PV CELL MODULE MODELING & ANN SIMULATION FOR SMART GRID APPLICATIONS

Size: px
Start display at page:

Download "PV CELL MODULE MODELING & ANN SIMULATION FOR SMART GRID APPLICATIONS"

Transcription

1 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. PV CELL MODULE MODELNG & ANN SMULATON FO SMAT GD APPLCATONS ADEL EL SHAHAT eearch Scietit, Mechatric-Gree Eergy Lab., Elect. & Cmp. Eg. Dept., OSU, USA, 3 adel.elhahat@ieee.rg, ahmed.@u.edu ABSTACT Thi paper prpe geeral ad pecific mdelig ad imulati fr Schtt ASE-3-DGF PV pael fr Smart Grid applicati. Thi i de, with the aid f MATLAB evirmet ad Artificial Neural Netwrk (ANN). Firt mdelig f PV cell mdule at mial cditi at 5 C, ad KW/m with -V curve at ( C, 5 C, 5 C, 75 C), al pwer ad irradiace. The, we prpe geeral mdelig ad imulati at mre prbable ituati fr variable value f temperature ad irradiace. The imulati reult at each irradiace value with variu temperature value ad crrepdig characteritic are well depicted i 3-D figure. Later, the ANN mdel fr the prped rage f irradiace ad temperature a mdel iput, with the crrepdig value f vltage, curret, ad pwer a utput i preeted. Fially, algebraic equati fr the ANN mdel are deduced. Keywrd: Mdelig, Simulati, Smart Grid, MATLAB, Neural Netwrk, PV Cell.. NTODUCTON Due t the imprtace f PV cell epecially i Smart Grid Eergy Sytem (SGES) thi paper i prped. Smart Grid Eergy Sytem (SGES) i recetly icreaig, particularly ite geerati. Thi iteret i becaue larger pwer plat are ecmically ufeaible i may regi due t icreaig ytem ad fuel ct, ad mre trict evirmetal regulati. additi, recet techlgical advace i mall geeratr, Pwer Electric, ad eergy trage device have prvided a ew pprtuity fr ditributed eergy reurce at the ditributi level [-3]. Phtvltaic ytem have becme icreaigly ppular ad are ideally uited fr ditributed ytem. May gvermet have prvided the much eeded icetive t prmte the utilizati f reewable eergie, ecuragig a mre decetralized apprach t pwer delivery ytem. pite f their relatively high ct, there ha bee very remarkable grwth i italled Phtvltaic ytem. ecet tudie hw a expetial icreae i the wrldwide italled phtvltaic pwer capacity. There i gig reearch aimed at reducig the ct ad achievig higher efficiecy. Furthermre, ew regulatry law madatig the ue f reewable eergy have expaded thi market arud the wrld. Curretly, phtvltaic geerati ytem are actively beig prmted i rder t mitigate evirmetal iue uch a the gree hue effect ad air plluti. Slar eergy i the wrld' majr reewable eergy urce ad i available everywhere i differet quatitie. Phtvltaic pael d t have ay mvig part, perate iletly ad geerate emii. Ather advatage i that lar techlgy i highly mdular ad ca be eaily aled t prvide the required pwer fr differet lad [], [5]. The fuel cell wa iveted by Sir William Grve i 839, but wa t ued i a practical applicati util prt-exchage membrae fuel cell (PEMFC), made by Geeral Electric, were emplyed i the Natial Aerautic ad Space Admiitrati (NASA) Gemii mii i the early 96. A a reult, the Alkalie Fuel Cell (AFC) wa ued fr a time by NASA, ad the ue f PEMFC became almt -exitet. the 99, the PEMFC regaied it tatu a the dmiat fuel cell type, ad fuel cell i geeral have received ciderable atteti a a alterative t fil fuel cmbuti. Much f the credit fr the revitalizati f the PEMFC mut be give t Ballard Pwer Sytem ad t the L Alam Natial Labratry. The PEMFC i w ee by may reearcher ad cmpaie a the ly fuel cell type uitable fr 9

2 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. vehicular applicati, due t a relatively high pwer deity, lw peratig temperature ad lid electrlyte. A igificat amut f fuel cell reearch fcue fudametal iue f perfrmace ad ct [6-9]. Ad fially, me f recet reearch advace example abut thi tpic are itrduced i [-]. Thi paper itrduce geeral ad pecific mdelig ad imulati fr Schtt ASE-3-DGF PV pael [3] fr Smart Grid (SG) applicati a hw i figure []. cell maufacture i m crytallie r ply crytallie ilic. Each cell i typically made f quare r rectagular wafer f dimei meaurig abut cm cm.3 mm. the dark, the PV cell behavir i imilar t that f a dide ad the well kw Shckley-ead equati ca be [], []. 3. ASE-3-DGF PV MODULE The ASE-3-DGF/5 i a idutrial-grade lar pwer mdule built t the highet tadard. Extremely pwerful ad reliable, the mdule deliver maximum perfrmace i large ytem that require higher vltage, icludig the mt challegig cditi f military, utility ad cmmercial itallati. Fr uperir perfrmace, quality ad peace f mid, the ASE-3-DGF/5 i rewed a the firt chice amg the wh recgize that t all lar mdule are created equal [3]. Figure : Simple Smart Grid Sytem with PV Geeratig Stati [].. PV CELL A cmmercial PV pael i ctructed frm a umber f PV cell. A PV cell i ctructed frm a p- hm jucti material. The hm jucti i a emicductr iterface that ccur betwee layer f imilar emicductr material. Thee material have equal bad gap ad they typically have differet dpig (emicductr) which there i a built i electric field. The abrpti f pht f eergy geerate DC pwer. Figure3: Picture f PV mdule [3] { { Figure : ASE-3-DGF/5 dide huig with bypa dide [3]. Figure : DC Pwer Geerati i a PV cell. The cr ecti f a PV cell i hw i figure. The mt cmm material ued i PV

3 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. Figure 5: Full quare emi-crytallie EFG cell. The electrical data applie t tadard tet cditi (STC): rradiace at the mdule level f, W/m with pectrum AM.5 ad a cell temperature f 5 C. The implet mdel f a PV cell equivalet circuit cit f a ideal curret urce i parallel with a ideal dide. The curret urce repreet the curret geerated by pht (fte deted a ph r L ), ad it utput i ctat uder ctat temperature ad ctat icidet radiati f light. The PV pael i uually repreeted by the igle expetial mdel r the duble expetial mdel. The igle expetial mdel i hw i fig. 6. The curret i expreed i term f vltage, curret ad temperature a hw i equati []. Table : Electrical data[3] Table : Dimei ad weight [3] Figure 6: Sigle expetial mdel f a PV Cell. q( V + ) = ph exp V + p () Table 3: Characteritic data [3] Figure 7: Duble expetial mdel f PV Cell. q( V + ) q( V + ) V + p = ph exp exp (). MODELNG A PV CELL The ue f equivalet electric circuit make it pible t mdel characteritic f a PV cell. The methd ued here i implemeted i MATLAB prgram fr imulati. The ame mdelig techique i al applicable fr mdelig a PV mdule. There are tw key parameter frequetly ued t characterize a PV cell. Shrtig tgether the termial f the cell, the pht geerated curret will fllw ut f the cell a a hrt-circuit curret ( ). Thu, ph =, whe there i cecti t the PV cell (pe-circuit), the pht geerated curret i huted iterally by the itriic p- jucti dide. Thi give the pe circuit vltage (V c ). The PV mdule r cell maufacturer uually prvide the value f thee parameter i their dataheet []. Where ph : the pht geerated curret; : the dark aturati curret; : aturati curret due t diffui; : i the aturati curret due t recmbiati i the pace charge layer; p : curret flwig i the hut reitace; : cell erie reitace; p : the cell (hut) reitace; A: the dide quality factr; q: the 9 electric charge,.6 C; k: the Bltzma ctat,.38 3 J/K; ad T: the ambiet temperature, i Kelvi. Eq. ad Eq. are bth liear. Furthermre, the parameter ( ph,,,, h ad A) vary with temperature, irradiace ad deped maufacturig tlerace a hw i figure 8. Numerical methd ad curve fittig ca be ued t etimate [], [].

