I have 3×1 positionArray such as [latitude, longitude, altitude] and units are [rad,rad,m].
This array is in the input for simulink. Is there any way to define different units for array in simulink?
In the "Signal Attributes" section, i tried:
rad, rad, m
rad_rad_m
[rad,rad,m]
{rad,rad,m}
nothing works.

Unit specifications are explained in following link, but there is no information about array units.

https://www.mathworks.com/help/simulink/ug/units-in-simulink.htmlI have 3×1 positionArray such as [latitude, longitude, altitude] and units are [rad,rad,m].
This array is in the input for simulink. Is there any way to define different units for array in simulink?
In the "Signal Attributes" section, i tried:
rad, rad, m
rad_rad_m
[rad,rad,m]
{rad,rad,m}
nothing works.

Unit specifications are explained in following link, but there is no information about array units.

https://www.mathworks.com/help/simulink/ug/units-in-simulink.html I have 3×1 positionArray such as [latitude, longitude, altitude] and units are [rad,rad,m].
This array is in the input for simulink. Is there any way to define different units for array in simulink?
In the "Signal Attributes" section, i tried:
rad, rad, m
rad_rad_m
[rad,rad,m]
{rad,rad,m}
nothing works.

Unit specifications are explained in following link, but there is no information about array units.

https://www.mathworks.com/help/simulink/ug/units-in-simulink.html signal attributes, unit, array MATLAB Answers — New Questions

​

i need to simulate lifi technology transmitter and reciever using matlab simulink….also need help to use led block in simulink….i have the led block that has terminals as anode and cathode…..need led block as having only input and output terminals as they are the final block of my transmitter section.i need to simulate lifi technology transmitter and reciever using matlab simulink….also need help to use led block in simulink….i have the led block that has terminals as anode and cathode…..need led block as having only input and output terminals as they are the final block of my transmitter section. i need to simulate lifi technology transmitter and reciever using matlab simulink….also need help to use led block in simulink….i have the led block that has terminals as anode and cathode…..need led block as having only input and output terminals as they are the final block of my transmitter section. lifi, simulink, led, visible light communication, transmitter, reciever, simulation MATLAB Answers — New Questions

​

1 month ago, I already export this data in RoadRunner.
and today I tried to export same data. but it was not working.
So how can I fix this error?1 month ago, I already export this data in RoadRunner.
and today I tried to export same data. but it was not working.
So how can I fix this error? 1 month ago, I already export this data in RoadRunner.
and today I tried to export same data. but it was not working.
So how can I fix this error? export, roadrunner MATLAB Answers — New Questions

​

Hi all,

I guess, that it is an easy issue, but I can not figure out how to apply rotatefactors on the SCORES matrix resulting from a previous PCA:

(1) I run a PCA following:

[wcoeff,score] = pca(zscore(DataSet))

(2) I would like to rotate my principal components using rotatefactors:

[NewCoeff,TransfMatrix] = rotatefactors(wcoeff);

NewCoeff should give the new coordinates of the variables on the varimax-rotated components.

My question is: How can I compute the coordinates ‘NewScores’ of my observations on the new components? My idea is to plot them on a biplot and on an other scatter-plot where each axis is a component.
I guess that there is something easy like:

NewScores = score * TransfMatrix

Am I right?

RomainHi all,

I guess, that it is an easy issue, but I can not figure out how to apply rotatefactors on the SCORES matrix resulting from a previous PCA:

(1) I run a PCA following:

[wcoeff,score] = pca(zscore(DataSet))

(2) I would like to rotate my principal components using rotatefactors:

[NewCoeff,TransfMatrix] = rotatefactors(wcoeff);

NewCoeff should give the new coordinates of the variables on the varimax-rotated components.

My question is: How can I compute the coordinates ‘NewScores’ of my observations on the new components? My idea is to plot them on a biplot and on an other scatter-plot where each axis is a component.
I guess that there is something easy like:

NewScores = score * TransfMatrix

Am I right?

Romain Hi all,

I guess, that it is an easy issue, but I can not figure out how to apply rotatefactors on the SCORES matrix resulting from a previous PCA:

(1) I run a PCA following:

[wcoeff,score] = pca(zscore(DataSet))

(2) I would like to rotate my principal components using rotatefactors:

[NewCoeff,TransfMatrix] = rotatefactors(wcoeff);

NewCoeff should give the new coordinates of the variables on the varimax-rotated components.

My question is: How can I compute the coordinates ‘NewScores’ of my observations on the new components? My idea is to plot them on a biplot and on an other scatter-plot where each axis is a component.
I guess that there is something easy like:

NewScores = score * TransfMatrix

Am I right?

