I use MATLAB-YALMIP to write the optimal trend program based on the IEEEE14 system, but I can’t converge. Please help to see if the program is wrong?
code:
% Optimal Power Flow Example
% Based on the IEEE 14-bus standard case (i.e., case14 in Matpower)
% Implemented in MATLAB with YALMIP
clear; clc;

% Load power system data
mpc = loadcase(‘case14’); % You can also load other data files, such as case9, case30, etc.

% Define variables and parameters
nb = size(mpc.bus, 1); % Number of buses (nodes)
nl = size(mpc.branch, 1); % Number of branches (lines)
ng = size(mpc.gen, 1); % Number of generators
nr = nb – ng; % Number of load buses

% Extract node data
bus_id = mpc.bus(:, 1); % Bus IDs
bus_type = mpc.bus(:, 2); % Bus types (1-PQ, 2-PV, 3-Slack)
Pd = mpc.bus(:, 3); % Active power demand (MW)
Qd = mpc.bus(:, 4); % Reactive power demand (MVAr)
Vmin = mpc.bus(:, 13); % Minimum bus voltage (p.u.)
Vmax = mpc.bus(:, 12); % Maximum bus voltage (p.u.)

% Extract generator data
gen_id = mpc.gen(:, 1); % Bus IDs where generators are connected
Pgmin = mpc.gen(:, 10); % Minimum generator active power output (MW)
Pgmax = mpc.gen(:, 9); % Maximum generator active power output (MW)
Qgmin = mpc.gen(:, 5); % Minimum generator reactive power output (MVAr)
Qgmax = mpc.gen(:, 4); % Maximum generator reactive power output (MVAr)
a2 = mpc.gencost(:, 5); % Generator cost curve coefficient for quadratic term
a1 = mpc.gencost(:, 6); % Generator cost curve coefficient for linear term
a0 = mpc.gencost(:, 7); % Generator cost curve constant term

% Extract branch data
fbus = mpc.branch(:, 1); % "From" bus (starting node) of each branch
tbus = mpc.branch(:, 2); % "To" bus (ending node) of each branch
Plmax = ones(nl, 1) * 4000; % Maximum branch active power flow limit (MW)
% Plmax = mpc.branch(:, 6) .* mpc.branch(:, 3) * 1e-3; % Maximum branch active power flow limit (MW)

% Calculate the bus admittance matrix
[Ybus, ~, ~] = makeYbus(mpc); % Use a Matpower function to calculate the bus admittance matrix
Gbus = real(Ybus); % Real part of bus admittance matrix (conductance)
Bbus = imag(Ybus); % Imaginary part of bus admittance matrix (susceptance)

% Define YALMIP variables
Pg = sdpvar(ng, 1, ‘full’); % Generator active power output variables
Qg = sdpvar(ng, 1, ‘full’); % Generator reactive power output variables
V = sdpvar(nb, 1, ‘full’); % Bus voltage magnitude variables
theta = sdpvar(nb, 1, ‘full’); % Bus voltage angle variables
Pl = sdpvar(nl, 1, ‘full’); % Branch active power flow variables

% Define the objective function
objective = sum(a2 .* Pg.^2 + a1 .* Pg + a0); % Minimize the total operating cost

% Define constraints
constraints = [];
% Node power balance constraints
for i = 1:nb
% Active power balance equation for node i
if ismember(i, gen_id)
record = find(gen_id == i);
constraints = [constraints, Pg(record) – Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, Qg(record) – Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
else
constraints = [constraints, -Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, -Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
end
end

% Generator output limit constraints
for i = 1:ng
% Minimum active power output constraint for generator i
constraints = [constraints, Pgmin(i) <= Pg(i)];
% Maximum active power output constraint for generator i
constraints = [constraints, Pg(i) <= Pgmax(i)];
% Minimum reactive power output constraint for generator i
constraints = [constraints, Qgmin(i) <= Qg(i)];
% Maximum reactive power output constraint for generator i
constraints = [constraints, Qg(i) <= Qgmax(i)];
end
% Bus voltage limit constraints
for i = 1:nb
% Minimum bus voltage magnitude constraint for bus i
constraints = [constraints, Vmin(i) <= V(i)];
% Maximum bus voltage magnitude constraint for bus i
constraints = [constraints, V(i) <= Vmax(i)];
end
% Branch power flow limit constraints
for i = 1:nl
% Calculate active power flow on branch i (assuming the "from" bus is the positive direction)
Pl(i) = V(fbus(i))^2 * Gbus(fbus(i), tbus(i)) – V(fbus(i)) * V(tbus(i)) * (Gbus(fbus(i), tbus(i)) * cos(theta(fbus(i) – theta(tbus(i))) + Bbus(fbus(i), tbus(i)) * sin(theta(fbus(i) – theta(tbus(i)))));
% Absolute value constraint for active power flow on branch i
constraints = [constraints, abs(Pl(i)) <= Plmax(i)];
end

% Solve the optimal power flow problem
result = solvesdp(constraints, objective); % Use YALMIP to solve the optimal power flow problem