4 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. Figure 8: Mdelig f a PV Pael[] There are three key peratig pit the V curve f a phtvltaic cell. They are the hrt circuit pit, maximum pwer pit ad the pe circuit pit. At the pe circuit pit the V curve, V = V c ad =. After ubtitutig thee value i the igle expetial equati () the equati ca be btaied []. qv c V c = ph exp (3) p At the hrt circuit pit the V curve, = ad V =. Similarly, uig equati (), we ca btai. q = ph exp () p At the maximum pwer pit f the V curve, we have = mpp ad V = V mpp. We ca ue thee value t btai the fllwig: q( Vmpp + mpp ) Vmpp + mpp (5) mpp = ph exp p The pwer traferred t the lad ca be expreed a P = V (6) We ca etimate the dide quality factr a: A = VT l( Ad V mpp h V mpp mpp + mpp ) l( V c Vc ) + ( V mpp c / ) (7) p = h (8) Vc Vc = ( ). exp( ) (9) AV p AV T Vc =.exp( ) () AV ph = ( + ) + (exp ) () AV p T T T A a very gd apprximati, the pht geerated curret, which i equal t, i directly prprtial t the irradiace, the iteity f illumiati, t PV cell [5]. Thu, if the value,, i kw frm the dataheet, uder the tadard tet cditi, G =W/m at the air ma (AM) =.5, the the pht geerated curret at ay ther irradiace, G (W/m), i give by: G = ( ) SC G G G () t huld be tified that, i a practical PV cell, there i a erie f reitace i a curret path thrugh the emicductr material, the metal grid, ctact, ad curret cllectig bu [6]. Thee reitive le are lumped tgether a a erie reiter ( ). t effect becme very cpicuu i a PV mdule that cit f may erie-cected cell, ad the value f reitace i multiplied by the umber f cell. Shut reitace i a l aciated with a mall leakage f curret thrugh a reitive path i parallel with the itriic device [6]. Thi ca be repreeted by a parallel reiter ( p ). t effect i much le cpicuu i a PV mdule cmpared t the erie reitace it may be igred [6] [7]. The ideality factr deted a A ad take the value betwee e ad tw (a t reach the miated characteritic) [7]. 5. PHOTOVOLTAC MODULE MODELNG A igle PV cell prduce a utput vltage le tha V, thu a umber f PV cell are cected i erie t achieve a deired utput vltage. Whe erie-cected cell are placed i a frame, it i called a a mdule. Whe the PV cell are wired tgether i erie, the curret utput i the ame a the igle cell, but the vltage utput i the um f each cell vltage. Al, multiple mdule ca be wired tgether i erie r parallel t deliver the vltage ad curret level eeded. The grup f mdule i called a array. The pael ctructi prvide prtecti fr idividual cell frm water, dut etc, a the lar cell are placed it a ecapulati f flat gla. Our cae here depict a typical cecti f 6 cell that are cected i erie [3]. The trategy f mdelig a PV mdule i differet frm mdelig a PV cell. t ue the ame PV cell mdel. The parameter are the all ame, but ly a vltage parameter (uch a the pe-circuit vltage) i differet ad mut be divided by the umber f cell. A electric mdel with mderate cmplexity [8] i hw i figure 9, ad prvide fairly accurate reult. The mdel cit f a curret urce ( ), a dide (D), ad a

5 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. erie reitace ( ). The effect f parallel reitace ( p ) i very mall i a igle mdule, thu the mdel de t iclude it. T make a better mdel, it al iclude temperature effect the hrt-circuit curret ( ) ad the revere aturati curret f dide ( ). t ue a igle dide with the dide ideality factr et t achieve the bet -V curve match. Figure9: Equivalet circuit ued i the imulati The equati (3) deribe the curret-vltage relatihip f the PV cell. V + = (exp( q ( )) ) (3) Where: i the cell curret (the ame a the mdule curret); V i the cell vltage = {mdule vltage} {N. f cell i erie}; T i the cell temperature i Kelvi (K). Firt, calculate the hrt-circuit curret ( ) at a give cell temperature (T): T = T ref [ + a ( T T ref () Where: at T ref i give i the dataheet (meaured uder irradiace f W/m ), T ref i the referece temperature f PV cell i Kelvi (K), uually 98K (5 C), a i the temperature cefficiet f i percet chage per degree temperature al give i the dataheet. The hrt-circuit curret ( ) i prprtial t the iteity f irradiace, thu at a give irradiace (G) i itrduced by Eq.. The revere aturati curret f dide ( ) at the referece temperature (T ref ) i give by the equati (5) with the dide ideality factr added: = qv c (exp( ) ) (5) The revere aturati curret ( ) i temperature depedat ad the at a give temperature (T) i calculated by the fllwig equati [8]. 3 T qe A g = ( ) exp( ( )) T T ref Tref Ak Tref Tref (6) The dide ideality factr (A) i ukw ad mut be etimated. t take a value betwee e ad tw; hwever, the mre accurate value i etimated by curve fittig [8] al, it ca be etimated by try )] ad errr util accurate value achieved. E g i the Bad gap eergy (. V (Si);. (GaA);.5 (CdTe);.75 (amrphu Si)). The erie reitace ( ) f the PV mdule ha a large impact the lpe f the -V curve ear the pe-circuit vltage (V c ), hece the value f i calculated by evaluatig the lpe d/dv f the -V curve at the V c [8]. The equati fr i derived by differetiatig the -V equati ad the rearragig it i term f a itrduced i equati (7). dv / q = Vc d qv c exp( ) (7) Where: dv d V c i the lpe f the -V curve at the V c (uig the -V curve i the dataheet the divide it by the umber f cell i erie); V c i the pecircuit vltage f cell (Dividig V c i the dataheet by the umber f cell i erie). Fially, the equati f -V characteritic i lved uig the Newt methd fr rapid cvergece f the awer, becaue the luti f curret i recurive by iclui f a erie reitace i the mdel [8]. The Newt methd i deribed a: f ( x ) x + = x f ' ( x ) (8) Where: f (x) i the derivative f the fucti, f(x) =, x i a preet value, ad x + i a ext value. V + f ( ) = (exp( q( )) ) = (9) By uig the abve equati the fllwig utput curret () i cmputed iteratively. + = V + (exp( q( )) ) q V + ( ) exp( q( )) () 6. SMULATON ESULTS The figure f -V characteritic at variu mdule temperature are imulated with the MATLAB mdel fr ur PV mdule are hw. Al, the P-V relati at variu mdule temperature are preeted. All f thee are de at variu irradiace value are itrduced. 3

6 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. Mdule Curret (A) ASE-3-DGF Phtvltaic Mdule -V Curve C 5 C 5 C Figure : V curve at (KW/m ;, 5, 5, 75 C) Mdule Curret (A) ASE-3-DGF Phtvltaic Mdule -V Curve C 5 C 5 C Figure : V curve (.75 KW/m ;, 5, 5, 75 C) Mdule Curret (A) ASE-3-DGF Phtvltaic Mdule -V Curve C 5 C 5 C Figure : V curve (.5 KW/m ;, 5, 5, 75 C) Mdule Output Pwer (W ) ASE-3-DGF Phtvltaic Mdule P-V Curve C 5 C 5 C Figure 5: P V curve (.75KW/m ;, 5, 5, 75 C) Mdule Output Pwer (W ) ASE-3-DGF Phtvltaic Mdule P-V Curve C 5 C 5 C Figure 6: P V curve (.5KW/m ;, 5, 5, 75 C) M dule O utput P w er (W ) ASE-3-DGF Phtvltaic Mdule P-V Curve C 5 C 5 C Figure 7: P V curve (.5KW/m ;, 5, 5, 75 C) Mdule Curret (A) ASE-3-DGF Phtvltaic Mdule -V Curve C 5 C 5 C Fially, a et f 3 D figure are prped t cver the mt prbable ituati at variu irradiace, variu temperature with the curret, the vltage, ad the pwer. Thee urface face relati will be cidered later a the learig r traiig data fr the geeral eural etwrk imulati Figure 3: V curve (.5 KW/m ;, 5, 5, 75 C) Mdule Output Pwer (W ) ASE-3-DGF Phtvltaic Mdule P-V Curve C 5 C 5 C Figure : P V curve at (KW/m ;, 5, 5, 75 C) rradiace (kw /m) Figure 8: Vltage & Temp.&(KW/m ) rradiace

7 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. rradiace (kw /m) Mdule Pwer (Watt) Figure 9: Pwer & Temp.&(KW/m ) rradiace 8 rradiace (kw/m) Mdule Curret (A) Figure 3: Curret & Temperature&(.75KW/m ) 6 8 rradiace (kw /m ) Mdule Curret (A) Figure : Curret & Temp.&(KW/m ) rradiace rradiace (kw /m) Figure : Vltage & Temperature&(.75KW/m ) rradiace (kw/m) Mdule Pwer (Watt) Figure : Pwer & Temperature&(.75KW/m ) rradiace (kw/m) Figure : Vltage & Temperature&(.5KW/m ) rradiace (kw/m) Mdule Pwer (Watt) 5 Figure 5: Pwer & Temperature&(.5KW/m ) rradiace (kw/m) Mdule Curret (A) Figure 6: Curret & Temperature&(.5KW/m ) rradiace (kw/m) Figure 7: Vltage & Temperature&(.5KW/m )

