LAB05ex1.mfunction LAB05ex1m = 1; .docx
•Download as DOCX, PDF•
0 likes•10 views
LAB05ex1.m function LAB05ex1 m = 1; % mass [kg] k = 4; % spring constant [N/m] omega0=sqrt(k/m); y0=0.1; v0=0; % initial conditions [t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0); % solve for 0<t<10 y=Y(:,1); v=Y(:,2); % retrieve y, v from Y figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v grid on; %------------------------------------------------------ function dYdt= f(t,Y,omega0) y = Y(1); v= Y(2); dYdt = [v; -omega0^2*y]; __MACOSX/._LAB05ex1.m LAB05ex1a.m function LAB05ex1a m = 1; % mass [kg] k = 4; % spring constant [N/m] c = 1; % friction coefficient [Ns/m] omega0 = sqrt(k/m); p = c/(2*m); y0 = 0.1; v0 = 0; % initial conditions [t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0,p); % solve for 0<t<10 y=Y(:,1); v=Y(:,2); % retrieve y, v from Y figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v grid on %------------------------------------------------------ function dYdt= f(t,Y,omega0,p) y = Y(1); v= Y(2); dYdt = [v; ?? ]; % fill-in dv/dt __MACOSX/._LAB05ex1a.m MAT275_LAB05.pdf MATLAB sessions: Laboratory 5 MAT 275 Laboratory 5 The Mass-Spring System In this laboratory we will examine harmonic oscillation. We will model the motion of a mass-spring system with differential equations. Our objectives are as follows: 1. Determine the effect of parameters on the solutions of differential equations. 2. Determine the behavior of the mass-spring system from the graph of the solution. 3. Determine the effect of the parameters on the behavior of the mass-spring. The primary MATLAB command used is the ode45 function. Mass-Spring System without Damping The motion of a mass suspended to a vertical spring can be described as follows. When the spring is not loaded it has length ℓ0 (situation (a)). When a mass m is attached to its lower end it has length ℓ (situation (b)). From the first principle of mechanics we then obtain mg︸︷︷︸ downward weight force + −k(ℓ − ℓ0)︸ ︷︷ ︸ upward tension force = 0. (L5.1) The term g measures the gravitational acceleration (g ≃ 9.8m/s2 ≃ 32ft/s2). The quantity k is a spring constant measuring its stiffness. We now pull downwards on the mass by an amount y and let the mass go (situation (c)). We expect the mass to oscillate around the position y = 0. The second principle of mechanics yields mg︸︷︷︸ weight + −k(ℓ + y − ℓ0)︸ ︷︷ ︸ upward tension force = m d2(ℓ + y) dt2︸ ︷︷ ︸ acceleration of mass , i.e., m d2y dt2 + ky = 0 (L5.2) using (L5.1). This ODE is second-order. (a) (b) (c) (d) y ℓ ℓ0 m k γ Equation (L5.2) is rewritten d2y dt2 + ω20y = 0 (L5.3) c⃝2011 Stefania Tracogna, SoMSS, ASU MATLAB sessions: Laboratory 5 where ω20 = k/m. Equation (L5.3) models simple harmonic motion. A numerica ...
Report
Share
Report
Share
![LAB05ex1.m
function LAB05ex1
m = 1; % mass [kg]
k = 4; % spring constant [N/m]
omega0=sqrt(k/m);
y0=0.1; v0=0; % initial conditions
[t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0); % solve for 0<t<10
y=Y(:,1); v=Y(:,2); % retrieve y, v from Y
figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v
grid on;
%------------------------------------------------------
function dYdt= f(t,Y,omega0)
y = Y(1); v= Y(2);
dYdt = [v; -omega0^2*y];
__MACOSX/._LAB05ex1.m
LAB05ex1a.m
function LAB05ex1a
m = 1; % mass [kg]](https://image.slidesharecdn.com/lab05ex1-221026091850-8e2b9f90/85/LAB05ex1-mfunction-LAB05ex1m-1-docx-1-320.jpg)
![k = 4; % spring constant [N/m]
c = 1; % friction coefficient [Ns/m]
omega0 = sqrt(k/m); p = c/(2*m);
y0 = 0.1; v0 = 0; % initial conditions
[t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0,p); % solve for
0<t<10
y=Y(:,1); v=Y(:,2); % retrieve y, v from Y
figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v
grid on
%------------------------------------------------------
function dYdt= f(t,Y,omega0,p)
y = Y(1); v= Y(2);
dYdt = [v; ?? ]; % fill-in dv/dt
__MACOSX/._LAB05ex1a.m
MAT275_LAB05.pdf
MATLAB sessions: Laboratory 5
MAT 275 Laboratory 5
The Mass-Spring System](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
From the Front LinesOur robotic equipment and its maintenanc.docx
From the Front Lines
Our robotic equipment and its maintenance represent a fixed cost of $23,320 per month. The cost-effectiveness of robotic-assisted surgery is related to patient volume: With only 10 cases, the fixed cost per case is $2,332, and with 40 cases, the fixed cost per case is $583.
Source: Alemozaffar, Chang, Kacker, Sun, DeWolf, & Wagner (2013).
MATLAB sessions: Laboratory 5
MAT 275 Laboratory 5
The Mass-Spring System
In this laboratory we will examine harmonic oscillation. We will model the motion of a mass-spring
system with differential equations.
Our objectives are as follows:
1. Determine the effect of parameters on the solutions of differential equations.
2. Determine the behavior of the mass-spring system from the graph of the solution.
3. Determine the effect of the parameters on the behavior of the mass-spring.
The primary MATLAB command used is the ode45 function.
Mass-Spring System without Damping
The motion of a mass suspended to a vertical spring can be described as follows. When the spring is
not loaded it has length ℓ0 (situation (a)). When a mass m is attached to its lower end it has length ℓ
(situation (b)). From the first principle of mechanics we then obtain
mg︸︷︷︸
downward weight force
+ −k(ℓ − ℓ0)︸ ︷︷ ︸
upward tension force
= 0. (L5.1)
The term g measures the gravitational acceleration (g ≃ 9.8m/s2 ≃ 32ft/s2). The quantity k is a spring
constant measuring its stiffness. We now pull downwards on the mass by an amount y and let the mass
go (situation (c)). We expect the mass to oscillate around the position y = 0. The second principle of
mechanics yields
mg︸︷︷︸
weight
+ −k(ℓ + y − ℓ0)︸ ︷︷ ︸
upward tension force
= m
d2(ℓ + y)
dt2︸ ︷︷ ︸
acceleration of mass
, i.e., m
d2y
dt2
+ ky = 0 (L5.2)
using (L5.1). This ODE is second-order.
(a) (b) (c) (d)
y
ℓ
ℓ0
m
k
γ
Equation (L5.2) is rewritten
d2y
dt2
+ ω20y = 0 (L5.3)
c⃝2011 Stefania Tracogna, SoMSS, ASU
MATLAB sessions: Laboratory 5
where ω20 = k/m. Equation (L5.3) models simple harmonic motion. A numerical solution with ini-
tial conditions y(0) = 0.1 meter and y′(0) = 0 (i.e., the mass is initially stretched downward 10cms
and released, see setting (c) in figure) is obtained by first reducing the ODE to first-order ODEs (see
Laboratory 4).
Let v = y′. Then v′ = y′′ = −ω20y = −4y. Also v(0) = y′(0) = 0. The following MATLAB program
implements the problem (with ω0 = 2).
function LAB05ex1
m = 1; % mass [kg]
k = 4; % spring constant [N/m]
omega0 = sqrt(k/m);
y0 = 0.1; v0 = 0; % initial conditions
[t,Y] = ode45(@f,[0,10],[y0,v0],[],omega0); % solve for 0<t<10
y = Y(:,1); v = Y(:,2); % retrieve y, v from Y
figure(1); plot(t,y,’b+-’,t,v,’ro-’); % time series for y and v
grid on;
%-----------------------------------------
function dYdt = f(t,Y,omega0)
y = Y(1); v = Y(2);
dYdt = [ v ; -omega0^2*y ];
Note that the parameter ω0 was passed as an argument to ode45 rather than set to its value ω0 = 2
directly in the funct ...
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docx
SAMPLE QUESTION:
Exercise 1: Consider the function
f (x,C)=
sin(C x)
Cx
(a) Create a vector x with 100 elements from -3*pi to 3*pi. Write f as an inline or anonymous function
and generate the vectors y1 = f(x,C1), y2 = f(x,C2) and y3 = f(x,C3), where C1 = 1, C2 = 2 and
C3 = 3. Make sure you suppress the output of x and y's vectors. Plot the function f (for the three
C's above), name the axis, give a title to the plot and include a legend to identify the plots. Add a
grid to the plot.
(b) Without using inline or anonymous functions write a function+function structure m-file that does
the same job as in part (a)
SAMPLE LAB WRITEUP:
MAT 275 MATLAB LAB 1 NAME: __________________________
LAB DAY and TIME:______________
Instructor: _______________________
Exercise 1
(a)
x = linspace(-3*pi,3*pi); % generating x vector - default value for number
% of pts linspace is 100
f= @(x,C) sin(C*x)./(C*x) % C will be just a constant, no need for ".*"
C1 = 1, C2 = 2, C3 = 3 % Using commans to separate commands
y1 = f(x,C1); y2 = f(x,C2); y3 = f(x,C3); % supressing the y's
plot(x,y1,'b.-', x,y2,'ro-', x,y3,'ks-') % using different markers for
% black and white plots
xlabel('x'), ylabel('y') % labeling the axis
title('f(x,C) = sin(Cx)/(Cx)') % adding a title
legend('C = 1','C = 2','C = 3') % adding a legend
grid on
Command window output:
f =
@(x,C)sin(C*x)./(C*x)
C1 =
1
C2 =
2
C3 =
3
(b)
M-file of structure function+function
function ex1
x = linspace(-3*pi,3*pi); % generating x vector - default value for number
% of pts linspace is 100
C1 = 1, C2 = 2, C3 = 3 % Using commans to separate commands
y1 = f(x,C1); y2 = f(x,C2); y3 = f(x,C3); % function f is defined below
plot(x,y1,'b.-', x,y2,'ro-', x,y3,'ks-') % using different markers for
% black and white plots
xlabel('x'), ylabel('y') % labeling the axis
title('f(x,C) = sin(Cx)/(Cx)') % adding a title
legend('C = 1','C = 2','C = 3') % adding a legend
grid on
end
function y = f(x,C)
y = sin(C*x)./(C*x);
end
Command window output:
C1 =
1
C2 =
2
C3 =
3
More instructions for the lab write-up:
1) You are not obligated to use the 'diary' function. It was presented only for you convenience. You
should be copying and pasting your code, plots, and results into some sort of "Word" type editor that
will allow you to import graphs and such. Make sure you always include the commands to generate
what is been asked and include the outputs (from command window and plots), unless the pr.
MATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docx
MATLAB sessions: Laboratory 6
MAT 275 Laboratory 6
Forced Equations and Resonance
In this laboratory we take a deeper look at second-order nonhomogeneous equations. We will concentrate
on equations with a periodic harmonic forcing term. This will lead to a study of the phenomenon known
as resonance. The equation we consider has the form
d2y
dt2
+ c
dy
dt
+ ω20y = cosωt. (L6.1)
This equation models the movement of a mass-spring system similar to the one described in Laboratory
5. The forcing term on the right-hand side of (L6.1) models a vibration, with amplitude 1 and frequency
ω (in radians per second = 12π rotation per second =
60
2π rotations per minute, or RPM) of the plate
holding the mass-spring system. All physical constants are assumed to be positive.
Let ω1 =
√
ω20 − c2/4. When c < 2ω0 the general solution of (L6.1) is
y(t) = e−
1
2 ct(c1 cos(ω1t) + c2 sin(ω1t)) + C cos (ωt− α) (L6.2)
with
C =
1√
(ω20 − ω2)
2
+ c2ω2
, (L6.3)
α =
⎧
⎨
⎩
arctan
(
cω
ω20−ω2
)
if ω0 > ω
π + arctan
(
cω
ω20−ω2
)
if ω0 < ω
(L6.4)
and c1 and c2 determined by the initial conditions. The first term in (L6.2) represents the complementary
solution, that is, the general solution to the homogeneous equation (independent of ω), while the second
term represents a particular solution of the full ODE.
Note that when c > 0 the first term vanishes for large t due to the decreasing exponential factor.
The solution then settles into a (forced) oscillation with amplitude C given by (L6.3). The objectives of
this laboratory are then to understand
1. the effect of the forcing term on the behavior of the solution for different values of ω, in particular
on the amplitude of the solution.
2. the phenomena of resonance and beats in the absence of friction.
The Amplitude of Forced Oscillations
We assume here that ω0 = 2 and c = 1 are fixed. Initial conditions are set to 0. For each value of ω, the
amplitude C can be obtained numerically by taking half the difference between the highs and the lows
of the solution computed with a MATLAB ODE solver after a sufficiently large time, as follows: (note
that in the M-file below we set ω = 1.4).
1 function LAB06ex1
2 omega0 = 2; c = 1; omega = 1.4;
3 param = [omega0,c,omega];
4 t0 = 0; y0 = 0; v0 = 0; Y0 = [y0;v0]; tf = 50;
5 options = odeset(’AbsTol’,1e-10,’RelTol’,1e-10);
6 [t,Y] = ode45(@f,[t0,tf],Y0,options,param);
7 y = Y(:,1); v = Y(:,2);
8 figure(1)
9 plot(t,y,’b-’); ylabel(’y’); grid on;
c⃝2011 Stefania Tracogna, SoMSS, ASU 1
MATLAB sessions: Laboratory 6
10 t1 = 25; i = find(t>t1);
11 C = (max(Y(i,1))-min(Y(i,1)))/2;
12 disp([’computed amplitude of forced oscillation = ’ num2str(C)]);
13 Ctheory = 1/sqrt((omega0^2-omega^2)^2+(c*omega)^2);
14 disp([’theoretical amplitude = ’ num2str(Ctheory)]);
15 %----------------------------------------------------------------
16 function dYdt = f(t,Y,param)
17 y = Y(1); v = Y(2);
18 omega0 = param(1); c = param(2); omega = param(3);
19 dYdt = [ v ; cos(omega ...
Lab 5 template Lab 5 - Your Name - MAT 275 Lab The M.docx
Lab 5 template
%% Lab 5 - Your Name - MAT 275 Lab
% The Mass-Spring System
%% EX 1 10 pts
%A) 1 pts | short comment
%
%B) 2 pts | short comment
%
%C) 1 pts | short comment
%
%D) 1 pts
%E) 2 pts | List the first 3-4 t values either in decimal format or as
%fractions involving pi
%F) 3 pts | comments. | (1 pts for including two distinct graphs, each with y(t) and v(t) plotted)
%% EX 2 10 pts
%A) 5 pts
% add commands to LAB05ex1 to compute and plot E(t). Then use ylim([~,~]) to change the yaxis limits.
% You don't need to include this code but at least one plot of E(t) and a comment must be
% included!
%B) 2 pts | write out main steps here
% first differentiate E(t) with respect to t using the chain rule. Then
% make substitutions using the expression for omega0 and using the
% differential equation
%C) 3 pts | show plot and comment
%% EX 3 10 pts
%A) 3 pts | modify the system of equations in LAB05ex1a
% write the t value and either a) show correponding graph or b) explain given matlab
% commands
%B) 2 pts | write t value and max |V| value; include figure
%note: velocity magnitude is like absolute value!
%C) 3 pts | include 3 figures here + comments.
% use title('text') to attach a title to the figure
%D) 2 pts | What needs to happen (in terms of the characteristic equation)
%in order for there to be no oscillations? Impose a condition on the
%characteristic equation to find the critical c value. Write out main steps
%% EX4 10 pts
% A) 5 pts | include 1 figure and comment
%B) 2 pts
% again find dE/dt using the chain rule and make substitutions based on the
% differential equation. You should reach an expression for dE/dt which is
% in terms of y'
%C) 3 pts | include one figure and comment
Exercise (1):
function LAB05ex1
m = 1; % mass [kg]
k = 9; % spring constant [N/m]
omega0=sqrt(k/m);
y0=0.4; v0=0; % initial conditions
[t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0); % solve for 0<t<10
y=Y(:,1); v=Y(:,2); % retrieve y, v from Y
figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v
grid on;
%------------------------------------------------------
function dYdt= f(t,Y,omega0)
y = Y(1); v= Y(2);
dYdt = [v; -omega0^2*y];
Exercise (1a):
function LAB05ex1a
m = 1; % mass [kg]
k = 9; % spring constant [N/m]
c = 1; % friction coefficient [Ns/m]
omega0 = sqrt(k/m); p = c/(2*m);
y0 = 0.4; v0 = 0; % initial conditions
[t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0,p); % solve for 0<t<10
y=Y(:,1); v=Y(:,2); % retrieve y, v from Y
figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v
grid on
%------------------------------------------------------
function dYdt= f(t,Y,omega0,p)
y = Y(1); v= Y(2);
dYdt = [v; ?? ]; % fill-in dv/dt
More instructions for the l ...
ENGINEERING SYSTEM DYNAMICS-TAKE HOME ASSIGNMENT 2018
1. Read Chapter 4 – System Dynamics for Mechanical Engineers by Matthew Davies and Tony L. Schmitz and implement Examples 4.1 to 4.12 in Matlab.
2. Read Chapter 7 – System Dynamics for Mechanical Engineers by Matthew Davies and Tony L. Schmitz and implement Examples 7.1 to 7.11 in Matlab.
3. Read Chapter 9 – System Dynamics for Mechanical Engineers by Matthew Davies and Tony L. Schmitz and implement Examples 9.1 to 9.6 in Matlab.
4. Read Chapter 11 – System Dynamics for Mechanical Engineers by Matthew Davies and Tony L. Schmitz and implement Examples 11.1 to 11.7 in Matlab.
5. Read Chapter 2 - System Dynamics for Engineering Students: Concepts and Applications by Nicolae Lobontiu and attempt problem 2.18 (page 63).
6. Read Chapter 3 - System Dynamics for Engineering Students: Concepts and Applications by Nicolae Lobontiu and attempt problem 3.13 (pp 98 - 100).
7. Read Chapter 4 - System Dynamics for Engineering Students: Concepts and Applications by Nicolae Lobontiu and attempt problem 4.20 (page 146).
8. Read Chapter 5 - System Dynamics for Engineering Students: Concepts and Applications by Nicolae Lobontiu and attempt problems 5.15 (page 198), 5.21 (pp 199 - 200) and 5.27 (pp 201 – 202).
damping_constant_spring
This document describes an experiment to determine the damping constant of a spring and hook system. A spring with an attached hook was stretched and released, and its motion was recorded. The effective mass and spring constant were calculated. The position over time was plotted and showed an exponentially decaying oscillation. The damping ratio was estimated by taking the logarithm of the amplitude ratios between peaks and finding the slope. This gave a damping ratio of 0.006 and a calculated damping constant of approximately 0.059.
Solutions Manual for Foundations Of MEMS 2nd Edition by Chang Liu
Full download : https://goo.gl/rLRJBK Solutions Manual for Foundations Of MEMS 2nd Edition by Chang Liu
Recommended
From the Front LinesOur robotic equipment and its maintenanc.docx
From the Front Lines
Our robotic equipment and its maintenance represent a fixed cost of $23,320 per month. The cost-effectiveness of robotic-assisted surgery is related to patient volume: With only 10 cases, the fixed cost per case is $2,332, and with 40 cases, the fixed cost per case is $583.
Source: Alemozaffar, Chang, Kacker, Sun, DeWolf, & Wagner (2013).
MATLAB sessions: Laboratory 5
MAT 275 Laboratory 5
The Mass-Spring System
In this laboratory we will examine harmonic oscillation. We will model the motion of a mass-spring
system with differential equations.
Our objectives are as follows:
1. Determine the effect of parameters on the solutions of differential equations.
2. Determine the behavior of the mass-spring system from the graph of the solution.
3. Determine the effect of the parameters on the behavior of the mass-spring.
The primary MATLAB command used is the ode45 function.
Mass-Spring System without Damping
The motion of a mass suspended to a vertical spring can be described as follows. When the spring is
not loaded it has length ℓ0 (situation (a)). When a mass m is attached to its lower end it has length ℓ
(situation (b)). From the first principle of mechanics we then obtain
mg︸︷︷︸
downward weight force
+ −k(ℓ − ℓ0)︸ ︷︷ ︸
upward tension force
= 0. (L5.1)
The term g measures the gravitational acceleration (g ≃ 9.8m/s2 ≃ 32ft/s2). The quantity k is a spring
constant measuring its stiffness. We now pull downwards on the mass by an amount y and let the mass
go (situation (c)). We expect the mass to oscillate around the position y = 0. The second principle of
mechanics yields
mg︸︷︷︸
weight
+ −k(ℓ + y − ℓ0)︸ ︷︷ ︸
upward tension force
= m
d2(ℓ + y)
dt2︸ ︷︷ ︸
acceleration of mass
, i.e., m
d2y
dt2
+ ky = 0 (L5.2)
using (L5.1). This ODE is second-order.
(a) (b) (c) (d)
y
ℓ
ℓ0
m
k
γ
Equation (L5.2) is rewritten
d2y
dt2
+ ω20y = 0 (L5.3)
c⃝2011 Stefania Tracogna, SoMSS, ASU
MATLAB sessions: Laboratory 5
where ω20 = k/m. Equation (L5.3) models simple harmonic motion. A numerical solution with ini-
tial conditions y(0) = 0.1 meter and y′(0) = 0 (i.e., the mass is initially stretched downward 10cms
and released, see setting (c) in figure) is obtained by first reducing the ODE to first-order ODEs (see
Laboratory 4).
Let v = y′. Then v′ = y′′ = −ω20y = −4y. Also v(0) = y′(0) = 0. The following MATLAB program
implements the problem (with ω0 = 2).
function LAB05ex1
m = 1; % mass [kg]
k = 4; % spring constant [N/m]
omega0 = sqrt(k/m);
y0 = 0.1; v0 = 0; % initial conditions
[t,Y] = ode45(@f,[0,10],[y0,v0],[],omega0); % solve for 0<t<10
y = Y(:,1); v = Y(:,2); % retrieve y, v from Y
figure(1); plot(t,y,’b+-’,t,v,’ro-’); % time series for y and v
grid on;
%-----------------------------------------
function dYdt = f(t,Y,omega0)
y = Y(1); v = Y(2);
dYdt = [ v ; -omega0^2*y ];
Note that the parameter ω0 was passed as an argument to ode45 rather than set to its value ω0 = 2
directly in the funct ...
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docx
SAMPLE QUESTION:
Exercise 1: Consider the function
f (x,C)=
sin(C x)
Cx
(a) Create a vector x with 100 elements from -3*pi to 3*pi. Write f as an inline or anonymous function
and generate the vectors y1 = f(x,C1), y2 = f(x,C2) and y3 = f(x,C3), where C1 = 1, C2 = 2 and
C3 = 3. Make sure you suppress the output of x and y's vectors. Plot the function f (for the three
C's above), name the axis, give a title to the plot and include a legend to identify the plots. Add a
grid to the plot.
(b) Without using inline or anonymous functions write a function+function structure m-file that does
the same job as in part (a)
SAMPLE LAB WRITEUP:
MAT 275 MATLAB LAB 1 NAME: __________________________
LAB DAY and TIME:______________
Instructor: _______________________
Exercise 1
(a)
x = linspace(-3*pi,3*pi); % generating x vector - default value for number
% of pts linspace is 100
f= @(x,C) sin(C*x)./(C*x) % C will be just a constant, no need for ".*"
C1 = 1, C2 = 2, C3 = 3 % Using commans to separate commands
y1 = f(x,C1); y2 = f(x,C2); y3 = f(x,C3); % supressing the y's
plot(x,y1,'b.-', x,y2,'ro-', x,y3,'ks-') % using different markers for
% black and white plots
xlabel('x'), ylabel('y') % labeling the axis
title('f(x,C) = sin(Cx)/(Cx)') % adding a title
legend('C = 1','C = 2','C = 3') % adding a legend
grid on
Command window output:
f =
@(x,C)sin(C*x)./(C*x)
C1 =
1
C2 =
2
C3 =
3
(b)
M-file of structure function+function
function ex1
x = linspace(-3*pi,3*pi); % generating x vector - default value for number
% of pts linspace is 100
C1 = 1, C2 = 2, C3 = 3 % Using commans to separate commands
y1 = f(x,C1); y2 = f(x,C2); y3 = f(x,C3); % function f is defined below
plot(x,y1,'b.-', x,y2,'ro-', x,y3,'ks-') % using different markers for
% black and white plots
xlabel('x'), ylabel('y') % labeling the axis
title('f(x,C) = sin(Cx)/(Cx)') % adding a title
legend('C = 1','C = 2','C = 3') % adding a legend
grid on
end
function y = f(x,C)
y = sin(C*x)./(C*x);
end
Command window output:
C1 =
1
C2 =
2
C3 =
3
More instructions for the lab write-up:
1) You are not obligated to use the 'diary' function. It was presented only for you convenience. You
should be copying and pasting your code, plots, and results into some sort of "Word" type editor that
will allow you to import graphs and such. Make sure you always include the commands to generate
what is been asked and include the outputs (from command window and plots), unless the pr.
MATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docx
MATLAB sessions: Laboratory 6
MAT 275 Laboratory 6
Forced Equations and Resonance
In this laboratory we take a deeper look at second-order nonhomogeneous equations. We will concentrate
on equations with a periodic harmonic forcing term. This will lead to a study of the phenomenon known
as resonance. The equation we consider has the form
d2y
dt2
+ c
dy
dt
+ ω20y = cosωt. (L6.1)
This equation models the movement of a mass-spring system similar to the one described in Laboratory
5. The forcing term on the right-hand side of (L6.1) models a vibration, with amplitude 1 and frequency
ω (in radians per second = 12π rotation per second =
60
2π rotations per minute, or RPM) of the plate
holding the mass-spring system. All physical constants are assumed to be positive.
Let ω1 =
√
ω20 − c2/4. When c < 2ω0 the general solution of (L6.1) is
y(t) = e−
1
2 ct(c1 cos(ω1t) + c2 sin(ω1t)) + C cos (ωt− α) (L6.2)
with
C =
1√
(ω20 − ω2)
2
+ c2ω2
, (L6.3)
α =
⎧
⎨
⎩
arctan
(
cω
ω20−ω2
)
if ω0 > ω
π + arctan
(
cω
ω20−ω2
)
if ω0 < ω
(L6.4)
and c1 and c2 determined by the initial conditions. The first term in (L6.2) represents the complementary
solution, that is, the general solution to the homogeneous equation (independent of ω), while the second
term represents a particular solution of the full ODE.
Note that when c > 0 the first term vanishes for large t due to the decreasing exponential factor.
The solution then settles into a (forced) oscillation with amplitude C given by (L6.3). The objectives of
this laboratory are then to understand
1. the effect of the forcing term on the behavior of the solution for different values of ω, in particular
on the amplitude of the solution.
2. the phenomena of resonance and beats in the absence of friction.
The Amplitude of Forced Oscillations
We assume here that ω0 = 2 and c = 1 are fixed. Initial conditions are set to 0. For each value of ω, the
amplitude C can be obtained numerically by taking half the difference between the highs and the lows
of the solution computed with a MATLAB ODE solver after a sufficiently large time, as follows: (note
that in the M-file below we set ω = 1.4).