% Output and analyze the results
if result.problem == 0 % If the solution is successful
disp(‘Optimal Power Flow Solved Successfully!’); % Display success message
disp(‘Total System Operating Cost:’); % Display total system operating cost
value(objective) % Display the objective function value
disp(‘Generator Active Power Output:’); % Display generator active power output
value(Pg) % Display the generator active power output variable values
disp(‘Generator Reactive Power Output:’); % Display generator reactive power output
value(Qg) % Display the generator reactive power output variable values
disp(‘Bus Voltage Magnitude:’); % Display bus voltage magnitude
value(V) % Display the bus voltage magnitude variable values
disp(‘Bus Voltage Angle (in degrees):’); % Display bus voltage angle (in degrees)
rad2deg(value(theta)) % Display bus voltage angle variable values and convert to degrees
disp(‘Branch Active Power Flow:’); % Display branch active power flow
value(Pl)
endI use MATLAB-YALMIP to write the optimal trend program based on the IEEEE14 system, but I can’t converge. Please help to see if the program is wrong?
code:
% Optimal Power Flow Example
% Based on the IEEE 14-bus standard case (i.e., case14 in Matpower)
% Implemented in MATLAB with YALMIP
clear; clc;

% Load power system data
mpc = loadcase(‘case14’); % You can also load other data files, such as case9, case30, etc.

% Define variables and parameters
nb = size(mpc.bus, 1); % Number of buses (nodes)
nl = size(mpc.branch, 1); % Number of branches (lines)
ng = size(mpc.gen, 1); % Number of generators
nr = nb – ng; % Number of load buses

% Extract node data
bus_id = mpc.bus(:, 1); % Bus IDs
bus_type = mpc.bus(:, 2); % Bus types (1-PQ, 2-PV, 3-Slack)
Pd = mpc.bus(:, 3); % Active power demand (MW)
Qd = mpc.bus(:, 4); % Reactive power demand (MVAr)
Vmin = mpc.bus(:, 13); % Minimum bus voltage (p.u.)
Vmax = mpc.bus(:, 12); % Maximum bus voltage (p.u.)

% Extract generator data
gen_id = mpc.gen(:, 1); % Bus IDs where generators are connected
Pgmin = mpc.gen(:, 10); % Minimum generator active power output (MW)
Pgmax = mpc.gen(:, 9); % Maximum generator active power output (MW)
Qgmin = mpc.gen(:, 5); % Minimum generator reactive power output (MVAr)
Qgmax = mpc.gen(:, 4); % Maximum generator reactive power output (MVAr)
a2 = mpc.gencost(:, 5); % Generator cost curve coefficient for quadratic term
a1 = mpc.gencost(:, 6); % Generator cost curve coefficient for linear term
a0 = mpc.gencost(:, 7); % Generator cost curve constant term

% Extract branch data
fbus = mpc.branch(:, 1); % "From" bus (starting node) of each branch
tbus = mpc.branch(:, 2); % "To" bus (ending node) of each branch
Plmax = ones(nl, 1) * 4000; % Maximum branch active power flow limit (MW)
% Plmax = mpc.branch(:, 6) .* mpc.branch(:, 3) * 1e-3; % Maximum branch active power flow limit (MW)

% Calculate the bus admittance matrix
[Ybus, ~, ~] = makeYbus(mpc); % Use a Matpower function to calculate the bus admittance matrix
Gbus = real(Ybus); % Real part of bus admittance matrix (conductance)
Bbus = imag(Ybus); % Imaginary part of bus admittance matrix (susceptance)

% Define YALMIP variables
Pg = sdpvar(ng, 1, ‘full’); % Generator active power output variables
Qg = sdpvar(ng, 1, ‘full’); % Generator reactive power output variables
V = sdpvar(nb, 1, ‘full’); % Bus voltage magnitude variables
theta = sdpvar(nb, 1, ‘full’); % Bus voltage angle variables
Pl = sdpvar(nl, 1, ‘full’); % Branch active power flow variables

% Define the objective function
objective = sum(a2 .* Pg.^2 + a1 .* Pg + a0); % Minimize the total operating cost

% Define constraints
constraints = [];
% Node power balance constraints
for i = 1:nb
% Active power balance equation for node i
if ismember(i, gen_id)
record = find(gen_id == i);
constraints = [constraints, Pg(record) – Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, Qg(record) – Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
else
constraints = [constraints, -Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, -Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
end
end

% Generator output limit constraints
for i = 1:ng
% Minimum active power output constraint for generator i
constraints = [constraints, Pgmin(i) <= Pg(i)];
% Maximum active power output constraint for generator i
constraints = [constraints, Pg(i) <= Pgmax(i)];
% Minimum reactive power output constraint for generator i
constraints = [constraints, Qgmin(i) <= Qg(i)];
% Maximum reactive power output constraint for generator i
constraints = [constraints, Qg(i) <= Qgmax(i)];
end
% Bus voltage limit constraints
for i = 1:nb
% Minimum bus voltage magnitude constraint for bus i
constraints = [constraints, Vmin(i) <= V(i)];
% Maximum bus voltage magnitude constraint for bus i
constraints = [constraints, V(i) <= Vmax(i)];
end
% Branch power flow limit constraints
for i = 1:nl
% Calculate active power flow on branch i (assuming the "from" bus is the positive direction)
Pl(i) = V(fbus(i))^2 * Gbus(fbus(i), tbus(i)) – V(fbus(i)) * V(tbus(i)) * (Gbus(fbus(i), tbus(i)) * cos(theta(fbus(i) – theta(tbus(i))) + Bbus(fbus(i), tbus(i)) * sin(theta(fbus(i) – theta(tbus(i)))));
% Absolute value constraint for active power flow on branch i
constraints = [constraints, abs(Pl(i)) <= Plmax(i)];
end

% Solve the optimal power flow problem
result = solvesdp(constraints, objective); % Use YALMIP to solve the optimal power flow problem