8 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. rradiace (kw/m) Mdule Pwer (Watt) Figure 8: Pwer & Temperature&(.5KW/m ) prduce a utput, which i the cmpared t the traiig patter. f there i a differece, the cecti weight are altered i uch a directi that the errr i decreaed. After the etwrk ha ru thrugh all the iput patter, if the errr i till greater tha the maximum deired tlerace, the ANN ru thrugh all the iput patter repeatedly util all the errr are withi the required tlerace [], []. B. Data Cllecti, Aalyi ad Prceig rradiace (kw/m) Mdule Curret (A).5 Figure 9: Curret & Temperature&(.5KW/m ) The eural etwrk ha the ability t deal with all previu relati a urface r mappig face, due t thi techique ability fr iterplati betwee pit with each ther ad al curve. 7. ATFCAL NEUAL NETWOKS (ANNS) TECHNQUE A ANN cit f very imple ad highly itercected prcer called eur. The eur are cected t each ther by weighted lik ver which igal ca pa. Each eur receive multiple iput frm ther eur i prprti t their cecti weight ad geerate a igle utput which may prpagate t everal ther eur [9]. Amg the variu kid f ANN that exit, the Back-prpagati learig algrithm ha becme the mt ppular ued methd i egieerig applicati. t ca be applied t ay feed-frward etwrk with differetiable activati fucti [], ad it i the type f etwrk ued i thi paper. A. Fudametal f Neural Netwrk The ANN mdelig i carried ut i tw tep; the firt tep i t trai the etwrk, wherea the ecd tep i t tet the etwrk with data, which were t ued fr traiig. t i imprtat that all the ifrmati the etwrk eed t lear i upplied t the etwrk a a data et. Whe each patter i read, the etwrk ue the iput data t 6 8 Quality, availability, reliability, repeatability, ad relevace f the data ued t develp ad ru the ytem i critical t it ucce. Data prceig tart frm the data cllecti ad aalyi fllwed by pre-prceig ad the feed t the eural etwrk. C. Netwrk Structure Deig Thugh theretically there exit a etwrk that ca imulate a prblem t ay accuracy, there i eay way t fid it. T defie a exact etwrk architecture uch a hw may hidde layer huld be ued, hw may uit huld there be withi a hidde layer fr a certai prblem i a paiful jb. ) Number f Hidde Layer Becaue etwrk with tw hidde layer ca repreet fucti with ay kid f hape, there i theretical rea t ue etwrk with mre tha tw hidde layer. geeral, it i trgly recmmeded that e hidde layer be the firt chice fr ay feed-frward etwrk deig [9- ]. ) Number f Hidde Uit (de) Ather imprtat iue i deigig a etwrk i hw may uit t place i each layer. Uig t few uit ca fail t detect the igal fully i a cmplicated data et, leadig t uder fittig. Uig t may uit will icreae the traiig time, perhap much that it becme impible t trai it adequately i a reaable perid f time. The bet umber f hidde uit deped may factr the umber f iput ad utput uit, the umber f traiig cae, the amut f ie i the target, the cmplexity f the errr fucti, the etwrk architecture, ad the traiig algrithm. The bet apprach t fid the ptimal umber f hidde uit i trial ad errr. 6

9 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. 3) itializig Back-Prpagati feedfrward etwrk Back-prpagati i the mt cmmly ued methd fr traiig multi-layer feed-frward etwrk. Fr mt etwrk, the learig prce i baed a uitable errr fucti, which i the miimized with repect t the weight ad bia. The algrithm fr evaluatig the derivative f the errr fucti i kw a back-prpagati, becaue it prpagate the errr backward thrugh the etwrk. ) Traiig the etwrk Traiig ccur accrdig t ay traiig fucti a previu ad we mut decide the traiig parameter with their default value: The rder ued fr traiig i fr example [et, tr] = trai (et, p, t) where p, t i the iput ad utput which are rmalized 5) Netwrk imulati T btai the utput f the etwrk, we mut imulate it the rder, which ca be ued, i a = im (et, p); where a: i the etwrk but rmalized if we wat t u - rmalize it we ue thi rder a = pttd (a, meat, tdt). T Perfrm a liear regrei betwee the etwrk repe the target, ad cmpute the crrelati cefficiet ue the rder ( value betwee the etwrk repe ad the target). [m, b, r] = ptreg (a, t) [Matlab\ tlbx]; where a, t are the etwrk utput ad deired r actual utput ad retur, M - Slpe f the liear regrei; B - Y itercept f the liear regrei; - egrei -value. = mea perfect crrelati. 6) Weight ad Bia The weight ad bia, ca be btaied frm traiig data by the rder; Net.iw {, } fr the weight frm iput layer t hidde layer; Net. {b} bia t a hidde layer Net.lw {, } fr the weight frm hidde layer t utput layer; Net. {b} bia t utput layer frm a hidde layer 7) Tetig the etwrk At firt the data fr tetig maily the iput ad the utput frm the etwrk i prepared ad the i cmpared with the deired r actual utput t tet the ability f the etwrk by uig the ame iitialized etwrk. [p, meap, tdp, t, meat, tdt] = pretd (p, t) et = trai (et, p) ; a = im (et, p); [a] = pttd (a, meat, tdt); [m, b, r]=ptreg (a, t) where: p, t are tetig data, a: i rmalized utput, a: u- rmalized utput 8) Derived mathematical equati Fially mathematical equati ca be derived [3], i rder t be ued i future t calculate the utput frm the iput data withut eedig t ctruct a eural etwrk by uig the weight ad bia accrdig t activati ad trafer fucti a whe uig {lgig 'pureli} a i thi paper, the ext equece have t be fllwed - Nrmalize the iput data a hw previu. - Calculate um f xi*w {i, j} +b {i} = hi fr each de i hidde layer where: xi: i the iput variable, hi: i a hidde layer iput frm iput layer w {i, j},b {i} are btaied befre, Net.iw{,} Net.b{} 3- Calculate the utput frm each de i hidde layer t utput layer (Fi) accrdig t trafer fucti here i lgig Fi =/(+exp(-hi) - Calculate the um f utput frm hidde layer t utput layer hi =Fi *Net.lw{,}+Net.b{} 5- Calculate the required utput accrdig t trafer fu. here i pureli [Matlab/tlbx] utput (yi) = hi accrdig t umber f the required utput 6- U rmalized y t btai the utput = y*tdt + meat...t btai the actual value 8. ANN PV MODULE MODEL WTH TS EGESSON FUNCTON Thi mdel ue the previu techique which ued ad verified befre i the field f reewable eergy like i [-7]. Thi mdel ue the previu 3D graph illutrated befre a traiig r learig data fr iput ad deired target. The iput i thi mdel are the rradiace ad Temperature; the utput are: Mdule Vltage, Curret, ad Pwer. Thi mdel with it hidde ad utput layer uitable eur umber i depicted i figure 3. Al, the geeral eural etwrk, ad traiig tate are preeted i figure 3, ad 3 repectively. 7

10 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. Figure 3: ANN PV Cell Mdule Mdel Figure 3: Neural Netwrk E5 = 6.35 G +.353T F5 = / (+ exp (- E5)) E6 = -.6G T F6 = / (+ exp (- E6)) E7 =.8 G +.37 T F7 = / (+ exp (- E7)) E8 =.653G.9 T F8 = / (+ exp (- E8)) E9 =.37 G.8735T F9 = / (+ exp (- E9)) The rmalized utput are: (7) (8) (9) (3) (3) V =.66 F +.8 F +.66 F3.3 F.7 F F F7 +.9 F8 -.7 F (3) = 6.97 F +.55 F +.88 F F F F F F F (33) P = 7.9 F +.78 F +.6 F F F F F F F (3) Figure 3: Traiig State The rmalized iput G : (Nrmalized rradiace); T : ( Nrmalized Temperature) are a fllw: G = (G -.65) / (.797) () T = (T ) / ( ) () Equati () ad () preet the rmalized iput fr irradiace ad temperature, al the fllwig equati lead t the required derived utput equati. E= -.388G.8968T F= / (+ exp (- E)) E =.8336 G. T F = / (+ exp (- E)) E3 = G 9.67T F3 = / (+ exp (- E3)) E = -.696G 9.75T F = / (+ exp (- E)) (3) () (5) (6) The u- rmalized ut put V = 5.6 V (35) = (36) P = P (37) 9. CONCLUSONS Thi paper preet a imple but efficiet phtvltaic mdelig trial fr bth pecific ad geeral e. t mdel each cmpet ad imulate them uig MATLAB. The reult hw that the PV mdel uig the equivalet circuit i mderate cmplexity prvide gd matchig with the real PV mdule. Simulati are baed Schtt ASE-3-DGF PV pael a a practical e. A -pecific mdelig ad imulati at mre prbable ituati fr variable value f temperature ad irradiace are preeted. The imulati reult at each irradiace value with variu temperature value ad crrepdig characteritic are well depicted i 3-D figure. ANN i ued fr the prped rage f irradiace ad temperature a mdel iput, with the crrepdig value f vltage, curret, ad pwer a utput with it algebraic equati. Thi eural etwrk uit i implemeted, uig the back prpagati (BP) learig algrithm due t it 8