1 function LAB06ex1
2 omega0 = 2; c = 1; omega = 1.4;
3 param = [omega0,c,omega];
4 t0 = 0; y0 = 0; v0 = 0; Y0 = [y0;v0]; tf = 50;
5 options = odeset(’AbsTol’,1e-10,’RelTol’,1e-10);
6 [t,Y] = ode45(@f,[t0,tf],Y0,options,param);
7 y = Y(:,1); v = Y(:,2);
8 figure(1)
9 plot(t,y,’b-’); ylabel(’y’); grid on;
c⃝2011 Stefania Tracogna, SoMSS, ASU 1
MATLAB sessions: Laboratory 6
10 t1 = 25; i = find(t>t1);
11 C = (max(Y(i,1))-min(Y(i,1)))/2;
12 disp([’computed amplitude of forced oscillation = ’ num2str(C)]);
13 Ctheory = 1/sqrt((omega0^2-omega^2)^2+(c*omega)^2);
14 disp([’theoretical amplitude = ’ num2str(Ctheory)]);
15 %----------------------------------------------------------------
16 function dYdt = f(t,Y,param)
17 y = Y(1); v = Y(2);
18 omega0 = param(1); c = param(2); omega = param(3);
19 dYdt = [ v ; cos(omega ...
Lab 5 template Lab 5 - Your Name - MAT 275 Lab The M.docx
Lab 5 template
%% Lab 5 - Your Name - MAT 275 Lab
% The Mass-Spring System
%% EX 1 10 pts
%A) 1 pts | short comment
%
%B) 2 pts | short comment
%
%C) 1 pts | short comment
%
%D) 1 pts
%E) 2 pts | List the first 3-4 t values either in decimal format or as
%fractions involving pi
%F) 3 pts | comments. | (1 pts for including two distinct graphs, each with y(t) and v(t) plotted)
%% EX 2 10 pts
%A) 5 pts
% add commands to LAB05ex1 to compute and plot E(t). Then use ylim([~,~]) to change the yaxis limits.
% You don't need to include this code but at least one plot of E(t) and a comment must be
% included!
%B) 2 pts | write out main steps here
% first differentiate E(t) with respect to t using the chain rule. Then
% make substitutions using the expression for omega0 and using the
% differential equation
%C) 3 pts | show plot and comment
%% EX 3 10 pts
%A) 3 pts | modify the system of equations in LAB05ex1a
% write the t value and either a) show correponding graph or b) explain given matlab
% commands
%B) 2 pts | write t value and max |V| value; include figure
%note: velocity magnitude is like absolute value!
%C) 3 pts | include 3 figures here + comments.
% use title('text') to attach a title to the figure
%D) 2 pts | What needs to happen (in terms of the characteristic equation)
%in order for there to be no oscillations? Impose a condition on the
%characteristic equation to find the critical c value. Write out main steps
%% EX4 10 pts
% A) 5 pts | include 1 figure and comment
%B) 2 pts
% again find dE/dt using the chain rule and make substitutions based on the
% differential equation. You should reach an expression for dE/dt which is
% in terms of y'
%C) 3 pts | include one figure and comment
Exercise (1):
function LAB05ex1
m = 1; % mass [kg]
k = 9; % spring constant [N/m]
omega0=sqrt(k/m);
y0=0.4; v0=0; % initial conditions
[t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0); % solve for 0<t<10
y=Y(:,1); v=Y(:,2); % retrieve y, v from Y
figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v
grid on;
%------------------------------------------------------
function dYdt= f(t,Y,omega0)
y = Y(1); v= Y(2);
dYdt = [v; -omega0^2*y];
Exercise (1a):
function LAB05ex1a
m = 1; % mass [kg]
k = 9; % spring constant [N/m]
c = 1; % friction coefficient [Ns/m]
omega0 = sqrt(k/m); p = c/(2*m);
y0 = 0.4; v0 = 0; % initial conditions
[t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0,p); % solve for 0<t<10
y=Y(:,1); v=Y(:,2); % retrieve y, v from Y
figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v
grid on
%------------------------------------------------------
function dYdt= f(t,Y,omega0,p)
y = Y(1); v= Y(2);
dYdt = [v; ?? ]; % fill-in dv/dt
More instructions for the l ...
ENGINEERING SYSTEM DYNAMICS-TAKE HOME ASSIGNMENT 2018
1. Read Chapter 4 – System Dynamics for Mechanical Engineers by Matthew Davies and Tony L. Schmitz and implement Examples 4.1 to 4.12 in Matlab.
2. Read Chapter 7 – System Dynamics for Mechanical Engineers by Matthew Davies and Tony L. Schmitz and implement Examples 7.1 to 7.11 in Matlab.
3. Read Chapter 9 – System Dynamics for Mechanical Engineers by Matthew Davies and Tony L. Schmitz and implement Examples 9.1 to 9.6 in Matlab.
4. Read Chapter 11 – System Dynamics for Mechanical Engineers by Matthew Davies and Tony L. Schmitz and implement Examples 11.1 to 11.7 in Matlab.
5. Read Chapter 2 - System Dynamics for Engineering Students: Concepts and Applications by Nicolae Lobontiu and attempt problem 2.18 (page 63).
6. Read Chapter 3 - System Dynamics for Engineering Students: Concepts and Applications by Nicolae Lobontiu and attempt problem 3.13 (pp 98 - 100).
7. Read Chapter 4 - System Dynamics for Engineering Students: Concepts and Applications by Nicolae Lobontiu and attempt problem 4.20 (page 146).
8. Read Chapter 5 - System Dynamics for Engineering Students: Concepts and Applications by Nicolae Lobontiu and attempt problems 5.15 (page 198), 5.21 (pp 199 - 200) and 5.27 (pp 201 – 202).
damping_constant_spring
This document describes an experiment to determine the damping constant of a spring and hook system. A spring with an attached hook was stretched and released, and its motion was recorded. The effective mass and spring constant were calculated. The position over time was plotted and showed an exponentially decaying oscillation. The damping ratio was estimated by taking the logarithm of the amplitude ratios between peaks and finding the slope. This gave a damping ratio of 0.006 and a calculated damping constant of approximately 0.059.
Solutions Manual for Foundations Of MEMS 2nd Edition by Chang Liu
Full download : https://goo.gl/rLRJBK Solutions Manual for Foundations Of MEMS 2nd Edition by Chang Liu
Adaptive dynamic programming for control
This document discusses developing near-optimal state feedback controllers for nonlinear discrete-time systems using iterative approximate dynamic programming (ADP) algorithms. Specifically:
1) An infinite-horizon optimal state feedback controller is developed for discrete-time systems based on the dual heuristic programming (DHP) algorithm.
2) A new optimal control scheme is developed using the generalized DHP (GDHP) algorithm and a discounted cost functional.
3) An infinite-horizon optimal stabilizing state feedback controller is designed based on the globalized dual heuristic programming (GHJB) algorithm.
4) Finite-horizon optimal controllers with an ε-error bound are proposed, where the number of optimal control steps can be determined
Fox And Mcdonald's Introduction To Fluid Mechanics 8th Edition Pritchard Solu...
Full Download : https://goo.gl/ixWZLs Fox And Mcdonald's Introduction To Fluid Mechanics 8th Edition Pritchard Solutions Manual
Solution of matlab chapter 3
This document provides solutions to 21 problems involving vector and matrix operations in MATLAB. Some key problems include:
- Calculating values of functions for given inputs using element-by-element operations
- Finding the length, unit vector, and angle between vectors
- Performing operations like addition, multiplication, exponentiation on vectors and using vectors in expressions
- Computing the center of mass and verifying vector identities
- Solving physics problems involving projectile motion using vector components
LAB #2Escape VelocityGoal Determine the initial veloc.docx
LAB #2
Escape Velocity
Goal: Determine the initial velocity an object is shot upward from the surface of the
earth so as to never return; illustrate scaling variables to simplify di↵erential equations.
Required tools: dfield and pplane ; second order di↵erential equations; convert-
ing a second order di↵erential equation with missing independent variable to a first
order di↵erential equation; converting a second order di↵erential equation to a system
of di↵erential equations.
Discussion
Newton’s Law of Gravitational Attraction tells us that in general an object of mass
m which is x miles above the surface of the earth will feel a force of approximately
F = �
0.0061m
⇣
1 +
x
4000
⌘2
pounds with the number “4000” an approximation to the radius of the earth in miles
and “0.0061” the acceleration due to gravity measured in miles/sec2.
On the other hand, according to Newton’s 2nd Law
F = m
d
2
x
dt
2
.
Thus equating these expressions for F leads to the di↵erential equation
d
2
x
dt
2
= �
0.0061
⇣
1 +
x
4000
⌘2 .
To study this equation using the Student Edition of Matlab, we need to change the
units to avoid overly large intervals. Rather than measuring distance in miles, we will
use multiples of the radius of the earth. This amounts to making the substitution
y =
x
4000
(scaling the dependent variable) resulting in the equation
d
2
y
dt
2
= �
0.0061
4000
1
(1 + y)2
. (A)
We can further simplify this equation by changing the units of time (scaling the
independent variable). If we let
s =
r
0.0061
4000
t
1
then by the Chain Rule
dy
dt
=
dy
ds
ds
dt
=
r
0.0061
4000
dy
ds
and hence
d
2
y
dt
2
=
d
dt
✓
dy
dt
◆
=
d
dt
r
0.0061
4000
dy
ds
!
d
2
y
dt
2
=
r
0.0061
4000
d
dt
✓
dy
ds
◆
d
2
y
dt
2
=
r
0.0061
4000
d
ds
✓
dy
ds
◆
ds
dt
�
d
2
y
dt
2
=
0.0061
4000
d
2
y
ds
2
Using the last expression, the di↵erential equation (A) becomes
d
2
y
ds
2
= �
1
(1 + y)2
. (B)
Hence y is now viewed as a function of s, instead of t.
Equation (B) is a second order di↵erential equation whose independent variable s
is missing. We can convert this special type of 2nd order di↵erential equation to a first
order equation using a simple technique. Let
v =
dy
ds
.
This is simply the velocity of the projectile using s as the unit of time instead of t and
y as the distance instead of x.
Since
dy
ds
= v, from the Chain Rule we obtain
d
2
y
ds
2
=
dv
ds
=
dv
dy
dy
ds
= v
dv
dy
.
Thus, equation (B) can be written as
v
dv
dy
= �
1
(1 + y)2
. (C)
Equation (C) is a 1st order di↵erential equation which may be solved to express v
as a function of y.
Assignment
(1) Enter equation (C) into dfield and plot a few solutions starting at y = 0 and
various initial values for the velocity v using the dfield default ranges for v and y.
You will need to enter it in the form
dv
dy
= �
1
v (1 + y)2
. (D)
(If you have trouble with dfield crashing, see the ...
Adaptive dynamic programming algorithm for uncertain nonlinear switched systems
Adaptive dynamic programming algorithm for uncertain nonlinear switched systemsInternational Journal of Power Electronics and Drive Systems
This paper studies an approximate dynamic programming (ADP) strategy of a group of nonlinear switched systems, where the external disturbances are considered. The neural network (NN) technique is regarded to estimate the unknown part of actor as well as critic to deal with the corresponding nominal system. The training technique is simul-taneously carried out based on the solution of minimizing the square error Hamilton function. The closed system’s tracking error is analyzed to converge to an attraction region of origin point with the uniformly ultimately bounded (UUB) description. The simulation results are implemented to determine the effectiveness of the ADP based controller.Feedback Control of Dynamic Systems 8th Edition Franklin Solutions Manual
Full download : https://alibabadownload.com/product/feedback-control-of-dynamic-systems-8th-edition-franklin-solutions-manual/ Feedback Control of Dynamic Systems 8th Edition Franklin Solutions Manual
Ch35 ssm
This document derives the energy of the first excited state of the harmonic oscillator using the Schrödinger equation. It first shows that the wave function for the first excited state is a1xe−ax2, where a is a constant. It then substitutes this wave function and its derivatives into the Schrödinger equation for the harmonic oscillator to obtain an expression for the energy E1 in terms of the angular frequency ω. Solving this expression yields the result that the energy of the first excited state is E1 = (3/2)ħω.
ECE 565 Project1
This document contains code and explanations for image processing tasks involving edge detection, thresholding, and segmentation.
It includes:
1) Code to perform spatial filtering of an image using a 3x3 mask and Sobel edge detection filters.
2) Application of the code to detect edges and segment a large blood vessel in a kidney image.
3) Implementation of Otsu's thresholding algorithm to automatically select a threshold for segmentation.
4) Comparison of Otsu's method to global thresholding on a polymersomes image, finding Otsu's performs better.
Modelling using differnt metods in matlab2 (2) (2) (2) (4) (1) (1).pptx
This document discusses modeling dynamic systems using MATLAB/Simulink. It provides examples of creating simple programs in MATLAB to solve equations and modeling the same programs in Simulink. It also discusses modeling a nonlinear pendulum system using different Simulink blocks, including individual blocks, state-space models, and transfer functions. Finally, it covers linearizing nonlinear systems and using the linearization tool in MATLAB to analyze systems.
On problem-of-parameters-identification-of-dynamic-object
This document discusses methods for identifying parameters of dynamic objects described by systems of ordinary differential equations. Specifically, it addresses problems with multiple initial boundary conditions that are not shared across points.
The paper proposes a new "conditions shift" method to transfer the initial boundary conditions in a way that eliminates differential links and multipoint conditions. This reduces the parameter identification problem to solving either an algebraic system or a quadratic programming problem.
Two cases are considered: case A where the number of conditions equals the number of conditionally free parameters, resulting in a single parameter vector solution. Case B where additional conditions on the parameters are needed in the form of equalities or inequalities, resulting in an optimization problem to select optimal parameter values.
Dynamics
1) The document discusses dynamics modeling for robotic manipulators using the Denavit-Hartenberg representation and Lagrangian mechanics. It describes using the Euler-Lagrange method to derive equations of motion for robotic links by computing kinetic and potential energy terms.
2) As an example, dynamics equations are derived for a simple 1 degree-of-freedom robotic arm. Kinetic and potential energy expressions are written and the Lagrangian is computed to obtain the equation of motion.
3) State-space modeling basics are reviewed using the example of a damped spring-mass system, showing how to write the system dynamics as state-space matrices to evaluate responses like step response.
Future CMB Experiments
The document provides an overview of CMB experiments, including:
1) It describes the natural units used to measure CMB temperature and polarization maps, and how the power spectrum is derived from harmonic coefficients of the maps.
