Maybe a noob question but, I would like to know how XPA and XPE ISE calculate power, voltages or curent. I mean, I undestand all about pre or post implemenatation stuff and what's the differnce between those tools(and the results they give) , but those results are based on what models?
Thanks!
Related
The problem is as follows:
I want to use a forecasting algorithm to predict heat demand of a not further specified household during the next 24 hours with a time resolution of only a few minutes within the next three or four hours and lower resolution within the following hours.
The algorithm should be adaptive and learn over time. I do not have much historic data since in the beginning I want the algorithm to be able to be used in different occasions. I only have very basic input like the assumed yearly heat demand and current outside temperature and time to begin with. So, it will be quite general and unprecise at the beginning but learn from its Errors over time.
The algorithm is asked to be implemented in Matlab if possible.
Does anyone know an apporach or an algortihm designed to predict sensible values after a short time by learning and adapting to current incoming data?
Well, this question is quite broad as essentially any algorithm for forcasting or data assimilation could do this task in principle.
The classic approach I would look into first would be Kalman filtering, which is a quite general approach at least once its generalizations to ensemble Filters etc. are taken into account (This is also implementable in MATLAB easily).
https://en.wikipedia.org/wiki/Kalman_filter
However the more important part than the actual inference algorithm is typically the design of the model you fit to your data. For your scenario you could start with a simple prediction from past values and add daily rhythms, influences of outside temperature etc. The more (correct) information you put into your model a priori the better your model should be at prediction.
For the full mathematical analysis of this type of problem I can recommend this book: https://doi.org/10.1017/CBO9781107706804
In order to turn this into a calibration problem, we need:
a model that predicts the heat demand depending on inputs and parameters,
observations of the heat demand.
Calibrating this model means tuning the parameters so that the model best predicts the heat demand.
If you go for Python, I suggest to use OpenTURNS, which provides several data assimilation methods, e.g. Kalman filtering (also called BLUE):
https://openturns.github.io/openturns/latest/user_manual/calibration.html
In mathematica I am have 10 data sets. I am trying to figure out how to predict future outcomes based on this data. The data almost follows a normal distribution. I want to predict what the average curve looks like based on the data I have. Is there any way to do this?
See the documentation for
https://reference.wolfram.com/language/howto/GetResultsForFittedModels.html
or for a time series
https://reference.wolfram.com/language/ref/TimeSeriesModelFit.html
Im currently designing a robot for my undergraduate project. One of the task of this robot is to follow the wall. For the purpose I'm using a PID control system, where the reference is given from a ultrasonic sensor. So my problem here is im having a hard time tuning the PID. I know i can find the P coefficient pretty easily by plotting the desired set point range vs desired motor output speed. Even then the robot is not so stable, so i though of adding DI part of PID. But how do find out roughly the values of these coefficients without just trying out random values (manual tuning)?
Thank you so much. Much appreciated. Sorry for a unrelated post on stackExchange. I believe programmers have a deep insight thinking than an engineer ;) Have a nice day
I am making a system generator model for radar pulse compression using HW Cosimulation of Spartan 6.
On internet there are three research papers which are close to what I want to find.
You can see the models in research paper.
Two are using FIR filter for time domain compression and third one is using FFT for freq domain compression, but I can't complete the whole model and generate FIR or FFT using Core gen.
So can someone help me realize the model please? Otherwise if someone has previously worked on it and can provide me with the model files, that would be really helpful.
PS. I have almost completed the time domain model but I do not know how to find out the coefficients of FIR filter.
Thank You. Anxiously waiting for reply.
The link to three research papers are on another site where i asked the same question. I hope admin do not disapprove.
http://forums.xilinx.com/t5/DSP-Tools/Pulse-Compression/m-p/404871#M7586
I am looking for a way to combine data from a compass and gyro in order to determine attitude after the fact. I will be working with a complete data set in which the 3D compass and gyro readings have been recorded at regular intervals, but I want to recover an estimate of attitude in post-processing.
I've considered simply using a Kalman filter, since they are so well documented, but would rather use something more appropriate to a case where the complete data set is known. I have a feeling the solution is "simply" a least squares problem, but I'm hoping someone here can point me in the direction of a paper or two dealing with this problem (or problems like it).
At this point, I'm not even sure what this filter would be called, so I'm having a hard time finding useful search terms. Any help would be appreciated.
Thanks so much!
If you understand the Kalman filter in details, you can also implement the so-called Kalman smoother which operates on the complete data set.
However, let me warn you about one thing. There is no such thing as Kalman Filter for programmers. Kalman filter is difficult to understand. You won't be able to implement and use it correctly if you do not understand it.
My implementation is almost what you are looking for. I used accelerometer and gyroscopes but no compasses. It is based on this manuscript, read it first. The most detailed description I have at the moment is slides 29-32 in my presentation on sensor fusion. It is an open source project, and I plan to release an updated version of the solver in the upcoming weeks.