destro resistnce bilit will be nd the more thret to our sides will be. Procedure of Erl Wrning Detection Thret Assessment Provide Erl Wrning Detection Sstem Indees. Erl wrning detection sstem indees minl comes from mterils nd technolog investigtion intelligence, whose inde sstem hs set up in the lst section. In this rticle, the method of Delphi is dopted to quntize prmeters. The bsic procedure of Delphi method is: setting gol, selecting eporters, designing evlution sheets nd feeding bck eperts opinions. Specificll, fter the opinions of eperts re collected, sttistic dt re collected, integrted, counted nd then fed bck to eperts nonmousl. This procedure will reccle severl times until the results re convergent nd consistent. Flow chrt of Delphi method is showed s Picture. 4

3 Definite Tsks Drft Indees Determine Eperts Distribute Questionnires (Attched to esures) Correct Integrte nd Anlze Determine Weights Confirm Indees Picture Flow Chrt of Delphi ethod Indees of erl wrning detection sstems re provided b mens of stndrd tbles which re showed s Tble. Pltform Seril umber Bse X Bse X Are Precision Tble Indees Time Sensitiveness umbers of nd Anlzing Period EC Cpbilit Bse X Bse X Quntize Indees of Erl Wrning Detection Sstems. According to sstem indees, eperts dopt the method of Delphi to quntize indees b their own eperience nd sstem bilities. Compred to the method of TODI, ttribute informtion ought to be replced s threten degree. Specill,,,, represent code numbers of erl wrning detecting sstems;,, 3, 4, 5, 6represent detecting re, detecting precision, time sensitiveness, numbers of detecting trgets nd nlzing trgets, detecting period nd EC cpbilit respectivel(pltform is qulittive inde which need not be quntized). represents detecting re threten degree of erl wrning detecting sstem. represents detecting precision threten degree of erl wrning detecting sstem. The rest cn be done in the sme 4

4 mnner.then 6 represents EC cpbilit threten degree of erl wrning detecting sstem. Dt rnge of 0, which mens eperts should evlute inde in this rnge. is [ ] Tble Attribute Informtion Description Attribute Informtion Since the circumscribed knowledge nd bilit of eperts, eperts could not quntif ll the indees precisel. A prt of threten degrees re showed s Formul (), Formul () nd Formul (3).,,,,, + λz z I z Jλ R () +, I, J,, + R + () uz,,,,,, uz z I z Ju u R (3) Confirm Reltive Threten Degree Weight of Erl Wrning Sstem. According to different erl wrning detection sstem, ttribute weight vectors represent si different ttribute weights T * w w, w, w, 7.Reference vectors w could be which re showed s = ( ). Dt rnge is [ ] 6 =.And then, relevnt weight vectors w is clculted w out through w =. * w Clculte Relevnt Threten Degree nd Totlit Threten Degree of Ech Erl Wrning Detection Sstem. In common incomplete ttribute informtion TODI method, superiorit is used to represented virtues or defect degree. This rticle evlutes erl wrning detection sstem with the help of the method. Therefore, superiorit is comprehended s relevnt threten degree. Higher relevnt threten degree will cuse higher dnger to our side. According to Formul (3), s(, z) represents the relevnt threten degree of sstem compred to z sstem on * clculted out b w m { w, w,, wj} ttribute informtion. j z w w, z > 0 + = s (, z ) = 0 z = 0 (3) j z w w, z < 0 + θ = m + = min ;θ represents ttenution coefficient which mens Introduction: + = { } { } eperts bilit of voiding mking error decision. The vlue of is positive correltion with the θ bilit of voiding mking errors. The rnge of θ is i 0 < θ < w w. = 43

5 According to Formul (4), (, ) threten degree of s could be clculted out, which mens the relevnt sstem compred to the other sstems on i s, s (, ) ( ) Lstl, totlit threten degree of z z = ( ) z ttribute informtion. = (4) i sstem could be clculted b Formul (5): z z= g = s (, ) (5) Structure the imum Threten Degree Selection odel of Erl Wrning Detection Sstem. Totlit threten degree of erl wrning detection sstem is higher, which mens more threten fctors to our side. Therefore, the mimum threten degree selection model should be structured. According to Formul (6), Formul (6b) nd Formul (6c), constrints could be estblished. B mens of ATLAB 03, the results cn be clculted out. { g( )} T = m, I (6) + st.. λ, z, I, z, J, λ R (6) z +, I, J,, + R (6b) uz,,,,,, uz z I z Ju u R (6c) Rnk Relevnt Threten Degree. According to Formul (7), totlit threten degree could be clculted out. The vlue of F ( ) is rnked from high to low vlue. g( ) min{ g( ) } I F ( ) =, I (7) m g min g m{ g( )} I contrr, min g( ) I I { } { ( ) } ( ) I represents the totlit mimum threten degree of ll the trgets. On the { } represents the totlit minimum threten degree of them. The vlues of F ( ) re corresponding with trgets. Higher vlue mens more dngerous to equipments. Cse Simultion In certin environment, there re severl erl wrning detection sstems showed in Tble 3.Tsk is to select the erl wrning detection sstem with the mimum threten to us. Tble 3 Prmeters of Erl Wrning Detection Sstems Pltform Seril umber Are Precision Time Sensitiveness umbers of nd Anlzing Period EC Cpbilit Spce-Bsed 5000 km 0.7m~0.95m 30min 30 8h weker Spce-Bsed km 0.3m~m 67min min generl Air-Bsed km 3 0.m~0.33m 5min 0 0s stronger Air-Bsed km 4 0.3m~0.46m 8min 80 7s generl Se-Bsed 686 km m~0.73m 7min 95 4s weker Ground-bsed km m~.m 5min s wek 44

