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# Engineering Applications of Correlation and Spectral Analysis, 2nd Edition

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• davidvictor
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Unformatted text preview: Waﬁawoaéﬁ w! 5%Uﬁwg / F’Koﬁ (T. P LYNN-f 6659f? ‘ -. 36%; 3- {29%) WW gm I . ,AFM. 9.: 22(3) éaﬁvégga, ,_. m. , d.,mm.MMWWWAVWMHWWW, p :: : 1m.(ix/12.23%:I? (055.35 * MIN (01"). = _ Z i E éaesuisszw " DST. . 1 .i .“42 _ m BIN/’55)! 2 .. imam??? .éﬁ’irza 2.._zx.é'%f\$§.ii.? ? '4) W31}; "(awmz_2»s¢ .. in Eérmw] gag a: 1 /g m: ' ' -' ' x10 (RN O 500 1000 1500 2000 2500 3000 3500 4000 HAW " Mmth MM=37 50 0 500 1000 1500 2000 M . 2500 3000 3500 4000 200 150 100 50 -50 2/19/07 5:10 PM C:\Documents and Settings\Jerome Pete...\DetermineAZDResolution.m 1 of l W—mH Jerome P. Lynch CEE619 — Advanced Dynamics Problem #2 — Homework #1 (990de % Load Battery Data clear load BatteryTimeHistory; % Determine Mean avex = mean(x): % Determine A/D Resolution xzeromean : x — round(mean{x)); MN : min(xzeromean) MX = max(xzeromean) span = MX—MN subplot(3,1,l) plOUX): subplot(3,l,2) plot(xzeromean); subplot(3,l,3) hist(xzeromean,span); 5f] SHEET\$ 22-3413 198 SHEETS 22—13%?» 263 SHEETS 22-141 .353" 0 2O 40 60 80 100 120 140 160 ‘E 80 200 0 20 4O 60 80 100 120 140 160 180 200 — Simulated ---- --Theory 0 50 100 1 50 200 250 300 Lags 2/19/07 8:04 PM C:\Documents and Settings\Jerome Peter Lynch\My Docume...\Prob3.m l of l % Jerome P. Lynch % CEE619 - Advanced Dynamics and Smart Structures % Problem #3 — Homework #1 clear; % Create Random White Noise Input for i21220000 w(i,1) : randn(l); t(i,l) = 0.0l*(i—1): end; subplot(3.l,l): plot(t,w); Process using SDOF system with Beta = 2 = 2; = l; u 2*b; = b*b; [x,t] = newmark{m,c,k,w,0.01,0.5,0.25.0,0); subplot(3,l,2); plot(t,x); W o B U w % Autocorrelation Function subplot(3,l,3) {ACF,Lags] = autocorr(x,300) % Theory Autocorrelation for k:G:3OO ACFt(k+l,l) = {std(w)“2)*(l+b*t(k+l,l))*exp(—b*t(k+l,l))+mean(w); end plot(Lags,ACF,'—b',Lags,ACFt,'mmr') xlabel('Lags') ylabel('Rxx') legend('Simulated','Theory'} 50 SI! JEKTS _ EGG SHEETS SEE-"34f? 5200 SHEETS. ’32 1 ’(3 W4. 25244? ‘1} r “ kegxangmaa 3 i i 1 3 At: 0‘0]. _ . I; 0,25 ML. ...w=,.935,z3”42’sc«. I : " Sim; H33“'.I"WH{T%”AJ:22\$§ é” . ' j. ..§<€\$2%A§zwt um I -:,g \$22214?“ .. Aé :2 ﬂbOHE-Vlcﬂ Sample Autocorrelation 0.5 «Inn. - .JIIIIIIIII _ :r‘._.1l ‘lO ﬁt - r . JIIIIIIIII m—"ulllllllll 'm 15 20 25 Sample Autocorrelation Function (ACF) JII .. h... Illllllllll II .—m ._ . “ﬁlm”. 50 1 00 Lag 30 W . gunning ‘lrﬂlr "il'lllllll W. Illlllllrl 35 40 -‘-‘!1rr 150 2/19/07 5:02 PM C:\Documents and Settings\Jerome Peter Lyn...\CreatNoiseandSine.m l of l % Jerome P. Lynch CEE619 ~ Advanced Dynamics and Smart Structures % Question #4 — Homework #1 d9 % Generate Noise and Sine Signal %clear; %for i=i:4000 % x(i,l) = ((30000 + round(randn(1)*10})/(2”16—1))*5; % t(i,1) = i*0.01; % y(i,1) = 0.0004*Sin(25.6637*t(i,l)); % tot(i,l) 2 x(i,l) + y(i.l): %end; %a = tot; %save RoofAccel t a % Load Signal load RoofAccel subplott2,l,l) plot(t.a); subplot(2,l,2) autocorr(a,150); ...
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• Winter '06
• Lynch

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