How to fix errors? "Index in position 2 exceeds array bounds. Index must not exceed 1".
Thanks for everyone’s help
Error in VU_MSSP_2024_QUYDAO (line 72)
Xv_2(s,i+1)=-inv(A_0(4,4))*(A_0(4,1)*Xh(s,i+1)+A_0(4,2)*Xeh(s,i+1)+A_0(4,3)*Xv_1(s,i+1)+A_0(4,5)*Xev_1(s,i+1)+A_0(4,6)*Xev_2(s,i+1)+B_0(4,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));

clc
clear all;
close all;
I_1=[1];
I_2=[1 0;0 1];
Eh=[1];
Ev=[1 0;0 0];
E=blkdiag(Eh,Ev);
E_c=blkdiag(Eh,Eh,Ev,Ev);
Ih=[1];
Iv=[1 0;0 1];
J=[Ih zeros(1,5);zeros(2,2) Iv zeros(2,2);0 Ih zeros(1,4); zeros(2,2) zeros(2,2) Iv];
A=[0.04 -0.01 0.3;0.01 0.9 0.3;0.15 0.01 -0.05];
H=[-0.02 0.01 0.02;0.05 -0.01 0.05;0.47 0.01 2.02];
C=[0.05 -0.02 0.05;-0.05 0.01 0.08];
B_u=[0.01;0.02;-0.01];
B_w=[-0.02;0.05;0.04];
D_u=[0.05;0.01];
D_w=[0.02;-0.03];

A_c=blkdiag(A,A);

B_wc=[B_w;B_w];
B_c=[B_u -B_u;zeros(3,1) zeros(3,1)];
H_c=[zeros(3,3) zeros(3,3); zeros(3,3) H];
N_E=[zeros(1,6);zeros(1,6);zeros(1,6);1 1 1 1 1 1;zeros(1,6);1 1 1 -1 1 1];
D_1=[D_u -D_u];

%——————–
a=0.8;
gamma=4;
K=[ -0.4957 -9.2860 -4.8612];
L=[ -1726 -1094 43;950 603 -24;-18404 -11652 457];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
A_0=J’*[A+B_u*K -B_u*K;zeros(3,3) A-L*H]*J;
B_0=J’*[B_w;B_w];
C_0=[C+D_u*K -D_u*K]*J;

n=80;
v=zeros(1,n);
w=zeros(1,n);
Xh=zeros(n,n); Xeh=zeros(n,n); Xv_1=zeros(n,n);Xv_2=zeros(n,n);Ev_1=zeros(n,n);Xev_2=zeros(n,n);Z=zeros(n,n);U=zeros(n,n);

for i=1:1:n-1
Xh(1,i)=0;
Xeh(1,i)=0;

end

for i=1:1:n-1
Xv_1(i,1)=0;
Xv_2(i,1)=0;
Xev_1(i,1)=0;
Xev_2(i,1)=0;
end

x1=zeros(6,1);x0=zeros(6,1);xc=zeros(6,1);
for i=1:1:n
v(1,i)=i;
w(1,i)=i;
end
for s=1:1:n
for i=1:1:n-1
x0(1,1)=Xh(s,i);x0(2,1)=Xeh(s,i);x0(3,1)=Xv_1(s,i);x0(4,1)=Xv_2(s,i);x0(5,1)=Xev_1(s,i);x0(6,1)=Xev_2(s,i);
x1=A_0*x0+B_0*5*exp(-0.1*(s+i))*sin(i+s);
Xh(s+1,i)=A_0(1,1)*Xh(s,i)+A_0(1,2)*Xeh(s,i)+A_0(1,3)*Xv_1(s,i)+A_0(1,4)*Xv_2(s,i)+A_0(1,5)*Xev_1(s,i)+A_0(1,6)*Xev_2(s,i)+B_0(1,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xeh(s+1,i)=A_0(2,1)*Xh(s,i)+A_0(2,2)*Xeh(s,i)+A_0(2,3)*Xv_1(s,i)+A_0(2,4)*Xv_2(s,i)+A_0(2,5)*Xev_1(s,i)+A_0(2,6)*Xev_2(s,i)+B_0(2,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_1(s,i+1)=A_0(3,1)*Xh(s,i)+A_0(3,2)*Xeh(s,i)+A_0(3,3)*Xv_1(s,i)+A_0(3,4)*Xv_2(s,i)+A_0(3,5)*Xev_1(s,i)+A_0(3,6)*Xev_2(s,i)+B_0(3,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_2(s,i+1)=-inv(A_0(4,4))*(A_0(4,1)*Xh(s,i+1)+A_0(4,2)*Xeh(s,i+1)+A_0(4,3)*Xv_1(s,i+1)+A_0(4,5)*Xev_1(s,i+1)+A_0(4,6)*Xev_2(s,i+1)+B_0(4,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Xev_1(s,i+1)=A_0(5,1)*Xh(s,i)+A_0(5,2)*Xeh(s,i)+A_0(5,3)*Xv_1(s,i)+A_0(5,4)*Xv_2(s,i)+A_0(5,5)*Xev_1(s,i)+A_0(5,6)*Xev_2(s,i)+B_0(5,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xev_2(s,i+1)=-inv(A_0(6,6))*(A_0(6,1)*Xh(s,i+1)+A_0(6,2)*Xeh(s,i+1)+A_0(6,3)*Xv_1(s,i+1)+A_0(6,4)*Xv_2(s,i+1)+A_0(6,5)*Xev_1(s,i+1)+B_0(6,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Z(s,i)=norm(C_0*x0+D_w*5*exp(-0.1*(s+i))*sin(i+s));
U(s,i)=K*([Xh(s,i);Xv_1(s,i);Xv_2(s,i)]-[Xeh(s,i);Xev_1(s,i);Xev_2(s,i)]);
end

end

for i=1:1:n-1
for s=1:1:n-1
x0(1,1)=Xh(s,i);x0(2,1)=Xeh(s,i);x0(3,1)=Xv_1(s,i);x0(4,1)=Xv_2(s,i);x0(5,1)=Xev_1(s,i);x0(6,1)=Xev_2(s,i);
x1=A_0*x0+B_0*5*exp(-0.1*(s+i))*sin(i+s);
Xh(s+1,i)=A_0(1,1)*Xh(s,i)+A_0(1,2)*Xeh(s,i)+A_0(1,3)*Xv_1(s,i)+A_0(1,4)*Xv_2(s,i)+A_0(1,5)*Xev_1(s,i)+A_0(1,6)*Xev_2(s,i)+B_0(1,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xeh(s+1,i)=A_0(2,1)*Xh(s,i)+A_0(2,2)*Xeh(s,i)+A_0(2,3)*Xv_1(s,i)+A_0(2,4)*Xv_2(s,i)+A_0(2,5)*Xev_1(s,i)+A_0(2,6)*Xev_2(s,i)+B_0(2,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_1(s,i+1)=A_0(3,1)*Xh(s,i)+A_0(3,2)*Xeh(s,i)+A_0(3,3)*Xv_1(s,i)+A_0(3,4)*Xv_2(s,i)+A_0(3,5)*Xev_1(s,i)+A_0(3,6)*Xev_2(s,i)+B_0(3,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_2(s,i+1)=-inv(A_0(4,4))*(A_0(4,1)*Xh(s,i+1)+A_0(4,2)*Xeh(s,i+1)+A_0(4,3)*Xv_1(s,i+1)+A_0(4,5)*Xev_1(s,i+1)+A_0(4,6)*Xev_2(s,i+1)+B_0(4,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Xev_1(s,i+1)=A_0(5,1)*Xh(s,i)+A_0(5,2)*Xeh(s,i)+A_0(5,3)*Xv_1(s,i)+A_0(5,4)*Xv_2(s,i)+A_0(5,5)*Xev_1(s,i)+A_0(5,6)*Xev_2(s,i)+B_0(5,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xev_2(s,i+1)=-inv(A_0(6,6))*(A_0(6,1)*Xh(s,i+1)+A_0(6,2)*Xeh(s,i+1)+A_0(6,3)*Xv_1(s,i+1)+A_0(6,4)*Xv_2(s,i+1)+A_0(6,5)*Xev_1(s,i+1)+B_0(6,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Z(s,i)=norm(C_0*x0+D_w*5*exp(-0.1*(s+i))*sin(i+s));
U(s,i)=K*([Xh(s,i);Xv_1(s,i);Xv_2(s,i)]-[Xeh(s,i);Xev_1(s,i);Xev_2(s,i)]);
end
end

figure(1)
surf(v,w,Xh)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x^h(i,j)’);
%axis([0 100 0 100 -0.15 0.06])
hold on;

figure(2)
surf(v,w,Xeh)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_e^h(i,j)’);
%axis([0 100 0 100 -0.5 0.1])
hold on;

figure(3)
surf(v,w,Xv_1)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_1^v(i,j)’);
hold on;

figure(4)
surf(v,w,Xv_2)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_2^v(i,j)’);
hold on;

figure(5)
surf(v,w,Xev_1)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_{e1}^v(i,j)’);
hold on;

figure(6)
surf(v,w,Xev_2)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_{e2}^v(i,j)’);
%axis([0 100 0 100 -0.8 0.2])
hold on;

figure(7)
surf(v,w,Z)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘||z(i,j)||’);
%axis([0 100 0 100 -0.8 0.2])
hold on;

figure(8)
surf(v,w,U)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘u(i,j)|’);
%axis([0 100 0 100 -0.8 0.2])
hold on;