11 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. beefit t have the ability t predict value i betwee learig value, al make iterplati betwee learig curve data. Thi i de with uitable umber f etwrk layer ad eur at miimum errr ad precie maer.. ACKNOWLEDGEMENT wuld like t thak M. Shaza M. Abd Al Meem fr her effrt i thi reearch editig. EFENCES: [] Ali Keyhai, Mhammad N. Marwali, ad Mi Dai, "tegrati f Gree ad eewable Eergy i Electric Pwer Sytem," Wiley, Jauary [] Ali Keyhai, "Cyber-Ctrlled Smart Micrgrid Sytem f the Future: The High Peetrati f eewable ad Gree Eergy Surce", New eearch Directi fr Future Cyber-Phyical Eergy Sytem, Sherat Baltimre City Ceter Htel Baltimre, Marylad, Jue 9 [3] Ali Keyhai, Ji-W Jug, Mi Dai, "Ctrl f eewable Eergy Surce i Smart Grid Sytem," Smart Grid Africa,8-3 July 8, Jhaeburg, Suth Africa [] [..], \Tred i phtvltaic applicati. urvey reprt f elected iea cutrie betwee 99 ad 6. [5] T. Markvart ad L. Cataer, Practical Hadbk f Phtvltaic, Fudametal ad Applicati. Elevier, 3. [6] Larmiie, J., ad Dick, A.. Fuel Cell Sytem Explaied, d Ed., Jh Wiley &S, New Yrk, 3. [7] She, J. "Emergig Eablig Techlgie i Vehicular Pwer Electric." Prceedig f the 3rd Aual Summer Wrkhp f the NDA telliget Vehicle Sympium,. [8] Ctamaga, P., ad Sriivaa, S. "Quatum jump i the PEMFC iece ad techlgy frm the 96 t the year : Part. Fudametal ietific apect." Jural f Pwer Surce,, pp. -5,. [9] Ahluwalia,., Wag, X., Laher, S., Siha, J., Yag, Y., ad Sriramulu, S. "Perfrmace f autmtive fuel cell ytem with atructured thi film catalyt." Prceedig f the 7 Fuel Cell Semiar ad Expiti, Sa Ati, TX, 7. [] Meimei Gu, Baiju Liu, Lg Li, Chag Liu, LifegWag, Zhehua Jiag, Preparati f ulfated ply(ether ether kete) ctaiig ami grup/epxy rei cmpite membrae ad their i itu crlikig fr applicati i fuel cell, Jural f Pwer Surce 95 () [] Tauqir A. Sherazi, Michael D. Guiver, David Kigt, Shujaat Ahmad, M. Akram Kahmiri, Xizhg Xue, adiati-grafted membrae baed plyethylee fr direct methal fuel cell, Jural f Pwer Surce 95 () 9 [] Xiu Qig Xig, KahWai Lum, Hee J Ph, Ya LigWu, Optimizati f aembly clampig preure perfrmace f prtexchage membrae fuel cell, Jural f Pwer Surce 95 () 6 68 [3] Schtt ASE-3-DGF PV pael data heet. Surce (Affrdable Slar webite) lar.cm/admi/prduct_dc/dc_pd--9- c_ae_3_83866.pdf [] Mater, Gilbert M. eewable ad Efficiet Electric Pwer Sytem Jh Wiley & S Ltd, [5] Meeger, ger & Jerry Vetre Phtvltaic Sytem Egieerig d Editi CC Pre, 3 [6] Catañer, Lui & Satiag Silvetre Mdellig Phtvltaic Sytem, Uig PSpice Jh Wiley & S Ltd, [7] [Gree, Marti A. Slar Cell; Operatig Priciple, Techlgy, ad Sytem Applicati Pretice Hall c., 98 [8] Walker, Geff. Evaluatig MPPT cverter tplgie uig a MATLAB PV mdel Autralaia Uiveritie Pwer Egieerig Cferece, AUPEC,Bribae, [9] TT Chw, Zhag GQ, Li Z, Sg CL.: Glbal ptimizati f abrpti chiller ytem by geetic algrithm ad eural etwrk. Eergy Buildig ;3:3 9. [] SA Kalgiru Applicati f artificial eural etwrk i eergy ytem: a review, Eergy Cver Maage 999; : [] SA. Kalgiru Applicati f artificial eural etwrk fr eergy ytem, Appl Eergy ; 67:7 35. [] SA. Kalgiru Lg-term perfrmace predicti f frced circulati lar dmetic water heatig Sytem uig artificial eural etwrk, Appl Eergy ; 66:

12 Jural f Theretical ad Applied frmati Techlgy 5 - JATT. All right reerved. [3] Arzu Seca, Kemal A.Yakut, Steri A.Kalgiru Thermdyamic aalyi f abrpti ytem uig artificial eural etwrk, eewable Eergy, Vlume 3, iue, Ja. 6, page 9 3. [] A. El Shahat, Geeratig Baic Sizig Deig egrei Neural Fucti fr HSPMSM i Aircraft EP-7, 3th teratial Cferece Aerpace Sciece & Aviati Techlgy, May 6 8, 9, ASAT 9 Military Techical Cllege, Cair, Egypt. [5] El Shahat, A ad El Shewy, H, Neural Uit fr PM Sychru Machie Perfrmace mprvemet ued fr eewable Eergy, ef: 93, The Third Ai Sham Uiverity teratial Cferece Evirmetal Egieerig (Aee- 3 ), April -6 9, Cair, Egypt. [6] A. El Shahat, ad H. El Shewy, Neural Uit fr PM Sychru Machie Perfrmace mprvemet ued fr eewable Eergy, Paper ef.: 9, Glbal Cferece eewable ad Eergy Efficiecy fr Deert egi (GCEEDE9), Amma, Jrda. [7] A. El Shahat, ad H. El Shewy, PM Sychru Mtr Ctrl Strategie with Their Neural Netwrk egrei Fucti, Jural f Electrical Sytem, Vl. 5, ue, Dec. 9.

MAXIMUM POWER POINT GENETIC IDENTIFICATION FUNCTION FOR PHOTOVOLTAIC SYSTEM

MAXIMUM POWER POINT GENETIC IDENTIFICATION FUNCTION FOR PHOTOVOLTAIC SYSTEM JAS 3 (3) Jue 2 El Shahat Maximum Pwer Pit Geetic detificati Fucti fr Phtvltaic System MAXMUM POWE PON GENEC DENFCAON FUNCON FO PHOOOLAC SYSEM Adel El Shahat Mechatrics-Gree Eergy Lab., Electrical & Cmputer

More information

STRUCTURES IN MIKE 21. Flow over sluice gates A-1

STRUCTURES IN MIKE 21. Flow over sluice gates A-1 A-1 STRUCTURES IN MIKE 1 Fl ver luice gate Fr a give gemetry f the luice gate ad k ater level uptream ad dtream f the tructure, the fl rate, ca be determied thrugh the equati f eergy ad mmetum - ee B Pedere,

More information

Chapter 5. Root Locus Techniques

Chapter 5. Root Locus Techniques Chapter 5 Rt Lcu Techique Itrducti Sytem perfrmace ad tability dt determied dby cled-lp l ple Typical cled-lp feedback ctrl ytem G Ope-lp TF KG H Zer -, - Ple 0, -, - K Lcati f ple eaily fud Variati f

More information

Estimation of Monthly Average Hourly Global Solar Radiation from the Daily Value in Çanakkale, Turkey

Estimation of Monthly Average Hourly Global Solar Radiation from the Daily Value in Çanakkale, Turkey Jural f Clea Eergy Techlgie, Vl. 5, N. 5, September 017 Etimati f Mthly Average Hurly Glbal Slar Radiati frm the Daily Value i Çaakkale, Turkey Özge Ayvazğluyükel ad Ümmüha Başara Filik lt f mdel perfrmed

More information

Axial Temperature Distribution in W-Tailored Optical Fibers

Axial Temperature Distribution in W-Tailored Optical Fibers Axial Temperature Distributi i W-Tailred Optical ibers Mhamed I. Shehata (m.ismail34@yah.cm), Mustafa H. Aly(drmsaly@gmail.cm) OSA Member, ad M. B. Saleh (Basheer@aast.edu) Arab Academy fr Sciece, Techlgy

More information

Chapter 3.1: Polynomial Functions

Chapter 3.1: Polynomial Functions Ntes 3.1: Ply Fucs Chapter 3.1: Plymial Fuctis I Algebra I ad Algebra II, yu ecutered sme very famus plymial fuctis. I this secti, yu will meet may ther members f the plymial family, what sets them apart

More information

Multi-objective Programming Approach for. Fuzzy Linear Programming Problems

Multi-objective Programming Approach for. Fuzzy Linear Programming Problems Applied Mathematical Scieces Vl. 7 03. 37 8-87 HIKARI Ltd www.m-hikari.cm Multi-bective Prgrammig Apprach fr Fuzzy Liear Prgrammig Prblems P. Padia Departmet f Mathematics Schl f Advaced Scieces VIT Uiversity

More information

Theoretical Stability Analysis of Isolated Bidirectional Dual Full Bridge DC-DC Converter

Theoretical Stability Analysis of Isolated Bidirectional Dual Full Bridge DC-DC Converter Aia Pwer Electric Jural, l. 7, N., Sep 03 heretical Stability Aalyi f Ilated Bidirectial Dual Full Bridge DC-DC Cverter K. Wu C. W. de Silva W. G. Dufrd Abtract hi per preet a ew methd fr theretical tability

More information

Alkaline Surfactant Polymer alternating with Miscible CO 2 (ASPaM) Seyed Hamidreza Ghazizadeh Behzadi And Dr. Brian F. Towler

Alkaline Surfactant Polymer alternating with Miscible CO 2 (ASPaM) Seyed Hamidreza Ghazizadeh Behzadi And Dr. Brian F. Towler Alkalie urfactat Plymer alteratig with Micible CO 2 (APaM) eyed Hamidreza Ghazizadeh Behzadi Ad Dr. Bria F. Twler OUTLINE Why AP ad why WAG APaM advatage Hw t imulate the APaM ectr Mdel Reult Cclui Why

More information

Solutions. Definitions pertaining to solutions

Solutions. Definitions pertaining to solutions Slutis Defiitis pertaiig t slutis Slute is the substace that is disslved. It is usually preset i the smaller amut. Slvet is the substace that des the disslvig. It is usually preset i the larger amut. Slubility