2) It shows the predicted scalar power spectrum, noting the first acoustic peak at l≈220 and subsequent Doppler peaks and troughs.
3) It discusses how instrument noise is characterized and how the likelihood function relates observed and theoretical power spectra.
4) It briefly describes how the CMB is predicted to be mildly polarized and how polarization is represented, as well as technologies used to detect CMB fluctuations like microwave horns.
Hermite integrators and 2-parameter subgroup of Riordan group
1. The document presents a generalization of 2-step Hermite integrators using a 2-parameter matrix M(a,b) whose elements are powers of a and b.
2. M(a,b) can be factorized into a lower triangular matrix L(a,b) and an upper triangular Pascal matrix Upas.
3. Explicit forms are derived for the inverse matrices M−1(a,b) which are needed for implementing higher order Hermite integrators.
Use the same variable names and write the function F - Force(x-ks-kc-l.pdf
Use the same variable names and write the function F = Force(x,ks,kc,l0,S) ; function l0 =
Compute_Rest_Length(x) ; function z = Equations_Of_Motion(xv,m,ks,kc,l0,S) ; in octave. And
answer the 6 questions below
#!/usr/bin/env octave
% Only modify this file where you see a "% TODO"
% Functions that do not contain this should not be modified.
% If you modify them for debugging, please remove/comment those modifications.
% This line tells octave that this is not a function file.
1;
% Workaround fltk bug. You might need to comment this line out.
graphics_toolkit("gnuplot");
% Meanings of commonly encountered variables:
% n = number of particles
% x = 2*n dimensional column vector with the positions of the particles
% v = 2*n dimensional column vector with the velocities of the particles
% Note that the ordering is always x1 y1 x2 y2 x3 y3 ...
% ks = spring constant
% kc = penalty collision strength
% l0 = matrix of spring rest lengths.
% l0(i,j) = initial length of spring between particles i and j (i<j)
% Note that l0(i,j) should not be used if i>=j.
% S = matrix indicating which springs exist
% S(i,j) = 1 if there is a spring between particles i and j (i<j)
% Note that S(i,j) should not be used if i>=j.
% m = mass of the particles (all particles have the same mass)
% Computes the total potential energy for the system.
function E = Potential_Energy(x,ks,kc,l0,S)
n = rows(S);
E = 0;
% For each pair of particles (i<j)
for i = 1:n
xi = x(2*i-1:2*i,1); % Position of particle i
for j = i+1:n
% If there is a spring between particles i and j
if S(i,j)>0
xj = x(2*j-1:2*j,1); % Position of particle j
% Compute the potential energy of the spring.
E = E + .5*ks/l0(i,j)*(norm(xi-xj)-l0(i,j))^2;
end
end
% Compute the potential energy of the penalty collision force for particle i
% If the particle is inside the radius 2 circle centered at the origin,
% then there is no collision (no force, zero potential energy).
% Otherwise, the energy rises quadratically with distance from the circle.
E = E + .5*kc*max(norm(xi)-2,0)^2;
end
end
% Computes the total energy for the system.
function E = Total_Energy(x,v,m,ks,kc,l0,S)
n = rows(S);
KE = 0;
% Kinetic energy for particle = 1/2 m ||v||^2
for i = 1:n
vi = v(2*i-1:2*i,1);
KE = KE + .5*m*(vi'*vi);
end
% Total energy is kinetic + potential
E = KE + Potential_Energy(x,ks,kc,l0,S);
end
% Computes the total force for all of the particles in the system. x is a 2*n
% dimensional vector. On exit, F should be a 2*n dimensional vector containing
% the total force on each of the n particles. The force can be deduced from the
% potential energy. In particular, F(k) is the negative partial derivative of PE
% with respect to x(k). Here k=1..2*n and PE is the quantity computed by
% Potential_Energy. Treat the Potential_Energy function as a regular math
% function, which is a function of its input x.
function F = Force(x,ks,kc,l0,S)
n = rows(S);
F = zeros(2*n,1);
% TODO
end
% Computes the total momentum p and angular momentum L for t.
Computer Controlled Systems (solutions manual). Astrom. 3rd edition 1997
This document contains solutions to problems in a textbook on computer-controlled systems. It provides solutions to problems from Chapter 2, which deals with modeling continuous systems as discrete-time systems using sampling. The document includes analytical solutions to several problems involving sampling continuous systems and determining the corresponding discrete-time models. It also contains the solutions presented in matrix form.
Please use the same variables and only write the TODO part #!-usr-bi.pdf
Please use the same variables and only write the TODO part
#!/usr/bin/env octave
% Only modify this file where you see a "% TODO"
% Functions that do not contain this should not be modified.
% If you modify them for debugging, please remove/comment those modifications.
% This line tells octave that this is not a function file.
1;
% Workaround fltk bug. You might need to comment this line out .
graphics_toolkit("gnuplot");
% Meanings of commonly encountered variables:
% n = number of particles
% x = 2*n dimensional column vector with the positions of the particles
% v = 2*n dimensional column vector with the velocities of the particles
% Note that the ordering is always x1 y1 x2 y2 x3 y3 ...
% ks = spring constant
% kc = penalty collision strength
% l0 = matrix of spring rest lengths.
% l0(i,j) = initial length of spring between particles i and j (i<j)
% Note that l0(i,j) should not be used if i>=j.
% S = matrix indicating which springs exist
% S(i,j) = 1 if there is a spring between particles i and j (i<j)
% Note that S(i,j) should not be used if i>=j.
% m = mass of the particles (all particles have the same mass)
% Computes the total potential energy for the system.
function E = Potential_Energy(x,ks,kc,l0,S)
n = rows(S);
E = 0;
% For each pair of particles (i<j)
for i = 1:n
xi = x(2*i-1:2*i,1); % Position of particle i
for j = i+1:n
% If there is a spring between particles i and j
if S(i,j)>0
xj = x(2*j-1:2*j,1); % Position of particle j
% Compute the potential energy of the spring.
E = E + .5*ks/l0(i,j)*(norm(xi-xj)-l0(i,j))^2;
end
end
% Compute the potential energy of the penalty collision force for particle i
% If the particle is inside the radius 2 circle centered at the origin,
% then there is no collision (no force, zero potential energy).
% Otherwise, the energy rises quadratically with distance from the circle.
E = E + .5*kc*max(norm(xi)-2,0)^2;
end
end
% Computes the total energy for the system.
function E = Total_Energy(x,v,m,ks,kc,l0,S)
n = rows(S);
KE = 0;
% Kinetic energy for particle = 1/2 m ||v||^2
for i = 1:n
vi = v(2*i-1:2*i,1);
KE = KE + .5*m*(vi'*vi);
end
% Total energy is kinetic + potential
E = KE + Potential_Energy(x,ks,kc,l0,S);
end
% Computes the total force for all of the particles in the system. x is a 2*n
% dimensional vector. On exit, F should be a 2*n dimensional vector containing
% the total force on each of the n particles. The force can be deduced from the
% potential energy. In particular, F(k) is the negative partial derivative of PE
% with respect to x(k). Here k=1..2*n and PE is the quantity computed by
% Potential_Energy. Treat the Potential_Energy function as a regular math
% function, which is a function of its input x.
function F = Force(x,ks,kc,l0,S)
n = rows(S);
F = zeros(2*n,1);
% TODO
end
% Computes the total momentum p and angular momentum L for the system
function [p L] = Momentum(x,v,m)
n = rows(x)/2;
p = zeros(2,1); % 2D vector; total momentum
L = 0; % Scalar; total angular momentum
for i = 1:.
Solutions_Manual_to_accompany_Applied_Nu.pdf
This document provides solutions to problems from a numerical methods textbook. It includes MATLAB code and step-by-step workings for problems related to numerical integration, differential equations, curve fitting, and other numerical methods topics. Solutions are provided for single-step and multi-step problems across 10 chapters. MATLAB code demonstrates the numerical methods and allows plotting of model outputs and errors. Equations, tables, and plots are used to show the results of applying numerical techniques to example problems.
Bazzucchi-Campolmi-Zatti
This document summarizes a mechanical engineering student project analyzing the kinematics and dynamics of a forging manipulator. It includes:
1) Modeling the hydraulic actuators as spring-damper systems and computing kinematics using vector equations of the degrees of freedom.
2) Computing static preloads on the actuators to achieve equilibrium.
3) Linearizing the equations of motion around the equilibrium position to determine natural frequencies and mode shapes.
4) Calculating frequency response functions by solving the linearized equations with an external forcing function.
adv-2015-16-solution-09
This document provides solutions to problems regarding disturbance rejection and integral control. It summarizes the disturbance models for different types of disturbances, including piecewise exponential, piecewise constant, and piecewise harmonic oscillations. It then designs state feedback and feedforward gains to reject disturbances. Finally, it derives the transfer function from disturbance to output and adds integral feedback to reject constant disturbances.
Solutions fox
This document contains 20 problems related to fluid mechanics and thermodynamics. Problem 1.20 provides data on measuring the height of a building using horizontal distance and angle measurements, and asks the reader to calculate height, uncertainty, and optimal measurement angle to minimize uncertainty. An Excel file shows calculations and plots uncertainty versus angle. The optimal angle is 31.4 degrees, which minimizes the height uncertainty of 0.95%.
PART B Please response to these two original posts below. Wh.docx
PART B
Please response to these two original posts below. When
responding to these posts, please either expand the
thought, add additional insights, or respectfully disagree
and explain why. Remember that we are after reasons
and arguments, and not simply the statement of
opinions.
Original Post 1
Are human lives intrinsically valuable? If so, in virtue of what? (Is
it our uniqueness, perhaps, or our autonomy, or something else?)
To begin, I would like to remind us that being intrinsically valuable
means having values for just being us and nothing else. I believe
that human lives are intrinsically valuable in virtue of our
uniqueness. As a bio nerd, I would like to state the fact that there
are a lot of crossover events during meiosis, which create trillions
of different DNA combinations. Hence, from a biological
standpoint, without considering other aspects, being you is
already valuable because you are that one sperm that won the
race and got fertilized. On a larger scale, there are hardly two
people whose look and behaviors are the same in the same
family, unless they are identical twins. However, identical twins
still act differently and have differences (such as fingerprints).
Since we are raised in different families, we are taught different
things and have different cultures. In general, we all have
different genetic information, appearances, personalities, senses
of humor, ambitions, talents, interests and life experiences. These
characteristics make up our “unique individual value” and make
us so unique and irreplaceable.
I would also love to discuss how our diversities enrich and
contribute to society, but that would be a talk about our extrinsic
values.
Original Post 2
Are human lives intrinsically valuable? If so, in virtue of what? (Is
it our uniqueness, perhaps, or our autonomy, or something else?)
I believe that human lives are intrinsically valuable due to a
number of reasons. Firstly, human lives aren’t replaceable. You
can’t replace a human being with another just like you can
replace a broken laptop with brand new one. Part of the reason
why we tend to think this way is that we were nurtured with the
notion that there is, indeed, a special value to human life. This
could be in virtue of our uniqueness-- the fact that we are
sentient and capable of complex thoughts and emotions
separates us from any other species on this planet. From a
scientific standpoint, this is also one of the reasons as to why
humans became the dominant species in today’s age.
Moreover, human lives aren’t disposable. I think this is largely due
to us humans having the ability to empathize with others. We
understand that it’s morally inappropriate to take the life of
another individual even if they’re complete strangers because
they’re another human being like us who has their own thoughts,
values, memories, and stories. In a way, we have a strong
emotional connection to our own species. As .
Part C Developing Your Design SolutionThe Production Cycle.docx
Part C Developing Your Design
Solution
The Production Cycle
Within the four stages of the design workflow there are two distinct parts.
The first three stages, as presented in Part B of this book, were described
as ‘The Hidden Thinking’ stages, as they are concerned with undertaking
the crucial behind-the-scenes preparatory work. You may have completed
them in terms of working through the book’s contents, but in visualisation
projects they will continue to command your attention, even if that is
reduced to a background concern.
You have now reached the second distinct part of the workflow which
involves developing your design solution. This stage follows a production
cycle, commencing with rationalising design ideas and moving through to
the development of a final solution.
The term cycle is appropriate to describe this stage as there are many loops
of iteration as you evolve rapidly between conceptual, practical and
technical thinking. The inevitability of this iterative cycle is, in large part,
again due to the nature of this pursuit being more about optimisation rather
than an expectation of achieving that elusive notion of perfection. Trade-
offs, compromises, and restrictions are omnipresent as you juggle ambition
and necessary pragmatism.
How you undertake this stage will differ considerably depending on the
nature of your task. The creation of a relatively simple, single chart to be
slotted into a report probably will not require the same rigour of a formal
production cycle that the development of a vast interactive visualisation to
be used by the public would demand. This is merely an outline of the most
you will need to do – you should edit, adapt and participate the steps to fit
with your context.
There are several discrete steps involved in this production cycle:
Conceiving ideas across the five layers of visualisation design.
Wireframing and storyboarding designs.
Developing prototypes or mock-up versions.
219
Testing.
Refining and completing.
Launching the solution.
Naturally, the specific approach for developing your design solution (from
prototyping through to launching) will vary hugely, depending particularly
on your skills and resources: it might be an Excel chart, or a Tableau
dashboard, an infographic created using Adobe Illustrator, or a web-based
interactive built with the D3.js library. As I have explained in the book’s
introduction, I’m not going to attempt to cover the myriad ways of
implementing a solution; that would be impossible to achieve as each task
and tool would require different instructions.
For the scope of this book, I am focusing on taking you through the first
two steps of this cycle – conceiving ideas and wireframing/storyboarding.
There are parallels here with the distinctions between architecture (design)
and engineering (execution) – I’m effectively chaperoning you through to
the conclusion of your design thinking.
To fulfil this, Part C presents a detailed breakdown of the many design
.
More Related Content
Similar to LAB05ex1.mfunction LAB05ex1m = 1; .docx
Adaptive dynamic programming for control
This document discusses developing near-optimal state feedback controllers for nonlinear discrete-time systems using iterative approximate dynamic programming (ADP) algorithms. Specifically:
1) An infinite-horizon optimal state feedback controller is developed for discrete-time systems based on the dual heuristic programming (DHP) algorithm.