"How to fix errors? "Index in position 2 exceeds array bounds. Index must not exceed 1".
Thanks for everyone’s help
Error in VU_MSSP_2024_QUYDAO (line 72)
Xv_2(s,i+1)=-inv(A_0(4,4))*(A_0(4,1)*Xh(s,i+1)+A_0(4,2)*Xeh(s,i+1)+A_0(4,3)*Xv_1(s,i+1)+A_0(4,5)*Xev_1(s,i+1)+A_0(4,6)*Xev_2(s,i+1)+B_0(4,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));

clc
clear all;
close all;
I_1=[1];
I_2=[1 0;0 1];
Eh=[1];
Ev=[1 0;0 0];
E=blkdiag(Eh,Ev);
E_c=blkdiag(Eh,Eh,Ev,Ev);
Ih=[1];
Iv=[1 0;0 1];
J=[Ih zeros(1,5);zeros(2,2) Iv zeros(2,2);0 Ih zeros(1,4); zeros(2,2) zeros(2,2) Iv];
A=[0.04 -0.01 0.3;0.01 0.9 0.3;0.15 0.01 -0.05];
H=[-0.02 0.01 0.02;0.05 -0.01 0.05;0.47 0.01 2.02];
C=[0.05 -0.02 0.05;-0.05 0.01 0.08];
B_u=[0.01;0.02;-0.01];
B_w=[-0.02;0.05;0.04];
D_u=[0.05;0.01];
D_w=[0.02;-0.03];

A_c=blkdiag(A,A);

B_wc=[B_w;B_w];
B_c=[B_u -B_u;zeros(3,1) zeros(3,1)];
H_c=[zeros(3,3) zeros(3,3); zeros(3,3) H];
N_E=[zeros(1,6);zeros(1,6);zeros(1,6);1 1 1 1 1 1;zeros(1,6);1 1 1 -1 1 1];
D_1=[D_u -D_u];

%——————–
a=0.8;
gamma=4;
K=[ -0.4957 -9.2860 -4.8612];
L=[ -1726 -1094 43;950 603 -24;-18404 -11652 457];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
A_0=J’*[A+B_u*K -B_u*K;zeros(3,3) A-L*H]*J;
B_0=J’*[B_w;B_w];
C_0=[C+D_u*K -D_u*K]*J;

n=80;
v=zeros(1,n);
w=zeros(1,n);
Xh=zeros(n,n); Xeh=zeros(n,n); Xv_1=zeros(n,n);Xv_2=zeros(n,n);Ev_1=zeros(n,n);Xev_2=zeros(n,n);Z=zeros(n,n);U=zeros(n,n);

for i=1:1:n-1
Xh(1,i)=0;
Xeh(1,i)=0;

end

for i=1:1:n-1
Xv_1(i,1)=0;
Xv_2(i,1)=0;
Xev_1(i,1)=0;
Xev_2(i,1)=0;
end

x1=zeros(6,1);x0=zeros(6,1);xc=zeros(6,1);
for i=1:1:n
v(1,i)=i;
w(1,i)=i;
end
for s=1:1:n
for i=1:1:n-1
x0(1,1)=Xh(s,i);x0(2,1)=Xeh(s,i);x0(3,1)=Xv_1(s,i);x0(4,1)=Xv_2(s,i);x0(5,1)=Xev_1(s,i);x0(6,1)=Xev_2(s,i);
x1=A_0*x0+B_0*5*exp(-0.1*(s+i))*sin(i+s);
Xh(s+1,i)=A_0(1,1)*Xh(s,i)+A_0(1,2)*Xeh(s,i)+A_0(1,3)*Xv_1(s,i)+A_0(1,4)*Xv_2(s,i)+A_0(1,5)*Xev_1(s,i)+A_0(1,6)*Xev_2(s,i)+B_0(1,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xeh(s+1,i)=A_0(2,1)*Xh(s,i)+A_0(2,2)*Xeh(s,i)+A_0(2,3)*Xv_1(s,i)+A_0(2,4)*Xv_2(s,i)+A_0(2,5)*Xev_1(s,i)+A_0(2,6)*Xev_2(s,i)+B_0(2,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_1(s,i+1)=A_0(3,1)*Xh(s,i)+A_0(3,2)*Xeh(s,i)+A_0(3,3)*Xv_1(s,i)+A_0(3,4)*Xv_2(s,i)+A_0(3,5)*Xev_1(s,i)+A_0(3,6)*Xev_2(s,i)+B_0(3,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_2(s,i+1)=-inv(A_0(4,4))*(A_0(4,1)*Xh(s,i+1)+A_0(4,2)*Xeh(s,i+1)+A_0(4,3)*Xv_1(s,i+1)+A_0(4,5)*Xev_1(s,i+1)+A_0(4,6)*Xev_2(s,i+1)+B_0(4,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Xev_1(s,i+1)=A_0(5,1)*Xh(s,i)+A_0(5,2)*Xeh(s,i)+A_0(5,3)*Xv_1(s,i)+A_0(5,4)*Xv_2(s,i)+A_0(5,5)*Xev_1(s,i)+A_0(5,6)*Xev_2(s,i)+B_0(5,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xev_2(s,i+1)=-inv(A_0(6,6))*(A_0(6,1)*Xh(s,i+1)+A_0(6,2)*Xeh(s,i+1)+A_0(6,3)*Xv_1(s,i+1)+A_0(6,4)*Xv_2(s,i+1)+A_0(6,5)*Xev_1(s,i+1)+B_0(6,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Z(s,i)=norm(C_0*x0+D_w*5*exp(-0.1*(s+i))*sin(i+s));
U(s,i)=K*([Xh(s,i);Xv_1(s,i);Xv_2(s,i)]-[Xeh(s,i);Xev_1(s,i);Xev_2(s,i)]);
end

end

for i=1:1:n-1
for s=1:1:n-1
x0(1,1)=Xh(s,i);x0(2,1)=Xeh(s,i);x0(3,1)=Xv_1(s,i);x0(4,1)=Xv_2(s,i);x0(5,1)=Xev_1(s,i);x0(6,1)=Xev_2(s,i);
x1=A_0*x0+B_0*5*exp(-0.1*(s+i))*sin(i+s);
Xh(s+1,i)=A_0(1,1)*Xh(s,i)+A_0(1,2)*Xeh(s,i)+A_0(1,3)*Xv_1(s,i)+A_0(1,4)*Xv_2(s,i)+A_0(1,5)*Xev_1(s,i)+A_0(1,6)*Xev_2(s,i)+B_0(1,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xeh(s+1,i)=A_0(2,1)*Xh(s,i)+A_0(2,2)*Xeh(s,i)+A_0(2,3)*Xv_1(s,i)+A_0(2,4)*Xv_2(s,i)+A_0(2,5)*Xev_1(s,i)+A_0(2,6)*Xev_2(s,i)+B_0(2,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_1(s,i+1)=A_0(3,1)*Xh(s,i)+A_0(3,2)*Xeh(s,i)+A_0(3,3)*Xv_1(s,i)+A_0(3,4)*Xv_2(s,i)+A_0(3,5)*Xev_1(s,i)+A_0(3,6)*Xev_2(s,i)+B_0(3,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_2(s,i+1)=-inv(A_0(4,4))*(A_0(4,1)*Xh(s,i+1)+A_0(4,2)*Xeh(s,i+1)+A_0(4,3)*Xv_1(s,i+1)+A_0(4,5)*Xev_1(s,i+1)+A_0(4,6)*Xev_2(s,i+1)+B_0(4,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Xev_1(s,i+1)=A_0(5,1)*Xh(s,i)+A_0(5,2)*Xeh(s,i)+A_0(5,3)*Xv_1(s,i)+A_0(5,4)*Xv_2(s,i)+A_0(5,5)*Xev_1(s,i)+A_0(5,6)*Xev_2(s,i)+B_0(5,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xev_2(s,i+1)=-inv(A_0(6,6))*(A_0(6,1)*Xh(s,i+1)+A_0(6,2)*Xeh(s,i+1)+A_0(6,3)*Xv_1(s,i+1)+A_0(6,4)*Xv_2(s,i+1)+A_0(6,5)*Xev_1(s,i+1)+B_0(6,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Z(s,i)=norm(C_0*x0+D_w*5*exp(-0.1*(s+i))*sin(i+s));
U(s,i)=K*([Xh(s,i);Xv_1(s,i);Xv_2(s,i)]-[Xeh(s,i);Xev_1(s,i);Xev_2(s,i)]);
end
end

figure(1)
surf(v,w,Xh)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x^h(i,j)’);
%axis([0 100 0 100 -0.15 0.06])
hold on;

figure(2)
surf(v,w,Xeh)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_e^h(i,j)’);
%axis([0 100 0 100 -0.5 0.1])
hold on;

figure(3)
surf(v,w,Xv_1)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_1^v(i,j)’);
hold on;

figure(4)
surf(v,w,Xv_2)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_2^v(i,j)’);
hold on;

figure(5)
surf(v,w,Xev_1)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_{e1}^v(i,j)’);
hold on;

figure(6)
surf(v,w,Xev_2)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_{e2}^v(i,j)’);
%axis([0 100 0 100 -0.8 0.2])
hold on;

figure(7)
surf(v,w,Z)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘||z(i,j)||’);
%axis([0 100 0 100 -0.8 0.2])
hold on;

figure(8)
surf(v,w,U)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘u(i,j)|’);
%axis([0 100 0 100 -0.8 0.2])
hold on;

" How to fix errors? "Index in position 2 exceeds array bounds. Index must not exceed 1".
Thanks for everyone’s help
Error in VU_MSSP_2024_QUYDAO (line 72)
Xv_2(s,i+1)=-inv(A_0(4,4))*(A_0(4,1)*Xh(s,i+1)+A_0(4,2)*Xeh(s,i+1)+A_0(4,3)*Xv_1(s,i+1)+A_0(4,5)*Xev_1(s,i+1)+A_0(4,6)*Xev_2(s,i+1)+B_0(4,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));

clc
clear all;
close all;
I_1=[1];
I_2=[1 0;0 1];
Eh=[1];
Ev=[1 0;0 0];
E=blkdiag(Eh,Ev);
E_c=blkdiag(Eh,Eh,Ev,Ev);
Ih=[1];
Iv=[1 0;0 1];
J=[Ih zeros(1,5);zeros(2,2) Iv zeros(2,2);0 Ih zeros(1,4); zeros(2,2) zeros(2,2) Iv];
A=[0.04 -0.01 0.3;0.01 0.9 0.3;0.15 0.01 -0.05];
H=[-0.02 0.01 0.02;0.05 -0.01 0.05;0.47 0.01 2.02];
C=[0.05 -0.02 0.05;-0.05 0.01 0.08];
B_u=[0.01;0.02;-0.01];
B_w=[-0.02;0.05;0.04];
D_u=[0.05;0.01];
D_w=[0.02;-0.03];

A_c=blkdiag(A,A);

B_wc=[B_w;B_w];
B_c=[B_u -B_u;zeros(3,1) zeros(3,1)];
H_c=[zeros(3,3) zeros(3,3); zeros(3,3) H];
N_E=[zeros(1,6);zeros(1,6);zeros(1,6);1 1 1 1 1 1;zeros(1,6);1 1 1 -1 1 1];
D_1=[D_u -D_u];

%——————–
a=0.8;
gamma=4;
K=[ -0.4957 -9.2860 -4.8612];
L=[ -1726 -1094 43;950 603 -24;-18404 -11652 457];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
A_0=J’*[A+B_u*K -B_u*K;zeros(3,3) A-L*H]*J;
B_0=J’*[B_w;B_w];
C_0=[C+D_u*K -D_u*K]*J;

n=80;
v=zeros(1,n);
w=zeros(1,n);
Xh=zeros(n,n); Xeh=zeros(n,n); Xv_1=zeros(n,n);Xv_2=zeros(n,n);Ev_1=zeros(n,n);Xev_2=zeros(n,n);Z=zeros(n,n);U=zeros(n,n);

for i=1:1:n-1
Xh(1,i)=0;
Xeh(1,i)=0;

end

for i=1:1:n-1
Xv_1(i,1)=0;
Xv_2(i,1)=0;
Xev_1(i,1)=0;
Xev_2(i,1)=0;
end

x1=zeros(6,1);x0=zeros(6,1);xc=zeros(6,1);
for i=1:1:n
v(1,i)=i;
w(1,i)=i;
end
for s=1:1:n
for i=1:1:n-1
x0(1,1)=Xh(s,i);x0(2,1)=Xeh(s,i);x0(3,1)=Xv_1(s,i);x0(4,1)=Xv_2(s,i);x0(5,1)=Xev_1(s,i);x0(6,1)=Xev_2(s,i);
x1=A_0*x0+B_0*5*exp(-0.1*(s+i))*sin(i+s);
Xh(s+1,i)=A_0(1,1)*Xh(s,i)+A_0(1,2)*Xeh(s,i)+A_0(1,3)*Xv_1(s,i)+A_0(1,4)*Xv_2(s,i)+A_0(1,5)*Xev_1(s,i)+A_0(1,6)*Xev_2(s,i)+B_0(1,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xeh(s+1,i)=A_0(2,1)*Xh(s,i)+A_0(2,2)*Xeh(s,i)+A_0(2,3)*Xv_1(s,i)+A_0(2,4)*Xv_2(s,i)+A_0(2,5)*Xev_1(s,i)+A_0(2,6)*Xev_2(s,i)+B_0(2,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_1(s,i+1)=A_0(3,1)*Xh(s,i)+A_0(3,2)*Xeh(s,i)+A_0(3,3)*Xv_1(s,i)+A_0(3,4)*Xv_2(s,i)+A_0(3,5)*Xev_1(s,i)+A_0(3,6)*Xev_2(s,i)+B_0(3,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_2(s,i+1)=-inv(A_0(4,4))*(A_0(4,1)*Xh(s,i+1)+A_0(4,2)*Xeh(s,i+1)+A_0(4,3)*Xv_1(s,i+1)+A_0(4,5)*Xev_1(s,i+1)+A_0(4,6)*Xev_2(s,i+1)+B_0(4,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Xev_1(s,i+1)=A_0(5,1)*Xh(s,i)+A_0(5,2)*Xeh(s,i)+A_0(5,3)*Xv_1(s,i)+A_0(5,4)*Xv_2(s,i)+A_0(5,5)*Xev_1(s,i)+A_0(5,6)*Xev_2(s,i)+B_0(5,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xev_2(s,i+1)=-inv(A_0(6,6))*(A_0(6,1)*Xh(s,i+1)+A_0(6,2)*Xeh(s,i+1)+A_0(6,3)*Xv_1(s,i+1)+A_0(6,4)*Xv_2(s,i+1)+A_0(6,5)*Xev_1(s,i+1)+B_0(6,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Z(s,i)=norm(C_0*x0+D_w*5*exp(-0.1*(s+i))*sin(i+s));
U(s,i)=K*([Xh(s,i);Xv_1(s,i);Xv_2(s,i)]-[Xeh(s,i);Xev_1(s,i);Xev_2(s,i)]);
end

