Airplane | A stable , optimized , well supported 1.17.1 Paper fork | Performance Testing library

Here is one way to do so using regex:

You could for example swap key and value of the dict and then simply access it with the list elements:

If the "img" column is a list of PIL images, you need to change the features of "img" from array to image, replace features with the following:

In the code provided class is declared as

Just put the camera into your AGV agent type. Make sure that your TransporterFleet is adding new transporters to a custom population of AGV agents (myPop), see below:

In your model, you can now change the camera using my3DWindow.setCamera(((AGV)randomFrom(myPop)).myCamera);

Unfortunately turning on/off Airplan mode on Android v4.2 is not supported. Have a look at the links below:-

• https://developer.android.com/about/versions/android-4.2.html
• How to programmatically enable/disable flight mode
• Programmatically setting ON/OFF airplane mode

Is there any reason for using Android 4.2? It is a very outdated OS (10 years old) and Android has improved vastly since then especially when it comes to BLE API and functionality. If you have the option of upgrading to a newer OS, I would recommend that you start with this as your first priority.

If upgrading to a newer OS is not an option, then I would personally try to fix the actual problem instead of finding a workaround. One possibility of this happening is that you are scanning too often, which can sometimes result in no scan results being returned for about 30 seconds. This is a tricky one as the Android OS doesn't return an error - it just doesn't return any results which seems to be similar to what you are seeing. Have a look at this link and search for the string "notable scan errors". I would also look at the Logcat log as you can sometimes find more info there.

Finally, it is possible that this is somehow a problem with the hardware (especially if it is an old device). Try installing nRF Connect and scanning for BLE devices. If the issue exists over there as well, then that proves that it's a hardware/device problem. If the issue doesn't exist over there, then this is a problem with your app.

You can use the following formula:

=TIME(LEFT(A2,LEN(A2)-2),RIGHT(A2,2),)

A2 = the arrival time cell that you want to convert.

Also, make sure to change your output column to whatever time format you're looking for. Screenshot of results in action included.

Disclaimer: it's been a few years since I've played with CNNs myself, so I can only pass on some general advice and suggestions.

First of all, I would like to talk about the results you've gotten so far. The first two networks you've trained seem to at least learn something from the training data because they perform better than just randomly guessing.

However: the performance on the test data indicates that the network has not learned anything meaningful because those numbers suggest the network is as good as (or only marginally better than) a random guess.

As for the third network: high accuracy for training data combined with low accuracy for testing data means that your network has overfitted. This means that the network has memorized the training data but has not learned any meaningful patterns.

There's no point in continuing to train a network that has started overfitting. So once the training accuracy increases and testing accuracy decreases for a few epochs consecutively, you can stop training.

Neural networks rely on loads of good training data to learn patterns from. Your dataset contains 15 classes with 15 images each, that is very little training data.

Of course, it would be great if you could get hold of additional high-quality training data to expand your dataset, but that is not always feasible. So a different approach is to artificially expand your dataset. You can easily do this by applying a bunch of transformations to the original training data. Think about: mirroring, rotating, zooming, and cropping.

Remember to not just apply these transformations willy-nilly, they must make sense! For example, if you want a network to recognize a chair, do you also want it to recognize chairs that are upside down? Or for detecting road signs: mirroring them makes no sense because the text, numbers, and graphics will never appear mirrored in real life.

From the brief description of the classes you have (planes and chairs and whatnot...), I think mirroring horizontally could be the best transformation to apply initially. That will already double your training dataset size.

Also, keep in mind that an artificially inflated dataset is never as good as one of the same size that contains all authentic, real images. A mirrored image contains much of the same information as its original, we merely hope it will delay the network from overfitting and hope that it will learn the important patterns instead.

This is a bit of side note, but try lowering the learning rate. Your network seems to overfit in only a few epochs which is very fast. Obviously, lowering the learning rate will not combat overfitting but it will happen more slowly. This means that you can hopefully find an epoch with better overall performance before overfitting takes place.

Note that a lower learning rate will never magically make a bad-performing network good. It's just one way to locate a set of parameters that performs a tad bit better.

During training, the training data is presented in batches to the network. This often happens in a fixed order over all iterations. This may lead to certain biases in the network.

First of all, make sure that the training data is shuffled at least once. You do not want to present the classes one by one, for example first all plane images, then all chairs, etc... This could lead to the network unlearning much of the first class by the end of each epoch.

Also, reshuffle the training data between epochs. This will again avoid potential minor biases because of training data order.

You've designed a convolutional neural network with only two convolution layers and two fully connected layers. Maybe this model is too shallow to learn to differentiate between the different classes.

Know that the convolution layers tend to first pick up small visual features and then tend to combine these in higher level patterns. So maybe adding a third convolution layer may help the network identify more meaningful patterns.

Obviously, network design is something you'll have to experiment with and making networks overly deep or complex is also a pitfall to watch out for!

I think you need: