I am creating model for electrical system (power supply with digital control) in Modelica.
I use Modelica.Electrical.Analog.Sources.PulseVoltage and Modelica.Blocks.Nonlinear.FixedDelay I want to connect them.
How to do a mix (interconnect) between Blocks.Nonlinear and Electrical components?
I need to generate pulses to my transistors which are shifted so I decided to use FixedDelay.
Is there better way to do a delay of pulsing voltage?
Best regards,
Peter
Instead of using Modelica.Electrical.Analog.Sources.PulseVoltage as source you can use Modelica.Blocks.Sources.Pulse and connect this with Modelica.Blocks.Nonlinear.FixedDelay and add finally Modelica.Electrical.Analog.Sources.SignalVoltage in order to generate an actual voltage out of your delayed pulses. I'm not sure if this is the best solution for the application you have but at least that way you would get delayed voltage pulses :)
/Dietmar/
"For nowadays my solution is"
Modelica.Electrical.Analog.Ideal.IdealClosingSwitch Switch1
Modelica.Blocks.Nonlinear.FixedDelay Delay1
Modelica.Blocks.Sources.Pulse Pulse1
Modelica.Blocks.Math.RealToBoolean Realtoboolean1
connect(Pulse1.y,Delay1.u)
connect(Delay1.y,Realtoboolean1.u)
connect(Realtoboolean1.y, Switch1.control)
I am not sure is perfect but it works...
Further I need to add digital control - another challenge
Best regards,
Peter
Related
I have been working with Anylogic for about 6 months now and my goal is to model a generic energy supply chain for an energy demand (e.g. storm and heat for a house). As a result I want to evaluate how suitable the components in the energy supply chain are to meet the energy demand.
My idea would be to model the components (Ex. PV->Battery Storage->House) as agents. I would have modeled the energy flow in the agents with SD and individual events of the components (e.g. charging and discharging at the battery) via state diagrams.
Currently I have two problems:
Which possibilities are there to create a variable interconnection of my components (agents). For example, if I do not want to evaluate the scenario PV->Battery Storage->House, but PV->Electrolysis->Tank->Fuel Cell->House. My current approach would be to visually connect the agents with ports and connectors and then pass input and output variables for DS calculation via set and get functions. Are there other possibilities, e.g. to realize such a connection via an input Excel? I have seen a similar solution in the video: "How to Build a True Digital Twin with Self-Configuring Models Using the Material Handling Library" by Benjamin Schumann, but I am not sure if this approach can be applied to SD.
To evaluate the energy supply chain, I would like to add information to the energy flow, for example the type (electricity, heat), generation price (depending on which components the energy flow went through) and others. Is there a way to add this information to a flow in SD? My current approach would be to model the energy flow as an agent population with appropriate parameters and variables. Then agents could die when energy is consumed or converted from electricity to heat type. However, I don't know if this fits with the SD modeling of the energy flow.
Maybe you can help me with my problems? I would basically be interested in the opinion of more experienced Anylogic users if my approaches would be feasible or if there are other or easier approaches. If you know of any tutorial videos or example models that address similar problems, I would also be happy to learn from them.
Best
Christoph
Sounds like what you need is a model that combine agent-based and system dynamics approaches with Agents populating the stocks (in your case energy that then gets converted into heat) depending on their connection. There is an example of AB-SD combination model in 'Example' models and I also found one on cloud.anylogic.com, although it is from a different domain.
Perhaps if you can put together a simple example and share then I'll be able to provide more help.
http://python.dronekit.io/develop/sitl_setup.html
there's a. mentioning that SITL is there for only a few pre-built vehicles. suppose i am designing a completely new model of flying vehicle, can i still simulate that?
Thanks a lot:) My drone design just has another set of mathematical expressions for its roll, pitch and yaw control. So, technically I need changes only in the AP_motors.cpp file (here the commands are converted into motor PWM values) only right? or is it more than that? Please guide me.