2) A new optimal control scheme is developed using the generalized DHP (GDHP) algorithm and a discounted cost functional.
3) An infinite-horizon optimal stabilizing state feedback controller is designed based on the globalized dual heuristic programming (GHJB) algorithm.
4) Finite-horizon optimal controllers with an ε-error bound are proposed, where the number of optimal control steps can be determined
Fox And Mcdonald's Introduction To Fluid Mechanics 8th Edition Pritchard Solu...
Full Download : https://goo.gl/ixWZLs Fox And Mcdonald's Introduction To Fluid Mechanics 8th Edition Pritchard Solutions Manual
Solution of matlab chapter 3
This document provides solutions to 21 problems involving vector and matrix operations in MATLAB. Some key problems include:
- Calculating values of functions for given inputs using element-by-element operations
- Finding the length, unit vector, and angle between vectors
- Performing operations like addition, multiplication, exponentiation on vectors and using vectors in expressions
- Computing the center of mass and verifying vector identities
- Solving physics problems involving projectile motion using vector components
LAB #2Escape VelocityGoal Determine the initial veloc.docx
LAB #2
Escape Velocity
Goal: Determine the initial velocity an object is shot upward from the surface of the
earth so as to never return; illustrate scaling variables to simplify di↵erential equations.
Required tools: dfield and pplane ; second order di↵erential equations; convert-
ing a second order di↵erential equation with missing independent variable to a first
order di↵erential equation; converting a second order di↵erential equation to a system
of di↵erential equations.
Discussion
Newton’s Law of Gravitational Attraction tells us that in general an object of mass
m which is x miles above the surface of the earth will feel a force of approximately
F = �
0.0061m
⇣
1 +
x
4000
⌘2
pounds with the number “4000” an approximation to the radius of the earth in miles
and “0.0061” the acceleration due to gravity measured in miles/sec2.
On the other hand, according to Newton’s 2nd Law
F = m
d
2
x
dt
2
.
Thus equating these expressions for F leads to the di↵erential equation
d
2
x
dt
2
= �
0.0061
⇣
1 +
x
4000
⌘2 .
To study this equation using the Student Edition of Matlab, we need to change the
units to avoid overly large intervals. Rather than measuring distance in miles, we will
use multiples of the radius of the earth. This amounts to making the substitution
y =
x
4000
(scaling the dependent variable) resulting in the equation
d
2
y
dt
2
= �
0.0061
4000
1
(1 + y)2
. (A)
We can further simplify this equation by changing the units of time (scaling the
independent variable). If we let
s =
r
0.0061
4000
t
1
then by the Chain Rule
dy
dt
=
dy
ds
ds
dt
=
r
0.0061
4000
dy
ds
and hence
d
2
y
dt
2
=
d
dt
✓
dy
dt
◆
=
d
dt
r
0.0061
4000
dy
ds
!
d
2
y
dt
2
=
r
0.0061
4000
d
dt
✓
dy
ds
◆
d
2
y
dt
2
=
r
0.0061
4000
d
ds
✓
dy
ds
◆
ds
dt
�
d
2
y
dt
2
=
0.0061
4000
d
2
y
ds
2
Using the last expression, the di↵erential equation (A) becomes
d
2
y
ds
2
= �
1
(1 + y)2
. (B)
Hence y is now viewed as a function of s, instead of t.
Equation (B) is a second order di↵erential equation whose independent variable s
is missing. We can convert this special type of 2nd order di↵erential equation to a first
order equation using a simple technique. Let
v =
dy
ds
.
This is simply the velocity of the projectile using s as the unit of time instead of t and
y as the distance instead of x.
Since
dy
ds
= v, from the Chain Rule we obtain
d
2
y
ds
2
=
dv
ds
=
dv
dy
dy
ds
= v
dv
dy
.
Thus, equation (B) can be written as
v
dv
dy
= �
1
(1 + y)2
. (C)
Equation (C) is a 1st order di↵erential equation which may be solved to express v
as a function of y.
Assignment
(1) Enter equation (C) into dfield and plot a few solutions starting at y = 0 and
various initial values for the velocity v using the dfield default ranges for v and y.
You will need to enter it in the form
dv
dy
= �
1
v (1 + y)2
. (D)
(If you have trouble with dfield crashing, see the ...
Adaptive dynamic programming algorithm for uncertain nonlinear switched systems
Adaptive dynamic programming algorithm for uncertain nonlinear switched systemsInternational Journal of Power Electronics and Drive Systems
This paper studies an approximate dynamic programming (ADP) strategy of a group of nonlinear switched systems, where the external disturbances are considered. The neural network (NN) technique is regarded to estimate the unknown part of actor as well as critic to deal with the corresponding nominal system. The training technique is simul-taneously carried out based on the solution of minimizing the square error Hamilton function. The closed system’s tracking error is analyzed to converge to an attraction region of origin point with the uniformly ultimately bounded (UUB) description. The simulation results are implemented to determine the effectiveness of the ADP based controller.Feedback Control of Dynamic Systems 8th Edition Franklin Solutions Manual
Full download : https://alibabadownload.com/product/feedback-control-of-dynamic-systems-8th-edition-franklin-solutions-manual/ Feedback Control of Dynamic Systems 8th Edition Franklin Solutions Manual
Ch35 ssm
This document derives the energy of the first excited state of the harmonic oscillator using the Schrödinger equation. It first shows that the wave function for the first excited state is a1xe−ax2, where a is a constant. It then substitutes this wave function and its derivatives into the Schrödinger equation for the harmonic oscillator to obtain an expression for the energy E1 in terms of the angular frequency ω. Solving this expression yields the result that the energy of the first excited state is E1 = (3/2)ħω.
ECE 565 Project1
This document contains code and explanations for image processing tasks involving edge detection, thresholding, and segmentation.
It includes:
1) Code to perform spatial filtering of an image using a 3x3 mask and Sobel edge detection filters.
2) Application of the code to detect edges and segment a large blood vessel in a kidney image.
3) Implementation of Otsu's thresholding algorithm to automatically select a threshold for segmentation.
4) Comparison of Otsu's method to global thresholding on a polymersomes image, finding Otsu's performs better.
Modelling using differnt metods in matlab2 (2) (2) (2) (4) (1) (1).pptx
This document discusses modeling dynamic systems using MATLAB/Simulink. It provides examples of creating simple programs in MATLAB to solve equations and modeling the same programs in Simulink. It also discusses modeling a nonlinear pendulum system using different Simulink blocks, including individual blocks, state-space models, and transfer functions. Finally, it covers linearizing nonlinear systems and using the linearization tool in MATLAB to analyze systems.
On problem-of-parameters-identification-of-dynamic-object
This document discusses methods for identifying parameters of dynamic objects described by systems of ordinary differential equations. Specifically, it addresses problems with multiple initial boundary conditions that are not shared across points.
The paper proposes a new "conditions shift" method to transfer the initial boundary conditions in a way that eliminates differential links and multipoint conditions. This reduces the parameter identification problem to solving either an algebraic system or a quadratic programming problem.
Two cases are considered: case A where the number of conditions equals the number of conditionally free parameters, resulting in a single parameter vector solution. Case B where additional conditions on the parameters are needed in the form of equalities or inequalities, resulting in an optimization problem to select optimal parameter values.
Dynamics
1) The document discusses dynamics modeling for robotic manipulators using the Denavit-Hartenberg representation and Lagrangian mechanics. It describes using the Euler-Lagrange method to derive equations of motion for robotic links by computing kinetic and potential energy terms.
2) As an example, dynamics equations are derived for a simple 1 degree-of-freedom robotic arm. Kinetic and potential energy expressions are written and the Lagrangian is computed to obtain the equation of motion.
3) State-space modeling basics are reviewed using the example of a damped spring-mass system, showing how to write the system dynamics as state-space matrices to evaluate responses like step response.
Future CMB Experiments
The document provides an overview of CMB experiments, including:
1) It describes the natural units used to measure CMB temperature and polarization maps, and how the power spectrum is derived from harmonic coefficients of the maps.
2) It shows the predicted scalar power spectrum, noting the first acoustic peak at l≈220 and subsequent Doppler peaks and troughs.
3) It discusses how instrument noise is characterized and how the likelihood function relates observed and theoretical power spectra.
4) It briefly describes how the CMB is predicted to be mildly polarized and how polarization is represented, as well as technologies used to detect CMB fluctuations like microwave horns.
Hermite integrators and 2-parameter subgroup of Riordan group
1. The document presents a generalization of 2-step Hermite integrators using a 2-parameter matrix M(a,b) whose elements are powers of a and b.
2. M(a,b) can be factorized into a lower triangular matrix L(a,b) and an upper triangular Pascal matrix Upas.
3. Explicit forms are derived for the inverse matrices M−1(a,b) which are needed for implementing higher order Hermite integrators.
Use the same variable names and write the function F - Force(x-ks-kc-l.pdf
Use the same variable names and write the function F = Force(x,ks,kc,l0,S) ; function l0 =
Compute_Rest_Length(x) ; function z = Equations_Of_Motion(xv,m,ks,kc,l0,S) ; in octave. And
answer the 6 questions below
#!/usr/bin/env octave
% Only modify this file where you see a "% TODO"
% Functions that do not contain this should not be modified.
% If you modify them for debugging, please remove/comment those modifications.
% This line tells octave that this is not a function file.
1;
% Workaround fltk bug. You might need to comment this line out.
graphics_toolkit("gnuplot");
% Meanings of commonly encountered variables:
% n = number of particles
% x = 2*n dimensional column vector with the positions of the particles
% v = 2*n dimensional column vector with the velocities of the particles
% Note that the ordering is always x1 y1 x2 y2 x3 y3 ...
% ks = spring constant
% kc = penalty collision strength
% l0 = matrix of spring rest lengths.
% l0(i,j) = initial length of spring between particles i and j (i<j)
% Note that l0(i,j) should not be used if i>=j.
% S = matrix indicating which springs exist
% S(i,j) = 1 if there is a spring between particles i and j (i<j)
% Note that S(i,j) should not be used if i>=j.
% m = mass of the particles (all particles have the same mass)
% Computes the total potential energy for the system.
function E = Potential_Energy(x,ks,kc,l0,S)
n = rows(S);
E = 0;
% For each pair of particles (i<j)
for i = 1:n
xi = x(2*i-1:2*i,1); % Position of particle i
for j = i+1:n
% If there is a spring between particles i and j
if S(i,j)>0
xj = x(2*j-1:2*j,1); % Position of particle j
% Compute the potential energy of the spring.
E = E + .5*ks/l0(i,j)*(norm(xi-xj)-l0(i,j))^2;
end
end
% Compute the potential energy of the penalty collision force for particle i
% If the particle is inside the radius 2 circle centered at the origin,
% then there is no collision (no force, zero potential energy).
% Otherwise, the energy rises quadratically with distance from the circle.
E = E + .5*kc*max(norm(xi)-2,0)^2;
end
end
% Computes the total energy for the system.
function E = Total_Energy(x,v,m,ks,kc,l0,S)
n = rows(S);
KE = 0;
% Kinetic energy for particle = 1/2 m ||v||^2
for i = 1:n
vi = v(2*i-1:2*i,1);
KE = KE + .5*m*(vi'*vi);
end
% Total energy is kinetic + potential
E = KE + Potential_Energy(x,ks,kc,l0,S);
end
% Computes the total force for all of the particles in the system. x is a 2*n
% dimensional vector. On exit, F should be a 2*n dimensional vector containing
% the total force on each of the n particles. The force can be deduced from the
% potential energy. In particular, F(k) is the negative partial derivative of PE
% with respect to x(k). Here k=1..2*n and PE is the quantity computed by
% Potential_Energy. Treat the Potential_Energy function as a regular math
% function, which is a function of its input x.
function F = Force(x,ks,kc,l0,S)
n = rows(S);
F = zeros(2*n,1);
% TODO
end
% Computes the total momentum p and angular momentum L for t.
Computer Controlled Systems (solutions manual). Astrom. 3rd edition 1997
This document contains solutions to problems in a textbook on computer-controlled systems. It provides solutions to problems from Chapter 2, which deals with modeling continuous systems as discrete-time systems using sampling. The document includes analytical solutions to several problems involving sampling continuous systems and determining the corresponding discrete-time models. It also contains the solutions presented in matrix form.
Please use the same variables and only write the TODO part #!-usr-bi.pdf
Please use the same variables and only write the TODO part
#!/usr/bin/env octave
% Only modify this file where you see a "% TODO"
% Functions that do not contain this should not be modified.
% If you modify them for debugging, please remove/comment those modifications.
% This line tells octave that this is not a function file.
1;
% Workaround fltk bug. You might need to comment this line out .
graphics_toolkit("gnuplot");
% Meanings of commonly encountered variables:
% n = number of particles
% x = 2*n dimensional column vector with the positions of the particles
% v = 2*n dimensional column vector with the velocities of the particles
% Note that the ordering is always x1 y1 x2 y2 x3 y3 ...
% ks = spring constant
% kc = penalty collision strength
% l0 = matrix of spring rest lengths.
% l0(i,j) = initial length of spring between particles i and j (i<j)
% Note that l0(i,j) should not be used if i>=j.
% S = matrix indicating which springs exist
% S(i,j) = 1 if there is a spring between particles i and j (i<j)
% Note that S(i,j) should not be used if i>=j.
% m = mass of the particles (all particles have the same mass)
% Computes the total potential energy for the system.
function E = Potential_Energy(x,ks,kc,l0,S)
n = rows(S);
E = 0;
% For each pair of particles (i<j)
for i = 1:n
xi = x(2*i-1:2*i,1); % Position of particle i
for j = i+1:n
% If there is a spring between particles i and j
if S(i,j)>0
xj = x(2*j-1:2*j,1); % Position of particle j
% Compute the potential energy of the spring.
E = E + .5*ks/l0(i,j)*(norm(xi-xj)-l0(i,j))^2;
end
end
% Compute the potential energy of the penalty collision force for particle i
% If the particle is inside the radius 2 circle centered at the origin,
% then there is no collision (no force, zero potential energy).