end

for i=1:1:n-1
for s=1:1:n-1
x0(1,1)=Xh(s,i);x0(2,1)=Xeh(s,i);x0(3,1)=Xv_1(s,i);x0(4,1)=Xv_2(s,i);x0(5,1)=Xev_1(s,i);x0(6,1)=Xev_2(s,i);
x1=A_0*x0+B_0*5*exp(-0.1*(s+i))*sin(i+s);
Xh(s+1,i)=A_0(1,1)*Xh(s,i)+A_0(1,2)*Xeh(s,i)+A_0(1,3)*Xv_1(s,i)+A_0(1,4)*Xv_2(s,i)+A_0(1,5)*Xev_1(s,i)+A_0(1,6)*Xev_2(s,i)+B_0(1,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xeh(s+1,i)=A_0(2,1)*Xh(s,i)+A_0(2,2)*Xeh(s,i)+A_0(2,3)*Xv_1(s,i)+A_0(2,4)*Xv_2(s,i)+A_0(2,5)*Xev_1(s,i)+A_0(2,6)*Xev_2(s,i)+B_0(2,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_1(s,i+1)=A_0(3,1)*Xh(s,i)+A_0(3,2)*Xeh(s,i)+A_0(3,3)*Xv_1(s,i)+A_0(3,4)*Xv_2(s,i)+A_0(3,5)*Xev_1(s,i)+A_0(3,6)*Xev_2(s,i)+B_0(3,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xv_2(s,i+1)=-inv(A_0(4,4))*(A_0(4,1)*Xh(s,i+1)+A_0(4,2)*Xeh(s,i+1)+A_0(4,3)*Xv_1(s,i+1)+A_0(4,5)*Xev_1(s,i+1)+A_0(4,6)*Xev_2(s,i+1)+B_0(4,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Xev_1(s,i+1)=A_0(5,1)*Xh(s,i)+A_0(5,2)*Xeh(s,i)+A_0(5,3)*Xv_1(s,i)+A_0(5,4)*Xv_2(s,i)+A_0(5,5)*Xev_1(s,i)+A_0(5,6)*Xev_2(s,i)+B_0(5,1)*5*exp(-0.1*(s+i))*sin(i+s);
Xev_2(s,i+1)=-inv(A_0(6,6))*(A_0(6,1)*Xh(s,i+1)+A_0(6,2)*Xeh(s,i+1)+A_0(6,3)*Xv_1(s,i+1)+A_0(6,4)*Xv_2(s,i+1)+A_0(6,5)*Xev_1(s,i+1)+B_0(6,1)*5*exp(-0.1*(s+i+1))*sin(i+s+1));
Z(s,i)=norm(C_0*x0+D_w*5*exp(-0.1*(s+i))*sin(i+s));
U(s,i)=K*([Xh(s,i);Xv_1(s,i);Xv_2(s,i)]-[Xeh(s,i);Xev_1(s,i);Xev_2(s,i)]);
end
end

figure(1)
surf(v,w,Xh)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x^h(i,j)’);
%axis([0 100 0 100 -0.15 0.06])
hold on;

figure(2)
surf(v,w,Xeh)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_e^h(i,j)’);
%axis([0 100 0 100 -0.5 0.1])
hold on;

figure(3)
surf(v,w,Xv_1)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_1^v(i,j)’);
hold on;

figure(4)
surf(v,w,Xv_2)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_2^v(i,j)’);
hold on;

figure(5)
surf(v,w,Xev_1)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_{e1}^v(i,j)’);
hold on;

figure(6)
surf(v,w,Xev_2)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘x_{e2}^v(i,j)’);
%axis([0 100 0 100 -0.8 0.2])
hold on;

figure(7)
surf(v,w,Z)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘||z(i,j)||’);
%axis([0 100 0 100 -0.8 0.2])
hold on;

figure(8)
surf(v,w,U)
xlabel(‘i’);
ylabel(‘j’);
zlabel(‘u(i,j)|’);
%axis([0 100 0 100 -0.8 0.2])
hold on;

" index exceeds array bounds. MATLAB Answers — New Questions

​

I encountered a weird problem when trying the example in
Analyze and Select Features for Pump Diagnostics – MATLAB & Simulink (mathworks.com)
In this manual, each signal is plot using a quite distinct color, like the examples below:

However, in my MATLAB, what I got is like below, that is all signals use very similar colors:

I there anybody knows what is wrong here?
Or, how to set the colormap or color each signal using distinct colors in Diagnostic Feature Designer?I encountered a weird problem when trying the example in
Analyze and Select Features for Pump Diagnostics – MATLAB & Simulink (mathworks.com)
In this manual, each signal is plot using a quite distinct color, like the examples below:

However, in my MATLAB, what I got is like below, that is all signals use very similar colors:

I there anybody knows what is wrong here?
Or, how to set the colormap or color each signal using distinct colors in Diagnostic Feature Designer? I encountered a weird problem when trying the example in
Analyze and Select Features for Pump Diagnostics – MATLAB & Simulink (mathworks.com)
In this manual, each signal is plot using a quite distinct color, like the examples below:

However, in my MATLAB, what I got is like below, that is all signals use very similar colors:

I there anybody knows what is wrong here?
Or, how to set the colormap or color each signal using distinct colors in Diagnostic Feature Designer? diagnostic feature designer, colormap MATLAB Answers — New Questions

​

Hello,
I am trying to analyse microstructural features in one of my image. Yet I have couple of problems on the image. First of all, one of my friends has denoised the image using python and he did not share the details of denosising. When I try to denoise image by myself, I cannot get close to his denosising so I am wodering what I should do and what he used for denoising so that he got such a clear image. I have added the image before and after denosising (hope you can see the zip file).
Also another question is that I was using the thresholded image to measure microstructural features like length, width and volume fraction of yellow and chunky purple ones via imageJ and python. I am wondering if I can do the same with Matlab in a better way. I know my thresholding could be better but with limited coding skills that is what I can do currently 🙂 So next question is how I can do a better thresholding to distinguish each phases which are gray, white and black in the original image

Thank you in advance.Hello,
I am trying to analyse microstructural features in one of my image. Yet I have couple of problems on the image. First of all, one of my friends has denoised the image using python and he did not share the details of denosising. When I try to denoise image by myself, I cannot get close to his denosising so I am wodering what I should do and what he used for denoising so that he got such a clear image. I have added the image before and after denosising (hope you can see the zip file).
Also another question is that I was using the thresholded image to measure microstructural features like length, width and volume fraction of yellow and chunky purple ones via imageJ and python. I am wondering if I can do the same with Matlab in a better way. I know my thresholding could be better but with limited coding skills that is what I can do currently 🙂 So next question is how I can do a better thresholding to distinguish each phases which are gray, white and black in the original image

Thank you in advance. Hello,
I am trying to analyse microstructural features in one of my image. Yet I have couple of problems on the image. First of all, one of my friends has denoised the image using python and he did not share the details of denosising. When I try to denoise image by myself, I cannot get close to his denosising so I am wodering what I should do and what he used for denoising so that he got such a clear image. I have added the image before and after denosising (hope you can see the zip file).
Also another question is that I was using the thresholded image to measure microstructural features like length, width and volume fraction of yellow and chunky purple ones via imageJ and python. I am wondering if I can do the same with Matlab in a better way. I know my thresholding could be better but with limited coding skills that is what I can do currently 🙂 So next question is how I can do a better thresholding to distinguish each phases which are gray, white and black in the original image

Thank you in advance. image analysis, image processing MATLAB Answers — New Questions

​

Hi,
Tried several solutions found in the forum but none of them worked,
in my program, when a cell is selected, all the info of the row is displayed in different edit cases, when the cell in the uitable is edited and enter is pressed, the wanted value changes properly but the CellSelectionCallback is called again and the indices this time are inexistent and returns an error.
Any way of fixing this?
Thanks in advanceHi,
Tried several solutions found in the forum but none of them worked,
in my program, when a cell is selected, all the info of the row is displayed in different edit cases, when the cell in the uitable is edited and enter is pressed, the wanted value changes properly but the CellSelectionCallback is called again and the indices this time are inexistent and returns an error.
Any way of fixing this?
Thanks in advance Hi,
Tried several solutions found in the forum but none of them worked,
in my program, when a cell is selected, all the info of the row is displayed in different edit cases, when the cell in the uitable is edited and enter is pressed, the wanted value changes properly but the CellSelectionCallback is called again and the indices this time are inexistent and returns an error.
Any way of fixing this?
Thanks in advance deselect MATLAB Answers — New Questions

​

Hi.

