Please help me with this problem.

It has to satisfy these conditions below.

Simulate the motion of the double pendulum for the following two initial conditions and observe the difference in motion (butterfly effect)

Initial conditions 1: Initial angles theta=pi/2, omg=pi/2 (initial speeds are all 0)

Initial conditions 2: Initial angles theta=pi/2, omg=pi/2+0.001 (initial speeds are all 0)

Precautions 1: Use RK4

Precautions 2: fps should be 30 frames per second

Precautions 3: Calculate by changing dt=1/30/50, 1/30/100, 1/30/200, 1/30/400, etc. and find a reliable size of dt

Precautions 4: Simulate 25 seconds of exercisePlease help me with this problem.

It has to satisfy these conditions below.

Simulate the motion of the double pendulum for the following two initial conditions and observe the difference in motion (butterfly effect)

Initial conditions 1: Initial angles theta=pi/2, omg=pi/2 (initial speeds are all 0)

Initial conditions 2: Initial angles theta=pi/2, omg=pi/2+0.001 (initial speeds are all 0)

Precautions 1: Use RK4

Precautions 2: fps should be 30 frames per second

Precautions 3: Calculate by changing dt=1/30/50, 1/30/100, 1/30/200, 1/30/400, etc. and find a reliable size of dt

Precautions 4: Simulate 25 seconds of exercise Please help me with this problem.

It has to satisfy these conditions below.

Simulate the motion of the double pendulum for the following two initial conditions and observe the difference in motion (butterfly effect)

Initial conditions 1: Initial angles theta=pi/2, omg=pi/2 (initial speeds are all 0)

Initial conditions 2: Initial angles theta=pi/2, omg=pi/2+0.001 (initial speeds are all 0)

Precautions 1: Use RK4

Precautions 2: fps should be 30 frames per second

Precautions 3: Calculate by changing dt=1/30/50, 1/30/100, 1/30/200, 1/30/400, etc. and find a reliable size of dt

Precautions 4: Simulate 25 seconds of exercise double pendulum, rk4, matlab MATLAB Answers — New Questions

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Hello. Currently I am doing a testing codes. But then, I found a loop problem which is too challenging for me since I am already facing it more than ten times (I am still a newbie). Below is my coding. My goals is to process the raw data and extract the features as stated in the code. Basically the problem that I am facing is that:
Error using tabular/vertcat Duplicate table row name: ‘2004.03.15.12.0’.
Error in Third_Test (line 55) Time_feature_matrix1 = [Time_feature_matrix1; df];

The file is too big so with that I share my link to GDrive: https://drive.google.com/drive/folders/1MltnJyAUh1BJpoPdNpqOUiHfhdbGF4wV?usp=drive_link