% Otherwise, the energy rises quadratically with distance from the circle.
E = E + .5*kc*max(norm(xi)-2,0)^2;
end
end
% Computes the total energy for the system.
function E = Total_Energy(x,v,m,ks,kc,l0,S)
n = rows(S);
KE = 0;
% Kinetic energy for particle = 1/2 m ||v||^2
for i = 1:n
vi = v(2*i-1:2*i,1);
KE = KE + .5*m*(vi'*vi);
end
% Total energy is kinetic + potential
E = KE + Potential_Energy(x,ks,kc,l0,S);
end
% Computes the total force for all of the particles in the system. x is a 2*n
% dimensional vector. On exit, F should be a 2*n dimensional vector containing
% the total force on each of the n particles. The force can be deduced from the
% potential energy. In particular, F(k) is the negative partial derivative of PE
% with respect to x(k). Here k=1..2*n and PE is the quantity computed by
% Potential_Energy. Treat the Potential_Energy function as a regular math
% function, which is a function of its input x.
function F = Force(x,ks,kc,l0,S)
n = rows(S);
F = zeros(2*n,1);
% TODO
end
% Computes the total momentum p and angular momentum L for the system
function [p L] = Momentum(x,v,m)
n = rows(x)/2;
p = zeros(2,1); % 2D vector; total momentum
L = 0; % Scalar; total angular momentum
for i = 1:.
Solutions_Manual_to_accompany_Applied_Nu.pdf
This document provides solutions to problems from a numerical methods textbook. It includes MATLAB code and step-by-step workings for problems related to numerical integration, differential equations, curve fitting, and other numerical methods topics. Solutions are provided for single-step and multi-step problems across 10 chapters. MATLAB code demonstrates the numerical methods and allows plotting of model outputs and errors. Equations, tables, and plots are used to show the results of applying numerical techniques to example problems.
Bazzucchi-Campolmi-Zatti
This document summarizes a mechanical engineering student project analyzing the kinematics and dynamics of a forging manipulator. It includes:
1) Modeling the hydraulic actuators as spring-damper systems and computing kinematics using vector equations of the degrees of freedom.
2) Computing static preloads on the actuators to achieve equilibrium.
3) Linearizing the equations of motion around the equilibrium position to determine natural frequencies and mode shapes.
4) Calculating frequency response functions by solving the linearized equations with an external forcing function.
adv-2015-16-solution-09
This document provides solutions to problems regarding disturbance rejection and integral control. It summarizes the disturbance models for different types of disturbances, including piecewise exponential, piecewise constant, and piecewise harmonic oscillations. It then designs state feedback and feedforward gains to reject disturbances. Finally, it derives the transfer function from disturbance to output and adds integral feedback to reject constant disturbances.
Solutions fox
This document contains 20 problems related to fluid mechanics and thermodynamics. Problem 1.20 provides data on measuring the height of a building using horizontal distance and angle measurements, and asks the reader to calculate height, uncertainty, and optimal measurement angle to minimize uncertainty. An Excel file shows calculations and plots uncertainty versus angle. The optimal angle is 31.4 degrees, which minimizes the height uncertainty of 0.95%.
Similar to LAB05ex1.mfunction LAB05ex1m = 1; .docx (20)
Fox And Mcdonald's Introduction To Fluid Mechanics 8th Edition Pritchard Solu...
Fox And Mcdonald's Introduction To Fluid Mechanics 8th Edition Pritchard Solu...
LAB #2Escape VelocityGoal Determine the initial veloc.docx
LAB #2Escape VelocityGoal Determine the initial veloc.docx
Adaptive dynamic programming algorithm for uncertain nonlinear switched systems
Adaptive dynamic programming algorithm for uncertain nonlinear switched systems
Feedback Control of Dynamic Systems 8th Edition Franklin Solutions Manual
Feedback Control of Dynamic Systems 8th Edition Franklin Solutions Manual
Modelling using differnt metods in matlab2 (2) (2) (2) (4) (1) (1).pptx
Modelling using differnt metods in matlab2 (2) (2) (2) (4) (1) (1).pptx
On problem-of-parameters-identification-of-dynamic-object
On problem-of-parameters-identification-of-dynamic-object
Hermite integrators and 2-parameter subgroup of Riordan group
Hermite integrators and 2-parameter subgroup of Riordan group
Use the same variable names and write the function F - Force(x-ks-kc-l.pdf
Use the same variable names and write the function F - Force(x-ks-kc-l.pdf
Computer Controlled Systems (solutions manual). Astrom. 3rd edition 1997
Computer Controlled Systems (solutions manual). Astrom. 3rd edition 1997
Please use the same variables and only write the TODO part #!-usr-bi.pdf
Please use the same variables and only write the TODO part #!-usr-bi.pdf
More from smile790243
PART B Please response to these two original posts below. Wh.docx
PART B
Please response to these two original posts below. When
responding to these posts, please either expand the
thought, add additional insights, or respectfully disagree
and explain why. Remember that we are after reasons
and arguments, and not simply the statement of
opinions.
Original Post 1
Are human lives intrinsically valuable? If so, in virtue of what? (Is
it our uniqueness, perhaps, or our autonomy, or something else?)
To begin, I would like to remind us that being intrinsically valuable
means having values for just being us and nothing else. I believe
that human lives are intrinsically valuable in virtue of our
uniqueness. As a bio nerd, I would like to state the fact that there
are a lot of crossover events during meiosis, which create trillions
of different DNA combinations. Hence, from a biological
standpoint, without considering other aspects, being you is
already valuable because you are that one sperm that won the
race and got fertilized. On a larger scale, there are hardly two
people whose look and behaviors are the same in the same
family, unless they are identical twins. However, identical twins
still act differently and have differences (such as fingerprints).
Since we are raised in different families, we are taught different
things and have different cultures. In general, we all have
different genetic information, appearances, personalities, senses
of humor, ambitions, talents, interests and life experiences. These
characteristics make up our “unique individual value” and make
us so unique and irreplaceable.
I would also love to discuss how our diversities enrich and
contribute to society, but that would be a talk about our extrinsic
values.
Original Post 2
Are human lives intrinsically valuable? If so, in virtue of what? (Is
it our uniqueness, perhaps, or our autonomy, or something else?)
I believe that human lives are intrinsically valuable due to a
number of reasons. Firstly, human lives aren’t replaceable. You
can’t replace a human being with another just like you can
replace a broken laptop with brand new one. Part of the reason
why we tend to think this way is that we were nurtured with the
notion that there is, indeed, a special value to human life. This
could be in virtue of our uniqueness-- the fact that we are
sentient and capable of complex thoughts and emotions
separates us from any other species on this planet. From a
scientific standpoint, this is also one of the reasons as to why
humans became the dominant species in today’s age.
Moreover, human lives aren’t disposable. I think this is largely due
to us humans having the ability to empathize with others. We
understand that it’s morally inappropriate to take the life of
another individual even if they’re complete strangers because
they’re another human being like us who has their own thoughts,
values, memories, and stories. In a way, we have a strong
emotional connection to our own species. As .
Part C Developing Your Design SolutionThe Production Cycle.docx
Part C Developing Your Design
Solution
The Production Cycle
Within the four stages of the design workflow there are two distinct parts.
The first three stages, as presented in Part B of this book, were described
as ‘The Hidden Thinking’ stages, as they are concerned with undertaking
the crucial behind-the-scenes preparatory work. You may have completed
them in terms of working through the book’s contents, but in visualisation
projects they will continue to command your attention, even if that is
reduced to a background concern.
You have now reached the second distinct part of the workflow which
involves developing your design solution. This stage follows a production
cycle, commencing with rationalising design ideas and moving through to
the development of a final solution.
The term cycle is appropriate to describe this stage as there are many loops
of iteration as you evolve rapidly between conceptual, practical and
technical thinking. The inevitability of this iterative cycle is, in large part,
again due to the nature of this pursuit being more about optimisation rather
than an expectation of achieving that elusive notion of perfection. Trade-
offs, compromises, and restrictions are omnipresent as you juggle ambition
and necessary pragmatism.
How you undertake this stage will differ considerably depending on the
nature of your task. The creation of a relatively simple, single chart to be
slotted into a report probably will not require the same rigour of a formal
production cycle that the development of a vast interactive visualisation to
be used by the public would demand. This is merely an outline of the most
you will need to do – you should edit, adapt and participate the steps to fit
with your context.
There are several discrete steps involved in this production cycle:
Conceiving ideas across the five layers of visualisation design.
Wireframing and storyboarding designs.
Developing prototypes or mock-up versions.
219
Testing.
Refining and completing.
Launching the solution.
Naturally, the specific approach for developing your design solution (from
prototyping through to launching) will vary hugely, depending particularly
on your skills and resources: it might be an Excel chart, or a Tableau
dashboard, an infographic created using Adobe Illustrator, or a web-based
interactive built with the D3.js library. As I have explained in the book’s
introduction, I’m not going to attempt to cover the myriad ways of
implementing a solution; that would be impossible to achieve as each task
and tool would require different instructions.
For the scope of this book, I am focusing on taking you through the first
two steps of this cycle – conceiving ideas and wireframing/storyboarding.
There are parallels here with the distinctions between architecture (design)
and engineering (execution) – I’m effectively chaperoning you through to
the conclusion of your design thinking.
To fulfil this, Part C presents a detailed breakdown of the many design
.
PART A You will create a media piece based around the theme of a.docx
PART A:
You will create a media piece based around the theme of “alternative facts.
Fake News:
Create a
series of 3
short, “fake news” articles or news videos. They should follow a specific theme. Make sure to have a clear understanding of WHY your fake news is being created (fake news is used by people, groups, companies, etc to convince an unsuspecting audience of something. It’s supposed to seem real, but the motivation behind it is to deceive. As part of this option, consider what your motivations are for your deception).
Part A: should be around 750 words for written tasks (or 250 for each 3 part task)
PART B:
The focus for this assignment is to demonstrate a
clear understanding of media conventions
, as well as
purpose
and
audience
. Therefore, along with your media product, you’ll also be required to submit a short
reflection
detailing why you created your product and for whom it was intended. You must discuss and analyze the elements within your media product (including why & how you used the persuasive techniques of ethos, logos and pathos) as well as the other elements of media you used and why.
.
Part 4. Implications to Nursing Practice & Implication to Patien.docx
Part 4. Implications to Nursing Practice & Implication to Patient Outcomes
Provide a paragraph summary addressing the topics implications to nursing practice and patient outcomes. This section is NOT another review of the literature or introduction of new topics related to the PICOT question.
You may find if helpful to begin each topic with -
Nurses need to know …
Important patient outcomes include …
Example
– please note this is an older previous students work and so some references are older than 5 years.
Be sure to provide the PICOT question to begin this post.
PICOT Question:
P=Patient Population
I=Intervention
C=Comparison
O=Outcome
T=Time (duration):
In patients in the hospital, (P)
how does frequently provided patient hand washing (I)
compared with patient initiated hand washing (C)
affect hospital acquired infection (O)
within the hospital stay (T)
Implications to Nursing Practice & Patient Outcomes
Nurses need to know that they play a significant role in the reduction of hospital acquired infection by ensuring by health care workers and patients wash hands since nurses have the most interactions with patients. Implementing hand hygiene protocol with patients can enhance awareness and decrease healthcare associated infection (HAI). Both nurses and patients need to know that HAI is associated with increased morbidity and mortality as well cost of treatment and length of hospital stay. Nurses and patients also need to know that most HAI is preventable. Gujral (2015) notes that proper hand hygiene is the single most important, simplest, and least expensive means of reducing prevalence of HAI and the spread of antimicrobial resistance. Nurse and patient hand washing plays a vital role in decreasing healthcare costs and infections in all settings.
References
Gujral, H. (2015.) Survey shows importance of hand washing for infection prevention. American Nurse Today, 10 (10), 20. Retrieved from hEp://www.nursingworld.org/AmericanNurseToday
.
PART AHepatitis C is a chronic liver infection that can be e.docx
PART A
Hepatitis C is a chronic liver infection that can be either silent (with no noticeable symptoms) or debilitating. Either way, 80% of infected persons experience continuing liver destruction. Chronic hepatitis C infection is the leading cause of liver transplants in the United States. The virus that causes it is blood borne, and therefore patients who undergo frequent procedures involving transfer of blood are particularly susceptible to infection. Kidney dialysis patients belong to this group. In 2008, a for-profit hemodialysis facility in New York was shut down after nine of its patients were confirmed as having become infected with hepatitis C while undergoing hemodialysis treatments there between 2001 and 2008.
When the investigation was conducted in 2008, investigators found that 20 of the facility’s 162 patients had been documented with hepatitis C infection at the time they began their association with the clinic. All the current patients were then offered hepatitis C testing, to determine how many had acquired hepatitis C during the time they were receiving treatment at the clinic. They were considered positive if enzyme-linked immunosorbent assay (ELISA) tests showed the presence of antibodies to the hepatitis C virus.
Health officials did not test the workers at the hemodialysis facility for hepatitis C because they did not view them as likely sources of the nine new infections. Why not?
Why do you think patients were tested for antibody to the virus instead of for the presence of the virus itself?
Ref.: Cowan, M. K. (2014) (4th Ed.). Microbiology: A Systems Approach, McGraw Hill
PART B
Summary:
Directions for the students: There are 4 essay questions. Please be sure to complete all of them with thorough substantive responses. Current APA Citations are required for all responses.
1. Precisely what is microbial death?
2. Why does a population of microbes not die instantaneously when exposed to an antimicrobial agent?
3. Explain what is wrong with this statement: “Prior to vaccination, the patient’s skin was sterilized with alcohol.” What would be a more correct wording?
4. Conduct additional research on the use of triclosan and other chemical agents in antimicrobial products today. Develop an opinion on whether this process should continue, providing evidence and citations to support your stance.
.
Part A post your answer to the following question1. How m.docx
Potential negative reactions from others to an adolescent questioning their sexual identity or gender role could negatively impact their social environment, behavior, and self-esteem. As social workers, we can play a role in creating a supportive environment for these adolescents by educating families and communities, advocating for inclusive policies, and providing counseling and resources to help adolescents accept themselves and develop coping strategies.