Im Looking to use the DQN agent for some initial study of a problem that Im working on. The problem requires that I modify the loss function used in training of the Q-approximation network in DQN. I have the equation that I need to use to modify the loss function, it will be an additional term added to the standard loss function, which is squares sum of target and predicted Q network values. The new term will be like sum of squares of predicted and some expert Q value.
I would like to know if its possible to just modify the loss function, add the new term, and still use the framework presented by Reinforcement Learning Toolbox? Or this is not possible and I would have to atleast write all the code for DQN agents learning part?

Thanks for your time and help!Hi.

Im Looking to use the DQN agent for some initial study of a problem that Im working on. The problem requires that I modify the loss function used in training of the Q-approximation network in DQN. I have the equation that I need to use to modify the loss function, it will be an additional term added to the standard loss function, which is squares sum of target and predicted Q network values. The new term will be like sum of squares of predicted and some expert Q value.
I would like to know if its possible to just modify the loss function, add the new term, and still use the framework presented by Reinforcement Learning Toolbox? Or this is not possible and I would have to atleast write all the code for DQN agents learning part?

Thanks for your time and help! Hi.

Im Looking to use the DQN agent for some initial study of a problem that Im working on. The problem requires that I modify the loss function used in training of the Q-approximation network in DQN. I have the equation that I need to use to modify the loss function, it will be an additional term added to the standard loss function, which is squares sum of target and predicted Q network values. The new term will be like sum of squares of predicted and some expert Q value.
I would like to know if its possible to just modify the loss function, add the new term, and still use the framework presented by Reinforcement Learning Toolbox? Or this is not possible and I would have to atleast write all the code for DQN agents learning part?

Thanks for your time and help! reinforcement learning, loss function, dqn MATLAB Answers — New Questions

​

% This is my current code. I am attempting to use inverse kinematics to
% plot the same semicircle i made using forward kinematics. My issue is
% that the function i am currently using doesn’t properly calculate the
% "joint2" angle (it prints the same value always)

% NOTE: the error i am having occurs mostly in STEP 4 of my code.

clear;
clc;

% Step one: Defining linked robot and confirming it is correct
L_1(1) = Link([0 199.07 0 pi/2]);
L_1(2) = Link([0 -27 -23.33 -pi/2]);
L_1(3) = Link([0 -12.18 0 0]);
L_1(4) = Link([0 0 0 -pi/2]);
L_1(5) = Link([0 0 107.92 0]);
L_1(6) = Link([0 0 0 0]);
L_1(7) = Link([0 0 133.99 0]);

robot1 = SerialLink(L_1);

figure(2);

robot1.plot([pi/2 0 pi/2 0 0.6542 1.36 0], ‘nojoints’);
hold on;
% Success

% ——————————————————————— %

% Step two: retrieving end effector coordinates and confirming thier
% position
coor = robot1.fkine([pi/2 0 pi/2 0 0.6542 1.36 0]);
xF = coor.t(1);
yF = coor.t(2);
zF = coor.t(3);
% disp([‘(‘, num2str(xF), ‘ , ‘, num2str(yF) , ‘ , ‘ num2str(zF), ‘)’]);
% plot3(xF,yF,zF, ‘o’, ‘Color’, ‘g’);
% Success

% ——————————————————————— %

% Step three: plotting a semicircle

radius_S3 = 200;
pointsNum = ( pi/(pi/16) + 1);
semiCircleArray = zeros(pointsNum, 3);

index = 1;
for theta = pi : -pi/16 : 0
xF = radius_S3 * cos(theta);
yF = -23.33;
zF = radius_S3 * sin(theta);
plot3(xF,yF,zF, ‘*’, ‘Color’, ‘r’);
% disp([‘(‘, num2str(xForward), ‘ , ‘, num2str(yForward) , ‘ , ‘ num2str(zForward), ‘)’]);

semiCircleArray(index, 🙂 = [xF, yF, zF];
index = index + 1;

end

plot3(semiCircleArray(:, 1), semiCircleArray(:, 2), semiCircleArray(:, 3), ‘o’, ‘Color’, ‘b’);

% Success

% ——————————————————————— %

% Step four: inserting array values into function to confirm angles.

joint1 = zeros(pointsNum, 1);
joint2 = zeros(pointsNum, 1);
for i = 1:pointsNum
[joint1(i), joint2(i)] = theta2Output(semiCircleArray(i, 3), semiCircleArray(i, 1));
disp([‘j1: ‘, num2str(joint1(i)), ‘ , j2: ‘, num2str(joint2(i))]);

end

% This is where something goes wrong, which confirms there is an issue
% with my function. The value of one joint angle slowly increments
% whilst the other stays unchanged.

title(‘Right Leg – J1 J2 J3’);
xlabel(‘x-axis’);
ylabel(‘y-axis’);
zlabel(‘z-axis’);

function [joint1 , joint2] = theta2Output(x, z)
l1 = 107.92;
l2 = 133.99;
x = x(:);
z = z(:);
term1 = x.^2;
term2 = z.^2;
term3 = l1^2;
term4 = l2^2;

cos_joint2 = (term1 + term2 – term3 – term4) / (2 * l2 * l1);
joint2 = acos(cos_joint2);

joint1 = atan2(x , z) – atan2((l2 * sin(joint2)) , (l1 + (l2*cos(joint2))));

end% This is my current code. I am attempting to use inverse kinematics to
% plot the same semicircle i made using forward kinematics. My issue is
% that the function i am currently using doesn’t properly calculate the
% "joint2" angle (it prints the same value always)

% NOTE: the error i am having occurs mostly in STEP 4 of my code.

clear;
clc;

% Step one: Defining linked robot and confirming it is correct
L_1(1) = Link([0 199.07 0 pi/2]);
L_1(2) = Link([0 -27 -23.33 -pi/2]);
L_1(3) = Link([0 -12.18 0 0]);
L_1(4) = Link([0 0 0 -pi/2]);
L_1(5) = Link([0 0 107.92 0]);
L_1(6) = Link([0 0 0 0]);
L_1(7) = Link([0 0 133.99 0]);

robot1 = SerialLink(L_1);

figure(2);

robot1.plot([pi/2 0 pi/2 0 0.6542 1.36 0], ‘nojoints’);
hold on;
% Success

% ——————————————————————— %

% Step two: retrieving end effector coordinates and confirming thier
% position
coor = robot1.fkine([pi/2 0 pi/2 0 0.6542 1.36 0]);
xF = coor.t(1);
yF = coor.t(2);
zF = coor.t(3);
% disp([‘(‘, num2str(xF), ‘ , ‘, num2str(yF) , ‘ , ‘ num2str(zF), ‘)’]);
% plot3(xF,yF,zF, ‘o’, ‘Color’, ‘g’);
% Success

% ——————————————————————— %

% Step three: plotting a semicircle

radius_S3 = 200;
pointsNum = ( pi/(pi/16) + 1);
semiCircleArray = zeros(pointsNum, 3);

index = 1;
for theta = pi : -pi/16 : 0
xF = radius_S3 * cos(theta);
yF = -23.33;
zF = radius_S3 * sin(theta);
plot3(xF,yF,zF, ‘*’, ‘Color’, ‘r’);
% disp([‘(‘, num2str(xForward), ‘ , ‘, num2str(yForward) , ‘ , ‘ num2str(zForward), ‘)’]);

semiCircleArray(index, 🙂 = [xF, yF, zF];
index = index + 1;

end

plot3(semiCircleArray(:, 1), semiCircleArray(:, 2), semiCircleArray(:, 3), ‘o’, ‘Color’, ‘b’);

% Success

% ——————————————————————— %

% Step four: inserting array values into function to confirm angles.

joint1 = zeros(pointsNum, 1);
joint2 = zeros(pointsNum, 1);
for i = 1:pointsNum
[joint1(i), joint2(i)] = theta2Output(semiCircleArray(i, 3), semiCircleArray(i, 1));
disp([‘j1: ‘, num2str(joint1(i)), ‘ , j2: ‘, num2str(joint2(i))]);

end

% This is where something goes wrong, which confirms there is an issue
% with my function. The value of one joint angle slowly increments
% whilst the other stays unchanged.

title(‘Right Leg – J1 J2 J3’);
xlabel(‘x-axis’);
ylabel(‘y-axis’);
zlabel(‘z-axis’);

function [joint1 , joint2] = theta2Output(x, z)
l1 = 107.92;
l2 = 133.99;
x = x(:);
z = z(:);
term1 = x.^2;
term2 = z.^2;
term3 = l1^2;
term4 = l2^2;

cos_joint2 = (term1 + term2 – term3 – term4) / (2 * l2 * l1);
joint2 = acos(cos_joint2);

joint1 = atan2(x , z) – atan2((l2 * sin(joint2)) , (l1 + (l2*cos(joint2))));

end % This is my current code. I am attempting to use inverse kinematics to
% plot the same semicircle i made using forward kinematics. My issue is
% that the function i am currently using doesn’t properly calculate the
% "joint2" angle (it prints the same value always)

