I have a set of figures that I’m saving using exportgraphics, after making some changes to the programm i get a new set of figures but exportgraphics is not overwriting it. I have been using this to export images for a long time and it used to overwrite, but since yesterday it isn’t doing so. Any ideas about what may be causing this?I have a set of figures that I’m saving using exportgraphics, after making some changes to the programm i get a new set of figures but exportgraphics is not overwriting it. I have been using this to export images for a long time and it used to overwrite, but since yesterday it isn’t doing so. Any ideas about what may be causing this? I have a set of figures that I’m saving using exportgraphics, after making some changes to the programm i get a new set of figures but exportgraphics is not overwriting it. I have been using this to export images for a long time and it used to overwrite, but since yesterday it isn’t doing so. Any ideas about what may be causing this? export, image, save MATLAB Answers — New Questions

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Can the USB-231 DAQ handle digital input/output? In Matlab, I am doing an addinput() but it is saying digital is not supported.Can the USB-231 DAQ handle digital input/output? In Matlab, I am doing an addinput() but it is saying digital is not supported. Can the USB-231 DAQ handle digital input/output? In Matlab, I am doing an addinput() but it is saying digital is not supported. matlab, data acquisition, mcc MATLAB Answers — New Questions

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Hey hello there!
So i have the following Issue. I am simulating the attitude (orientation) of a satellite via simulink. My initial conditions are usually a spinning satellite that gets slowed down. And when i reach small turn rates (derivative of the magnetic field is very low) my simulation breaks down and my step size decreases so drastically that it takes almost 30min to simulate 1s. And that cant be right somehow.
My tolerance settings are:
Absolute Tolerance: 10*e-8
Relative Tolerance: 10*e-6
Maybe they are low, but they work for most of the time with reasonable step sizes. (as one can see in the graph below)

This is how the step-size graph looks like for an edge case where i get the solver exceptiosn with error control but only for "short" time frames that still take minutes to compute.
At those time intervals the derivative of the magnetic field approaches 0 (1st graph below) and starts to do weird stuff. But when i tell the derivative that it should be 0 when close to 0 the simulation slows down even faster and my magnetic field derivative just starts to flicker (2nd graph).

Are there any general things one has to consider? Does stuff like this happen often in simulations?
Any Ideas how to fix this?

Some more additional Information:
Simulation Overview:

So what the simulation basically does is. It tries to bring the angular velocity of the satellite close to zero. This is done by "measuring" the magnetic field of earth and then trying to align the satellite with earths magnetic field (as it can be roughly assumed constant). This is done via this logic:

Here the graph of my angular Velocity around the x-Axis and the Angular Acceleration Graph when those anomalies occur:
(The Angular acceleration graph is zoomed in on the y-axis as otherwise one would only see the spikes and the angular velocity graph is zoomed in a lot in both axis)

One can clearly see that the Angular Velocity first runs smoothly and then for some reason flattens out until increasing not so smoothly.

If you need more information i will gladly provide them.
Thanks for your Help :)Hey hello there!
So i have the following Issue. I am simulating the attitude (orientation) of a satellite via simulink. My initial conditions are usually a spinning satellite that gets slowed down. And when i reach small turn rates (derivative of the magnetic field is very low) my simulation breaks down and my step size decreases so drastically that it takes almost 30min to simulate 1s. And that cant be right somehow.
My tolerance settings are:
Absolute Tolerance: 10*e-8
Relative Tolerance: 10*e-6
Maybe they are low, but they work for most of the time with reasonable step sizes. (as one can see in the graph below)

This is how the step-size graph looks like for an edge case where i get the solver exceptiosn with error control but only for "short" time frames that still take minutes to compute.
At those time intervals the derivative of the magnetic field approaches 0 (1st graph below) and starts to do weird stuff. But when i tell the derivative that it should be 0 when close to 0 the simulation slows down even faster and my magnetic field derivative just starts to flicker (2nd graph).

Are there any general things one has to consider? Does stuff like this happen often in simulations?
Any Ideas how to fix this?

Some more additional Information:
Simulation Overview:

So what the simulation basically does is. It tries to bring the angular velocity of the satellite close to zero. This is done by "measuring" the magnetic field of earth and then trying to align the satellite with earths magnetic field (as it can be roughly assumed constant). This is done via this logic:

Here the graph of my angular Velocity around the x-Axis and the Angular Acceleration Graph when those anomalies occur:
(The Angular acceleration graph is zoomed in on the y-axis as otherwise one would only see the spikes and the angular velocity graph is zoomed in a lot in both axis)

One can clearly see that the Angular Velocity first runs smoothly and then for some reason flattens out until increasing not so smoothly.