PART BPlease response to these two original posts below..docx
PART B
Please response to these two original posts below. When responding to
these posts, please either expand the thought, add additional insights, or
respectfully disagree and explain why. Remember that we are after reasons
and arguments, and not simply the statement of opinions.
Original Post 1
"What is moral relativism? Why might people be attracted to it? Is
it plausible?"
First of all, moral relativism is the view that moral truths are
subjective and depend on each individual's standpoints. Based
on this, everyone's moral view is legitimate. This can be attracted
because it sounds liberating and there is no need to argue for a
particular position. Moral relativism seems convincing in some
cases. For example, some people are okay with giving money to
homeless people, thinking that it's good to provide for the people
in need. Some people, on the other hand, claim that they can
work to satisfy their own needs. Moral relativism works well in
these cases because they all seem legitimate. However, there are
cases that moral relativism does not seem reasonable. For
example, child sacrifice in some cultures seems cruel and
uncivilized to most people. Hence, moral relativism is not
absolutely true.
Original Post 2
“Is your death bad for you, specifically, or only (at most) for others? Why
might someone claim that it isn’t bad for you?”
I'd start off by acknowledging what the two ancient philosophers,
Lucretius and Epicurus, outlined about death. They made the
point that death isn't necessarily bad for you since no suffering
takes place and that you yourself don't realize your own death. In
this way, one could make the claim that death isn't intrinsically
bad for you.
Another perspective I wanted to add was the influence of death
(both on you and others around you). Specifically, the event of
death itself may not be bad for you, but the idea of impending
death could impact one's life. Some may live freely, totally care-
free, accepting of death and enjoy life in the moment. Others may
be frightened by the idea of death that they live in constant fear
and hence death causing their mental health to take its toll. In
this way, I'd argue that death could, in fact, be bad for you. One
common reason for being afraid of death is the fear of being
forgotten. Not to mention the death of an individual certainly
affects others; death doesn't affect one's life but also all that is
connected to it. Focusing back to the point, it's clear that the
very idea of death directly affects the concerned individual. The
fact that those who live in fear of death are looking for legacies
and footprints to leave after they leave this world is telling of how
death could be arguably bad for you before it even happens.
PART A
Pick one or more questions below and write a substantive post
with >100 words. Please try to provide evidence(s) to support
your idea(s).
Questions:
• Do we have a duty to work out whe.
Part A (50 Points)Various men and women throughout history .docx
Part A (50 Points):
Various men and women throughout history have made important contributions to the development of statistical science. Select any one (1) individual from the list below and write a 2 page summary of their influence on statistics. Be specific in detail to explain the concepts they developed and how this advanced our understanding and application of statistics.
Florence Nightingale
Francis Galton
Thomas Bayes
Part B (50 Points):
Select any one statistical concept you learned in this course and explain how it can be applied to our understanding of the Covid-19 pandemic (2 pages). You should use a specific example and include at least one diagram to illustrate your answer.
Please note: Your work must be original and not copied directly from other sources. No citations are needed. Be sure to submit this assignment in Blackboard on the due date specified.
.
Part A1. K2. D3. N4. C5. A6. O7. F8. Q9. H10..docx
This document discusses urinary tract infections (UTIs). It begins with a matching exercise identifying structures of the urinary system. The second part addresses UTIs in more detail. It defines a UTI, discusses the microorganisms that cause UTIs and where they enter the body. It also explains common signs and symptoms of UTIs, as well as diagnostic tests and treatments. The document concludes by noting that UTIs are more common in women and describes some ways women can reduce their risk.
Part A Develop an original age-appropriate activity for your .docx
The document describes developing two original age-appropriate activities for preschoolers. The first activity uses either Froebel's cube gift, parquetry gift, or Lincoln Logs and identifies two skills it develops. The second activity promotes the same skills but is based on the Montessori method. The summary describes each activity and notes two key differences between them.
Part 3 Social Situations2. Identify multicultural challenges th.docx
Part 3: Social Situations
2. Identify multicultural challenges that your chosen individual may face as a recent
refugee.
• What are some of the issues that can arise for someone who has recently
immigrated to a new country?
• Explain how these multicultural challenges could impact your chosen individual’s
four areas of development?
3. Suggest plans of action or resources that you feel should be provided to this family to
assist them in proper develop
Part 3: Social Situations
• Proposal paper which identifies multicultural challenges that your chosen individual may face as a recent refugee.
• Suggested plan of action and/or resources which should be implemented to address the multicultural challenges.
• 2-3 Pages in length
• APA Formatting
• Submission will be checked for plagiaris
.
Part A (1000 words) Annotated Bibliography - Create an annota.docx
Part A
(1000 words): Annotated Bibliography - Create an annotated bibliography that focuses on ONE particular aspect of current Software Engineering that face a world with different cultural standards. At least seven (7) peer-reviewed articles must be used for this exercise.
Part B
(3000 words):
Research Report
- Write a report of the analysis and synthesis using the
(Part A
) foundational
Annotated Bibliography
.
Part C (500 words): Why is it important to try to minimize complexity in a software system.
Part D (500 words): What are the advantages and disadvantages to companies that are developing software products that use cloud servers to support their development process?
Part E (500 words): Explain why each microservice should maintain its own data. Explain how data in service replicas can be kept consistent?
.
Part 6 Disseminating Results Create a 5-minute, 5- to 6-sli.docx
Part 6: Disseminating Results
Create a 5-minute, 5- to 6-slide narrated PowerPoint presentation of your Evidence-Based Project:
· Be sure to incorporate any feedback or changes from your presentation submission in Module 5.
· Explain how you would disseminate the results of your project to an audience. Provide a rationale for why you selected this dissemination strategy.
Points Range: 81 (81%) - 90 (90%)
The narrated presentation accurately and completely summarizes the evidence-based project. The narrated presentation is professional in nature and thoroughly addresses all components of the evidence-based project.
The narrated presentation accurately and clearly explains in detail how to disseminate the results of the project to an audience, citing specific and relevant examples.
The narrated presentation accurately and clearly provides a justification that details the selection of this dissemination strategy that is fully supported by specific and relevant examples.
The narrated presentation provides a complete, detailed, and specific synthesis of two outside resources related to the dissemination strategy explained. The narrated presentation fully integrates at least two outside resources and two or three course-specific resources that fully support the presentation.
Written Expression and Formatting—Paragraph Development and Organization:
Paragraphs make clear points that support well-developed ideas, flow logically, and demonstrate continuity of ideas. Sentences are carefully focused—neither long and rambling nor short and lacking substance. A clear and comprehensive purpose statement and introduction is provided which delineates all required criteria.
Points Range: 5 (5%) - 5 (5%)
Paragraphs and sentences follow writing standards for flow, continuity, and clarity.
A clear and comprehensive purpose statement, introduction, and conclusion is provided which delineates all required criteria.
Written Expression and Formatting—English Writing Standards:
Correct grammar, mechanics, and proper punctuation.
Points Range: 5 (5%) - 5 (5%)
Uses correct grammar, spelling, and punctuation with no errors.
Evidenced Based Change
Leslie Hill
Walden University
Introduction/PurposeChange is inevitable.Health care organizations need change to improve.There are challenges that need to be addressed(Baraka-Johnson et al. 2019).Challenges should be addressed using evidence-based research.These changes enhance professionalism therefore improving quality of care and quality of life.The purpose of this paper is to identify an existing problem in health care and suggest a change idea that would be effective in addressing the problem. The paper also articulates risks associated with the change process, how to distribute the change information and how to implement change successfully.
Organizational CultureThe Organization is a hospice facilityOffers end of life care for pain and symptom managementThe health care providers cu.
Part 3 Social Situations • Proposal paper which identifies multicul.docx
Part 3: Social Situations • Proposal paper which identifies multicultural challenges that your chosen individual may face as a recent refugee. • Suggested plan of action and/or resources which should be implemented to address the multicultural challenges. • 2-3 Pages in length • APA Formatting • Submission will be checked for plagiarism
Part 3: Social Situations 2. Identify multicultural challenges that your chosen individual may face as a recent refugee. • What are some of the issues that can arise for someone who has recently immigrated to a new country? • Explain how these multicultural challenges could impact your chosen individual’s four areas of development? 3. Suggest plans of action or resources that you feel should be provided to this family to assist them in proper development.
.
Part 3 Social Situations 2. Identify multicultural challenges that .docx
Part 3: Social Situations 2. Identify multicultural challenges that your chosen individual may face as a recent refugee. • What are some of the issues that can arise for someone who has recently immigrated to a new country? • Explain how these multicultural challenges could impact your chosen individual’s four areas of development? 3. Suggest plans of action or resources that you feel should be provided to this family to assist them in proper development.
Part 3: Social Situations • Proposal paper which identifies multicultural challenges that your chosen individual may face as a recent refugee. • Suggested plan of action and/or resources which should be implemented to address the multicultural challenges. • 2-3 Pages in length • APA Formatting • Submission will be checked for plagiarism
.
Part 2The client is a 32-year-old Hispanic American male who c.docx
Part 2
The client is a 32-year-old Hispanic American male who came to the United States when he was in high school with his father. His mother died back in Mexico when he was in school. He presents today to the PMHNPs office for an initial appointment for complaints of depression. The client was referred by his PCP after “routine” medical work-up to rule out an organic basis for his depression. He has no other health issues except for some occasional back pain and “stiff” shoulders which he attributes to his current work as a laborer in a warehouse. the “Montgomery- Asberg Depression Rating Scale (MADRS)” and obtained a score of 51 (indicating severe depression). reports that he always felt like an outsider as he was “teased” a lot for being “black” in high school. States that he had few friends, and basically kept to himself. He also reports a remarkably diminished interest in engaging in usual activities, states that he has gained 15 pounds in the last 2 months. He is also troubled with insomnia which began about 6 months ago, but have been progressively getting worse. He does report poor concentration which he reports is getting in “trouble” at work.
· Decision #1: start Zoloft 25mg orally daily
· Which decision did you select?
· Why did you select this decision? Support your response with evidence and references to the Learning Resources.
· What were you hoping to achieve by making this decision? Support your response with evidence and references to the Learning Resources.
· Explain any difference between what you expected to achieve with Decision #1 and the results of the decision. Why were they different?
· Decision #2: Client returns to clinic in four weeks, reports a 25% decrease in symptoms but concerned over the new onset of erectile dysfunction
*add Augmentin Wellbutrin IR 150mg in the morning
· Why did you select this decision? Support y our response with evidence and references to the Learning Resources.
· What were you hoping to achieve by making this decision? Support your response with evidence and references to the Learning Resources.
· Explain any difference between what you expected to achieve with Decision #2 and the results of the decision. Why were they different?
· Decision #3: Client returns to clinic in four weeks, Client stated that depressive symptoms have decreased even more and his erectile dysfunction has abated
· Client reports that he has been feeling “jittery” and sometimes “nervous”
*change to Wellbutrin XL 150mg daily
· Why did you select this decision? Support your response with evidence and references to the Learning Resources.
· What were you hoping to achieve by making this decision? Support your response with evidence and references to the Learning Resources.
· Explain any difference between what you expected to achieve with Decision #3 and the results of the decision. Why were they different?
Explain how ethical considerations might impact your treatment plan and communication with clients.
Conclusion.
Part 2For this section of the template, focus on gathering deta.docx
Part 2:
For this section of the template, focus on gathering details about common, specific learning disabilities. These disabilities fall under the IDEA disability categories you researched for the chart above. Review the textbook and the topic study materials and use them to complete the chart.
Learning Disability Definition Characteristics Common Assessments for Diagnosis Potential Effect on Learning and Other Areas of Life Basic Strategies for Addressing the Disability
Attention Deficit Hyperactivity Disorder (ADHD)
Auditory Processing Disorder (APD)
Dyscalculia
Dysgraphia
Dyslexia
Dysphasia/Aphasia
Dyspraxia
Language Processing Disorder (LPD)
Non-Verbal Learning Disabilities
Visual Perceptual/Visual Motor Deficit
.
Part 2 Observation Summary and Analysis • Summary paper of observat.docx
Part 2: Observation Summary and Analysis • Summary paper of observation findings for each area of development and connection to the observed participant. • Comprehensive description of the observed participant. • Analyzed observation experience with course material to determine whetherthe participant is developmentally on track for each area of development. • 4 Pages in length • APA Formatting • Submission will be checked for plagiarism
Part 2: Observation Summary and Analysis 1. Review and implement any comments from your instructor for Part 1: Observation. 2. Describe the participant that you observed. • Share your participant’s first name (can be fictional name if participant wants to remain anonymous), age, physical attributes, and you initial impressions. 3. Analyze your observation findings for each area of development (physical, cognitive, social/emotional, and spiritual/moral). • Explain how your observations support the 3-5 bullets for each area of development that you identified in your Development Observation Guidefrom Part 1: Observation. • Explain whether or not your participant is developmentally on track for each area of development. 4. What stood out the most to you about the observation? 5. Include at least 2 credible sources
.
Part 2 Observation Summary and Analysis 1. Review and implement any.docx
Part 2: Observation Summary and Analysis 1. Review and implement any comments from your instructor for Part 1: Observation. 2. Describe the participant that you observed. • Share your participant’s first name (can be fictional name if participant wants to remain anonymous), age, physical attributes, and you initial impressions. 3. Analyze your observation findings for each area of development (physical, cognitive, social/emotional, and spiritual/moral). • Explain how your observations support the 3-5 bullets for each area of development that you identified in your Development Observation Guidefrom Part 1: Observation. • Explain whether or not your participant is developmentally on track for each area of development. 4. What stood out the most to you about the observation? 5. Include at least 2 credible sources
Part 2: Observation Summary and Analysis • Summary paper of observation findings for each area of development and connection to the observed participant. • Comprehensive description of the observed participant. • Analyzed observation experience with course material to determine whetherthe participant is developmentally on track for each area of development. • 4-6 Pages in length • APA Formatting • Submission will be checked for plagiarism
.