% Output and analyze the results
if result.problem == 0 % If the solution is successful
disp(‘Optimal Power Flow Solved Successfully!’); % Display success message
disp(‘Total System Operating Cost:’); % Display total system operating cost
value(objective) % Display the objective function value
disp(‘Generator Active Power Output:’); % Display generator active power output
value(Pg) % Display the generator active power output variable values
disp(‘Generator Reactive Power Output:’); % Display generator reactive power output
value(Qg) % Display the generator reactive power output variable values
disp(‘Bus Voltage Magnitude:’); % Display bus voltage magnitude
value(V) % Display the bus voltage magnitude variable values
disp(‘Bus Voltage Angle (in degrees):’); % Display bus voltage angle (in degrees)
rad2deg(value(theta)) % Display bus voltage angle variable values and convert to degrees
disp(‘Branch Active Power Flow:’); % Display branch active power flow
value(Pl)
end I use MATLAB-YALMIP to write the optimal trend program based on the IEEEE14 system, but I can’t converge. Please help to see if the program is wrong?
code:
% Optimal Power Flow Example
% Based on the IEEE 14-bus standard case (i.e., case14 in Matpower)
% Implemented in MATLAB with YALMIP
clear; clc;

% Load power system data
mpc = loadcase(‘case14’); % You can also load other data files, such as case9, case30, etc.

% Define variables and parameters
nb = size(mpc.bus, 1); % Number of buses (nodes)
nl = size(mpc.branch, 1); % Number of branches (lines)
ng = size(mpc.gen, 1); % Number of generators
nr = nb – ng; % Number of load buses

% Extract node data
bus_id = mpc.bus(:, 1); % Bus IDs
bus_type = mpc.bus(:, 2); % Bus types (1-PQ, 2-PV, 3-Slack)
Pd = mpc.bus(:, 3); % Active power demand (MW)
Qd = mpc.bus(:, 4); % Reactive power demand (MVAr)
Vmin = mpc.bus(:, 13); % Minimum bus voltage (p.u.)
Vmax = mpc.bus(:, 12); % Maximum bus voltage (p.u.)

% Extract generator data
gen_id = mpc.gen(:, 1); % Bus IDs where generators are connected
Pgmin = mpc.gen(:, 10); % Minimum generator active power output (MW)
Pgmax = mpc.gen(:, 9); % Maximum generator active power output (MW)
Qgmin = mpc.gen(:, 5); % Minimum generator reactive power output (MVAr)
Qgmax = mpc.gen(:, 4); % Maximum generator reactive power output (MVAr)
a2 = mpc.gencost(:, 5); % Generator cost curve coefficient for quadratic term
a1 = mpc.gencost(:, 6); % Generator cost curve coefficient for linear term
a0 = mpc.gencost(:, 7); % Generator cost curve constant term

% Extract branch data
fbus = mpc.branch(:, 1); % "From" bus (starting node) of each branch
tbus = mpc.branch(:, 2); % "To" bus (ending node) of each branch
Plmax = ones(nl, 1) * 4000; % Maximum branch active power flow limit (MW)
% Plmax = mpc.branch(:, 6) .* mpc.branch(:, 3) * 1e-3; % Maximum branch active power flow limit (MW)

% Calculate the bus admittance matrix
[Ybus, ~, ~] = makeYbus(mpc); % Use a Matpower function to calculate the bus admittance matrix
Gbus = real(Ybus); % Real part of bus admittance matrix (conductance)
Bbus = imag(Ybus); % Imaginary part of bus admittance matrix (susceptance)

% Define YALMIP variables
Pg = sdpvar(ng, 1, ‘full’); % Generator active power output variables
Qg = sdpvar(ng, 1, ‘full’); % Generator reactive power output variables
V = sdpvar(nb, 1, ‘full’); % Bus voltage magnitude variables
theta = sdpvar(nb, 1, ‘full’); % Bus voltage angle variables
Pl = sdpvar(nl, 1, ‘full’); % Branch active power flow variables

% Define the objective function
objective = sum(a2 .* Pg.^2 + a1 .* Pg + a0); % Minimize the total operating cost

% Define constraints
constraints = [];
% Node power balance constraints
for i = 1:nb
% Active power balance equation for node i
if ismember(i, gen_id)
record = find(gen_id == i);
constraints = [constraints, Pg(record) – Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, Qg(record) – Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
else
constraints = [constraints, -Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, -Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
end
end

% Generator output limit constraints
for i = 1:ng
% Minimum active power output constraint for generator i
constraints = [constraints, Pgmin(i) <= Pg(i)];
% Maximum active power output constraint for generator i
constraints = [constraints, Pg(i) <= Pgmax(i)];
% Minimum reactive power output constraint for generator i
constraints = [constraints, Qgmin(i) <= Qg(i)];
% Maximum reactive power output constraint for generator i
constraints = [constraints, Qg(i) <= Qgmax(i)];
end
% Bus voltage limit constraints
for i = 1:nb
% Minimum bus voltage magnitude constraint for bus i
constraints = [constraints, Vmin(i) <= V(i)];
% Maximum bus voltage magnitude constraint for bus i
constraints = [constraints, V(i) <= Vmax(i)];
end
% Branch power flow limit constraints
for i = 1:nl
% Calculate active power flow on branch i (assuming the "from" bus is the positive direction)
Pl(i) = V(fbus(i))^2 * Gbus(fbus(i), tbus(i)) – V(fbus(i)) * V(tbus(i)) * (Gbus(fbus(i), tbus(i)) * cos(theta(fbus(i) – theta(tbus(i))) + Bbus(fbus(i), tbus(i)) * sin(theta(fbus(i) – theta(tbus(i)))));
% Absolute value constraint for active power flow on branch i
constraints = [constraints, abs(Pl(i)) <= Plmax(i)];
end