If you need more information i will gladly provide them.
Thanks for your Help 🙂 Hey hello there!
So i have the following Issue. I am simulating the attitude (orientation) of a satellite via simulink. My initial conditions are usually a spinning satellite that gets slowed down. And when i reach small turn rates (derivative of the magnetic field is very low) my simulation breaks down and my step size decreases so drastically that it takes almost 30min to simulate 1s. And that cant be right somehow.
My tolerance settings are:
Absolute Tolerance: 10*e-8
Relative Tolerance: 10*e-6
Maybe they are low, but they work for most of the time with reasonable step sizes. (as one can see in the graph below)

This is how the step-size graph looks like for an edge case where i get the solver exceptiosn with error control but only for "short" time frames that still take minutes to compute.
At those time intervals the derivative of the magnetic field approaches 0 (1st graph below) and starts to do weird stuff. But when i tell the derivative that it should be 0 when close to 0 the simulation slows down even faster and my magnetic field derivative just starts to flicker (2nd graph).

Are there any general things one has to consider? Does stuff like this happen often in simulations?
Any Ideas how to fix this?

Some more additional Information:
Simulation Overview:

So what the simulation basically does is. It tries to bring the angular velocity of the satellite close to zero. This is done by "measuring" the magnetic field of earth and then trying to align the satellite with earths magnetic field (as it can be roughly assumed constant). This is done via this logic:

Here the graph of my angular Velocity around the x-Axis and the Angular Acceleration Graph when those anomalies occur:
(The Angular acceleration graph is zoomed in on the y-axis as otherwise one would only see the spikes and the angular velocity graph is zoomed in a lot in both axis)

One can clearly see that the Angular Velocity first runs smoothly and then for some reason flattens out until increasing not so smoothly.

If you need more information i will gladly provide them.
Thanks for your Help 🙂 simulink, solver, step-size MATLAB Answers — New Questions

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hello, i found a model regarding UPFC detailed model and i went throught the article written regarding the simulation and i refered back to the model but i do not understand the scope can someone explain them to me please.hello, i found a model regarding UPFC detailed model and i went throught the article written regarding the simulation and i refered back to the model but i do not understand the scope can someone explain them to me please. hello, i found a model regarding UPFC detailed model and i went throught the article written regarding the simulation and i refered back to the model but i do not understand the scope can someone explain them to me please. matlab, simulink, power_electronics_control MATLAB Answers — New Questions

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Using the Black/Scholes Option Pricing Model, calculate the value of the call option given:
S= 70; X=80; T=3 months; s2=.49; Rf =10%Using the Black/Scholes Option Pricing Model, calculate the value of the call option given:
S= 70; X=80; T=3 months; s2=.49; Rf =10% Using the Black/Scholes Option Pricing Model, calculate the value of the call option given:
S= 70; X=80; T=3 months; s2=.49; Rf =10% mba MATLAB Answers — New Questions

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I’m creating a Poisson distribution. I then want to produce a cdf evaluated at an array of values using this distribution. However, the output of the cdf matlab function when applied to a Poisson distribution is giving me all one value. I can alter that value by changing lambda of the Poisson distribution, but it’s still all one and the same.
This is my first time using Poisson distributions in Matlab, so admittedly there is a strong possiblity there is some user error involved here, but after staring at this code and rewriting it for the last two hours I’m stumped.
The code is below, along with a plot of the evaluated cdf over an array that includes 5000 points regularly spaced from 0.00001 to 0.05. The CDF value is roughly 0.95 for all evaluated points, but it should be a nice smooth ‘S’ shape starting at 0 and climbing up as the XX value increases. Why am I getting this nonsensical output?
Computer is a Dell laptop running Windows 10.
XX = 0.00001:0.00001:0.05;
p = makedist(‘Poisson’,’lambda’,0.05);
C = cdf(p,XX);
plot(C)I’m creating a Poisson distribution. I then want to produce a cdf evaluated at an array of values using this distribution. However, the output of the cdf matlab function when applied to a Poisson distribution is giving me all one value. I can alter that value by changing lambda of the Poisson distribution, but it’s still all one and the same.
This is my first time using Poisson distributions in Matlab, so admittedly there is a strong possiblity there is some user error involved here, but after staring at this code and rewriting it for the last two hours I’m stumped.
The code is below, along with a plot of the evaluated cdf over an array that includes 5000 points regularly spaced from 0.00001 to 0.05. The CDF value is roughly 0.95 for all evaluated points, but it should be a nice smooth ‘S’ shape starting at 0 and climbing up as the XX value increases. Why am I getting this nonsensical output?
Computer is a Dell laptop running Windows 10.
XX = 0.00001:0.00001:0.05;
p = makedist(‘Poisson’,’lambda’,0.05);
C = cdf(p,XX);
plot(C) I’m creating a Poisson distribution. I then want to produce a cdf evaluated at an array of values using this distribution. However, the output of the cdf matlab function when applied to a Poisson distribution is giving me all one value. I can alter that value by changing lambda of the Poisson distribution, but it’s still all one and the same.
This is my first time using Poisson distributions in Matlab, so admittedly there is a strong possiblity there is some user error involved here, but after staring at this code and rewriting it for the last two hours I’m stumped.
The code is below, along with a plot of the evaluated cdf over an array that includes 5000 points regularly spaced from 0.00001 to 0.05. The CDF value is roughly 0.95 for all evaluated points, but it should be a nice smooth ‘S’ shape starting at 0 and climbing up as the XX value increases. Why am I getting this nonsensical output?
Computer is a Dell laptop running Windows 10.
XX = 0.00001:0.00001:0.05;
p = makedist(‘Poisson’,’lambda’,0.05);
C = cdf(p,XX);
plot(C) poisson, cdf, pdf, makedist, fitdist MATLAB Answers — New Questions

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K = 8.99e9;

% Input the number of charges
n = input(‘Enter number of charges: ‘);

% Initialize arrays to store charge values and coordinates
q = zeros(1, n);
x = zeros(1, n);
y = zeros(1, n);

minx = Inf;
maxx = -Inf;
miny = Inf;
maxy = -Inf;
% Input charge values
for i = 1:n
q(i) = input([‘Enter the charge in coulombs for charge ‘, num2str(i), ‘: ‘]);
end

% Input coordinates of the charges
for i = 1:n
x(i) = input([‘Enter x coordinate for charge ‘, num2str(i), ‘: ‘]);
y(i) = input([‘Enter y coordinate for charge ‘, num2str(i), ‘: ‘]);
if x(i) > maxx
maxx = x(i);
end
if x(i) < minx
minx = x(i);
end
if y(i) > maxy
maxy = y(i);
end
if y(i) < miny
miny = y(i);
end
end

% Define the grid for visualization
[X, Y] = meshgrid((minx-1):0.1:(maxx+1), (miny-1):0.1:(maxy+1));
Ex = zeros(size(X));
Ey = zeros(size(X));

% Calculate the electric field components
for i = 1:n
R = ((X – x(i)).^2 + (Y – y(i)).^2 ).^1.5; % Distance cubed
Ex = Ex + K * q(i) * (X – x(i)) ./ R;
Ey = Ey + K * q(i) * (Y – y(i)) ./ R;
end

% Plot the electric field vectors
figure;
quiver(X, Y, Ex, Ey);
xlabel(‘x (m)’);
ylabel(‘y (m)’);
title(‘Electric Field Vectors’);
axis equal;
grid on;

% Plot the potential distribution (if needed)
% Potential at each point
V = zeros(size(X));
for i = 1:n
R = sqrt((X – x(i)).^2 + (Y – y(i)).^2 ); % Distance
V = V + K * q(i) ./ R;
end

figure;
contourf(X, Y, V, 50, ‘LineColor’, ‘none’);
colorbar;
xlabel(‘x (m)’);
ylabel(‘y (m)’);
title(‘Potential Distribution’);
axis equal;
grid on;
i wrote the following code and i am trying to improve a couple of things
1 i want the graph to fit the boundaries so it wont draw a grid that has field lines in parts and the rest is grid
2 when i enter a negative point charge it gets problamatic this is physically correct but i want to try and write something to deal with it
3 the field lines are write but they do not really reflect the behavoir well especially when i write something like 3 + and -1 on a destince 1 from each other they look just like 1 and -1 a bit better in the voltage aspect
now this is for improvment that seem okayish yet from my trial and error i could not implement

now the problamtic request is for the next part of the code i am trying to write i am trying to draw a visullization of the field between 2 finite planes
i started this part by writing an approximation for the derivative and checking that it works

h = logspace(-1,-15,100);
real_d = -sin(1);
real_d2 = -sin(1+h);
di_d = (cos(1+h)-cos(1))./h ;
simt_d = (cos(1+h)-cos(1-h))./(2*h) ;
plot (log10(h) , log10(real_d – di_d),’*’)
hold on
plot (log10(h) , log10(real_d – simt_d),’*’)
hold o
xlabel ("log(h) from x=1")
ylabel ("log(di) from -sin(1)")

i derived the equations i need
yet i am having a hard time trying to implment them
% Constants
d = 0.5; % distance between plates in cm
R = 10.0; % radius of the plates in cm
V = 1.0; % potential difference in V
h = d / 20; % step size in cm
r_max = R + 5 * d; % maximum r value in cm
z_max = 10 * d; % maximum z value in cm
tolerance = 0.0001; % convergence criterion (0.01%)