% NOTE: the error i am having occurs mostly in STEP 4 of my code.

clear;
clc;

% Step one: Defining linked robot and confirming it is correct
L_1(1) = Link([0 199.07 0 pi/2]);
L_1(2) = Link([0 -27 -23.33 -pi/2]);
L_1(3) = Link([0 -12.18 0 0]);
L_1(4) = Link([0 0 0 -pi/2]);
L_1(5) = Link([0 0 107.92 0]);
L_1(6) = Link([0 0 0 0]);
L_1(7) = Link([0 0 133.99 0]);

robot1 = SerialLink(L_1);

figure(2);

robot1.plot([pi/2 0 pi/2 0 0.6542 1.36 0], ‘nojoints’);
hold on;
% Success

% ——————————————————————— %

% Step two: retrieving end effector coordinates and confirming thier
% position
coor = robot1.fkine([pi/2 0 pi/2 0 0.6542 1.36 0]);
xF = coor.t(1);
yF = coor.t(2);
zF = coor.t(3);
% disp([‘(‘, num2str(xF), ‘ , ‘, num2str(yF) , ‘ , ‘ num2str(zF), ‘)’]);
% plot3(xF,yF,zF, ‘o’, ‘Color’, ‘g’);
% Success

% ——————————————————————— %

% Step three: plotting a semicircle

radius_S3 = 200;
pointsNum = ( pi/(pi/16) + 1);
semiCircleArray = zeros(pointsNum, 3);

index = 1;
for theta = pi : -pi/16 : 0
xF = radius_S3 * cos(theta);
yF = -23.33;
zF = radius_S3 * sin(theta);
plot3(xF,yF,zF, ‘*’, ‘Color’, ‘r’);
% disp([‘(‘, num2str(xForward), ‘ , ‘, num2str(yForward) , ‘ , ‘ num2str(zForward), ‘)’]);

semiCircleArray(index, 🙂 = [xF, yF, zF];
index = index + 1;

end

plot3(semiCircleArray(:, 1), semiCircleArray(:, 2), semiCircleArray(:, 3), ‘o’, ‘Color’, ‘b’);

% Success

% ——————————————————————— %

% Step four: inserting array values into function to confirm angles.

joint1 = zeros(pointsNum, 1);
joint2 = zeros(pointsNum, 1);
for i = 1:pointsNum
[joint1(i), joint2(i)] = theta2Output(semiCircleArray(i, 3), semiCircleArray(i, 1));
disp([‘j1: ‘, num2str(joint1(i)), ‘ , j2: ‘, num2str(joint2(i))]);

end

% This is where something goes wrong, which confirms there is an issue
% with my function. The value of one joint angle slowly increments
% whilst the other stays unchanged.

title(‘Right Leg – J1 J2 J3’);
xlabel(‘x-axis’);
ylabel(‘y-axis’);
zlabel(‘z-axis’);

function [joint1 , joint2] = theta2Output(x, z)
l1 = 107.92;
l2 = 133.99;
x = x(:);
z = z(:);
term1 = x.^2;
term2 = z.^2;
term3 = l1^2;
term4 = l2^2;

cos_joint2 = (term1 + term2 – term3 – term4) / (2 * l2 * l1);
joint2 = acos(cos_joint2);

joint1 = atan2(x , z) – atan2((l2 * sin(joint2)) , (l1 + (l2*cos(joint2))));

end inverse kinematics, serial robot MATLAB Answers — New Questions

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We have the same issue as https://www.mathworks.com/matlabcentral/answers/2128851-why-is-the-mathworks-service-host-causing-issues-with-my-cluster-and-or-hpc and I see there that "For R2024a and newer, regardless of your licensing, the MathWorks Service Host is required"
When running in a cluster, this process is repeated every time MATLAB is started on a new node.
This not only causes uneccessary delays but it also piles up lots of data on the users’ HOME space.
If we have 300 nodes, at ~640MB per node under $HOME/.MathWorks/ServiceHost a user would require more than 180GB just to host this data. This makes version 2024 not suitable for cluster environments.

How do we fix this issue?We have the same issue as https://www.mathworks.com/matlabcentral/answers/2128851-why-is-the-mathworks-service-host-causing-issues-with-my-cluster-and-or-hpc and I see there that "For R2024a and newer, regardless of your licensing, the MathWorks Service Host is required"
When running in a cluster, this process is repeated every time MATLAB is started on a new node.
This not only causes uneccessary delays but it also piles up lots of data on the users’ HOME space.
If we have 300 nodes, at ~640MB per node under $HOME/.MathWorks/ServiceHost a user would require more than 180GB just to host this data. This makes version 2024 not suitable for cluster environments.

How do we fix this issue? We have the same issue as https://www.mathworks.com/matlabcentral/answers/2128851-why-is-the-mathworks-service-host-causing-issues-with-my-cluster-and-or-hpc and I see there that "For R2024a and newer, regardless of your licensing, the MathWorks Service Host is required"
When running in a cluster, this process is repeated every time MATLAB is started on a new node.
This not only causes uneccessary delays but it also piles up lots of data on the users’ HOME space.
If we have 300 nodes, at ~640MB per node under $HOME/.MathWorks/ServiceHost a user would require more than 180GB just to host this data. This makes version 2024 not suitable for cluster environments.

How do we fix this issue? mathworks service host cluster hpc MATLAB Answers — New Questions

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I have a C++ code that I have compiled in Visual Studio 2022 as a DLL. I use MATLAB to call the DLL using the function loadlibrary. The code executes no problem. However, I want to enter the Visual Studio debugger to step through the code once MATLAB calls the DLL. Years ago in a previous version of Visual Studio and MATLAB, a friend showed me how to connect Visual Studio to MATLAB. In Visual Studio, I would select "Build->Attach to process" and pick the MATLAB process that would appear in the window. Then, I could add a breakpoint in the C code, and when MATLAB called the DLL, the Visual Studio debugger would stop code execution at the break point and let me step through the code. I’m unable to do that now for some reason. When I attach MATLAB to Visual Studio, the breakpoint turns to an empty red outline circle with a caution triangle on it. When I hover over it, the tooltip says "The breakpoint will not currently be hit. No symbols have been loaded for this document." The Symbol Status column of the Debug->Windows->Modules dialog says "Cannot find or open the PDB file." I’m unsure what to do here. I’m sure I’m just forgetting a step that I was shown years back. Any help would be appreciated. Note Visual Studio did create a pdb file along with the DLL and both are in the same directory. Also, I compiled in debug mode, not release mode.I have a C++ code that I have compiled in Visual Studio 2022 as a DLL. I use MATLAB to call the DLL using the function loadlibrary. The code executes no problem. However, I want to enter the Visual Studio debugger to step through the code once MATLAB calls the DLL. Years ago in a previous version of Visual Studio and MATLAB, a friend showed me how to connect Visual Studio to MATLAB. In Visual Studio, I would select "Build->Attach to process" and pick the MATLAB process that would appear in the window. Then, I could add a breakpoint in the C code, and when MATLAB called the DLL, the Visual Studio debugger would stop code execution at the break point and let me step through the code. I’m unable to do that now for some reason. When I attach MATLAB to Visual Studio, the breakpoint turns to an empty red outline circle with a caution triangle on it. When I hover over it, the tooltip says "The breakpoint will not currently be hit. No symbols have been loaded for this document." The Symbol Status column of the Debug->Windows->Modules dialog says "Cannot find or open the PDB file." I’m unsure what to do here. I’m sure I’m just forgetting a step that I was shown years back. Any help would be appreciated. Note Visual Studio did create a pdb file along with the DLL and both are in the same directory. Also, I compiled in debug mode, not release mode. I have a C++ code that I have compiled in Visual Studio 2022 as a DLL. I use MATLAB to call the DLL using the function loadlibrary. The code executes no problem. However, I want to enter the Visual Studio debugger to step through the code once MATLAB calls the DLL. Years ago in a previous version of Visual Studio and MATLAB, a friend showed me how to connect Visual Studio to MATLAB. In Visual Studio, I would select "Build->Attach to process" and pick the MATLAB process that would appear in the window. Then, I could add a breakpoint in the C code, and when MATLAB called the DLL, the Visual Studio debugger would stop code execution at the break point and let me step through the code. I’m unable to do that now for some reason. When I attach MATLAB to Visual Studio, the breakpoint turns to an empty red outline circle with a caution triangle on it. When I hover over it, the tooltip says "The breakpoint will not currently be hit. No symbols have been loaded for this document." The Symbol Status column of the Debug->Windows->Modules dialog says "Cannot find or open the PDB file." I’m unsure what to do here. I’m sure I’m just forgetting a step that I was shown years back. Any help would be appreciated. Note Visual Studio did create a pdb file along with the DLL and both are in the same directory. Also, I compiled in debug mode, not release mode. dll, visual-studio, debug, loadlibrary MATLAB Answers — New Questions

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I’m trying to find the minimum of a multivariable function. I’ve tried using both fminunc and fminsearch but the results seem incorrect. I’m just confused on what I’m doing wrong and why its not working.