% Solve the optimal power flow problem
result = solvesdp(constraints, objective); % Use YALMIP to solve the optimal power flow problem

% Output and analyze the results
if result.problem == 0 % If the solution is successful
disp(‘Optimal Power Flow Solved Successfully!’); % Display success message
disp(‘Total System Operating Cost:’); % Display total system operating cost
value(objective) % Display the objective function value
disp(‘Generator Active Power Output:’); % Display generator active power output
value(Pg) % Display the generator active power output variable values
disp(‘Generator Reactive Power Output:’); % Display generator reactive power output
value(Qg) % Display the generator reactive power output variable values
disp(‘Bus Voltage Magnitude:’); % Display bus voltage magnitude
value(V) % Display the bus voltage magnitude variable values
disp(‘Bus Voltage Angle (in degrees):’); % Display bus voltage angle (in degrees)
rad2deg(value(theta)) % Display bus voltage angle variable values and convert to degrees
disp(‘Branch Active Power Flow:’); % Display branch active power flow
value(Pl)
end yalmip, opf, matlab, optimal power flow MATLAB Answers — New Questions

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The 3 DOF Vehicle Body Block supports steering angle and wheel speed as inputs, but the 6 DOF Vehicle Body Block only seems to support suspension and external forces and moments as inputs.
Is there a way to use the 6 DOF block with steering angle and wheel speed control inputs, and perhaps an additional environment input? I know the Constant Radius Reference Application uses the 6DOF block under the hood, but this is a huge step up in complexity from the 2-dimensional 3DOF block. Looking to make the jump from 2-D to 3-D, but preferably still using typical control inputs.The 3 DOF Vehicle Body Block supports steering angle and wheel speed as inputs, but the 6 DOF Vehicle Body Block only seems to support suspension and external forces and moments as inputs.
Is there a way to use the 6 DOF block with steering angle and wheel speed control inputs, and perhaps an additional environment input? I know the Constant Radius Reference Application uses the 6DOF block under the hood, but this is a huge step up in complexity from the 2-dimensional 3DOF block. Looking to make the jump from 2-D to 3-D, but preferably still using typical control inputs. The 3 DOF Vehicle Body Block supports steering angle and wheel speed as inputs, but the 6 DOF Vehicle Body Block only seems to support suspension and external forces and moments as inputs.
Is there a way to use the 6 DOF block with steering angle and wheel speed control inputs, and perhaps an additional environment input? I know the Constant Radius Reference Application uses the 6DOF block under the hood, but this is a huge step up in complexity from the 2-dimensional 3DOF block. Looking to make the jump from 2-D to 3-D, but preferably still using typical control inputs. vehicle dynamics, 6dof, simulink MATLAB Answers — New Questions

​

I need to take output from a "PCQI: A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images" code. But whenever i run this, it doesnt show anything at all. Not even an error. How can i solve this?

Demo.m
clc;
clear;

im1=imread(‘ref.png’);
im2=imread(‘contrast_changed.png’);
im1=double(rgb2gray(im1));
im2=double(rgb2gray(im2));
[mpcqi, pcqi_map] = PCQI(im1, im2);
PCQI.m (default value for window = fspecial(‘gaussian’, 11, 1.5); L = 256)
function [mpcqi, pcqi_map]= PCQI(img1, img2, window, L)
if (nargin < 2 || nargin > 4)
mpcqi = -Inf;
pcqi_map = -Inf;
return;
end

if (size(img1) ~= size(img2))
mpcqi = -Inf;
pcqi_map = -Inf;
return;
end

[M N] = size(img1);

if (nargin == 2)
if ((M < 11) || (N < 11))
mpcqi = -Inf;
pcqi_map = -Inf;
return
end
window = fspecial(‘gaussian’, 11, 1.5);
L = 256;
end

if (nargin == 3)
[H W] = size(window);
if ((H*W) < 4 || (H > M) || (W > N))
mpcqi = -Inf;
pcqi_map = -Inf;
return
end
L = 255;
end

if (nargin == 4)
[H W] = size(window);
if ((H*W) < 4 || (H > M) || (W > N))
mpcqi = -Inf;
pcqi_map = -Inf;
return
end
end

window = window/sum(sum(window));

mu1 = filter2(window, img1, ‘valid’);
mu2 = filter2(window, img2, ‘valid’);
mu1_sq = mu1.*mu1;
mu2_sq = mu2.*mu2;
mu1_mu2 = mu1.*mu2;
sigma1_sq = filter2(window, img1.*img1, ‘valid’) – mu1_sq;
sigma2_sq = filter2(window, img2.*img2, ‘valid’) – mu2_sq;
sigma12 = filter2(window, img1.*img2, ‘valid’) – mu1_mu2;

sigma1_sq = max(0, sigma1_sq);
sigma2_sq = max(0, sigma2_sq);

C=3;

pcqi_map = (4/pi) *atan((sigma12 + C )./(sigma1_sq + C ));
pcqi_map = pcqi_map .*((sigma12 + C) ./(sqrt(sigma1_sq).*sqrt(sigma2_sq) + C));
pcqi_map = pcqi_map .*exp(-abs(mu1-mu2)/L);

mpcqi = mean2(pcqi_map);

returnI need to take output from a "PCQI: A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images" code. But whenever i run this, it doesnt show anything at all. Not even an error. How can i solve this?