% Define the grid
r_points = floor(r_max / h) + 1;
z_points = 2 * floor(z_max / h) + 1;
phi = zeros(r_points, z_points);

% Set boundary conditions for the finite plates
for i = 1:floor(R / h) + 1
% Positive plate at z = d/2
phi(i, floor(z_max / h) + 1 + floor(d / (2 * h))) = 0.5 * V;
% Negative plate at z = -d/2
phi(i, floor(z_max / h) + 1 – floor(d / (2 * h))) = -0.5 * V;
end

% Relaxation method
max_diff = tolerance + 1; % Initialize max difference
while max_diff > tolerance
max_diff = 0;
phi_new = phi;
for i = 2:r_points-1
for j = 2:z_points-1
% Skip the points on the plates
if (i * h <= R) && (j == floor(z_max / h) + 1 + floor(d / (2 * h)) || j == floor(z_max / h) + 1 – floor(d / (2 * h)))
continue;
end
phi_new(i, j) = 0.25 * ( …
phi(i + 1, j) * (1 + h / (2 * (i * h))) + …
phi(i – 1, j) * (1 – h / (2 * (i * h))) + …
phi(i, j + 1) + …
phi(i, j – 1));
max_diff = max(max_diff, abs(phi_new(i, j) – phi(i, j)));
end
end
phi = phi_new;
end

% Plotting the potential
r = 0:h:r_max;
z = -z_max:h:z_max;
[R, Z] = meshgrid(r, z);
figure;
contourf(R, Z, phi’, 50, ‘LineColor’, ‘none’);
colorbar;
xlabel(‘r (cm)’);
ylabel(‘z (cm)’);
title(‘Electric Potential in the r-z Plane’);

the drawing seems weird too if some one can give a bit of guidenss on the use of relaxationK = 8.99e9;

% Input the number of charges
n = input(‘Enter number of charges: ‘);

% Initialize arrays to store charge values and coordinates
q = zeros(1, n);
x = zeros(1, n);
y = zeros(1, n);

minx = Inf;
maxx = -Inf;
miny = Inf;
maxy = -Inf;
% Input charge values
for i = 1:n
q(i) = input([‘Enter the charge in coulombs for charge ‘, num2str(i), ‘: ‘]);
end

% Input coordinates of the charges
for i = 1:n
x(i) = input([‘Enter x coordinate for charge ‘, num2str(i), ‘: ‘]);
y(i) = input([‘Enter y coordinate for charge ‘, num2str(i), ‘: ‘]);
if x(i) > maxx
maxx = x(i);
end
if x(i) < minx
minx = x(i);
end
if y(i) > maxy
maxy = y(i);
end
if y(i) < miny
miny = y(i);
end
end

% Define the grid for visualization
[X, Y] = meshgrid((minx-1):0.1:(maxx+1), (miny-1):0.1:(maxy+1));
Ex = zeros(size(X));
Ey = zeros(size(X));

% Calculate the electric field components
for i = 1:n
R = ((X – x(i)).^2 + (Y – y(i)).^2 ).^1.5; % Distance cubed
Ex = Ex + K * q(i) * (X – x(i)) ./ R;
Ey = Ey + K * q(i) * (Y – y(i)) ./ R;
end

% Plot the electric field vectors
figure;
quiver(X, Y, Ex, Ey);
xlabel(‘x (m)’);
ylabel(‘y (m)’);
title(‘Electric Field Vectors’);
axis equal;
grid on;

% Plot the potential distribution (if needed)
% Potential at each point
V = zeros(size(X));
for i = 1:n
R = sqrt((X – x(i)).^2 + (Y – y(i)).^2 ); % Distance
V = V + K * q(i) ./ R;
end

figure;
contourf(X, Y, V, 50, ‘LineColor’, ‘none’);
colorbar;
xlabel(‘x (m)’);
ylabel(‘y (m)’);
title(‘Potential Distribution’);
axis equal;
grid on;
i wrote the following code and i am trying to improve a couple of things
1 i want the graph to fit the boundaries so it wont draw a grid that has field lines in parts and the rest is grid
2 when i enter a negative point charge it gets problamatic this is physically correct but i want to try and write something to deal with it
3 the field lines are write but they do not really reflect the behavoir well especially when i write something like 3 + and -1 on a destince 1 from each other they look just like 1 and -1 a bit better in the voltage aspect
now this is for improvment that seem okayish yet from my trial and error i could not implement