More information

x z Increasing the size of the sample increases the power (reduces the probability of a Type II error) when the significance level remains fixed.

x z Increasing the size of the sample increases the power (reduces the probability of a Type II error) when the significance level remains fixed. ] z-tet for the mea, μ If the P-value i a mall or maller tha a pecified value, the data are tatitically igificat at igificace level. Sigificace tet for the hypothei H 0: = 0 cocerig the ukow mea of a populatio

More information

BIO752: Advanced Methods in Biostatistics, II TERM 2, 2010 T. A. Louis. BIO 752: MIDTERM EXAMINATION: ANSWERS 30 November 2010

BIO752: Advanced Methods in Biostatistics, II TERM 2, 2010 T. A. Louis. BIO 752: MIDTERM EXAMINATION: ANSWERS 30 November 2010 BIO752: Advaced Methds i Bistatistics, II TERM 2, 2010 T. A. Luis BIO 752: MIDTERM EXAMINATION: ANSWERS 30 Nvember 2010 Questi #1 (15 pits): Let X ad Y be radm variables with a jit distributi ad assume

More information

State space systems analysis

State space systems analysis State pace ytem aalyi Repreetatio of a ytem i tate-pace (tate-pace model of a ytem To itroduce the tate pace formalim let u tart with a eample i which the ytem i dicuio i a imple electrical circuit with

More information

MATH Midterm Examination Victor Matveev October 26, 2016

MATH Midterm Examination Victor Matveev October 26, 2016 MATH 33- Midterm Examiati Victr Matveev Octber 6, 6. (5pts, mi) Suppse f(x) equals si x the iterval < x < (=), ad is a eve peridic extesi f this fucti t the rest f the real lie. Fid the csie series fr

More information

Isolated Word Recogniser

Isolated Word Recogniser Lecture 5 Iolated Word Recogitio Hidde Markov Model of peech State traitio ad aligmet probabilitie Searchig all poible aligmet Dyamic Programmig Viterbi Aligmet Iolated Word Recogitio 8. Iolated Word Recogier

More information

If σis unknown. Properties of t distribution. 6.3 One and Two Sample Inferences for Means. What is the correct multiplier? t

If σis unknown. Properties of t distribution. 6.3 One and Two Sample Inferences for Means. What is the correct multiplier? t /8/009 6.3 Oe a Tw Samle Iferece fr Mea If i kw a 95% Cfiece Iterval i 96 ±.96 96.96 ± But i ever kw. If i ukw Etimate by amle taar eviati The etimate taar errr f the mea will be / Uig the etimate taar

More information

Chapter 9. Key Ideas Hypothesis Test (Two Populations)

Chapter 9. Key Ideas Hypothesis Test (Two Populations) Chapter 9 Key Idea Hypothei Tet (Two Populatio) Sectio 9-: Overview I Chapter 8, dicuio cetered aroud hypothei tet for the proportio, mea, ad tadard deviatio/variace of a igle populatio. However, ofte

More information

ENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ]

ENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ] ENGI 441 Cetral Limit Therem Page 11-01 Cetral Limit Therem [Navidi, secti 4.11; Devre sectis 5.3-5.4] If X i is t rmally distributed, but E X i, V X i ad is large (apprximately 30 r mre), the, t a gd

More information

Quantum Mechanics for Scientists and Engineers. David Miller

Quantum Mechanics for Scientists and Engineers. David Miller Quatum Mechaics fr Scietists ad Egieers David Miller Time-depedet perturbati thery Time-depedet perturbati thery Time-depedet perturbati basics Time-depedet perturbati thery Fr time-depedet prblems csider

More information

ECEN620: Network Theory Broadband Circuit Design Fall 2014

ECEN620: Network Theory Broadband Circuit Design Fall 2014 ECE60: etwork Theory Broadbad Circuit Deig Fall 04 Lecture 3: PLL Aalyi Sam Palermo Aalog & Mixed-Sigal Ceter Texa A&M Uiverity Ageda & Readig PLL Overview & Applicatio PLL Liear Model Phae & Frequecy

More information

Ch. 1 Introduction to Estimation 1/15

Ch. 1 Introduction to Estimation 1/15 Ch. Itrducti t stimati /5 ample stimati Prblem: DSB R S f M f s f f f ; f, φ m tcsπf t + φ t f lectrics dds ise wt usually white BPF & mp t s t + w t st. lg. f & φ X udi mp cs π f + φ t Oscillatr w/ f

More information

Phys 2310 Wed. Oct. 4, 2017 Today s Topics

Phys 2310 Wed. Oct. 4, 2017 Today s Topics Phy 30 Wed. Oct. 4, 07 Tday Tpic Ctiue Chapter 33: Gemetric Optic Readig r Next Time By Mday: Readig thi Week Fiih Ch. 33 Lee, Mirrr ad Prim Hmewrk Due Oct., 07 Y&F Ch. 3: #3., 3.5 Ch. 33: #33.3, 33.7,

More information

COMPARISONS INVOLVING TWO SAMPLE MEANS. Two-tail tests have these types of hypotheses: H A : 1 2

COMPARISONS INVOLVING TWO SAMPLE MEANS. Two-tail tests have these types of hypotheses: H A : 1 2 Tetig Hypothee COMPARISONS INVOLVING TWO SAMPLE MEANS Two type of hypothee:. H o : Null Hypothei - hypothei of o differece. or 0. H A : Alterate Hypothei hypothei of differece. or 0 Two-tail v. Oe-tail

More information

Design and implementation of multiple-output power supply for electric vehicle

Design and implementation of multiple-output power supply for electric vehicle Available lie at www.ciecedirect.cm Prcedia Egieerig ( 6 67 Iteratial Cferece Pwer Electric ad Egieerig Alicati (PEEA Deig ad imlemetati f multile-utut wer uly fr electric vehicle Chig-ug Chu a*, Chua-Jyu

More information

ELEC 372 LECTURE NOTES, WEEK 4 Dr. Amir G. Aghdam Concordia University

ELEC 372 LECTURE NOTES, WEEK 4 Dr. Amir G. Aghdam Concordia University ELEC 37 LECTURE NOTES, WEE 4 Dr Amir G Aghdam Cocordia Uiverity Part of thee ote are adapted from the material i the followig referece: Moder Cotrol Sytem by Richard C Dorf ad Robert H Bihop, Pretice Hall

More information

Fourier Series & Fourier Transforms

Fourier Series & Fourier Transforms Experimet 1 Furier Series & Furier Trasfrms MATLAB Simulati Objectives Furier aalysis plays a imprtat rle i cmmuicati thery. The mai bjectives f this experimet are: 1) T gai a gd uderstadig ad practice

More information

Fourier Method for Solving Transportation. Problems with Mixed Constraints

Fourier Method for Solving Transportation. Problems with Mixed Constraints It. J. Ctemp. Math. Scieces, Vl. 5, 200,. 28, 385-395 Furier Methd fr Slvig Trasprtati Prblems with Mixed Cstraits P. Padia ad G. Nataraja Departmet f Mathematics, Schl f Advaced Scieces V I T Uiversity,

More information

A Study on Estimation of Lifetime Distribution with Covariates Under Misspecification

A Study on Estimation of Lifetime Distribution with Covariates Under Misspecification Prceedigs f the Wrld Cgress Egieerig ad Cmputer Sciece 2015 Vl II, Octber 21-23, 2015, Sa Fracisc, USA A Study Estimati f Lifetime Distributi with Cvariates Uder Misspecificati Masahir Ykyama, Member,

More information

Hidden Markov Model Parameters

Hidden Markov Model Parameters .PPT 5/04/00 Lecture 6 HMM Traiig Traiig Hidde Markov Model Iitial model etimate Viterbi traiig Baum-Welch traiig 8.7.PPT 5/04/00 8.8 Hidde Markov Model Parameter c c c 3 a a a 3 t t t 3 c a t A Hidde

More information

Increasing Voltage Gain by New Structure of Inductive Switching DC-DC Converter

Increasing Voltage Gain by New Structure of Inductive Switching DC-DC Converter AUT Jural f Electrical Egeerg AUT J. Elec. Eg., 49((0773-78 DO: 0.060/eej.07.555.4978 creag Vltage Ga by New Structure f ductie Switchg D-D erter S. Nabati, A. Siadata *, S. B. Mzafari Departmet f Electrical

More information

Performance-Based Plastic Design (PBPD) Procedure

Performance-Based Plastic Design (PBPD) Procedure Performace-Baed Platic Deig (PBPD) Procedure 3. Geeral A outlie of the tep-by-tep, Performace-Baed Platic Deig (PBPD) procedure follow, with detail to be dicued i ubequet ectio i thi chapter ad theoretical

More information

Fig. 1: Streamline coordinates

Fig. 1: Streamline coordinates 1 Equatio of Motio i Streamlie Coordiate Ai A. Soi, MIT 2.25 Advaced Fluid Mechaic Euler equatio expree the relatiohip betwee the velocity ad the preure field i ivicid flow. Writte i term of treamlie coordiate,

More information

TESTS OF SIGNIFICANCE

TESTS OF SIGNIFICANCE TESTS OF SIGNIFICANCE Seema Jaggi I.A.S.R.I., Library Aveue, New Delhi eema@iari.re.i I applied ivetigatio, oe i ofte itereted i comparig ome characteritic (uch a the mea, the variace or a meaure of aociatio

More information

Statistical Inference Procedures

Statistical Inference Procedures Statitical Iferece Procedure Cofidece Iterval Hypothei Tet Statitical iferece produce awer to pecific quetio about the populatio of iteret baed o the iformatio i a ample. Iferece procedure mut iclude a

More information

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I liear regreio, we coider the frequecy ditributio of oe variable (Y) at each of everal level of a ecod variable (X). Y i kow a the depedet variable.