Demo.m
clc;
clear;

im1=imread(‘ref.png’);
im2=imread(‘contrast_changed.png’);
im1=double(rgb2gray(im1));
im2=double(rgb2gray(im2));
[mpcqi, pcqi_map] = PCQI(im1, im2);
PCQI.m (default value for window = fspecial(‘gaussian’, 11, 1.5); L = 256)
function [mpcqi, pcqi_map]= PCQI(img1, img2, window, L)
if (nargin < 2 || nargin > 4)
mpcqi = -Inf;
pcqi_map = -Inf;
return;
end

if (size(img1) ~= size(img2))
mpcqi = -Inf;
pcqi_map = -Inf;
return;
end

[M N] = size(img1);

if (nargin == 2)
if ((M < 11) || (N < 11))
mpcqi = -Inf;
pcqi_map = -Inf;
return
end
window = fspecial(‘gaussian’, 11, 1.5);
L = 256;
end

if (nargin == 3)
[H W] = size(window);
if ((H*W) < 4 || (H > M) || (W > N))
mpcqi = -Inf;
pcqi_map = -Inf;
return
end
L = 255;
end

if (nargin == 4)
[H W] = size(window);
if ((H*W) < 4 || (H > M) || (W > N))
mpcqi = -Inf;
pcqi_map = -Inf;
return
end
end

window = window/sum(sum(window));

mu1 = filter2(window, img1, ‘valid’);
mu2 = filter2(window, img2, ‘valid’);
mu1_sq = mu1.*mu1;
mu2_sq = mu2.*mu2;
mu1_mu2 = mu1.*mu2;
sigma1_sq = filter2(window, img1.*img1, ‘valid’) – mu1_sq;
sigma2_sq = filter2(window, img2.*img2, ‘valid’) – mu2_sq;
sigma12 = filter2(window, img1.*img2, ‘valid’) – mu1_mu2;

sigma1_sq = max(0, sigma1_sq);
sigma2_sq = max(0, sigma2_sq);

C=3;

pcqi_map = (4/pi) *atan((sigma12 + C )./(sigma1_sq + C ));
pcqi_map = pcqi_map .*((sigma12 + C) ./(sqrt(sigma1_sq).*sqrt(sigma2_sq) + C));
pcqi_map = pcqi_map .*exp(-abs(mu1-mu2)/L);

mpcqi = mean2(pcqi_map);

return I need to take output from a "PCQI: A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images" code. But whenever i run this, it doesnt show anything at all. Not even an error. How can i solve this?

Demo.m
clc;
clear;

im1=imread(‘ref.png’);
im2=imread(‘contrast_changed.png’);
im1=double(rgb2gray(im1));
im2=double(rgb2gray(im2));
[mpcqi, pcqi_map] = PCQI(im1, im2);
PCQI.m (default value for window = fspecial(‘gaussian’, 11, 1.5); L = 256)
function [mpcqi, pcqi_map]= PCQI(img1, img2, window, L)
if (nargin < 2 || nargin > 4)
mpcqi = -Inf;
pcqi_map = -Inf;
return;
end

if (size(img1) ~= size(img2))
mpcqi = -Inf;
pcqi_map = -Inf;
return;
end

[M N] = size(img1);

if (nargin == 2)
if ((M < 11) || (N < 11))
mpcqi = -Inf;
pcqi_map = -Inf;
return
end
window = fspecial(‘gaussian’, 11, 1.5);
L = 256;
end

if (nargin == 3)
[H W] = size(window);
if ((H*W) < 4 || (H > M) || (W > N))
mpcqi = -Inf;
pcqi_map = -Inf;
return
end
L = 255;
end

if (nargin == 4)
[H W] = size(window);
if ((H*W) < 4 || (H > M) || (W > N))
mpcqi = -Inf;
pcqi_map = -Inf;
return
end
end

window = window/sum(sum(window));

mu1 = filter2(window, img1, ‘valid’);
mu2 = filter2(window, img2, ‘valid’);
mu1_sq = mu1.*mu1;
mu2_sq = mu2.*mu2;
mu1_mu2 = mu1.*mu2;
sigma1_sq = filter2(window, img1.*img1, ‘valid’) – mu1_sq;
sigma2_sq = filter2(window, img2.*img2, ‘valid’) – mu2_sq;
sigma12 = filter2(window, img1.*img2, ‘valid’) – mu1_mu2;

sigma1_sq = max(0, sigma1_sq);
sigma2_sq = max(0, sigma2_sq);

C=3;

pcqi_map = (4/pi) *atan((sigma12 + C )./(sigma1_sq + C ));
pcqi_map = pcqi_map .*((sigma12 + C) ./(sqrt(sigma1_sq).*sqrt(sigma2_sq) + C));
pcqi_map = pcqi_map .*exp(-abs(mu1-mu2)/L);

mpcqi = mean2(pcqi_map);

return image analysis, pcqi MATLAB Answers — New Questions

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Hi I am porting a script I had in Python to Matlab. Also returning to MATLAB after a decade, so pardon me if it’s too basic.

The key issue I am having is, retrieving premapped items from a cell array. Example, I have:

label_names={"assds", "Sasas", "Asasa", "assds"}
indices={[1 3 2]} %note that this can have varying lengths from 1 to full length of the label array.
label_names(indices) %of course returns an error

Error is "Unable to use a value of type cell as an index."
What is the best way to achieve this – retreive items based on a cell array of indices – as cleanly as possible?

PS: The reason they are cell arrays has to do with varying lengths. To avoid X-Y problem, lets assume rest of the code is done optimally.

