I am using TraCI function: traci.simulation.findroute.m to find a route for the vehicle. The documentation of the function says that it will find the route which uses shortest travel time. I am wondering, if the function computes the travel time only using the travel distance and speed, or the function also take the transportation condition (like the traffic flow) into consideration.
Hope someone could help me.
Thanks a lot!
You can configure which values TraCI takes into account when routing. The according parameter is the so-called routing mode (I don't know whether it is part of the matlab interface already). More details can be found here: https://sumo.dlr.de/docs/Simulation/Routing.html#travel-time_values_for_routing
SUMO uses the algorithms Djikstra or A* to compute the shortest or the fastest route when provided with a set of demand definitions and/or edge-weights (check DUAROUTER).
When using A*, the parameter euclidean distance/maximum vehicle speed is computed to design the routes.
To take the current traffic conditions into account, DUAROUTER needs to be iteratively called in conjunction with current SUMO simulation step, a job that is performed using the tool duaIterate.py
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Could someone please spend a few words to explain to someone who does not come from a formal methods background what is the difference between verifying a specification using Symbolic Model Checking and doing the same using Concrete Model Checking, when the search is bounded in time? I am referring to the definition of SMC and Concrete MC made in UPPAAL.
In particular, I wrote a program that uses UPPAAL Java API to verify a query against a network of timed automata. If the query is verified, UPPAAL returns a symbolic trace to parse or something else if it is not. If the verification takes too long I decided to halt the verification process, return a message and move on with the next query to verify. Everything is good so far.
Recently, I have been playing around with UPPAAL Stratego which natively offers the possibility of choosing a maximum time or depth of exploration to bound the search. However, this options can be applied only when the verification is carried out using Concrete Model Checking.
My question is : is there any difference in halting the symbolic verification process, as I am doing in my Java program and what UPPAAL Stratego does natively? In both case I don't get an answer (or a trace) but what about the "reliability" of the exploration?
Which would be better (i.e. more complete) between the two options? Halting the symbolic verification or halting the concrete verification?
My understanding so far is that in Symbolic Model Checking, the possible states are defined by using intervals of variables whilst in Concrete Model Checking variables assume an actual value. My view is that, in terms of "completeness", halting the SMC after some time is more "complete" since the exploration of the state space happens systematically using BFS or DFS algorithm and, if I use BFS, I can be "sure" that within N steps nothing bad happens. But again, my background in model checking is not rich, so I might have get it completely wrong.
Also, if could drop any reference to the strategies, it would be really appreciated.
Thanks!
When using SUMO to create a grid network, it seems we have to define route for different types of vehicles. But for a large grid network such as 10*10, it would be impossible to manually input the routes for different flow, especially when considering turning at intersections.
My goal is to have a large network, let flow run throught it with certain turning probabilities at intersections. Then I wish I could use traCI to control the signal lights.
There are a few ways as to how you could manage multiple routes:
Define trip and/or flow with to edge and from edge attributes. The DUAROUTER application will find the shortest route possible or the best route possible (if edge-weights are provided)
The above (point 1) can also be achieved if fromTaz/toTaz (Traffic Assignment Zones) are assigned
NOTE - For both point 1 and 2, the via attribute can force the vehicles to travel through a given edge or a given set of edges.
Another way to generate multiple routes is to generate the 10*10 network and to note down (in the program) all the connections (this is done so that SUMO does not throw any no connection errors). A simple program can be written in conjunction with TraCI, that turns the vehicle from a given edge to a different edge on any junction. Given that this will be time consuming, but if your focus is not on the overall simulation time, this approach will be the most apt for you.
Another way is to add rerouter devices on all edges leading to a junction. You can define new destinations and routes here. This will be the easiest solution for a large network.
Is it possible in AWS XRay to create the sampling rule somehow that will sample all the calls for some service with duration greater than some value?
The only way right now to find the lagging sub-service is to sample 100% and then filter by service name and duration. This is pretty expensive having 10K+ segments per second.
AWS X-Ray dev here. Biased sampling on longer duration can skew your service graph statistics and cause false negatives. If you are ok with this data skew, depend on which X-Ray SDK you are using you might be able to achieve conditional sampling by making explicit sampling decisions on segment close. This would require you to override some certain part of the SDK behaviors.
We know this is a popular feature request and we are working on improving it. Please use our AWS X-Ray public forum https://forums.aws.amazon.com/forum.jspa?forumID=241&start=0 for latest feature launch or provide any additional comments.
There is a cubic block of fractured rock; the question is:
how to simulate fluid flow from top-side to down-side or left-side to right-side?
Is FEA (FEM,...) the only practical solution?
If so for the question above in its simplest conditions, that is, flow can happen only through fractures; no interaction between matrix and the fluid; etc etc how to have a quick simulation with FEA?
Is this practical someone with professionality in FEA could do this in a few minutes? Suppose there is already a suitable mesh generated.
If not what would you recommend to get started rapidly to be able to solve such simple cases?
Is there anybody having experience with similar problem (flow modeling); if so what did you use and how did you fulfilled the job?
Note that we are aware of availability of many FEM packages e.g., FEniCS, OpenFoam, ....
Your question refers to simulation of the fluid in the porous medium, e.g. the rock.
I highly recommend using LBM instead of FEM-based methods. LBM simulates the flow in porous media by nature. Phys Review E contains publications about that approach. What is even more attractive, LBM can be also easily parallelized on GPU.
There are a number of numerical techniques that could be used to solve this problem, finite elements being probably the most common. If you have a mesh of the fluid flow domain already (presumably the voids/cracks in the rock) it would be very straightforward to set up and run the flow model with pretty much any CFD package (finite element based or not) and most people with any exposure to FEA should be able to do it. I am assuming that you want to understand the fluid flow within the rock in some detail, rather than just evaluate the effects of the rock on the flow in some larger flow domain. In the latter case, there are other approaches which might be more computationally efficient.
You could use the one-dimensional form of Darcy's Law.
I am trying to implement a function approximator (aggregation) using a rule-based fuzzy control system. So as to simplify my implementation (and have better understanding) I am trying to approximate y=x^2 (the simplest non-linear function). As far as i understand i have to map my input (e.g. uniform samples over [-1,1]) to fuzzy sets (fuzzyfication) and then use a defuzzyfication method to take crisp values. Is there any simple explanation of this procedure because fuzzy control system literature is a bit mess.
This is sort of a broad question, but I'll give it a go since it has sat unanswered for so long.
First, I believe you need to refine your objective (at least as it stated here). I would hesitate to use the term "function approximation" in this context. If I follow your question correctly, the objective is map a non-linear function into another domain via fuzzy methods.
To do so, you first need to define your fuzzy set membership functions. (This link is a good example of the process.) Without additional information, the I recommend the triangular function due to its ease in implementation. The number of fuzzy sets, their placement and width (or support), and degree of overlap is application specific. You've indicated that your input domain is [-1,1], so you might find that three fuzzy sets does the trick, i.e Negative, Zero, and Positive.
From there, you need to craft a set of rules, i.e. if x is Negative then...
With rules in place, you can then define the defuzzification process. In short, this step weights the activation of each rule according to the needs of the application.
I don't believe I can contribute more fully until the output is better defined. You state "use a defuzzyfication method to take crisp values." - what does this set of crisp values mean? What is the range? Etc. Furthermore, you'll get more a response if you can identify the areas in which you are stuck (i.e. more specific questions).