More information

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing Commet o Dicuio Sheet 18 ad Workheet 18 ( 9.5-9.7) A Itroductio to Hypothei Tetig Dicuio Sheet 18 A Itroductio to Hypothei Tetig We have tudied cofidece iterval for a while ow. Thee are method that allow

More information

Author. Introduction. Author. o Asmir Tobudic. ISE 599 Computational Modeling of Expressive Performance

Author. Introduction. Author. o Asmir Tobudic. ISE 599 Computational Modeling of Expressive Performance ISE 599 Cmputatial Mdelig f Expressive Perfrmace Playig Mzart by Aalgy: Learig Multi-level Timig ad Dyamics Strategies by Gerhard Widmer ad Asmir Tbudic Preseted by Tsug-Ha (Rbert) Chiag April 5, 2006

More information

Every gas consists of a large number of small particles called molecules moving with very high velocities in all possible directions.

Every gas consists of a large number of small particles called molecules moving with very high velocities in all possible directions. Kietic thery f gases ( Kietic thery was develped by Berlli, Jle, Clasis, axwell ad Bltzma etc. ad represets dyamic particle r micrscpic mdel fr differet gases sice it thrws light the behir f the particles

More information

Analysis of a Positive Output Super-Lift Luo Boost Converter

Analysis of a Positive Output Super-Lift Luo Boost Converter Ausha eade et al. t. Jural f Egeerg esearch ad Applicats SSN: 8-96, l. 6, ssue, (Part - 5) February 06, pp.7-78 ESEACH ACE www.ijera.cm OPEN ACCESS Aalys f a Psitive Output Super-ift u Bst Cverter Ausha

More information

8.6 Order-Recursive LS s[n]

8.6 Order-Recursive LS s[n] 8.6 Order-Recurive LS [] Motivate ti idea wit Curve Fittig Give data: 0,,,..., - [0], [],..., [-] Wat to fit a polyomial to data.., but wic oe i te rigt model?! Cotat! Quadratic! Liear! Cubic, Etc. ry

More information

Grade 3 Mathematics Course Syllabus Prince George s County Public Schools

Grade 3 Mathematics Course Syllabus Prince George s County Public Schools Ctet Grade 3 Mathematics Curse Syllabus Price Gerge s Cuty Public Schls Prerequisites: Ne Curse Descripti: I Grade 3, istructial time shuld fcus fur critical areas: (1) develpig uderstadig f multiplicati

More information

Variations on the theme of slacks-based measure of efficiency in DEA

Variations on the theme of slacks-based measure of efficiency in DEA GRIPS Plicy Ifrmati Ceter Dicui Paper : 8-4 Variati the theme f lac-baed meaure f efficiecy i DEA Karu Te Natial Graduate Ititute fr Plicy Studie 7-22- Rppgi, Miat-u, Ty 6-8677, Japa te@gripacp Abtract:

More information

Verification of Quality Parameters of a Solar Panel and Modification in Formulae of its Series Resistance

Verification of Quality Parameters of a Solar Panel and Modification in Formulae of its Series Resistance Verificatin f Quality Parameters f a Slar Panel and Mdificatin in Frmulae f its Series Resistance Sanika Gawhane Pune-411037-India Onkar Hule Pune-411037- India Chinmy Kulkarni Pune-411037-India Ojas Pandav

More information

E o and the equilibrium constant, K

E o and the equilibrium constant, K lectrchemical measuremets (Ch -5 t 6). T state the relati betwee ad K. (D x -b, -). Frm galvaic cell vltage measuremet (a) K sp (D xercise -8, -) (b) K sp ad γ (D xercise -9) (c) K a (D xercise -G, -6)

More information

Strategy in practice: a quantitative approach to target setting

Strategy in practice: a quantitative approach to target setting MPRA Muich Peral RePEc Archive Strategy i practice: a quatitative apprach t target ettig Iree Fafaliu ad Paagiti Zervpul Uiverity f Piraeu, Ope Uiverity f Cypru 4. Jauary 2014 Olie at http://mpra.ub.ui-mueche.de/54054/

More information

Chapter 7, Solution 1C.

Chapter 7, Solution 1C. hapter 7, Solutio 1. he velocity of the fluid relative to the immered olid body ufficietly far away from a body i called the free-tream velocity,. he uptream or approach velocity i the velocity of the

More information

M6c: Design of Stable Open. Channels. future conditions, reasonable cost, minimal. These objectives must be met considering

M6c: Design of Stable Open. Channels. future conditions, reasonable cost, minimal. These objectives must be met considering M6c: Deig f Stale Ope Chael Adequate cveyace capacity Stale chael Prvide aquatic life haitat Thee jective mut e met ciderig future cditi, reaale ct, miimal lad cumpti, ad afety. Trapezidal Secti (Figure

More information

D.S.G. POLLOCK: TOPICS IN TIME-SERIES ANALYSIS STATISTICAL FOURIER ANALYSIS

D.S.G. POLLOCK: TOPICS IN TIME-SERIES ANALYSIS STATISTICAL FOURIER ANALYSIS STATISTICAL FOURIER ANALYSIS The Furier Represetati f a Sequece Accrdig t the basic result f Furier aalysis, it is always pssible t apprximate a arbitrary aalytic fucti defied ver a fiite iterval f the

More information

Sound Absorption Characteristics of Membrane- Based Sound Absorbers

Sound Absorption Characteristics of Membrane- Based Sound Absorbers Purdue e-pubs Publicatis f the Ray W. Schl f Mechaical Egieerig 8-28-2003 Sud Absrpti Characteristics f Membrae- Based Sud Absrbers J Stuart Blt, blt@purdue.edu Jih Sg Fllw this ad additial wrks at: http://dcs.lib.purdue.edu/herrick

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 30 Sigal & Sytem Prof. Mark Fowler Note Set #8 C-T Sytem: Laplace Traform Solvig Differetial Equatio Readig Aigmet: Sectio 6.4 of Kame ad Heck / Coure Flow Diagram The arrow here how coceptual flow

More information

Assignment 1 - Solutions. ECSE 420 Parallel Computing Fall November 2, 2014

Assignment 1 - Solutions. ECSE 420 Parallel Computing Fall November 2, 2014 Aigmet - Solutio ECSE 420 Parallel Computig Fall 204 ovember 2, 204. (%) Decribe briefly the followig term, expoe their caue, ad work-aroud the idutry ha udertake to overcome their coequece: (i) Memory

More information

UNITARY CYCLIC DOA ALGORITHMS FOR COHERENT CYCLOSTATIONARY SIGNALS ZHIGANG LIU AND JINKUAN WANG

UNITARY CYCLIC DOA ALGORITHMS FOR COHERENT CYCLOSTATIONARY SIGNALS ZHIGANG LIU AND JINKUAN WANG INTERNATIONAL JOURNAL OF INFORMATION AND SYSTEMS SCIENCES Vlume 23, Number, Page 23-38 2005 Ititute fr Scietific Cmputig ad Ifrmati UNITARY CYCLIC DOA ALGORITMS FOR COERENT CYCLOSTATIONARY SIGNALS ZIGANG

More information

Last time: Ground rules for filtering and control system design

Last time: Ground rules for filtering and control system design 6.3 Stochatic Etimatio ad Cotrol, Fall 004 Lecture 7 Lat time: Groud rule for filterig ad cotrol ytem deig Gral ytem Sytem parameter are cotaied i w( t ad w ( t. Deired output i grated by takig the igal

More information

ALE 26. Equilibria for Cell Reactions. What happens to the cell potential as the reaction proceeds over time?

ALE 26. Equilibria for Cell Reactions. What happens to the cell potential as the reaction proceeds over time? Name Chem 163 Secti: Team Number: AL 26. quilibria fr Cell Reactis (Referece: 21.4 Silberberg 5 th editi) What happes t the ptetial as the reacti prceeds ver time? The Mdel: Basis fr the Nerst quati Previusly,

More information

Ch5 Appendix Q-factor and Smith Chart Matching

Ch5 Appendix Q-factor and Smith Chart Matching Ch5 Appedx -factr ad mth Chart Matchg 5B-1 We-Cha a udwg, F Crcut Deg hery ad Applcat, Chapter 8 -type matchg etwrk w-cmpet Matchg Netwrk hee etwrk ue tw reactve cmpet t trafrm the lad mpedace t the dered

More information

STRONG DEVIATION THEOREMS FOR THE SEQUENCE OF CONTINUOUS RANDOM VARIABLES AND THE APPROACH OF LAPLACE TRANSFORM

STRONG DEVIATION THEOREMS FOR THE SEQUENCE OF CONTINUOUS RANDOM VARIABLES AND THE APPROACH OF LAPLACE TRANSFORM Joural of Statitic: Advace i Theory ad Applicatio Volume, Number, 9, Page 35-47 STRONG DEVIATION THEORES FOR THE SEQUENCE OF CONTINUOUS RANDO VARIABLES AND THE APPROACH OF LAPLACE TRANSFOR School of athematic

More information

Introduction to Control Systems

Introduction to Control Systems Itroductio to Cotrol Sytem CLASSIFICATION OF MATHEMATICAL MODELS Icreaig Eae of Aalyi Static Icreaig Realim Dyamic Determiitic Stochatic Lumped Parameter Ditributed Parameter Liear Noliear Cotat Coefficiet

More information

The Excel FFT Function v1.1 P. T. Debevec February 12, The discrete Fourier transform may be used to identify periodic structures in time ht.