Thanks!Hi I am porting a script I had in Python to Matlab. Also returning to MATLAB after a decade, so pardon me if it’s too basic.

The key issue I am having is, retrieving premapped items from a cell array. Example, I have:

label_names={"assds", "Sasas", "Asasa", "assds"}
indices={[1 3 2]} %note that this can have varying lengths from 1 to full length of the label array.
label_names(indices) %of course returns an error

Error is "Unable to use a value of type cell as an index."
What is the best way to achieve this – retreive items based on a cell array of indices – as cleanly as possible?

PS: The reason they are cell arrays has to do with varying lengths. To avoid X-Y problem, lets assume rest of the code is done optimally.

Thanks! Hi I am porting a script I had in Python to Matlab. Also returning to MATLAB after a decade, so pardon me if it’s too basic.

The key issue I am having is, retrieving premapped items from a cell array. Example, I have:

label_names={"assds", "Sasas", "Asasa", "assds"}
indices={[1 3 2]} %note that this can have varying lengths from 1 to full length of the label array.
label_names(indices) %of course returns an error

Error is "Unable to use a value of type cell as an index."
What is the best way to achieve this – retreive items based on a cell array of indices – as cleanly as possible?

PS: The reason they are cell arrays has to do with varying lengths. To avoid X-Y problem, lets assume rest of the code is done optimally.

Thanks! cell array, indexing MATLAB Answers — New Questions

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This is not circle finding in an image, which has been well covered, but in an array.
I have an array P with several approximately-circular areas of a low value. I would like to find the centres of those areas to calibrate my device. Here is a contour 2D plot of P. The contour plot looks a bit odd because I only obtained a patch of data around each circle (the box) and it is doing a linear interpolation between those.
This is a one-time calibration so I’m happy to do some manual setting up work.
Thanks in advance!
Jack

This is how I display the contour:
figure(1)
pcolor(X,Y,P);
shading interp
hold on
colorbar
hold off

PS this is my first question so do say if there is anything I can improve for next time.This is not circle finding in an image, which has been well covered, but in an array.
I have an array P with several approximately-circular areas of a low value. I would like to find the centres of those areas to calibrate my device. Here is a contour 2D plot of P. The contour plot looks a bit odd because I only obtained a patch of data around each circle (the box) and it is doing a linear interpolation between those.
This is a one-time calibration so I’m happy to do some manual setting up work.
Thanks in advance!
Jack

This is how I display the contour:
figure(1)
pcolor(X,Y,P);
shading interp
hold on
colorbar
hold off

PS this is my first question so do say if there is anything I can improve for next time. This is not circle finding in an image, which has been well covered, but in an array.
I have an array P with several approximately-circular areas of a low value. I would like to find the centres of those areas to calibrate my device. Here is a contour 2D plot of P. The contour plot looks a bit odd because I only obtained a patch of data around each circle (the box) and it is doing a linear interpolation between those.
This is a one-time calibration so I’m happy to do some manual setting up work.
Thanks in advance!
Jack

This is how I display the contour:
figure(1)
pcolor(X,Y,P);
shading interp
hold on
colorbar
hold off

PS this is my first question so do say if there is anything I can improve for next time. circles, finding, arrays, variables, matlab MATLAB Answers — New Questions

​

Hello everyone,
This is a follow-up to an earlier question I had asked about capturing lossless images with Matlab. Earlier, I was using USB Webcam Package’s snapshot() function to capture, yet it seemed that there was some compression going on possibly because it was not possible to access YUY2 (I am not sure if this is relevant as I want to capture images rather than recording videos). Now I have moved on to use the Image Acquisition Toolbox and the interface generated the following code to capture images:
v = videoinput("winvideo", 1, "YUY2_3840x2160"); % Video device
v.ReturnedColorspace = "RGB";
src = getselectedsource(v);
src.Exposure = 0; % Camera parameters

image1 = getsnapshot(v); % Capturing the image

filename = "snapshot1.bmp"; % I changed the code to save the image as .bmp
imwrite(image1, filename, "bmp"); % Saving the file
Now the code is using the getsnapshot() function instead. Should this apply any additional compression on Matlab’s side of things? I know that the camera may do something but I want to ensure that Matlab does not.Hello everyone,
This is a follow-up to an earlier question I had asked about capturing lossless images with Matlab. Earlier, I was using USB Webcam Package’s snapshot() function to capture, yet it seemed that there was some compression going on possibly because it was not possible to access YUY2 (I am not sure if this is relevant as I want to capture images rather than recording videos). Now I have moved on to use the Image Acquisition Toolbox and the interface generated the following code to capture images:
v = videoinput("winvideo", 1, "YUY2_3840x2160"); % Video device
v.ReturnedColorspace = "RGB";
src = getselectedsource(v);
src.Exposure = 0; % Camera parameters

image1 = getsnapshot(v); % Capturing the image

filename = "snapshot1.bmp"; % I changed the code to save the image as .bmp
imwrite(image1, filename, "bmp"); % Saving the file
Now the code is using the getsnapshot() function instead. Should this apply any additional compression on Matlab’s side of things? I know that the camera may do something but I want to ensure that Matlab does not. Hello everyone,
This is a follow-up to an earlier question I had asked about capturing lossless images with Matlab. Earlier, I was using USB Webcam Package’s snapshot() function to capture, yet it seemed that there was some compression going on possibly because it was not possible to access YUY2 (I am not sure if this is relevant as I want to capture images rather than recording videos). Now I have moved on to use the Image Acquisition Toolbox and the interface generated the following code to capture images:
v = videoinput("winvideo", 1, "YUY2_3840x2160"); % Video device
v.ReturnedColorspace = "RGB";
src = getselectedsource(v);
src.Exposure = 0; % Camera parameters

image1 = getsnapshot(v); % Capturing the image

filename = "snapshot1.bmp"; % I changed the code to save the image as .bmp
imwrite(image1, filename, "bmp"); % Saving the file
Now the code is using the getsnapshot() function instead. Should this apply any additional compression on Matlab’s side of things? I know that the camera may do something but I want to ensure that Matlab does not. image acquisition, lossless image, bitmap, camera, recording, video, image MATLAB Answers — New Questions

