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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$
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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
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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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