The Excel FFT Function v1.1 P. T. Debevec February 12, The discrete Fourier transform may be used to identify periodic structures in time ht. The Excel FFT Fucti v P T Debevec February 2, 26 The discrete Furier trasfrm may be used t idetify peridic structures i time ht series data Suppse that a physical prcess is represeted by the fucti f time,

More information

are specified , are linearly independent Otherwise, they are linearly dependent, and one is expressed by a linear combination of the others

are specified , are linearly independent Otherwise, they are linearly dependent, and one is expressed by a linear combination of the others Chater 3. Higher Order Liear ODEs Kreyszig by YHLee;4; 3-3. Hmgeeus Liear ODEs The stadard frm f the th rder liear ODE ( ) ( ) = : hmgeeus if r( ) = y y y y r Hmgeeus Liear ODE: Suersiti Pricile, Geeral

More information

Statistics and Chemical Measurements: Quantifying Uncertainty. Normal or Gaussian Distribution The Bell Curve

Statistics and Chemical Measurements: Quantifying Uncertainty. Normal or Gaussian Distribution The Bell Curve Statitic ad Chemical Meauremet: Quatifyig Ucertaity The bottom lie: Do we trut our reult? Should we (or ayoe ele)? Why? What i Quality Aurace? What i Quality Cotrol? Normal or Gauia Ditributio The Bell

More information

Study in Cylindrical Coordinates of the Heat Transfer Through a Tow Material-Thermal Impedance

Study in Cylindrical Coordinates of the Heat Transfer Through a Tow Material-Thermal Impedance Research ural f Applied Scieces, Egieerig ad echlgy (): 9-63, 3 ISSN: 4-749; e-issn: 4-7467 Maxwell Scietific Orgaiati, 3 Submitted: uly 4, Accepted: September 8, Published: May, 3 Study i Cylidrical Crdiates

More information

IntroEcono. Discrete RV. Continuous RV s

IntroEcono. Discrete RV. Continuous RV s ItroEcoo Aoc. Prof. Poga Porchaiwiekul, Ph.D... ก ก e-mail: Poga.P@chula.ac.th Homepage: http://pioeer.chula.ac.th/~ppoga (c) Poga Porchaiwiekul, Chulalogkor Uiverity Quatitative, e.g., icome, raifall

More information

Adaptive control design for a Mimo chemical reactor

Adaptive control design for a Mimo chemical reactor Automatio, Cotrol ad Itelliget Sytem 013; 1(3): 64-70 Publihed olie July 10, 013 (http://www.ciecepublihiggroup.com/j/aci) doi: 10.11648/j.aci.0130103.15 Adaptive cotrol deig for a Mimo chemical reactor

More information

Mean residual life of coherent systems consisting of multiple types of dependent components

Mean residual life of coherent systems consisting of multiple types of dependent components Mea residual life f cheret systems csistig f multiple types f depedet cmpets Serka Eryilmaz, Frak P.A. Cle y ad Tahai Cle-Maturi z February 20, 208 Abstract Mea residual life is a useful dyamic characteristic

More information

20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE

20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE 20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE If the populatio tadard deviatio σ i ukow, a it uually will be i practice, we will have to etimate it by the ample tadard deviatio. Sice σ i ukow,

More information

System Control. Lesson #19a. BME 333 Biomedical Signals and Systems - J.Schesser

System Control. Lesson #19a. BME 333 Biomedical Signals and Systems - J.Schesser Sytem Cotrol Leo #9a 76 Sytem Cotrol Baic roblem Say you have a ytem which you ca ot alter but it repoe i ot optimal Example Motor cotrol for exokeleto Robotic cotrol roblem that ca occur Utable Traiet

More information

ENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ]

ENGI 4421 Central Limit Theorem Page Central Limit Theorem [Navidi, section 4.11; Devore sections ] ENGI 441 Cetral Limit Therem Page 11-01 Cetral Limit Therem [Navidi, secti 4.11; Devre sectis 5.3-5.4] If X i is t rmally distributed, but E X i, V X i ad is large (apprximately 30 r mre), the, t a gd

More information

θ * f * θ F (s) ξ s s+h a sin(ωt- φ)

θ * f * θ F (s) ξ s s+h a sin(ωt- φ) Perfrmace Imprvemet ad Limitati i Extremum Seekig Ctrl Mirlav Krtic Departmet f AMES Uiverity f Califria, Sa Dieg La Jlla, CA 993-4 krtic@ucd.edu phe: 69-8-374 fax: 69-534-778 http://www-ame.ucd.edu/reearch/krtic

More information

Chapter 8. Root Locus Techniques

Chapter 8. Root Locus Techniques Chapter 8 Rt Lcu Technique Intrductin Sytem perfrmance and tability dt determined dby cled-lp l ple Typical cled-lp feedback cntrl ytem G Open-lp TF KG H Zer -, - Ple 0, -, -4 K 4 Lcatin f ple eaily fund

More information

Generalized Fibonacci Like Sequence Associated with Fibonacci and Lucas Sequences

Generalized Fibonacci Like Sequence Associated with Fibonacci and Lucas Sequences Turkih Joural of Aalyi ad Number Theory, 4, Vol., No. 6, 33-38 Available olie at http://pub.ciepub.com/tjat//6/9 Sciece ad Educatio Publihig DOI:.69/tjat--6-9 Geeralized Fiboacci Like Sequece Aociated

More information

Lecture 30: Frequency Response of Second-Order Systems

Lecture 30: Frequency Response of Second-Order Systems Lecture 3: Frequecy Repoe of Secod-Order Sytem UHTXHQF\ 5HVSRQVH RI 6HFRQGUGHU 6\VWHPV A geeral ecod-order ytem ha a trafer fuctio of the form b + b + b H (. (9.4 a + a + a It ca be table, utable, caual

More information

Super-efficiency Models, Part II

Super-efficiency Models, Part II Super-efficiec Mdels, Part II Emilia Niskae The 4th f Nvember S steemiaalsi Ctets. Etesis t Variable Returs-t-Scale (0.4) S steemiaalsi Radial Super-efficiec Case Prblems with Radial Super-efficiec Case

More information

IMPROVING PERFORMANCE IN FREE SPACE OPTICAL COMMUNICATION (FSOC) CHANNEL THROUGH THE DUAL DIFFUSER MODULATION (DDM) DUE TO ATMOSPHERIC TURBULENCE

IMPROVING PERFORMANCE IN FREE SPACE OPTICAL COMMUNICATION (FSOC) CHANNEL THROUGH THE DUAL DIFFUSER MODULATION (DDM) DUE TO ATMOSPHERIC TURBULENCE Jural f Theretical ad Appli frmati Techlgy th February 4. Vl. 6. - 4 JATT & LL. All right reerv. : 99-864.jatit.rg E-: 87-39 MPOVG PEFOMACE FEE PACE OPTCAL COMMUCATO (FOC) CHAEL THOUGH THE DUAL DFFUE MODULATO

More information

Design and Implementation of Cosine Transforms Employing a CORDIC Processor

Design and Implementation of Cosine Transforms Employing a CORDIC Processor C16 1 Desig ad Implemetati f Csie Trasfrms Emplyig a CORDIC Prcessr Sharaf El-Di El-Nahas, Ammar Mttie Al Hsaiy, Magdy M. Saeb Arab Academy fr Sciece ad Techlgy, Schl f Egieerig, Alexadria, EGYPT ABSTRACT

More information

VIII. Interval Estimation A. A Few Important Definitions (Including Some Reminders)

VIII. Interval Estimation A. A Few Important Definitions (Including Some Reminders) VIII. Iterval Etimatio A. A Few Importat Defiitio (Icludig Some Remider) 1. Poit Etimate - a igle umerical value ued a a etimate of a parameter.. Poit Etimator - the ample tatitic that provide the poit

More information

A Multilevel Cartesian Non-uniform Grid Time Domain Algorithm

A Multilevel Cartesian Non-uniform Grid Time Domain Algorithm A Multilevel Carteia N-uifrm Grid Time Dmai Algrithm Ju Meg 1, Amir ag, Vitaliy Lmaki 3*, ad Eric Michiele 4 1 Departmet f Electrical ad Cmputer Egieerig, Uiverity f Illii at Urbaa Champaig, Urbaa, IL

More information

A New Method for Finding an Optimal Solution. of Fully Interval Integer Transportation Problems