now the problamtic request is for the next part of the code i am trying to write i am trying to draw a visullization of the field between 2 finite planes
i started this part by writing an approximation for the derivative and checking that it works

h = logspace(-1,-15,100);
real_d = -sin(1);
real_d2 = -sin(1+h);
di_d = (cos(1+h)-cos(1))./h ;
simt_d = (cos(1+h)-cos(1-h))./(2*h) ;
plot (log10(h) , log10(real_d – di_d),’*’)
hold on
plot (log10(h) , log10(real_d – simt_d),’*’)
hold o
xlabel ("log(h) from x=1")
ylabel ("log(di) from -sin(1)")

i derived the equations i need
yet i am having a hard time trying to implment them
% Constants
d = 0.5; % distance between plates in cm
R = 10.0; % radius of the plates in cm
V = 1.0; % potential difference in V
h = d / 20; % step size in cm
r_max = R + 5 * d; % maximum r value in cm
z_max = 10 * d; % maximum z value in cm
tolerance = 0.0001; % convergence criterion (0.01%)

% Define the grid
r_points = floor(r_max / h) + 1;
z_points = 2 * floor(z_max / h) + 1;
phi = zeros(r_points, z_points);

% Set boundary conditions for the finite plates
for i = 1:floor(R / h) + 1
% Positive plate at z = d/2
phi(i, floor(z_max / h) + 1 + floor(d / (2 * h))) = 0.5 * V;
% Negative plate at z = -d/2
phi(i, floor(z_max / h) + 1 – floor(d / (2 * h))) = -0.5 * V;
end

% Relaxation method
max_diff = tolerance + 1; % Initialize max difference
while max_diff > tolerance
max_diff = 0;
phi_new = phi;
for i = 2:r_points-1
for j = 2:z_points-1
% Skip the points on the plates
if (i * h <= R) && (j == floor(z_max / h) + 1 + floor(d / (2 * h)) || j == floor(z_max / h) + 1 – floor(d / (2 * h)))
continue;
end
phi_new(i, j) = 0.25 * ( …
phi(i + 1, j) * (1 + h / (2 * (i * h))) + …
phi(i – 1, j) * (1 – h / (2 * (i * h))) + …
phi(i, j + 1) + …
phi(i, j – 1));
max_diff = max(max_diff, abs(phi_new(i, j) – phi(i, j)));
end
end
phi = phi_new;
end

% Plotting the potential
r = 0:h:r_max;
z = -z_max:h:z_max;
[R, Z] = meshgrid(r, z);
figure;
contourf(R, Z, phi’, 50, ‘LineColor’, ‘none’);
colorbar;
xlabel(‘r (cm)’);
ylabel(‘z (cm)’);
title(‘Electric Potential in the r-z Plane’);

the drawing seems weird too if some one can give a bit of guidenss on the use of relaxation K = 8.99e9;

% Input the number of charges
n = input(‘Enter number of charges: ‘);

% Initialize arrays to store charge values and coordinates
q = zeros(1, n);
x = zeros(1, n);
y = zeros(1, n);

minx = Inf;
maxx = -Inf;
miny = Inf;
maxy = -Inf;
% Input charge values
for i = 1:n
q(i) = input([‘Enter the charge in coulombs for charge ‘, num2str(i), ‘: ‘]);
end

% Input coordinates of the charges
for i = 1:n
x(i) = input([‘Enter x coordinate for charge ‘, num2str(i), ‘: ‘]);
y(i) = input([‘Enter y coordinate for charge ‘, num2str(i), ‘: ‘]);
if x(i) > maxx
maxx = x(i);
end
if x(i) < minx
minx = x(i);
end
if y(i) > maxy
maxy = y(i);
end
if y(i) < miny
miny = y(i);
end
end

% Define the grid for visualization
[X, Y] = meshgrid((minx-1):0.1:(maxx+1), (miny-1):0.1:(maxy+1));
Ex = zeros(size(X));
Ey = zeros(size(X));

% Calculate the electric field components
for i = 1:n
R = ((X – x(i)).^2 + (Y – y(i)).^2 ).^1.5; % Distance cubed
Ex = Ex + K * q(i) * (X – x(i)) ./ R;
Ey = Ey + K * q(i) * (Y – y(i)) ./ R;
end