Romain pca, rotatefactors, scores MATLAB Answers — New Questions

​

I want to design a single patch antenna (rectangle) and I want to output the beam pattern, S11, as a graph. Is there an expert who can make it S-band resonant frequency 3GHz??

For your information, I can’t use an antenna toolI want to design a single patch antenna (rectangle) and I want to output the beam pattern, S11, as a graph. Is there an expert who can make it S-band resonant frequency 3GHz??

For your information, I can’t use an antenna tool I want to design a single patch antenna (rectangle) and I want to output the beam pattern, S11, as a graph. Is there an expert who can make it S-band resonant frequency 3GHz??

For your information, I can’t use an antenna tool antenna, patch antenna MATLAB Answers — New Questions

​

Hi Community,
I have a stress vs. strain plot with approximately 30 different loops, and I need to calculate the area enclosed by each loop. I’ve attached a file containing the stress vs. strain data.
Any help or guidance would be greatly appreciated!
Thank you!Hi Community,
I have a stress vs. strain plot with approximately 30 different loops, and I need to calculate the area enclosed by each loop. I’ve attached a file containing the stress vs. strain data.
Any help or guidance would be greatly appreciated!
Thank you! Hi Community,
I have a stress vs. strain plot with approximately 30 different loops, and I need to calculate the area enclosed by each loop. I’ve attached a file containing the stress vs. strain data.
Any help or guidance would be greatly appreciated!
Thank you! matlab MATLAB Answers — New Questions

​

Post Content Post Content misra c 2012 violation MATLAB Answers — New Questions

​

Hey,
I have an EV model where velocity is controlled via PMSM FOC blockset and when using ideal converter, the model is correctly following the reference speed and generating torque as expected and even when I put MOSFET (Ideal Switching), the model response is as expected, however, when I parameterized the same MOSFETs using preparameterized MOSFET or even I do the parameterizaton on my own using N-Channel MOSFET using datasheet, the response is 0.
I have done the tuning via trial and error and even tried to change the gains to see even some response.
I am attaching the Model here, any expert in this modelling can have look and guide on the issue.Hey,
I have an EV model where velocity is controlled via PMSM FOC blockset and when using ideal converter, the model is correctly following the reference speed and generating torque as expected and even when I put MOSFET (Ideal Switching), the model response is as expected, however, when I parameterized the same MOSFETs using preparameterized MOSFET or even I do the parameterizaton on my own using N-Channel MOSFET using datasheet, the response is 0.
I have done the tuning via trial and error and even tried to change the gains to see even some response.
I am attaching the Model here, any expert in this modelling can have look and guide on the issue. Hey,
I have an EV model where velocity is controlled via PMSM FOC blockset and when using ideal converter, the model is correctly following the reference speed and generating torque as expected and even when I put MOSFET (Ideal Switching), the model response is as expected, however, when I parameterized the same MOSFETs using preparameterized MOSFET or even I do the parameterizaton on my own using N-Channel MOSFET using datasheet, the response is 0.
I have done the tuning via trial and error and even tried to change the gains to see even some response.
I am attaching the Model here, any expert in this modelling can have look and guide on the issue. simscape, power_electronics_control MATLAB Answers — New Questions

​

I am trying to use readBasemapImage to read satellite imagery. However, the image I get has an enormous attribution block, most of which appears to be empty space instead of attribution. Is there a way to read the basemap so that block is an appropriate size, without increasing the zoom level (for better performance panning around the image)? For some reason, if I use "Run with R2024a" in the question editor, it works fine, but doesn’t on my personal computer or laptop (image attached)?
[img, rast, attrib] = readBasemapImage("satellite", [38.7 39.1], [-77.5 -76.9], 14);
mapshow(img, rast);I am trying to use readBasemapImage to read satellite imagery. However, the image I get has an enormous attribution block, most of which appears to be empty space instead of attribution. Is there a way to read the basemap so that block is an appropriate size, without increasing the zoom level (for better performance panning around the image)? For some reason, if I use "Run with R2024a" in the question editor, it works fine, but doesn’t on my personal computer or laptop (image attached)?
[img, rast, attrib] = readBasemapImage("satellite", [38.7 39.1], [-77.5 -76.9], 14);
mapshow(img, rast); I am trying to use readBasemapImage to read satellite imagery. However, the image I get has an enormous attribution block, most of which appears to be empty space instead of attribution. Is there a way to read the basemap so that block is an appropriate size, without increasing the zoom level (for better performance panning around the image)? For some reason, if I use "Run with R2024a" in the question editor, it works fine, but doesn’t on my personal computer or laptop (image attached)?
[img, rast, attrib] = readBasemapImage("satellite", [38.7 39.1], [-77.5 -76.9], 14);
mapshow(img, rast); image MATLAB Answers — New Questions