The data:
x=[1.0e-5;1.0e-5;1.0e-5;1.4e-5;1.4e-5;1.4e-5;1.8e-5;1.8e-5;1.8e-5];
y=[1.3e-4;1.55e-4;1.7e-4;1.3e-4;1.55e-4;1.7e-4;1.3e-4;1.55e-4;1.7e-4];
z=[18.488;21.45;24.52;20.93; 26.08; 29.41;25.44;30.98;34.19];

My code:
coeffs=fit([x,y],z,’Poly22′);
[~,idx]=min(z);
x(idx)
y(idx)
xval=fminunc(coeffs,[x(idx) y(idx)])
and
A=[x(:).^[0:2] y(:).^[1:2] x(:).*y(:)];
a=Az(:);
x2=linspace(0,2.00e-5,100);
y2=linspace(0,2.00e-4,100);
[X,Y]=meshgrid(x2,y2);
M=[X(:).^(0:2) Y(:).^(1:2) X(:).*Y(:)];
Z=X;
Z(:)=M*a;
f=@(xy) -14.19 -4.028e+05.*xy(:,1) + 2.774e+05.*xy(:,2)+ 1.473e+10.*xy(:,1).^2 + 7.05e+09.*xy(:,1).*xy(:,2) + -6.256e+08.*xy(:,2).^2;
fminsearch(f,[1.00e-5 1.55e-4])
Both methods give me results that seem incorrect. The fmincon gives me results at:
xval =
1.0e+06 *
0.324231351627941 -1.604585119026901
which based on the data, doesn’t make sense at all. What am I doing wrong?I’m trying to find the minimum of a multivariable function. I’ve tried using both fminunc and fminsearch but the results seem incorrect. I’m just confused on what I’m doing wrong and why its not working.

The data:
x=[1.0e-5;1.0e-5;1.0e-5;1.4e-5;1.4e-5;1.4e-5;1.8e-5;1.8e-5;1.8e-5];
y=[1.3e-4;1.55e-4;1.7e-4;1.3e-4;1.55e-4;1.7e-4;1.3e-4;1.55e-4;1.7e-4];
z=[18.488;21.45;24.52;20.93; 26.08; 29.41;25.44;30.98;34.19];

My code:
coeffs=fit([x,y],z,’Poly22′);
[~,idx]=min(z);
x(idx)
y(idx)
xval=fminunc(coeffs,[x(idx) y(idx)])
and
A=[x(:).^[0:2] y(:).^[1:2] x(:).*y(:)];
a=Az(:);
x2=linspace(0,2.00e-5,100);
y2=linspace(0,2.00e-4,100);
[X,Y]=meshgrid(x2,y2);
M=[X(:).^(0:2) Y(:).^(1:2) X(:).*Y(:)];
Z=X;
Z(:)=M*a;
f=@(xy) -14.19 -4.028e+05.*xy(:,1) + 2.774e+05.*xy(:,2)+ 1.473e+10.*xy(:,1).^2 + 7.05e+09.*xy(:,1).*xy(:,2) + -6.256e+08.*xy(:,2).^2;
fminsearch(f,[1.00e-5 1.55e-4])
Both methods give me results that seem incorrect. The fmincon gives me results at:
xval =
1.0e+06 *
0.324231351627941 -1.604585119026901
which based on the data, doesn’t make sense at all. What am I doing wrong? I’m trying to find the minimum of a multivariable function. I’ve tried using both fminunc and fminsearch but the results seem incorrect. I’m just confused on what I’m doing wrong and why its not working.

The data:
x=[1.0e-5;1.0e-5;1.0e-5;1.4e-5;1.4e-5;1.4e-5;1.8e-5;1.8e-5;1.8e-5];
y=[1.3e-4;1.55e-4;1.7e-4;1.3e-4;1.55e-4;1.7e-4;1.3e-4;1.55e-4;1.7e-4];
z=[18.488;21.45;24.52;20.93; 26.08; 29.41;25.44;30.98;34.19];

My code:
coeffs=fit([x,y],z,’Poly22′);
[~,idx]=min(z);
x(idx)
y(idx)
xval=fminunc(coeffs,[x(idx) y(idx)])
and
A=[x(:).^[0:2] y(:).^[1:2] x(:).*y(:)];
a=Az(:);
x2=linspace(0,2.00e-5,100);
y2=linspace(0,2.00e-4,100);
[X,Y]=meshgrid(x2,y2);
M=[X(:).^(0:2) Y(:).^(1:2) X(:).*Y(:)];
Z=X;
Z(:)=M*a;
f=@(xy) -14.19 -4.028e+05.*xy(:,1) + 2.774e+05.*xy(:,2)+ 1.473e+10.*xy(:,1).^2 + 7.05e+09.*xy(:,1).*xy(:,2) + -6.256e+08.*xy(:,2).^2;
fminsearch(f,[1.00e-5 1.55e-4])
Both methods give me results that seem incorrect. The fmincon gives me results at:
xval =
1.0e+06 *
0.324231351627941 -1.604585119026901
which based on the data, doesn’t make sense at all. What am I doing wrong? optimization, fminunc, fminsearch, matlab, functions MATLAB Answers — New Questions

​

I would like to use "Optimizable KNN" for this training, but the whole NNC section is disabled for some reasons. Please help to solve this. The code is attached in the bottom.

% Define the file path
imageFolderPath = "C:UsersxxooxOneDriveデスクトップMATLAB worksDataMathWorks ImagesRoadside Ground Cover";

% Create an imageDatastore
imds = imageDatastore(imageFolderPath, …
‘IncludeSubfolders’, true, …
‘LabelSource’, ‘foldernames’);

% Display the count of each label
labelCount = countEachLabel(imds);
disp(‘Initial label count:’);
disp(labelCount);

% Split the datastore into training and testing subsets (85% training, 15% testing)
[imdsTrain, imdsTest] = splitEachLabel(imds, 0.85, ‘randomized’);

% Display the count of each label in the training datastore
labelCountTrain = countEachLabel(imdsTrain);
disp(‘Training label count:’);
disp(labelCountTrain);

% Count the images labeled as "Snow" in the training datastore
numSnowTrain = sum(imdsTrain.Labels == "Snow");
disp([‘Number of "Snow" images in the training datastore: ‘, num2str(numSnowTrain)]);

% Define the file path to the specific image
imageFilePath = fullfile(imageFolderPath, ‘No Snow’, ‘RoadsideA_1.jpg’);

% Read the image
img = imread(imageFilePath);

% Convert the image from RGB to HSV color space
hsvImage = rgb2hsv(img);

% Extract the saturation channel
saturationChannel = hsvImage(:,:,2);

% Calculate the mean saturation value
meanSaturation = mean(saturationChannel(:));

% Display the mean saturation
disp([‘The mean saturation for the "No Snow" labeled image "RoadsideA_1.jpg" is: ‘, num2str(meanSaturation)]);

% Calculate the standard deviation of the saturation values
stdSaturation = std(saturationChannel(:));

disp([‘The standard deviation of the saturation for the "No Snow" labeled image "RoadsideA_1.jpg" is: ‘, num2str(stdSaturation)]);

% Initialize arrays to store the results
fileNames = imdsTrain.Files;
labels = imdsTrain.Labels;
meanSaturations = zeros(length(fileNames), 1);
stdSaturations = zeros(length(fileNames), 1);