% Plot the electric field vectors
figure;
quiver(X, Y, Ex, Ey);
xlabel(‘x (m)’);
ylabel(‘y (m)’);
title(‘Electric Field Vectors’);
axis equal;
grid on;

% Plot the potential distribution (if needed)
% Potential at each point
V = zeros(size(X));
for i = 1:n
R = sqrt((X – x(i)).^2 + (Y – y(i)).^2 ); % Distance
V = V + K * q(i) ./ R;
end

figure;
contourf(X, Y, V, 50, ‘LineColor’, ‘none’);
colorbar;
xlabel(‘x (m)’);
ylabel(‘y (m)’);
title(‘Potential Distribution’);
axis equal;
grid on;
i wrote the following code and i am trying to improve a couple of things
1 i want the graph to fit the boundaries so it wont draw a grid that has field lines in parts and the rest is grid
2 when i enter a negative point charge it gets problamatic this is physically correct but i want to try and write something to deal with it
3 the field lines are write but they do not really reflect the behavoir well especially when i write something like 3 + and -1 on a destince 1 from each other they look just like 1 and -1 a bit better in the voltage aspect
now this is for improvment that seem okayish yet from my trial and error i could not implement

now the problamtic request is for the next part of the code i am trying to write i am trying to draw a visullization of the field between 2 finite planes
i started this part by writing an approximation for the derivative and checking that it works

h = logspace(-1,-15,100);
real_d = -sin(1);
real_d2 = -sin(1+h);
di_d = (cos(1+h)-cos(1))./h ;
simt_d = (cos(1+h)-cos(1-h))./(2*h) ;
plot (log10(h) , log10(real_d – di_d),’*’)
hold on
plot (log10(h) , log10(real_d – simt_d),’*’)
hold o
xlabel ("log(h) from x=1")
ylabel ("log(di) from -sin(1)")

i derived the equations i need
yet i am having a hard time trying to implment them
% Constants
d = 0.5; % distance between plates in cm
R = 10.0; % radius of the plates in cm
V = 1.0; % potential difference in V
h = d / 20; % step size in cm
r_max = R + 5 * d; % maximum r value in cm
z_max = 10 * d; % maximum z value in cm
tolerance = 0.0001; % convergence criterion (0.01%)

% Define the grid
r_points = floor(r_max / h) + 1;
z_points = 2 * floor(z_max / h) + 1;
phi = zeros(r_points, z_points);

% Set boundary conditions for the finite plates
for i = 1:floor(R / h) + 1
% Positive plate at z = d/2
phi(i, floor(z_max / h) + 1 + floor(d / (2 * h))) = 0.5 * V;
% Negative plate at z = -d/2
phi(i, floor(z_max / h) + 1 – floor(d / (2 * h))) = -0.5 * V;
end

% Relaxation method
max_diff = tolerance + 1; % Initialize max difference
while max_diff > tolerance
max_diff = 0;
phi_new = phi;
for i = 2:r_points-1
for j = 2:z_points-1
% Skip the points on the plates
if (i * h <= R) && (j == floor(z_max / h) + 1 + floor(d / (2 * h)) || j == floor(z_max / h) + 1 – floor(d / (2 * h)))
continue;
end
phi_new(i, j) = 0.25 * ( …
phi(i + 1, j) * (1 + h / (2 * (i * h))) + …
phi(i – 1, j) * (1 – h / (2 * (i * h))) + …
phi(i, j + 1) + …
phi(i, j – 1));
max_diff = max(max_diff, abs(phi_new(i, j) – phi(i, j)));
end
end
phi = phi_new;
end

% Plotting the potential
r = 0:h:r_max;
z = -z_max:h:z_max;
[R, Z] = meshgrid(r, z);
figure;
contourf(R, Z, phi’, 50, ‘LineColor’, ‘none’);
colorbar;
xlabel(‘r (cm)’);
ylabel(‘z (cm)’);
title(‘Electric Potential in the r-z Plane’);

the drawing seems weird too if some one can give a bit of guidenss on the use of relaxation plotting MATLAB Answers — New Questions

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It says "Unable to find suitable response variable ~"

Here is the code:

img = imread("C:UsersxxooxOneDriveデスクトップMATLAB worksDataMathWorks Imagesocean.jpg"); % Load the unlabeled image
load ("C:UsersxxooxOneDriveデスクトップMATLAB worksComputer Vision for Engineering and ScienceC2-MachineLearningForComputerVisionModule 2gcClassifierSaturation.mat","gcClassifierSaturation") % Load the model trained using hand-selected features
load ("C:UsersxxooxOneDriveデスクトップMATLAB worksComputer Vision for Engineering and ScienceC2-MachineLearningForComputerVisionModule 2gcClassifierBag.mat","gcClassifierBag") % Load the model trained using automatically generated features
load ("C:UsersxxooxOneDriveデスクトップMATLAB worksComputer Vision for Engineering and ScienceC2-MachineLearningForComputerVisionModule 2bag.mat","bag") % Load bag of visual words object created by bagOfFeatures