You can actually, but at this time there is no detailed documentation to do so. At the moment the best bet is by using RealFlight 8 to design the vehicle (and also to simulate the physics) and then use SITL to control it. You can check how to connect RealFlight to SITL at here http://ardupilot.org/dev/docs/sitl-with-realflight.html. There is also a discussion about building a custom model for RealFlight 8 with SITL at here https://discuss.ardupilot.org/t/building-realflight8-models/23106/34.
Hi: I want to do a sound waves simulation that include wave propagation, absorbing and reflection in 3D space.
I do some searches and I found this question in stackoverflow but it talk about electromagnetic waves not sound waves.
I know i can reimplement the FDTD method for sound waves but how about the sources and does it act like the electromagnetic waves ? Is there any resources to start with ?
Thanks in advance.
Hope this can give you some inputs...
As far as i know, in EM simulations obstacles (and thus terrain) are not considered at all. With sound you have to consider reflection, diffraction, etc
there are different standards to calculate the noise originated from different sources (I'll list the europe ones, the one i know of):
traffic, NMPB (NMPB-Routes-96) is THE standard. All the noise calculations have to be done with that one (at least in my country). Results aren't very good. A "new" algorithm is SonRoad (i think it uses inverse ray-tracing)... from my tests it works great.
trains: Schall03
industries, ISO 9613
a list of all the used models in CadnaA (a professional software) so you can google them all: http://www.datakustik.com/en/products/cadnaa/modeling-and-calculation/calculation-standards/
another pro software is SoundPlan, somewhere on the web there is a free "SoundPlan-ReferenceManual.pdf" 800-pages with the mathematical description of the implemented algorithms... i haven't had any luck with google today tough
An easy way to do this is use the SoundPlan software. Multiple sound propagation methods such as ISO9613-2, CONCAWE and Nord2000 are implemented. It has basic 3D visualization with sound pressure level contours.
I want to compare 2 audio files programmatically.
For example: I have a sound file in my iPhone app, and then I record another one. I want to check if the existing sound matches the recorded sound or not ( - similar to voice recognition).
How can I accomplish this?
Have a server doing audio fingerprinting computation that is not suitable for mobile device anyway. And then your mobile app uploads your files to the server and gets the analysis result for display. So I don't think programming language implementing it matters much. Following are a few AF implementations.
Java: http://www.redcode.nl/blog/2010/06/creating-shazam-in-java/
VC++: http://code.google.com/p/musicip-libofa/
C#: https://web.archive.org/web/20190128062416/https://www.codeproject.com/Articles/206507/Duplicates-detector-via-audio-fingerprinting
I know the question has been asked a long time ago, but a clear answer could help someone else.
The libraries from Echoprint ( website: echoprint.me/start ) will help you solve the following problems :
De-duplicate a big collection
Identify (Track, Artist ...) a song on a hard drive or on a server
Run an Echoprint server with your data
Identify a song on an iOS device
PS: For more music-oriented features, you can check the list of APIs here.
If you want to implement Fingerprinting by yourself, you should read the docs listed as references here, and probably have a look at musicip-libofa on Google Code
Hope this will help ;)
Apply bandpass filter to reduce noise
Normalize for amplitude
Calculate the cross-correlation
It can be fairly Mhz intensive.
The DSP details are in the well known text:
Digital Signal Processing by
Alan V. Oppenheim and Ronald W. Schafer
I think as well you may try to select a few second sample from both audio track, mnormalise them in amplitude and reduce noise with a band pass filter and after try to use a correlator.
for instance you may take a 5 second sample of one of the thwo and made it slide over the second one computing a cross corelation for any time you shift. (be carefull that if you take a too small pachet you may have high correlation when not expeced and you will soffer the side effect due to the croping of the signal and the crosscorrelation).
After yo can collect an array with al the results of the cross correlation and get the index of the maximun.
You should then set experimentally up threshould o decide when yo assume the pachet to b the same. this will change depending on the quality of the audio track you are comparing.
I implemented a correator to receive and distinguish preamble in wireless communication. My script is actually done in matlab. if you are interested i can try to find the common part and send it to you.