Here the Code:
% Initialize empty tables for each bearing
Time_feature_matrix1 = table();
Time_feature_matrix2 = table();
Time_feature_matrix3 = table();
Time_feature_matrix4 = table();
% Specify the test set and bearing numbers
test_set = 3;
bearing_numbers = [1, 2, 3, 4];
% Set the path to the directory containing the data files
path = ‘file directory’;
% Get list of files in the directory
files = dir(fullfile(path, ‘*’));
% Loop through the files in the directory
for j = 1:length(files)
filename = files(j).name;
if files(j).isdir % Skip directories
continue;
end
file_path = fullfile(path, filename); % Full path of the file
dataset = readtable(file_path, ‘Delimiter’, ‘t’, ‘FileType’, ‘text’); % Read the dataset
% Loop through each bearing number
for j = 1:length(bearing_numbers)
bearing_no = bearing_numbers(j);
% Extract the bearing data
bearing_data = dataset{:, bearing_no};
% Calculate features
feature_matrix = [
max(bearing_data), % Max
min(bearing_data), % Min
mean(bearing_data), % Mean
std(bearing_data, 1), % Std
sqrt(mean(bearing_data.^2)), % RMS
compute_skewness(bearing_data), % Skewness
compute_kurtosis(bearing_data), % Kurtosis
max(bearing_data) / sqrt(mean(bearing_data.^2)), % CrestFactor
sqrt(mean(bearing_data.^2)) / mean(bearing_data) % FormFactor
];
df = array2table(feature_matrix.’); % Transpose for correct orientation
df.Properties.VariableNames = {‘Max’, ‘Min’, ‘Mean’, ‘Std’, ‘RMS’, ‘Skewness’, ‘Kurtosis’, ‘CrestFactor’, ‘FormFactor’};
df.Properties.RowNames = {[filename(1:end-4)]}; % Append bearing number
df.Properties.RowNames = {rowName};
% Append the table to the corresponding bearing’s feature matrix
switch bearing_no
case 1
if ~ismember(rowName, Time_feature_matrix1.Properties.RowNames)
Time_feature_matrix1 = [Time_feature_matrix1; df];
end
case 2
if ~ismember(rowName, Time_feature_matrix2.Properties.RowNames)
Time_feature_matrix2 = [Time_feature_matrix2; df];
end
case 3
if ~ismember(rowName, Time_feature_matrix3.Properties.RowNames)
Time_feature_matrix3 = [Time_feature_matrix3; df];
end
case 4
if ~ismember(rowName, Time_feature_matrix4.Properties.RowNames)
Time_feature_matrix4 = [Time_feature_matrix4; df];
end
% case 1
% Time_feature_matrix1 = [Time_feature_matrix1; df];
% case 2
% Time_feature_matrix2 = [Time_feature_matrix2; df];
% case 3
% Time_feature_matrix3 = [Time_feature_matrix3; df];
% case 4
% Time_feature_matrix4 = [Time_feature_matrix4; df];
end
end
end
% Define function to compute skewness
function skewness = compute_skewness(x)
n = length(x);
third_moment = sum((x – mean(x)).^3) / n;
s_3 = std(x, 1)^3;
skewness = third_moment / s_3;
end
% Define function to compute kurtosis
function kurtosis = compute_kurtosis(x)
n = length(x);
fourth_moment = sum((x – mean(x)).^4) / n;
s_4 = std(x, 1)^4;
kurtosis = fourth_moment / s_4 – 3;
endHello. Currently I am doing a testing codes. But then, I found a loop problem which is too challenging for me since I am already facing it more than ten times (I am still a newbie). Below is my coding. My goals is to process the raw data and extract the features as stated in the code. Basically the problem that I am facing is that:
Error using tabular/vertcat Duplicate table row name: ‘2004.03.15.12.0’.
Error in Third_Test (line 55) Time_feature_matrix1 = [Time_feature_matrix1; df];

The file is too big so with that I share my link to GDrive: https://drive.google.com/drive/folders/1MltnJyAUh1BJpoPdNpqOUiHfhdbGF4wV?usp=drive_link

Here the Code:
% Initialize empty tables for each bearing
Time_feature_matrix1 = table();
Time_feature_matrix2 = table();
Time_feature_matrix3 = table();
Time_feature_matrix4 = table();
% Specify the test set and bearing numbers
test_set = 3;
bearing_numbers = [1, 2, 3, 4];
% Set the path to the directory containing the data files
path = ‘file directory’;
% Get list of files in the directory
files = dir(fullfile(path, ‘*’));
% Loop through the files in the directory
for j = 1:length(files)
filename = files(j).name;
if files(j).isdir % Skip directories
continue;
end
file_path = fullfile(path, filename); % Full path of the file
dataset = readtable(file_path, ‘Delimiter’, ‘t’, ‘FileType’, ‘text’); % Read the dataset
% Loop through each bearing number
for j = 1:length(bearing_numbers)
bearing_no = bearing_numbers(j);
% Extract the bearing data
bearing_data = dataset{:, bearing_no};
% Calculate features
feature_matrix = [
max(bearing_data), % Max
min(bearing_data), % Min
mean(bearing_data), % Mean
std(bearing_data, 1), % Std
sqrt(mean(bearing_data.^2)), % RMS
compute_skewness(bearing_data), % Skewness
compute_kurtosis(bearing_data), % Kurtosis
max(bearing_data) / sqrt(mean(bearing_data.^2)), % CrestFactor
sqrt(mean(bearing_data.^2)) / mean(bearing_data) % FormFactor
];
df = array2table(feature_matrix.’); % Transpose for correct orientation
df.Properties.VariableNames = {‘Max’, ‘Min’, ‘Mean’, ‘Std’, ‘RMS’, ‘Skewness’, ‘Kurtosis’, ‘CrestFactor’, ‘FormFactor’};
df.Properties.RowNames = {[filename(1:end-4)]}; % Append bearing number
df.Properties.RowNames = {rowName};
% Append the table to the corresponding bearing’s feature matrix
switch bearing_no
case 1
if ~ismember(rowName, Time_feature_matrix1.Properties.RowNames)
Time_feature_matrix1 = [Time_feature_matrix1; df];
end
case 2
if ~ismember(rowName, Time_feature_matrix2.Properties.RowNames)
Time_feature_matrix2 = [Time_feature_matrix2; df];
end
case 3
if ~ismember(rowName, Time_feature_matrix3.Properties.RowNames)
Time_feature_matrix3 = [Time_feature_matrix3; df];
end
case 4
if ~ismember(rowName, Time_feature_matrix4.Properties.RowNames)
Time_feature_matrix4 = [Time_feature_matrix4; df];
end
% case 1
% Time_feature_matrix1 = [Time_feature_matrix1; df];
% case 2
% Time_feature_matrix2 = [Time_feature_matrix2; df];
% case 3
% Time_feature_matrix3 = [Time_feature_matrix3; df];
% case 4
% Time_feature_matrix4 = [Time_feature_matrix4; df];
end
end
end
% Define function to compute skewness
function skewness = compute_skewness(x)
n = length(x);
third_moment = sum((x – mean(x)).^3) / n;
s_3 = std(x, 1)^3;
skewness = third_moment / s_3;
end
% Define function to compute kurtosis
function kurtosis = compute_kurtosis(x)
n = length(x);
fourth_moment = sum((x – mean(x)).^4) / n;
s_4 = std(x, 1)^4;
kurtosis = fourth_moment / s_4 – 3;
end Hello. Currently I am doing a testing codes. But then, I found a loop problem which is too challenging for me since I am already facing it more than ten times (I am still a newbie). Below is my coding. My goals is to process the raw data and extract the features as stated in the code. Basically the problem that I am facing is that:
Error using tabular/vertcat Duplicate table row name: ‘2004.03.15.12.0’.
Error in Third_Test (line 55) Time_feature_matrix1 = [Time_feature_matrix1; df];