​

Hi,
I have developed a function to be converted into exe file, the function include optimizer fmincon. The function is working well from matlab but when I converted into exe file using Application complier and start to run the exe file, the optimizer fmincon have this error "array indices must be positive integers or logical values".
Not sure what is wrong. Any help.Hi,
I have developed a function to be converted into exe file, the function include optimizer fmincon. The function is working well from matlab but when I converted into exe file using Application complier and start to run the exe file, the optimizer fmincon have this error "array indices must be positive integers or logical values".
Not sure what is wrong. Any help. Hi,
I have developed a function to be converted into exe file, the function include optimizer fmincon. The function is working well from matlab but when I converted into exe file using Application complier and start to run the exe file, the optimizer fmincon have this error "array indices must be positive integers or logical values".
Not sure what is wrong. Any help. application complier, fmincon, array indices must be positive integers or logical MATLAB Answers — New Questions

​

I have written a script to find the maximum and minimum values of 4 time series and then truncate each time series to these values as they are all of different length.
I want it to record the maximum and minimum values of all the time series first (lines 15-19), then after that store the overall smallest and largest values to new variables (lines 20-23).
At the moment, its constantly updating variables as it loops through, so its not using the overall smallest/largest values to truncate the time series.
I have tried putting lines 15-19 in another loop so it executes this first, but it doesnt seem to make any difference. Can anybody help?
%import all the csv files in the folder
csvFiles = dir(fullfile(folderPath, ‘*.csv’));
%read in all the csv files
for i = 1:length(csvFiles)
%get the individual file pathway
csvFilePath = fullfile(folderPath, csvFiles(i).name);
%read the file in
data = readmatrix(csvFilePath);
%remove nan’s from the timeseries
data = data(~any(isnan(data), 2), :);
%remove non-unique datapoints from the timeseries
[~, id] = unique(data(:,1)) ;
data = data(id,:);
for a = 1:length(csvFiles)
%find and save the maximum value of all the time series
maxVal(1) = max(data(:,1));
%find and save the minimum value of all the time series
minVal(1) = min(data(:,1));
%find and save the mean dt of all the time series
dt(1) = mean(diff(data(:,1)));
end
%find the time series that ends soonest
smallestMaxValue = min(maxVal)
%find the time series that starts latest
largestMinValue = max(minVal)
%truncate all the time series to the shortest one
truncateRow = find(data(:,1) > smallestMaxValue, 1, ‘first’); %find the index of the first value in the first column when the shortest time series is exceeded
if ~isempty(truncateRow)
truncatedData = data(1:truncateRow-1, :); %if there is a value in truncateRow, then truncate the dataset up to this point
else
truncatedData = data; %if there is no value in truncateRow (e.g. shortest time series), then leave the data as it is
end
endI have written a script to find the maximum and minimum values of 4 time series and then truncate each time series to these values as they are all of different length.
I want it to record the maximum and minimum values of all the time series first (lines 15-19), then after that store the overall smallest and largest values to new variables (lines 20-23).
At the moment, its constantly updating variables as it loops through, so its not using the overall smallest/largest values to truncate the time series.
I have tried putting lines 15-19 in another loop so it executes this first, but it doesnt seem to make any difference. Can anybody help?
%import all the csv files in the folder
csvFiles = dir(fullfile(folderPath, ‘*.csv’));
%read in all the csv files
for i = 1:length(csvFiles)
%get the individual file pathway
csvFilePath = fullfile(folderPath, csvFiles(i).name);
%read the file in
data = readmatrix(csvFilePath);
%remove nan’s from the timeseries
data = data(~any(isnan(data), 2), :);
%remove non-unique datapoints from the timeseries
[~, id] = unique(data(:,1)) ;
data = data(id,:);
for a = 1:length(csvFiles)
%find and save the maximum value of all the time series
maxVal(1) = max(data(:,1));
%find and save the minimum value of all the time series
minVal(1) = min(data(:,1));
%find and save the mean dt of all the time series
dt(1) = mean(diff(data(:,1)));
end
%find the time series that ends soonest
smallestMaxValue = min(maxVal)
%find the time series that starts latest
largestMinValue = max(minVal)
%truncate all the time series to the shortest one
truncateRow = find(data(:,1) > smallestMaxValue, 1, ‘first’); %find the index of the first value in the first column when the shortest time series is exceeded
if ~isempty(truncateRow)
truncatedData = data(1:truncateRow-1, :); %if there is a value in truncateRow, then truncate the dataset up to this point
else
truncatedData = data; %if there is no value in truncateRow (e.g. shortest time series), then leave the data as it is
end
end I have written a script to find the maximum and minimum values of 4 time series and then truncate each time series to these values as they are all of different length.
I want it to record the maximum and minimum values of all the time series first (lines 15-19), then after that store the overall smallest and largest values to new variables (lines 20-23).
At the moment, its constantly updating variables as it loops through, so its not using the overall smallest/largest values to truncate the time series.
I have tried putting lines 15-19 in another loop so it executes this first, but it doesnt seem to make any difference. Can anybody help?
%import all the csv files in the folder
csvFiles = dir(fullfile(folderPath, ‘*.csv’));
%read in all the csv files
for i = 1:length(csvFiles)
%get the individual file pathway
csvFilePath = fullfile(folderPath, csvFiles(i).name);
%read the file in
data = readmatrix(csvFilePath);
%remove nan’s from the timeseries
data = data(~any(isnan(data), 2), :);
%remove non-unique datapoints from the timeseries
[~, id] = unique(data(:,1)) ;
data = data(id,:);
for a = 1:length(csvFiles)
%find and save the maximum value of all the time series
maxVal(1) = max(data(:,1));
%find and save the minimum value of all the time series
minVal(1) = min(data(:,1));
%find and save the mean dt of all the time series
dt(1) = mean(diff(data(:,1)));
end
%find the time series that ends soonest
smallestMaxValue = min(maxVal)
%find the time series that starts latest
largestMinValue = max(minVal)
%truncate all the time series to the shortest one
truncateRow = find(data(:,1) > smallestMaxValue, 1, ‘first’); %find the index of the first value in the first column when the shortest time series is exceeded
if ~isempty(truncateRow)
truncatedData = data(1:truncateRow-1, :); %if there is a value in truncateRow, then truncate the dataset up to this point
else
truncatedData = data; %if there is no value in truncateRow (e.g. shortest time series), then leave the data as it is
end
end for loop, loop, nested loop MATLAB Answers — New Questions