It would be a too long code to be pasted hene in the forum. if you want just let me know and i will send it to ya asap.
cheers
Yamaha InfoSound and ShopKick application use technologies that allow to transfer data using ultrasound. That is playing an inaudible signal (>18kHz) that can be picked up by modern mobile phones (iOS, Android).
What is the approach used in such technologies? What kind of modulation they use?
I see several problems with this approach. First, 18kHz is not inaudible. Many people cannot hear it, especially as they age, but I know I certainly can (I do regular hearing tests, work-related). Also, most phones have different low-pass filters on their A/D converters, and many devices, especially older Android ones (I've personally seen that happen), filter everything below 16 kHz or so. Your app therefore is not guaranteed to work on any hardware. The iPhone should probably be able to do it.
In terms of modulation, it could be anything really, but I would definitely rule out AM. Sound has next to zero robustness when it comes to volume. If I were to implement something like that, I would go with FSK. I would think that PSK would fail due to acoustic reflections and such. The difficulty is that you're working with non-robust energy transfer within a very narrow bandwidth. I certainly do not doubt that it can be achieved, but I don't see something like this proving reliable. Just IMHO, that is.
Update: Now that i think about it, a plain on-off would work with a single tone if you're not transferring any data, just some short signals.
Can't say for Yamaha InfoSound and ShopKick, but what we used in our project was a variation of frequency modulation: the frequency of the carrier is modulated by a digital binary signal, where 0 and 1 correspond to 17 kHz and 18 kHz respectively. As for demodulator, we tried heterodyne. More details you could find here: http://rnd.azoft.com/mobile-app-transering-data-using-ultrasound/
There's nothing special in being ultrasound, the principle is the same as data transmission through a modem, so any digital modulation is -in principle- feasible. You only have a specific frequency band (above 18khz) and some practical requisites (the medium is very unreliable, I guess) that suggest to use a simple-robust scheme with low-bit rate.
I don't know how they do it but this is how I do it:
If it is a string then make sure it's not a long one (the longer the higher is the error probability ). Lets assume we're working with the vital part of the ASCII code, namely up to character number 127, then all you need is 7 bits per character. Transform this character into bits and modulate those bits using QFSK (there are several modulations to choose from, frequency shift based ones have turned out to be the most robust I've tried from the conventional ones... I've created my own modulation scheme for this use case). Select the carrier frequencies as 18.5,19,19.5, and 20 kHz (if you want to be mathematically strict in your design, select frequency values that assure you both orthogonality and phase continuity at symbol transitions, if you can't, a good workaround to avoid abrupt symbols transitions is to multiply your symbols by a window of the same size, eg. a Gaussian or Bartlet ). In my experience you can move this values in the range from 17.5 to 20.5 kHz (if you go lower it will start to bother people using your app, if you go higher the average type microphone frequency response will attenuate your transmission and induce unwanted errors).
On the receiver side implement a correlation or matched filter receiver (an FFT receiver works as well, specially a zero padded one but it might be a little bit slower, I wouldn't recommend Goertzel because frequency shift due to Doppler effect or speaker-microphone non-linearities could affect your reception). Once you have received the bit stream make characters with them and you will recover your message
If you face too many broadcasting errors, try selecting a higher amount of samples per symbol or band-pass filtering each frequency value before giving them to the demodulator, using an error correction code such as BCH or Reed Solomon is sometimes the only way to assure an error free communication.
One topic everybody always forgets to talk about is synchronization (to know on the receiver side when the transmission has begun), you have to be creative here and make a lot a tests with a lot of phones before you can derive an actual detection threshold that works on all, notice that this might also be distance dependent
If you are unfamiliar with these subjects I would recommend a couple of great books:
Digital Modulation Techniques from Fuqin Xiong
DIGITAL COMMUNICATIONS Fundamentals and Applications from BERNARD SKLAR
Digital Communications from John G. Proakis
You might have luck with a library I created for sound-based modems, libquiet. It gives you a handful of profiles to work from, including a slow "Ultrasonic whisper" profile with spectral content above 19kHz. The library is written in C but would require some work to interface with iOS.