The file is too big so with that I share my link to GDrive: https://drive.google.com/drive/folders/1MltnJyAUh1BJpoPdNpqOUiHfhdbGF4wV?usp=drive_link

Here the Code:
% Initialize empty tables for each bearing
Time_feature_matrix1 = table();
Time_feature_matrix2 = table();
Time_feature_matrix3 = table();
Time_feature_matrix4 = table();
% Specify the test set and bearing numbers
test_set = 3;
bearing_numbers = [1, 2, 3, 4];
% Set the path to the directory containing the data files
path = ‘file directory’;
% Get list of files in the directory
files = dir(fullfile(path, ‘*’));
% Loop through the files in the directory
for j = 1:length(files)
filename = files(j).name;
if files(j).isdir % Skip directories
continue;
end
file_path = fullfile(path, filename); % Full path of the file
dataset = readtable(file_path, ‘Delimiter’, ‘t’, ‘FileType’, ‘text’); % Read the dataset
% Loop through each bearing number
for j = 1:length(bearing_numbers)
bearing_no = bearing_numbers(j);
% Extract the bearing data
bearing_data = dataset{:, bearing_no};
% Calculate features
feature_matrix = [
max(bearing_data), % Max
min(bearing_data), % Min
mean(bearing_data), % Mean
std(bearing_data, 1), % Std
sqrt(mean(bearing_data.^2)), % RMS
compute_skewness(bearing_data), % Skewness
compute_kurtosis(bearing_data), % Kurtosis
max(bearing_data) / sqrt(mean(bearing_data.^2)), % CrestFactor
sqrt(mean(bearing_data.^2)) / mean(bearing_data) % FormFactor
];
df = array2table(feature_matrix.’); % Transpose for correct orientation
df.Properties.VariableNames = {‘Max’, ‘Min’, ‘Mean’, ‘Std’, ‘RMS’, ‘Skewness’, ‘Kurtosis’, ‘CrestFactor’, ‘FormFactor’};
df.Properties.RowNames = {[filename(1:end-4)]}; % Append bearing number
df.Properties.RowNames = {rowName};
% Append the table to the corresponding bearing’s feature matrix
switch bearing_no
case 1
if ~ismember(rowName, Time_feature_matrix1.Properties.RowNames)
Time_feature_matrix1 = [Time_feature_matrix1; df];
end
case 2
if ~ismember(rowName, Time_feature_matrix2.Properties.RowNames)
Time_feature_matrix2 = [Time_feature_matrix2; df];
end
case 3
if ~ismember(rowName, Time_feature_matrix3.Properties.RowNames)
Time_feature_matrix3 = [Time_feature_matrix3; df];
end
case 4
if ~ismember(rowName, Time_feature_matrix4.Properties.RowNames)
Time_feature_matrix4 = [Time_feature_matrix4; df];
end
% case 1
% Time_feature_matrix1 = [Time_feature_matrix1; df];
% case 2
% Time_feature_matrix2 = [Time_feature_matrix2; df];
% case 3
% Time_feature_matrix3 = [Time_feature_matrix3; df];
% case 4
% Time_feature_matrix4 = [Time_feature_matrix4; df];
end
end
end
% Define function to compute skewness
function skewness = compute_skewness(x)
n = length(x);
third_moment = sum((x – mean(x)).^3) / n;
s_3 = std(x, 1)^3;
skewness = third_moment / s_3;
end
% Define function to compute kurtosis
function kurtosis = compute_kurtosis(x)
n = length(x);
fourth_moment = sum((x – mean(x)).^4) / n;
s_4 = std(x, 1)^4;
kurtosis = fourth_moment / s_4 – 3;
end data processing MATLAB Answers — New Questions

