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Is there an open-source heat exchanger library written in Modelica which covers different types of heat exchangers? Like on this web page https://en.wikipedia.org/wiki/Heat_exchanger
Based on your previous posts and your Wikipedia link I assume that you need heat exchanger models for power plant simulation.
The one in MSL (Modelica.Fluid.Examples.HeatExchanger) is a generic, discretized model, not calculating heat transfer/pressure drop from plate or tube/shell geometry or condensation/evaporation as can be found in various norms and textbooks. However, you can extend the model to include e.g. number of tube tiers/rows etc. and you can add your own two-phase heat transfer models based on Modelica.Fluid.Pipes.BaseClasses.HeatTransfer.PartialFlowHeatTransfer. But this requires a bit of work from your side.
Otherwise, there are a number of freely available options — among others:
ClaRa library by XRG/TLK Thermo (www.claralib.com) is extremely capable and aimed for power plant simulation. Note that the version you can find on GitHub is not the official library maintained by the original authors.
ThermoPower by Francesco Casella (github.com/casella/ThermoPower) has been around for many years and its models are well proven and documented.
Modelica Buildings Library (github.com/lbl-srg/modelica-buildings) has a number of different heat exchanger models that are all very easy to use but are without geometrical information. This library uses MSL fluid connectors.
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I found it much better on visualisation but not many people using it. Why isn't mathematica not as popular as matlab or python?
MATLAB and Mathematica are different packages for different user groups. Both software tools have similar freeware versions including Octave (MATLAB), Maxima (Mathematica), and others.
MATLAB has realized its objectives as being a software package for prototyping, idea testing, and simulations. It is more popular with engineers and some scientists. This popularity is based, in part, on marketing to students and instructors, ease of use, and availability.
Mathematica is a difficult and yet powerful tool. It has a set of language options and the usage has been personally frustrating. I was excited about integration with R language, but I have found that integration to be lacking, so far. It is more popular with a few engineers, some scientists, and mathematicians. Mathematica's latest editions have desirable licensing for students and home users. While not free, expense is reasonable.
Both tools have limited use in some industries.
Licensing is much more expensive for industrial partners. Industry continues to tighten its belt. Therefore, if you plan to be in industry, it may be highly beneficial to experience the freeware options, which nowadays includes the packages mentioned above as well as R, Python, and others.
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Apple introduced Core ML. There are many third parties providing trained models.
But what if I want to create a model myself? How can I do that and what tools & technologies can I use?
Core ML doesn't provide a way to train your own models. You only can convert existing ones to Apple 'mlmodel' format.
To create your own neural networks, use Caffe or Keras frameworks and then convert those models to CoreML format. For traditional machine learning algorithms Core ML is also compatible with Scikit-learn* and XGBoost.
You can also train and run neural networks on iOS without Core ML, just use Caffe 2 or TensorFlow. I support a long list of iOS-compatible machine learning libraries, if you're interested.
All libraries mentioned above have a great documentation and a lot of tutorials for you to start creating your own models.
*LibSVM is also compatible, but scikit-learn uses it under the hood anyway.
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Does anyone know of an online resource where I can find stochastic matrices for an nth order Markov chain describing the probability of a note being played based on the previous n notes (for different musical genres, if possible)? I am looking for something similar to the second-order matrix found on this page: http://algorithmiccomposer.com/2010/04/openmusic-markov-chains-and-omlea.html
If not, or otherwise, what would be the best way to construct such a matrix for each genre? The article states that this can be done by hand or by analysing existing pieces of music. How could large amounts of music for each genre be processed to generate these matrices?
I have been doing research on this topic. The matrix you are looking for is highly dependant upon what kind of music you want to generate.
One of the people I work with wrote this paper that is the method used for this. It is based on using viewpoint to look at the music and then basically creating a transition matrix for all these viewpoints: http://www.ehu.es/cs-ikerbasque/conklin/papers/jnmr95.pdf
You can contact me if you need more specific info or collaboration.
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I am new in the field of neural networks and would like to learn how to implement NNs in MATLAB. Can I have links for some step-by-step explanation of NNs using MATLAB?
It would be really helpful if applications deal with examples in Civil Engineering.
You can take a course of Andrew Ng (I believe he made most of us young people discover machine learning and become interested in this field):
https://class.coursera.org/ml-004/class
The homework includes the neural network coding by matlab.
IF you're using 2013a or later you can try typing in command window:
nnstar
They have a lot of examples with various data and different types of problems, and also after training you can have all the code you need to write the same thing on your own.
Also, you can check here the same thing, code with explanation:
Matlab Neural Network Toolbox
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I have a requirement of building an Inertial Measurement Unit (IMU) from the following sensors:
Accelerometer
Gyroscope
Magnetometer
I must integrate this data to derive the attitude of the sensor platform and the external forces involved (eg. subtract tilt from linear acceleration).
I must then use this information to compliment a standard GPS unit to provide higher consistent measurements than can be provided by GPS alone.
I do understand the basic requirements of this problem:
Integrate sensors. (To cancel noise, subtract acceleration).
Remove noise. (Kalman filter)
Integrate IMU measurement into GPS.
Whilst there are various libraries currently around that would do this for me (http://code.google.com/p/sf9domahrs/) I need to understand the mechanisms involved to a level where I am able to explain the techniques to other individuals after I have implemented the solution.
I have been looking at the following resources, but I am unsure which I should go for...
I need something covering Sensor Fusion, Filtering, IMU, Integration.
Multisensor-Fusion-Integration-Intelligent-Systems
Positioning-Systems-Inertial-Navigation-Integration
Mechatronics-Intelligent-Systems-Off-road-Vehicles
Autonomous-Flying-Robots-Unmanned-Vehicles
I hope someone experienced in this area can provide any recommendations.
Many thanks.
I have implemented sensor fusion for the Shimmer platform. These have been a big help:
An introduction to inertial navigation
An Introduction to the Kalman Filter
Pedestrian Localisation for Indoor Environments