Part 2Data collectionfrom your change study initiative,.docx
Part 2:
Data collection
from your change study initiative, sample, method, display of the results of the data itself, process, and method of analysis (graphs, charts, frequency counts, descriptive statistics of the data, narrative)
Part 3: Interpretation of the results of the Data
Collection and
Analysis, address likely resistance, and provide recommendations for continuing
the study
or evaluating your change study/initiative.
.
More from smile790243 (20)
PART B Please response to these two original posts below. Wh.docx
PART B Please response to these two original posts below. Wh.docx
Part C Developing Your Design SolutionThe Production Cycle.docx
Part C Developing Your Design SolutionThe Production Cycle.docx
PART A You will create a media piece based around the theme of a.docx
PART A You will create a media piece based around the theme of a.docx
Part 4. Implications to Nursing Practice & Implication to Patien.docx
Part 4. Implications to Nursing Practice & Implication to Patien.docx
PART AHepatitis C is a chronic liver infection that can be e.docx
PART AHepatitis C is a chronic liver infection that can be e.docx
Part A post your answer to the following question1. How m.docx
Part A post your answer to the following question1. How m.docx
PART BPlease response to these two original posts below..docx
PART BPlease response to these two original posts below..docx
Part A (50 Points)Various men and women throughout history .docx
Part A (50 Points)Various men and women throughout history .docx
Part A1. K2. D3. N4. C5. A6. O7. F8. Q9. H10..docx
Part A1. K2. D3. N4. C5. A6. O7. F8. Q9. H10..docx
Part A Develop an original age-appropriate activity for your .docx
Part A Develop an original age-appropriate activity for your .docx
Part 3 Social Situations2. Identify multicultural challenges th.docx
Part 3 Social Situations2. Identify multicultural challenges th.docx
Part A (1000 words) Annotated Bibliography - Create an annota.docx
Part A (1000 words) Annotated Bibliography - Create an annota.docx
Part 6 Disseminating Results Create a 5-minute, 5- to 6-sli.docx
Part 6 Disseminating Results Create a 5-minute, 5- to 6-sli.docx
Part 3 Social Situations • Proposal paper which identifies multicul.docx
Part 3 Social Situations • Proposal paper which identifies multicul.docx
Part 3 Social Situations 2. Identify multicultural challenges that .docx
Part 3 Social Situations 2. Identify multicultural challenges that .docx
Part 2The client is a 32-year-old Hispanic American male who c.docx
Part 2The client is a 32-year-old Hispanic American male who c.docx
Part 2For this section of the template, focus on gathering deta.docx
Part 2For this section of the template, focus on gathering deta.docx
Part 2 Observation Summary and Analysis • Summary paper of observat.docx
Part 2 Observation Summary and Analysis • Summary paper of observat.docx
Part 2 Observation Summary and Analysis 1. Review and implement any.docx
Part 2 Observation Summary and Analysis 1. Review and implement any.docx
Part 2Data collectionfrom your change study initiative,.docx
Part 2Data collectionfrom your change study initiative,.docx
Recently uploaded
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
How to Make a Field Mandatory in Odoo 17
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Physical pharmaceutics notes for B.pharm students
HYPERTENSION - SLIDE SHARE PRESENTATION.
IT WILL BE HELPFULL FOR THE NUSING STUDENTS
IT FOCUSED ON MEDICAL MANAGEMENT AND NURSING MANAGEMENT.
HIGHLIGHTS ON HEALTH EDUCATION.
Temple of Asclepius in Thrace. Excavation results
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
How to Setup Warehouse & Location in Odoo 17 Inventory
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Stack Memory Organization of 8086 Microprocessor
The stack memory organization of 8086 microprocessor.
Walmart Business+ and Spark Good for Nonprofits.pdf
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...Nguyen Thanh Tu Collection
https://app.box.com/s/qhtvq32h4ybf9t49ku85x0n3xl4jhr15Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Recently uploaded (20)
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
Lifelines of National Economy chapter for Class 10 STUDY MATERIAL PDF
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
B. Ed Syllabus for babasaheb ambedkar education university.pdf
B. Ed Syllabus for babasaheb ambedkar education university.pdf
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
SWOT analysis in the project Keeping the Memory @live.pptx
SWOT analysis in the project Keeping the Memory @live.pptx
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering.pptx
LAB05ex1.mfunction LAB05ex1m = 1; .docx
- 1. LAB05ex1.m function LAB05ex1 m = 1; % mass [kg] k = 4; % spring constant [N/m] omega0=sqrt(k/m); y0=0.1; v0=0; % initial conditions [t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0); % solve for 0<t<10 y=Y(:,1); v=Y(:,2); % retrieve y, v from Y figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v grid on; %------------------------------------------------------ function dYdt= f(t,Y,omega0) y = Y(1); v= Y(2); dYdt = [v; -omega0^2*y]; __MACOSX/._LAB05ex1.m LAB05ex1a.m function LAB05ex1a m = 1; % mass [kg]
- 2. k = 4; % spring constant [N/m] c = 1; % friction coefficient [Ns/m] omega0 = sqrt(k/m); p = c/(2*m); y0 = 0.1; v0 = 0; % initial conditions [t,Y]=ode45(@f,[0,10],[y0,v0],[],omega0,p); % solve for 0<t<10 y=Y(:,1); v=Y(:,2); % retrieve y, v from Y figure(1); plot(t,y,'b+-',t,v,'ro-'); % time series for y and v grid on %------------------------------------------------------ function dYdt= f(t,Y,omega0,p) y = Y(1); v= Y(2); dYdt = [v; ?? ]; % fill-in dv/dt __MACOSX/._LAB05ex1a.m MAT275_LAB05.pdf MATLAB sessions: Laboratory 5 MAT 275 Laboratory 5 The Mass-Spring System
- 3. In this laboratory we will examine harmonic oscillation. We will model the motion of a mass-spring system with differential equations. Our objectives are as follows: 1. Determine the effect of parameters on the solutions of differential equations. 2. Determine the behavior of the mass-spring system from the graph of the solution. 3. Determine the effect of the parameters on the behavior of the mass-spring. The primary MATLAB command used is the ode45 function. Mass-Spring System without Damping The motion of a mass suspended to a vertical spring can be described as follows. When the spring is not loaded it has length ℓ0 (situation (a)). When a mass m is attached to its lower end it has length ℓ (situation (b)). From the first principle of mechanics we then obtain mg︸︷︷︸ downward weight force + −k(ℓ − ℓ0)︸ ︷︷ ︸ upward tension force = 0. (L5.1) The term g measures the gravitational acceleration (g ≃ 9.8m/s2 ≃ 32ft/s2). The quantity k is a spring
- 4. constant measuring its stiffness. We now pull downwards on the mass by an amount y and let the mass go (situation (c)). We expect the mass to oscillate around the position y = 0. The second principle of mechanics yields mg︸︷︷︸ weight + −k(ℓ + y − ℓ0)︸ ︷︷ ︸ upward tension force = m d2(ℓ + y) dt2︸ ︷︷ ︸ acceleration of mass , i.e., m d2y dt2 + ky = 0 (L5.2) using (L5.1). This ODE is second-order. (a) (b) (c) (d) y ℓ ℓ0 m
- 5. k γ Equation (L5.2) is rewritten d2y dt2 + ω20y = 0 (L5.3) c⃝2011 Stefania Tracogna, SoMSS, ASU MATLAB sessions: Laboratory 5 where ω20 = k/m. Equation (L5.3) models simple harmonic motion. A numerical solution with ini- tial conditions y(0) = 0.1 meter and y′(0) = 0 (i.e., the mass is initially stretched downward 10cms and released, see setting (c) in figure) is obtained by first reducing the ODE to first-order ODEs (see Laboratory 4). Let v = y′. Then v′ = y′′ = −ω20y = −4y. Also v(0) = y′(0) = 0. The following MATLAB program implements the problem (with ω0 = 2). function LAB05ex1 m = 1; % mass [kg] k = 4; % spring constant [N/m] omega0 = sqrt(k/m);
- 6. y0 = 0.1; v0 = 0; % initial conditions [t,Y] = ode45(@f,[0,10],[y0,v0],[],omega0); % solve for 0<t<10 y = Y(:,1); v = Y(:,2); % retrieve y, v from Y figure(1); plot(t,y,’b+-’,t,v,’ro-’); % time series for y and v grid on; %----------------------------------------- function dYdt = f(t,Y,omega0) y = Y(1); v = Y(2); dYdt = [ v ; -omega0^2*y ]; Note that the parameter ω0 was passed as an argument to ode45 rather than set to its value ω0 = 2 directly in the function f. The advantage is that its value can easily be changed in the driver part of the program rather than in the function, for example when multiple plots with different values of ω0 need to be compared in a single MATLAB figure window. 0 1 2 3 4 5 6 7 8 9 10 −0.2 −0.15 −0.1 −0.05 0
- 7. 0.05 0.1 0.15 0.2 Figure L5a: Harmonic motion 1. From the graph in Fig. L5a answer the following questions. (a) Which curve represents y = y(t)? How do you know? (b) What is the period of the motion? Answer this question first graphically (by reading the period from the graph) and then analytically (by finding the period using ω0). (c) We say that the mass comes to rest if, after a certain time, the position of the mass remains within an arbitrary small distance from the equilibrium position. Will the mass ever come to rest? Why? c⃝2011 Stefania Tracogna, SoMSS, ASU MATLAB sessions: Laboratory 5 (d) What is the amplitude of the oscillations for y? (e) What is the maximum velocity (in magnitude) attained by the mass, and when is it attained?
- 8. Make sure you give all the t-values at which the velocity is maximum and the corresponding maximum value. The t-values can be determined by magnifying the MATLAB figure using the magnify button , and by using the periodicity of the velocity function. (f) How does the size of the mass m and the stiffness k of the spring affect the motion? Support your answer first with a theoretical analysis on how ω0 – and therefore the period of the oscillation – is related to m and k, and then graphically by running LAB05ex1.m first with m = 5 and k = 4 and then with m = 1 and k = 16. Include the corresponding graphs. 2. The energy of the mass-spring system is given by the sum of the potential energy and kinetic energy. In absence of damping, the energy is conserved. (a) Plot the quantity E = 1 2 mv2 + 1 2 ky2 as a function of time. What do you observe? (pay close attention to the y-axis scale and, if necessary, use ylim to get a better graph). Does the graph confirm the fact that the energy is conserved? (b) Show analytically that dE dt = 0.(Note that this proves that the energy is constant).
- 9. (c) Plot v vs y (phase plot). Does the curve ever get close to the origin? Why or why not? What does that mean for the mass-spring system? Mass-Spring System with Damping When the movement of the mass is damped due to viscous effects (e.g., the mass moves in a cylinder containing oil, situation (d)), an additional term proportional to the velocity must be added. The resulting equation becomes m d2y dt2 + c dy dt + ky = 0 or d2y dt2 + 2p dy dt + ω20y = 0 (L5.4) by setting p = c 2m
- 10. . The program LAB05ex1 is updated by modifying the function f: function LAB05ex1a m = 1; % mass [kg] k = 4; % spring constant [N/m] c = 1; % friction coefficient [Ns/m] omega0 = sqrt(k/m); p = c/(2*m); y0 = 0.1; v0 = 0; % initial conditions [t,Y] = ode45(@f,[0,10],[y0,v0],[],omega0,p); % solve for 0<t<10 y = Y(:,1); v = Y(:,2); % retrieve y, v from Y figure(1); plot(t,y,’b+-’,t,v,’ro-’); % time series for y and v grid on; %------------------------------------------- function dYdt = f(t,Y,omega0,p) y = Y(1); v = Y(2); dYdt = [ v ; ?? ]; % fill-in dv/dt 3. Fill in LAB05ex1a.m to reproduce Fig. L5b and then answer the following questions.
- 11. (a) For what minimal time t1 will the mass-spring system satisfy |y(t)| < 0.01 for all t > t1? You can answer the question either by magnifying the MATLAB figure using the magnify button (include a graph that confirms your answer), or use the following MATLAB commands (explain): c⃝2011 Stefania Tracogna, SoMSS, ASU MATLAB sessions: Laboratory 5 0 1 2 3 4 5 6 7 8 9 10 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 0.2 y(t) v(t)=y’(t)
- 12. Figure L5b: Damped harmonic motion for i=1:length(y) m(i)=max(abs(y(i:end))); end i = find(m<0.01); i = i(1); disp([’|y|<0.01 for t>t1 with ’ num2str(t(i-1)) ’<t1<’ num2str(t(i))]) (b) What is the maximum (in magnitude) velocity attained by the mass, and when is it attained? Answer by using the magnify button and include the corresponding picture. (c) How does the size of c affect the motion? To support your answer, run the file LAB05ex1.m for c = 2, c = 4, c = 6 and c = 8. Include the corresponding graphs with a title indicating the value of c used. (d) Determine analytically the smallest (critical) value of c such that no oscillation appears in the solution. 4. (a) Plot the quantity E = 1 2 mv2 + 1 2 ky2 as a function of time. What do you observe? Is the energy conserved in this case?
- 13. (b) Show analytically that dE dt < 0 for c > 0 while dE dt > 0 for c < 0. (c) Plot v vs y (phase plot). Comment on the behavior of the curve in the context of the motion of the spring. Does the graph ever get close to the origin? Why or why not? c⃝2011 Stefania Tracogna, SoMSS, ASU __MACOSX/._MAT275_LAB05.pdf Emerging Markets Case Study Format a. Cover page b. Executive Summary (Brief Teaching Case) c. Table of Contents 1. Introduction 1.1 Case-context /Country Background 1.2 Current Situation Analysis 1.3 Research Question/Purpose of your review 1.4 How is the report structured 2. Institutional Factors affecting the Success/Failures in Emerging Markets context 2.1 Formal Institutional Factors 2.2 Informal Institutional Factors
- 14. 3. Organizational /Individual-level Factors affecting Success/Failures in Emerging Markets context 3.1 Internal Tangible & Intangible Resources 3.2 Competencies 3.3 Dynamic Capabilities 3.4 Key Problems faced 4. Strategic Options & Analysis 5. Recommendations 6. Conclusion 7. References