​

Hi everyone,
First, thank you! This is a fantastic community from which I’m learning so much. This is my first question (hopefully, I’ll be able to contribute answers in the future!).
I have a system consisting of 10 elements, where each element can exist in one of four states (or classes). This means the system has 410410 possible states. For each element, I have 61 features that can be used to predict its state. I’ve experimented with different neural networks (FF networks have worked well so far), mainly focusing on predicting the labels of individual elements.
However, I’ve encountered some challenges:
The classes are naturally imbalanced.
The problem is non-deterministic, meaning two identical feature vectors can correspond to different labels.
I’ve been addressing these issues with relative success by applying techniques such as downsampling, oversampling, data augmentation, and soft labels (the latter has been the most effective).
Now, I want to predict the probability of the entire system being in each of its 410410 states. One issue I’ve noticed is that a misclassification error of 0.05 has minimal impact when the classification is close to random (e.g., 0.25), but it has a significant impact when probabilities are closer to 1 or 0.
What I’d like to do next is implement a loss function that considers the entire system rather than individual elements, while still being based on predictions for each element. My idea is to:
Take batches of 10 observations (corresponding to the 10 elements of the system).
Compute the probability of each element belonging to each of the 4 classes.
Calculate the probability of the system being in each of its 410410 possible states based on these predictions.
Sort these probabilities and use the known labels to find the index of the correct state.
Minimize this index.
Does this approach make sense? Is it feasible? And if so, how could it be implemented?
Many thanks!
DavidHi everyone,
First, thank you! This is a fantastic community from which I’m learning so much. This is my first question (hopefully, I’ll be able to contribute answers in the future!).
I have a system consisting of 10 elements, where each element can exist in one of four states (or classes). This means the system has 410410 possible states. For each element, I have 61 features that can be used to predict its state. I’ve experimented with different neural networks (FF networks have worked well so far), mainly focusing on predicting the labels of individual elements.
However, I’ve encountered some challenges:
The classes are naturally imbalanced.
The problem is non-deterministic, meaning two identical feature vectors can correspond to different labels.
I’ve been addressing these issues with relative success by applying techniques such as downsampling, oversampling, data augmentation, and soft labels (the latter has been the most effective).
Now, I want to predict the probability of the entire system being in each of its 410410 states. One issue I’ve noticed is that a misclassification error of 0.05 has minimal impact when the classification is close to random (e.g., 0.25), but it has a significant impact when probabilities are closer to 1 or 0.
What I’d like to do next is implement a loss function that considers the entire system rather than individual elements, while still being based on predictions for each element. My idea is to:
Take batches of 10 observations (corresponding to the 10 elements of the system).
Compute the probability of each element belonging to each of the 4 classes.
Calculate the probability of the system being in each of its 410410 possible states based on these predictions.
Sort these probabilities and use the known labels to find the index of the correct state.
Minimize this index.
Does this approach make sense? Is it feasible? And if so, how could it be implemented?
Many thanks!
David Hi everyone,
First, thank you! This is a fantastic community from which I’m learning so much. This is my first question (hopefully, I’ll be able to contribute answers in the future!).
I have a system consisting of 10 elements, where each element can exist in one of four states (or classes). This means the system has 410410 possible states. For each element, I have 61 features that can be used to predict its state. I’ve experimented with different neural networks (FF networks have worked well so far), mainly focusing on predicting the labels of individual elements.
However, I’ve encountered some challenges:
The classes are naturally imbalanced.
The problem is non-deterministic, meaning two identical feature vectors can correspond to different labels.
I’ve been addressing these issues with relative success by applying techniques such as downsampling, oversampling, data augmentation, and soft labels (the latter has been the most effective).
Now, I want to predict the probability of the entire system being in each of its 410410 states. One issue I’ve noticed is that a misclassification error of 0.05 has minimal impact when the classification is close to random (e.g., 0.25), but it has a significant impact when probabilities are closer to 1 or 0.
What I’d like to do next is implement a loss function that considers the entire system rather than individual elements, while still being based on predictions for each element. My idea is to:
Take batches of 10 observations (corresponding to the 10 elements of the system).
Compute the probability of each element belonging to each of the 4 classes.
Calculate the probability of the system being in each of its 410410 possible states based on these predictions.
Sort these probabilities and use the known labels to find the index of the correct state.
Minimize this index.
Does this approach make sense? Is it feasible? And if so, how could it be implemented?
Many thanks!
David neural network, classification, loss function, fast forward, trainnet MATLAB Answers — New Questions

​