​

Note: Workaround is to remove .NET 9.0 from PC to resolve conflicts with 6.0.
This might be a .NET issue as a whole, but I only see it using Matlab, so I am posting here to help others out facing the same issues.
I have a .dll from one of the groups I work with for communicating to our devices. The .dll was built in .NET 6.0 (core) (x64). I have been able to use it in the past in Matlab, but have now run into issues since installing Visual Studio 2022. It was discovered that Matlab would reference the most recent installation of .NET (9.0 in my case) which was installed by VS 2022. When I set "core" as the environment in Matlab (dotnetenv("core")), I would then make the .NET assembly visible to Matlab using NET.addAssembly("insert path here"). When Matlab got to this step, it would buffer for about 10 seconds then crash immediately with no crash report. Getting help from my local IT, we pin pointed it to the .NET 9.0 install causing the problem.
To get around this, you can either uninstall 9.0 or set up an environment variable in your system properties called "DOTNET_ROOT" that can force a path to be referenced.
So I would like to ask if anyone else has faced this issue in Matlab? Is it an unexpected error that needs an error handle? I am naive to .net, and really only know the particular dll I interact with to do my methods, etc.Note: Workaround is to remove .NET 9.0 from PC to resolve conflicts with 6.0.
This might be a .NET issue as a whole, but I only see it using Matlab, so I am posting here to help others out facing the same issues.
I have a .dll from one of the groups I work with for communicating to our devices. The .dll was built in .NET 6.0 (core) (x64). I have been able to use it in the past in Matlab, but have now run into issues since installing Visual Studio 2022. It was discovered that Matlab would reference the most recent installation of .NET (9.0 in my case) which was installed by VS 2022. When I set "core" as the environment in Matlab (dotnetenv("core")), I would then make the .NET assembly visible to Matlab using NET.addAssembly("insert path here"). When Matlab got to this step, it would buffer for about 10 seconds then crash immediately with no crash report. Getting help from my local IT, we pin pointed it to the .NET 9.0 install causing the problem.
To get around this, you can either uninstall 9.0 or set up an environment variable in your system properties called "DOTNET_ROOT" that can force a path to be referenced.
So I would like to ask if anyone else has faced this issue in Matlab? Is it an unexpected error that needs an error handle? I am naive to .net, and really only know the particular dll I interact with to do my methods, etc. Note: Workaround is to remove .NET 9.0 from PC to resolve conflicts with 6.0.
This might be a .NET issue as a whole, but I only see it using Matlab, so I am posting here to help others out facing the same issues.
I have a .dll from one of the groups I work with for communicating to our devices. The .dll was built in .NET 6.0 (core) (x64). I have been able to use it in the past in Matlab, but have now run into issues since installing Visual Studio 2022. It was discovered that Matlab would reference the most recent installation of .NET (9.0 in my case) which was installed by VS 2022. When I set "core" as the environment in Matlab (dotnetenv("core")), I would then make the .NET assembly visible to Matlab using NET.addAssembly("insert path here"). When Matlab got to this step, it would buffer for about 10 seconds then crash immediately with no crash report. Getting help from my local IT, we pin pointed it to the .NET 9.0 install causing the problem.
To get around this, you can either uninstall 9.0 or set up an environment variable in your system properties called "DOTNET_ROOT" that can force a path to be referenced.
So I would like to ask if anyone else has faced this issue in Matlab? Is it an unexpected error that needs an error handle? I am naive to .net, and really only know the particular dll I interact with to do my methods, etc. .net MATLAB Answers — New Questions

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Hello everyone! Can I have some files about VPP Virtual Power Plants research for reference during my studies? Please help me.Hello everyone! Can I have some files about VPP Virtual Power Plants research for reference during my studies? Please help me. Hello everyone! Can I have some files about VPP Virtual Power Plants research for reference during my studies? Please help me. vpp, model MATLAB Answers — New Questions

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