​

I have some queries related to the speed of pretrained networks used for feature extraction.
Query 1:
I am using pre-trained network Googlenet (installed from MATLAB Add-Ons) for feature extraction from images. Right now, I am able to extract features from 5 frames in one second. If I do not have option to enhance the computational power of my machine (on which I am performing feature extraction), how can I increase the speed of feature extraction using the same network (means how can I be able to extract features from, say 20 or 30 frames per second).
Query 2:
What are the different factors that determine the speed of pre-trained networks for feature extraction? Can anyone please elaborate this one?
If my queries are not clear, feel free to comment.I have some queries related to the speed of pretrained networks used for feature extraction.
Query 1:
I am using pre-trained network Googlenet (installed from MATLAB Add-Ons) for feature extraction from images. Right now, I am able to extract features from 5 frames in one second. If I do not have option to enhance the computational power of my machine (on which I am performing feature extraction), how can I increase the speed of feature extraction using the same network (means how can I be able to extract features from, say 20 or 30 frames per second).
Query 2:
What are the different factors that determine the speed of pre-trained networks for feature extraction? Can anyone please elaborate this one?
If my queries are not clear, feel free to comment. I have some queries related to the speed of pretrained networks used for feature extraction.
Query 1:
I am using pre-trained network Googlenet (installed from MATLAB Add-Ons) for feature extraction from images. Right now, I am able to extract features from 5 frames in one second. If I do not have option to enhance the computational power of my machine (on which I am performing feature extraction), how can I increase the speed of feature extraction using the same network (means how can I be able to extract features from, say 20 or 30 frames per second).
Query 2:
What are the different factors that determine the speed of pre-trained networks for feature extraction? Can anyone please elaborate this one?
If my queries are not clear, feel free to comment. deep learning, pre-trained networks, googlenet, machine learning, feature extraction, speed of neural networks MATLAB Answers — New Questions

​

Whenever I try to open the matlab coder from MATLAB, the MATLAB will crash and gives this error
Gtk-Message: 12:36:13.523: Failed to load module "overlay-scrollbar"
[0527/123639.510619:ERROR:gl_utils.cc(319)] [.WebGL-0x25dc130]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels
I am using MATLAB R2022b, the Ubuntu version is 18.04.6 and the GNU version is gcc 11.4. Can somebody help explain this error?Whenever I try to open the matlab coder from MATLAB, the MATLAB will crash and gives this error
Gtk-Message: 12:36:13.523: Failed to load module "overlay-scrollbar"
[0527/123639.510619:ERROR:gl_utils.cc(319)] [.WebGL-0x25dc130]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels
I am using MATLAB R2022b, the Ubuntu version is 18.04.6 and the GNU version is gcc 11.4. Can somebody help explain this error? Whenever I try to open the matlab coder from MATLAB, the MATLAB will crash and gives this error
Gtk-Message: 12:36:13.523: Failed to load module "overlay-scrollbar"
[0527/123639.510619:ERROR:gl_utils.cc(319)] [.WebGL-0x25dc130]GL Driver Message (OpenGL, Performance, GL_CLOSE_PATH_NV, High): GPU stall due to ReadPixels
I am using MATLAB R2022b, the Ubuntu version is 18.04.6 and the GNU version is gcc 11.4. Can somebody help explain this error? matlab coder, crash, ubuntu 18.04, matlab r2022b MATLAB Answers — New Questions

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