Can you draw the petri net model of Travelling salesman problem?
Is it possible in petri net to have places with different kinds?
Related
I have generated a Kaplan Meier survival curve on the consumer data (the event of interest is 'Churn'). I have the survival curve for both buyers and nonbuyers. Before jumping into the use of the model. I want to know how I can evaluate the truthness of the curve?
I have already tried creating a separate curve for the two different consumers cohorts (who joined in a different year) for the span of 36 months. I noticed that these curves are not similar at all. I believe this is not the right way to evaluate. Can somebody tell me what can be tried to evaluate the survival curve apart from the statistical methods?
I have made measurements on sensors (light,humidity etc.) and I result in statistical curves/graphs. When I do the same experiments, I get a curve that looks like the previous in general, not the same of course. What I want is to model the curve and result to an equation so that when I run the experiment again and take a similar curve(graph) to say this is light sensor, or this is humidity sensor..etc. The problem is that I do not know whether this is feasible, and where to start from.. Do I need Machine Learning? Something else? Thanks...
You can use simple neural network which would learn how to determine type of sensor given measurement. To train the neural net you need data which means you would need to gather several dozens or hundreds of measurements and label them (the more data the more accurate predictions from neural network)
Hovewer, if the measurements for given sensor are very similar and from specified range, you don't really need machine learning. You just need to compute to which type of sensor your new measurement is the most similar to.
One possible approach would be to :
Take a few measures for each class of sensor
For each class create a vector of fixed length that would contain averaged values of measurement, for example if your light sensor measurements from 3 experiments look like this:
[1,4,5,3,8]
[1,3,4,3,7]
[1,3,5,3,6]
Then you average it to single vector:
[1, 3.33, 4.66, 3, 7]
When you take a new measure and want to determine it's class, you compute Mean Absolute Error of the new measurement for avereged vector of each class. The class with the lowest error is the sensor that the measurement was taken with
I'm working on rolling noise emissions from cars.
I have a model for rolling noise emissions which gives me sound pressure levels in third octave bands (29 in total, between 20Hz and 8kHz), depending on vehicle speed, and road/tyre combinaison.
I'd like to fit a real recording of tire/road noise to the model, while keeping the spectral properties of the recording. The signal of rolling noise is mainly stochastic
In blue, the spectrum of the recording using Welch's method, and in red, the levels i'd like to reach.
What kind of methods can i use in this particular case?
Thank you for your help!
This is quite dependent on the structure of the model you're trying to fit, but you should be able to use an optimization method (such as fmincon in MATLAB) to adjust the model parameters until you reach an acceptable level of error between your measurements and the model.
I am studying the rasterization algorithm and try to make a list of papers which were seminal in this area. For example "A Parallel Algorithm for Polygon Rasterization" would be one.
The one or group of papers I am looking for at the moment, are the papers that introduced the concept of interpolating vertex attributes (RGB, n, st, etc.) across the surface of a triangle using the inverse projection method.
Basically, my goal is to get back to the source of the technique.
Any other fundamental/seminal paper you could actually recommend in this area would be helpful as well. Thanks
To answer the question in part, the Wikipedia article on Gouraud shading mentions Gouraud's PhD thesis and apparently a follow-up paper as sources.
Gouraud, Henri (1971). Computer Display of Curved Surfaces, Doctoral
Thesis. University of Utah. Gouraud, Henri (1971).
"Continuous shading
of curved surfaces". IEEE Transactions on Computers C–20 (6): 623–629.
doi:10.1109/T-C.1971.223313.
I have been doing research in the traveling salesman problem, and I have a question about how it is formulated. Or this might be a question on classification or name of sub-problems or variations on the problem.
In the traveling salesman problem are the cities places in a space and the distances between the cities measured to form a graph with weighted connections, or can the weights on the edges be arbitrarily chosen, even though they might make it impossible to lay the cities out on a map?
If one of those is considered the standard traveling salesman problem, is there a name for the other one?
TSP can be defined in a lot of ways. You're describing the symmetric Euclidean TSP, where weights correspond to the actual distances between the nodes and traveling clockwise on a tour between the nodes would give. As suggested by Phpdna, the triangle inequality is satisfied.
However, that's not the standard definition of the TSP. In fact, this IS the sub-problem or special case. The general problem can have any weight between each pair of nodes, and it doesn't have to be a Euclidean distance.
For example, if you were trying to formulate the shortest tour by the cost of travel rather than distance, you'd have the cost of travel between cities as the weight between the vertices... that could be anything. City A might be closest to city B on a Euclidean map, but the cost of travel from A to B might be phenomenally greater than from A to C to B for whatever reason. This is the general scenario. But either way, they're both NP-hard.
In the metric tsp it's satisfy the triangle inequality but if you have one-way streets or obstacles like mountains, canyons and so on it's not the metric tsp.