% Step 1: Create a table of saturation-based predictor features
% Convert the image to HSV color space
hsvImg = rgb2hsv(img);
% Extract the saturation channel
saturation = hsvImg(:,:,2);
% Calculate the mean and standard deviation of the saturation
avgSat = mean(saturation(:));
stdSat = std(saturation(:));
% Create a table of saturation-based predictor features
gcTableSaturation = table(avgSat, stdSat);

% Step 2: Use gcClassifierSaturation.predictFcn to classify the unlabeled image
% Use the classifier to predict the label
prediction = gcClassifierSaturation.predictFcn(gcTableSaturation);
% Attach the prediction to the table
gcTableSaturation.prediction = prediction;

% Step 3: Create a table of bagOfFeatures-based predictor features
% Encode the image using the bag of features
featureVector = encode(bag, img);
% Create a table of predictor features for the unlabeled image
gcTableBag = array2table(featureVector);
gcTableBag.Properties.VariableNames = arrayfun(@(x) [‘f’ num2str(x)], 1:500, ‘UniformOutput’, false);

% Step 4: Use gcClassifierBag.predictFcn to classify the unlabeled image
% Use the classifier to predict the label
predictionBag = gcClassifierBag.predictFcn(gcTableBag);
% Attach the prediction to the table
gcTableBag.prediction = predictionBag;It says "Unable to find suitable response variable ~"

Here is the code:

img = imread("C:UsersxxooxOneDriveデスクトップMATLAB worksDataMathWorks Imagesocean.jpg"); % Load the unlabeled image
load ("C:UsersxxooxOneDriveデスクトップMATLAB worksComputer Vision for Engineering and ScienceC2-MachineLearningForComputerVisionModule 2gcClassifierSaturation.mat","gcClassifierSaturation") % Load the model trained using hand-selected features
load ("C:UsersxxooxOneDriveデスクトップMATLAB worksComputer Vision for Engineering and ScienceC2-MachineLearningForComputerVisionModule 2gcClassifierBag.mat","gcClassifierBag") % Load the model trained using automatically generated features
load ("C:UsersxxooxOneDriveデスクトップMATLAB worksComputer Vision for Engineering and ScienceC2-MachineLearningForComputerVisionModule 2bag.mat","bag") % Load bag of visual words object created by bagOfFeatures

% Step 1: Create a table of saturation-based predictor features
% Convert the image to HSV color space
hsvImg = rgb2hsv(img);
% Extract the saturation channel
saturation = hsvImg(:,:,2);
% Calculate the mean and standard deviation of the saturation
avgSat = mean(saturation(:));
stdSat = std(saturation(:));
% Create a table of saturation-based predictor features
gcTableSaturation = table(avgSat, stdSat);

% Step 2: Use gcClassifierSaturation.predictFcn to classify the unlabeled image
% Use the classifier to predict the label
prediction = gcClassifierSaturation.predictFcn(gcTableSaturation);
% Attach the prediction to the table
gcTableSaturation.prediction = prediction;

% Step 3: Create a table of bagOfFeatures-based predictor features
% Encode the image using the bag of features
featureVector = encode(bag, img);
% Create a table of predictor features for the unlabeled image
gcTableBag = array2table(featureVector);
gcTableBag.Properties.VariableNames = arrayfun(@(x) [‘f’ num2str(x)], 1:500, ‘UniformOutput’, false);

% Step 4: Use gcClassifierBag.predictFcn to classify the unlabeled image
% Use the classifier to predict the label
predictionBag = gcClassifierBag.predictFcn(gcTableBag);
% Attach the prediction to the table
gcTableBag.prediction = predictionBag; It says "Unable to find suitable response variable ~"

Here is the code:

img = imread("C:UsersxxooxOneDriveデスクトップMATLAB worksDataMathWorks Imagesocean.jpg"); % Load the unlabeled image
load ("C:UsersxxooxOneDriveデスクトップMATLAB worksComputer Vision for Engineering and ScienceC2-MachineLearningForComputerVisionModule 2gcClassifierSaturation.mat","gcClassifierSaturation") % Load the model trained using hand-selected features
load ("C:UsersxxooxOneDriveデスクトップMATLAB worksComputer Vision for Engineering and ScienceC2-MachineLearningForComputerVisionModule 2gcClassifierBag.mat","gcClassifierBag") % Load the model trained using automatically generated features
load ("C:UsersxxooxOneDriveデスクトップMATLAB worksComputer Vision for Engineering and ScienceC2-MachineLearningForComputerVisionModule 2bag.mat","bag") % Load bag of visual words object created by bagOfFeatures