6 The pper uses the method of Delphi to evlute the tsk bove to improve the relibilit of decision-mking nd get the result more precisel. However, since the tsk is jmmed for time. Prts of ttribute informtion re represented s incomplete stle, which re showed in Tble 4 below. Pltfor m Seri l um ber Tble 4 Eperts Evlution Result of Erl Wrning Detection Sstems umbers of Time Are nd Precision Sensitiveness Period Anlzing EC Cpbilit Spce-B sed 6 =0.83 < < 0.65 < 3 < 4 = = 0.4 = Spce-B sed =0.53 = = < 4 < 0 5 = < 6 < 0 Air-Bs ed Air-Bs ed < 3 < 4 3 = = = = < 36 < 0.4 < < 0.9 < 4 < 0 43 = = = 0.83 < < Se-Bs ed 5 0. < 5 < = < 53 < 0.66 < 54 < 55 = 0.93 = Groundbsed 6 < < 0.30 < 6 < 3 < 63 < 054 < 64 < 65 = = According to wht eperts hve provided, weight vectors might be clculted s w = ( 4,6,,7,3,) T.Therefore, reference weight cn be represent s * w = m { w, w, w3, w4, w5, w6} = w4 = 7. Then relevnt weight vector is w =,,,,, After wht hs mentioned bove, ccording to Formul 5, Formul 5 (), Formul 5(b) nd Formul 5(c), optimiztion model nd constrints could be cquired. 6 6 T = m s(, z) z= = = < < < 3 < = = =0.34 =0.53 = = < 4 < = = < 3 < 4 3 = = = = < 36 < 0.87 st < 4 < < 4 < = = = < 46 < 6 0. < 5 < = < 53 < 3 44 < 54 < = =0.36 < 6 < < 6 < < 63 < = = =0.4 T 45

7 Decision mtri A= could be cquired through clculting the optimiztion model b 6 6 mens of ATLAB A = According to Formul 3, relevnt threten degree mtri of ech two sstems could be cquired s= According to Formul 4, totlit relevnt threten degree could be cquired, which mens tht g( ) represents the totlit threten degree of sstem compred to the others. g( ), g( 3 ), g( 4 ), g( 5 ) nd g( 6 ) could be epressed s sme mening. g=[ g( ), g( ), g( 3), g( 4), g( 5), g( 6) ] = [ ,.4677, 3.594, 5.343, 49.07, ] According to Formul 6, relevnt threten degree could be stndrdized into rnking mtri for convenient to compre with ech other. F= F ( ), F ( ), F ( ), F ( ), F ( ), F ( ), = 0.69,,0.9693,0.65,0, Si [ ] [ ] sstems which cuse different levels of threten to our side re showed in the picture below. Picture Threten Degree Rnk of Erl Wrning Detection Sstem 46

8 Conclusion: > 3 > 6 > 4 > > 5 represents the rnk of si erl wrning detection sstem. Reference [] J.Q. Wng,.E. Li. TODI method with multi-vlue neutrosophic sets.control nd Decision, 05():. [] Y.P. Jing, X. Ling. OPERATIOS RESEARCH AD AAGEET SCIECE, 05():7. [3] Hwng C.L., Yoon K. ultiple ttribute decision mking: ethods nd Applictions.ew York:Springer-Verlg,98. [4] Peide Liu, Fei Teng, An Etended TODI ethod for ultiple Attribute Group Decision-king Bsed on -Dimension Uncertin Linguistic Vrible.COPLEXITY, 04Wile Periodicls:4-6. [5] H.B. Jin, Z.h. Hu, S.B. Wn. Reserch on Evlution Sstem of the Joint Air Intelligence Erl Wrning nd Surveillnce Sstem.Journl of Detection &Control, 009():46 [6] J.h. Wng, X.P. Wu. Effectiveness nlsis of ship communiction securit equipment bsed on fuzz snthetic evlution. SHIP SCIECE AD TECHOLOGY, 009, 3(4):03. [7] Khnemn D,Khnemn D. Judgement under uncertint:heuristics nd bises. Science, 974, 85(457):4-30. [8] Bell D E. Regret in decision mking under uncertint.opertions Reserch, Journl of Economic Theor,987,4():