A New Method for Finding an Optimal Solution. of Fully Interval Integer Transportation Problems Applied Matheatical Scieces, Vl. 4, 200,. 37, 89-830 A New Methd fr Fidig a Optial Sluti f Fully Iterval Iteger Trasprtati Prbles P. Padia ad G. Nataraja Departet f Matheatics, Schl f Advaced Scieces,

More information

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall Oct. Heat Equatio M aximum priciple I thi lecture we will dicu the maximum priciple ad uiquee of olutio for the heat equatio.. Maximum priciple. The heat equatio alo ejoy maximum priciple a the Laplace

More information

Examination No. 3 - Tuesday, Nov. 15

Examination No. 3 - Tuesday, Nov. 15 NAME (lease rit) SOLUTIONS ECE 35 - DEVICE ELECTRONICS Fall Semester 005 Examiati N 3 - Tuesday, Nv 5 3 4 5 The time fr examiati is hr 5 mi Studets are allwed t use 3 sheets f tes Please shw yur wrk, artial

More information

Erick L. Oberstar Fall 2001 Project: Sidelobe Canceller & GSC 1. Advanced Digital Signal Processing Sidelobe Canceller (Beam Former)

Erick L. Oberstar Fall 2001 Project: Sidelobe Canceller & GSC 1. Advanced Digital Signal Processing Sidelobe Canceller (Beam Former) Erick L. Obertar Fall 001 Project: Sidelobe Caceller & GSC 1 Advaced Digital Sigal Proceig Sidelobe Caceller (Beam Former) Erick L. Obertar 001 Erick L. Obertar Fall 001 Project: Sidelobe Caceller & GSC

More information

Difference tests (1): parametric

Difference tests (1): parametric NST B Eperimetal Pychology Statitic practical Differece tet (): parametric Rudolf Cardial & Mike Aitke / 3 December 003; Departmet of Eperimetal Pychology Uiverity of Cambridge Hadout: Awer to Eample (from

More information

ME 410 MECHANICAL ENGINEERING SYSTEMS LABORATORY REGRESSION ANALYSIS

ME 410 MECHANICAL ENGINEERING SYSTEMS LABORATORY REGRESSION ANALYSIS ME 40 MECHANICAL ENGINEERING REGRESSION ANALYSIS Regreio problem deal with the relatiohip betwee the frequec ditributio of oe (depedet) variable ad aother (idepedet) variable() which i (are) held fied

More information

[1 & α(t & T 1. ' ρ 1

[1 & α(t & T 1. ' ρ 1 NAME 89.304 - IGNEOUS & METAMORPHIC PETROLOGY DENSITY & VISCOSITY OF MAGMAS I. Desity The desity (mass/vlume) f a magma is a imprtat parameter which plays a rle i a umber f aspects f magma behavir ad evluti.

More information

A Review of Time Jitter and Digital Systems

A Review of Time Jitter and Digital Systems A Review f Time Jitter ad Digital Sytem Victr S. Reihardt Raythe Space ad Airbre Sytem El Segud, CA/USA Abtract Time jitter i a imprtat parameter fr determiig the perfrmace f digital ytem. Thi paper review

More information

the results to larger systems due to prop'erties of the projection algorithm. First, the number of hidden nodes must

the results to larger systems due to prop'erties of the projection algorithm. First, the number of hidden nodes must M.E. Aggune, M.J. Dambrg, M.A. El-Sharkawi, R.J. Marks II and L.E. Atlas, "Dynamic and static security assessment f pwer systems using artificial neural netwrks", Prceedings f the NSF Wrkshp n Applicatins

More information

5.1 Two-Step Conditional Density Estimator

5.1 Two-Step Conditional Density Estimator 5.1 Tw-Step Cditial Desity Estimatr We ca write y = g(x) + e where g(x) is the cditial mea fucti ad e is the regressi errr. Let f e (e j x) be the cditial desity f e give X = x: The the cditial desity

More information

The Performance of Feedback Control Systems

The Performance of Feedback Control Systems The Performace of Feedbac Cotrol Sytem Objective:. Secify the meaure of erformace time-domai the firt te i the deig roce Percet overhoot / Settlig time T / Time to rie / Steady-tate error e. ut igal uch

More information

Comparative analysis of bayesian control chart estimation and conventional multivariate control chart

Comparative analysis of bayesian control chart estimation and conventional multivariate control chart America Jural f Theretical ad Applied Statistics 3; ( : 7- ublished lie Jauary, 3 (http://www.sciecepublishiggrup.cm//atas di:.648/.atas.3. Cmparative aalysis f bayesia ctrl chart estimati ad cvetial multivariate

More information

Chapter 9 Compressible Flow 667

Chapter 9 Compressible Flow 667 Chapter 9 Cmpreible Flw 667 9.57 Air flw frm a tank thrugh a nzzle int the tandard atmphere, a in Fig. P9.57. A nrmal hck tand in the exit f the nzzle, a hwn. Etimate (a) the tank preure; and (b) the ma

More information

ELEC 372 LECTURE NOTES, WEEK 1 Dr. Amir G. Aghdam Concordia University

ELEC 372 LECTURE NOTES, WEEK 1 Dr. Amir G. Aghdam Concordia University EEC 37 ECTURE NOTES, WEEK Dr Amir G Aghdam Cocordia Uiverity Part of thee ote are adapted from the material i the followig referece: Moder Cotrol Sytem by Richard C Dorf ad Robert H Bihop, Pretice Hall

More information

Another Look at Estimation for MA(1) Processes With a Unit Root

Another Look at Estimation for MA(1) Processes With a Unit Root Aother Look at Etimatio for MA Procee With a Uit Root F. Jay Breidt Richard A. Davi Na-Jug Hu Murray Roeblatt Colorado State Uiverity Natioal Tig-Hua Uiverity U. of Califoria, Sa Diego http://www.tat.colotate.edu/~rdavi/lecture

More information

Last time: Completed solution to the optimum linear filter in real-time operation

Last time: Completed solution to the optimum linear filter in real-time operation 6.3 tochatic Etimatio ad Cotrol, Fall 4 ecture at time: Completed olutio to the oimum liear filter i real-time operatio emi-free cofiguratio: t D( p) F( p) i( p) dte dp e π F( ) F( ) ( ) F( p) ( p) 4444443

More information

On the affine nonlinearity in circuit theory

On the affine nonlinearity in circuit theory O the affie liearity i circuit thery Emauel Gluski The Kieret Cllege the Sea f Galilee; ad Ort Braude Cllege (Carmiel), Israel. gluski@ee.bgu.ac.il; http://www.ee.bgu.ac.il/~gluski/ E. Gluski, O the affie

More information

Economics of a reservation system for morning commute

Economics of a reservation system for morning commute Paper ubmitted t the ITEA Aual Cferece Traprtati Ecmic (Kuhm ectar), 04 Ecmic f a reervati ytem fr mrig cmmute Wei Liu, Hai Yag ad Fagi Zhag Departmet f Civil ad Evirmetal Egieerig, The Hg Kg Uiverity

More information

Tools Hypothesis Tests

Tools Hypothesis Tests Tool Hypothei Tet The Tool meu provide acce to a Hypothei Tet procedure that calculate cofidece iterval ad perform hypothei tet for mea, variace, rate ad proportio. It i cotrolled by the dialog box how

More information

Social Studies 201 Notes for November 14, 2003

Social Studies 201 Notes for November 14, 2003 1 Social Studie 201 Note for November 14, 2003 Etimation of a mean, mall ample ize Section 8.4, p. 501. When a reearcher ha only a mall ample ize available, the central limit theorem doe not apply to the

More information

Markov processes and the Kolmogorov equations

Markov processes and the Kolmogorov equations Chapter 6 Markv prcesses ad the Klmgrv equatis 6. Stchastic Differetial Equatis Csider the stchastic differetial equati: dx(t) =a(t X(t)) dt + (t X(t)) db(t): (SDE) Here a(t x) ad (t x) are give fuctis,

More information

Note 8 Root-Locus Techniques

Note 8 Root-Locus Techniques Lecture Nte f Ctrl Syte I - ME 43/Alyi d Sythei f Lier Ctrl Syte - ME862 Nte 8 Rt-Lcu Techique Deprtet f Mechicl Egieerig, Uiverity Of Sktchew, 57 Cpu Drive, Skt, S S7N 5A9, Cd Lecture Nte f Ctrl Syte

More information

, the random variable. and a sample size over the y-values 0:1:10.

, the random variable. and a sample size over the y-values 0:1:10. Lecture 3 (4//9) 000 HW PROBLEM 3(5pts) The estimatr i (c) f PROBLEM, p 000, where { } ~ iid bimial(,, is 000 e f the mst ppular statistics It is the estimatr f the ppulati prprti I PROBLEM we used simulatis

More information

Professor: Mihnea UDREA DIGITAL SIGNAL PROCESSING. Grading: Web: MOODLE. 1. Introduction. General information

Professor: Mihnea UDREA DIGITAL SIGNAL PROCESSING. Grading: Web:   MOODLE. 1. Introduction. General information Geeral iformatio DIGITL SIGL PROCESSIG Profeor: ihea UDRE B29 mihea@comm.pub.ro Gradig: Laboratory: 5% Proect: 5% Tet: 2% ial exam : 5% Coure quiz: ±% Web: www.electroica.pub.ro OODLE 2 alog igal proceig

More information