% Step 1: Create a table of saturation-based predictor features
% Convert the image to HSV color space
hsvImg = rgb2hsv(img);
% Extract the saturation channel
saturation = hsvImg(:,:,2);
% Calculate the mean and standard deviation of the saturation
avgSat = mean(saturation(:));
stdSat = std(saturation(:));
% Create a table of saturation-based predictor features
gcTableSaturation = table(avgSat, stdSat);

% Step 2: Use gcClassifierSaturation.predictFcn to classify the unlabeled image
% Use the classifier to predict the label
prediction = gcClassifierSaturation.predictFcn(gcTableSaturation);
% Attach the prediction to the table
gcTableSaturation.prediction = prediction;

% Step 3: Create a table of bagOfFeatures-based predictor features
% Encode the image using the bag of features
featureVector = encode(bag, img);
% Create a table of predictor features for the unlabeled image
gcTableBag = array2table(featureVector);
gcTableBag.Properties.VariableNames = arrayfun(@(x) [‘f’ num2str(x)], 1:500, ‘UniformOutput’, false);

% Step 4: Use gcClassifierBag.predictFcn to classify the unlabeled image
% Use the classifier to predict the label
predictionBag = gcClassifierBag.predictFcn(gcTableBag);
% Attach the prediction to the table
gcTableBag.prediction = predictionBag; matlab, machine learning, image classification MATLAB Answers — New Questions

​

Hi everybody,

I’m relatively new to MatLab, and to numerical analysis in general. I have this problem I have to solve, using the Crank-Nicolson method, and I don’t have the slightest idea how to start my code…
I’m trying to solve two non-linear forced pendulum equation, which I adimensionalized as follows :

Can anyone give me a hint on how to start my process?

Thanks!

KenaHi everybody,

I’m relatively new to MatLab, and to numerical analysis in general. I have this problem I have to solve, using the Crank-Nicolson method, and I don’t have the slightest idea how to start my code…
I’m trying to solve two non-linear forced pendulum equation, which I adimensionalized as follows :

Can anyone give me a hint on how to start my process?

Thanks!

Kena Hi everybody,

I’m relatively new to MatLab, and to numerical analysis in general. I have this problem I have to solve, using the Crank-Nicolson method, and I don’t have the slightest idea how to start my code…
I’m trying to solve two non-linear forced pendulum equation, which I adimensionalized as follows :

Can anyone give me a hint on how to start my process?

Thanks!

Kena matlab, crank, nicolson, nonlinear, method MATLAB Answers — New Questions

​

Hi, I am trying to make an implicit 3D plot in Matlab. A Matlab Help page says to use the following (as an example):

f = @(x,y,z) x.^2 + y.^2 – z.^2;
interval = [-5 5 -5 5 0 5];
fimplicit3(f,interval)

This works fine. But if I try to complicate things a bit by changing f to:
f = @(x,y,z) x.^2 + y.^2 – z.^2 + x.*y.

then I get the error message "Error: Invalid expression. Check for missing or extra characters".

Any idea what the problem is?

Thank you!Hi, I am trying to make an implicit 3D plot in Matlab. A Matlab Help page says to use the following (as an example):

f = @(x,y,z) x.^2 + y.^2 – z.^2;
interval = [-5 5 -5 5 0 5];
fimplicit3(f,interval)

This works fine. But if I try to complicate things a bit by changing f to:
f = @(x,y,z) x.^2 + y.^2 – z.^2 + x.*y.

then I get the error message "Error: Invalid expression. Check for missing or extra characters".

Any idea what the problem is?

Thank you! Hi, I am trying to make an implicit 3D plot in Matlab. A Matlab Help page says to use the following (as an example):

f = @(x,y,z) x.^2 + y.^2 – z.^2;
interval = [-5 5 -5 5 0 5];
fimplicit3(f,interval)

This works fine. But if I try to complicate things a bit by changing f to:
f = @(x,y,z) x.^2 + y.^2 – z.^2 + x.*y.

then I get the error message "Error: Invalid expression. Check for missing or extra characters".

Any idea what the problem is?

Thank you! plot MATLAB Answers — New Questions

​