% Loop through each image in the training datastore
for i = 1:length(fileNames)
% Read the image
img = imread(fileNames{i});

% Convert the image from RGB to HSV color space
hsvImage = rgb2hsv(img);

% Extract the saturation channel
saturationChannel = hsvImage(:,:,2);

% Calculate the mean and standard deviation of the saturation
meanSaturations(i) = mean(saturationChannel(:));
stdSaturations(i) = std(saturationChannel(:));
end

% Create a table with the results
trainingTable = table(fileNames, labels, meanSaturations, stdSaturations, …
‘VariableNames’, {‘Filename’, ‘Label’, ‘MeanSaturation’, ‘StdSaturation’});

% Display the table
disp(trainingTable);

% Create the scatter plot
figure;
gscatter(trainingTable.MeanSaturation, trainingTable.StdSaturation, trainingTable.Label, ‘rb’, ‘ox’);
xlabel(‘Mean Saturation’);
ylabel(‘Standard Deviation of Saturation’);
title(‘Grouped Scatter Plot of Mean Saturation and Standard Deviation of Saturation’);
legend(‘No Snow’, ‘Snow’);

% Save the figure
saveas(gcf, ‘GroupedScatterPlot.png’);

% Display the result
disp(‘Scatter plot created and saved as GroupedScatterPlot.png’);I would like to use "Optimizable KNN" for this training, but the whole NNC section is disabled for some reasons. Please help to solve this. The code is attached in the bottom.

% Define the file path
imageFolderPath = "C:UsersxxooxOneDriveデスクトップMATLAB worksDataMathWorks ImagesRoadside Ground Cover";

% Create an imageDatastore
imds = imageDatastore(imageFolderPath, …
‘IncludeSubfolders’, true, …
‘LabelSource’, ‘foldernames’);

% Display the count of each label
labelCount = countEachLabel(imds);
disp(‘Initial label count:’);
disp(labelCount);

% Split the datastore into training and testing subsets (85% training, 15% testing)
[imdsTrain, imdsTest] = splitEachLabel(imds, 0.85, ‘randomized’);

% Display the count of each label in the training datastore
labelCountTrain = countEachLabel(imdsTrain);
disp(‘Training label count:’);
disp(labelCountTrain);

% Count the images labeled as "Snow" in the training datastore
numSnowTrain = sum(imdsTrain.Labels == "Snow");
disp([‘Number of "Snow" images in the training datastore: ‘, num2str(numSnowTrain)]);

% Define the file path to the specific image
imageFilePath = fullfile(imageFolderPath, ‘No Snow’, ‘RoadsideA_1.jpg’);

% Read the image
img = imread(imageFilePath);

% Convert the image from RGB to HSV color space
hsvImage = rgb2hsv(img);

% Extract the saturation channel
saturationChannel = hsvImage(:,:,2);

% Calculate the mean saturation value
meanSaturation = mean(saturationChannel(:));

% Display the mean saturation
disp([‘The mean saturation for the "No Snow" labeled image "RoadsideA_1.jpg" is: ‘, num2str(meanSaturation)]);

% Calculate the standard deviation of the saturation values
stdSaturation = std(saturationChannel(:));

disp([‘The standard deviation of the saturation for the "No Snow" labeled image "RoadsideA_1.jpg" is: ‘, num2str(stdSaturation)]);

% Initialize arrays to store the results
fileNames = imdsTrain.Files;
labels = imdsTrain.Labels;
meanSaturations = zeros(length(fileNames), 1);
stdSaturations = zeros(length(fileNames), 1);

% Loop through each image in the training datastore
for i = 1:length(fileNames)
% Read the image
img = imread(fileNames{i});

% Convert the image from RGB to HSV color space
hsvImage = rgb2hsv(img);

% Extract the saturation channel
saturationChannel = hsvImage(:,:,2);

% Calculate the mean and standard deviation of the saturation
meanSaturations(i) = mean(saturationChannel(:));
stdSaturations(i) = std(saturationChannel(:));
end

% Create a table with the results
trainingTable = table(fileNames, labels, meanSaturations, stdSaturations, …
‘VariableNames’, {‘Filename’, ‘Label’, ‘MeanSaturation’, ‘StdSaturation’});

% Display the table
disp(trainingTable);

% Create the scatter plot
figure;
gscatter(trainingTable.MeanSaturation, trainingTable.StdSaturation, trainingTable.Label, ‘rb’, ‘ox’);
xlabel(‘Mean Saturation’);
ylabel(‘Standard Deviation of Saturation’);
title(‘Grouped Scatter Plot of Mean Saturation and Standard Deviation of Saturation’);
legend(‘No Snow’, ‘Snow’);

% Save the figure
saveas(gcf, ‘GroupedScatterPlot.png’);

% Display the result
disp(‘Scatter plot created and saved as GroupedScatterPlot.png’); I would like to use "Optimizable KNN" for this training, but the whole NNC section is disabled for some reasons. Please help to solve this. The code is attached in the bottom.

% Define the file path
imageFolderPath = "C:UsersxxooxOneDriveデスクトップMATLAB worksDataMathWorks ImagesRoadside Ground Cover";

% Create an imageDatastore
imds = imageDatastore(imageFolderPath, …
‘IncludeSubfolders’, true, …
‘LabelSource’, ‘foldernames’);

% Display the count of each label
labelCount = countEachLabel(imds);
disp(‘Initial label count:’);
disp(labelCount);

% Split the datastore into training and testing subsets (85% training, 15% testing)
[imdsTrain, imdsTest] = splitEachLabel(imds, 0.85, ‘randomized’);

% Display the count of each label in the training datastore
labelCountTrain = countEachLabel(imdsTrain);
disp(‘Training label count:’);
disp(labelCountTrain);

% Count the images labeled as "Snow" in the training datastore
numSnowTrain = sum(imdsTrain.Labels == "Snow");
disp([‘Number of "Snow" images in the training datastore: ‘, num2str(numSnowTrain)]);

% Define the file path to the specific image
imageFilePath = fullfile(imageFolderPath, ‘No Snow’, ‘RoadsideA_1.jpg’);

% Read the image
img = imread(imageFilePath);

% Convert the image from RGB to HSV color space
hsvImage = rgb2hsv(img);

% Extract the saturation channel
saturationChannel = hsvImage(:,:,2);

% Calculate the mean saturation value
meanSaturation = mean(saturationChannel(:));

% Display the mean saturation
disp([‘The mean saturation for the "No Snow" labeled image "RoadsideA_1.jpg" is: ‘, num2str(meanSaturation)]);

% Calculate the standard deviation of the saturation values
stdSaturation = std(saturationChannel(:));

disp([‘The standard deviation of the saturation for the "No Snow" labeled image "RoadsideA_1.jpg" is: ‘, num2str(stdSaturation)]);

% Initialize arrays to store the results
fileNames = imdsTrain.Files;
labels = imdsTrain.Labels;
meanSaturations = zeros(length(fileNames), 1);
stdSaturations = zeros(length(fileNames), 1);

% Loop through each image in the training datastore
for i = 1:length(fileNames)
% Read the image
img = imread(fileNames{i});

% Convert the image from RGB to HSV color space
hsvImage = rgb2hsv(img);

% Extract the saturation channel
saturationChannel = hsvImage(:,:,2);

% Calculate the mean and standard deviation of the saturation
meanSaturations(i) = mean(saturationChannel(:));
stdSaturations(i) = std(saturationChannel(:));
end

% Create a table with the results
trainingTable = table(fileNames, labels, meanSaturations, stdSaturations, …
‘VariableNames’, {‘Filename’, ‘Label’, ‘MeanSaturation’, ‘StdSaturation’});

% Display the table
disp(trainingTable);

% Create the scatter plot
figure;
gscatter(trainingTable.MeanSaturation, trainingTable.StdSaturation, trainingTable.Label, ‘rb’, ‘ox’);
xlabel(‘Mean Saturation’);
ylabel(‘Standard Deviation of Saturation’);
title(‘Grouped Scatter Plot of Mean Saturation and Standard Deviation of Saturation’);
legend(‘No Snow’, ‘Snow’);

% Save the figure
saveas(gcf, ‘GroupedScatterPlot.png’);

% Display the result
disp(‘Scatter plot created and saved as GroupedScatterPlot.png’); matlab, plotting, machine learing, knn, nnc MATLAB Answers — New Questions

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