I have a very complex CAD model of a car, and my task is to present it in VR. Since it's so complex, every single screw and stuff is in there, it currently is impossible to work with.
Unfortunately, I'm a total Catia beginner. Is there any way to get a model that only includes the visible surface and reduces the amount of data?
With the DMO-license (DMU Optimizer 2) you can use the function called Silhoutte which will create the enveloping hull. But with Silhoutte model size will not decrease, but even increase.
So requiring a small model it might make more sense to use the function Simplification - also in DMO workbench. In my tests the model size (.cgr) was reduced to 1/10 of the original size (Catia product with parts).
Results can be saved in formats .cgr, .wrl, .model or .stl.
More information in Catia docu:
http://catiadoc.free.fr/online/CATIAfr_C2/dmougCATIAfrs.htm
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I'm working on a simple project in which I'm trying to describe the relationship between two positively correlated variables and determine if that relationship is changing over time, and if so, to what degree. I feel like this is something people probably do pretty often, but maybe I'm just not using the correct terminology because google isn't helping me very much.
I've plotted the variables on a scatter plot and know how to determine the correlation coefficient and plot a linear regression. I thought this may be a good first step because the linear regression tells me what I can expect y to be for a given x value. This means I can quantify how "far away" each data point is from the regression line (I think this is called the squared error?). Now I'd like to see what the error looks like for each data point over time. For example, if I have 100 data points and the most recent 20 are much farther away from where the regression line/function says it should be, maybe I could say that the relationship between the variables is showing signs of changing? Does that make any sense at all or am I way off base?
I have a suspicion that there is a much simpler way to do this and/or that I'm going about it in the wrong way. I'd appreciate any guidance you can offer!
I can suggest two strands of literature that study changing relationships over time. Typing these names into google should provide you with a large number of references so I'll stick to more concise descriptions.
(1) Structural break modelling. As the name suggest, this assumes that there has been a sudden change in parameters (e.g. a correlation coefficient). This is applicable if there has been a policy change, change in measurement device, etc. The estimation approach is indeed very close to the procedure you suggest. Namely, you would estimate the squared error (or some other measure of fit) on the full sample and the two sub-samples (before and after break). If the gains in fit are large when dividing the sample, then you would favour the model with the break and use different coefficients before and after the structural change.
(2) Time-varying coefficient models. This approach is more subtle as coefficients will now evolve more slowly over time. These changes can originate from the time evolution of some observed variables or they can be modeled through some unobserved latent process. In the latter case the estimation typically involves the use of state-space models (and thus the Kalman filter or some more advanced filtering techniques).
I hope this helps!
I would like to get suggestions about a time series problem. The data is about strain gauge on the wing of flight which is measured using different sensors. Basically, we are creating the anomalies by simulating the physics model. We have a baseline which is working fine and then created some anomalies by changing some of the factors and recorded over time. Our aim is to create a model which can find out the anomaly during the live testing(it can be a crack on the wing), basically a real time anomaly detection using statistical methods or machine learning.
A few thoughts - sorted roughly from top-to-bottom based on time investiment (assuming little/no prior ML knowledge):
start simple and validate: for what you've described this could be as simple as
create a training / validation dataset using your simulator - since you can simulate, do so for significant episodes of both "standard" and extreme forces applied to the wing
choose a real time smoother: e.g., exponential averaging or moving average, determine a proper parameter for each of your input sensor signals. smooth the input signals.
determine threshold values:
- create rough but sensible lower bound threshold values "by eye"
- use simple statistics to determine a decent threshold value (e.g., using a moving fixed length window of appropriate size, and setting the threshold at a multiple of the standard deviation in that window slid across the entire signal)
in either case, testing on further simulated (and - ideally also - real data)
If an effort like this works "good enough" - stop and move on to next (facet of) problem. If not
follow the first two steps (simulate and smooth data)
take an "autoregressive" approach create training / validation input/output pairs by running a sliding window of fixed length over the input signal(s). train a simple supervised learner on thes pairs, for each input signal or all together, to produce a (set of) time series anamoly detectors trained on your simulated data. cross-validate with the validation portion of your data.
use this model (or one like it) on your validation data to test performance - and ideall collect real data (not simulated) to validate your model even further on.
If this sort of approach produces "good enough" results - stop, and move onto the next facet of the problem.
If not - examine and try any number of anomoly detection approaches coded in a variety languages listed on an aggregator like the awesome repo for time series anomaly detection
I'm using detectron2 for solving a segmentation task,
I'm trying to classify an object into 4 classes,
so I have used COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml.
I have applied 4 kind of augmentation transforms and after training I get about 0.1
total loss.
But for some reason the accuracy of the bbox is not great on some images on the test set,
the bbox is drawn either larger or smaller or doesn't cover the whole object.
Moreover sometimes the predictor draws few bboxes, it assumes there are few different objects although there is only a single object.
Are there any suggestions how to improve it's accuracy?
Are there any good practice approaches how to resolve this issue?
Any suggestion or reference material will be helpful.
I would suggest the following:
Ensure that your training set has the object you want to detect in all sizes: in this way, the network learns that the size of the object can be different and less prone to overfitting (detector could assume your object should be only big for example).
Add data. Rather than applying all types of augmentations, try adding much more data. The phenomenon of detecting different objects although there is only one object leads me to believe that your network does not generalize well. Personally I would opt for at least 500 annotations per class.
The biggest step towards improvement will be achieved by means of (2).
Once you have a decent baseline, you could also experiment with augmentations.
I am very new to java and using ELKI. I have three dimensional objects have information about their uncertainty ( a multivariate gaussian). I would like to use FDBSCAN to cluster my data. I am wondering if it is possible to do this in ELKI using the UncertainiObject class. However, I am not sure how to do this.
Any help or pointers to examples will be very useful.
Yes, you can use, e.g., SimpleGaussianContinuousUncertainObject to model uncertain data with Gaussian uncertainty. But if you want a full multivariate Gaussian, you will have to modify its source code. It is not a very complicated class.
Many of the algorithms assume you can put a bounding box around uncertain objects, in order to prune the search space (otherwise, you will always be in O(n^2)). This is more difficult with rotated Gaussians!
The key difficulty with using all of these is actually data input. There is no standard file format for specifying objects with uncertainty. Apparently, most people that work with uncertain data just use certain data, and add an artificial uncertainty to it. But even that needs a lot of parameters to tune, and I am not convinced by this approach.
I created a multi-class SVM model using libSVM for categorizing images. I optimized for the C and G parameters using grid search and used the RBF kernel.
The classes are 1) animal 2) floral 3) landscape 4) portrait.
My training set is 100 images from each category, and for each image, I extracted a 920-length vector using Lear's Gist Descriptor C code: http://lear.inrialpes.fr/software.
Upon testing my model on 50 images/category, I achieved ~50% accuracy, which is twice as good as random (25% since there are four classes).
I'm relatively new to computer vision, but familiar with machine learning techniques. Any suggestions on how to improve accuracy effectively?
Thanks so much and I look forward to your responses!
This is very very very open research challenge. And there isn't necessarily a single answer that is theoretically guaranteed to be better.
Given your categories, it's not a bad start though, but keep in mind that Gist was originally designed as a global descriptor for scene classification (albeit has empirically proven useful for other image categories). On the representation side, I recommend trying color-based features like patch-based histograms as well as popular low-level gradient features like SIFT. If you're just beginning to learn about computer vision, then I would say SVM is plenty for what you're doing depending on the variability in your image set, e.g. illumination, view-angle, focus, etc.