d3nbg / FlightStatusClassifier Public

This lists all test data. You can manage this data through Data acquisition.

Test data

Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.

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Sample name	Expected outcome	Length
take_off.flight2-0	take_off	3m 16s	Show classification Show in data acquisition Show in feature explorer
landed.flight0-0	grounded	48m 49s	Show classification Show in data acquisition Show in feature explorer
cruising.flight0-2	cruising	4m 41s	Show classification Show in data acquisition Show in feature explorer
landing.flight0-0	landing	1m 3s	Show classification Show in data acquisition Show in feature explorer
cruising.flight2-2	cruising	1m 15s	Show classification Show in data acquisition Show in feature explorer
circling.flight2-0	circling	4m 27s	Show classification Show in data acquisition Show in feature explorer

Model testing output

Every learning block has a threshold. This can be the minimum confidence that a neural network needs to have, or the maximum anomaly score before a sample is tagged as an anomaly. You can configure these thresholds to tweak the sensitivity of these learning blocks. This affects both live classification and model testing.

Classifier

Minimum confidence rating

Should be numeric

These users will be notified over email when this job finishes.

	Name
	v vimkat
	d d3nbg

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Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

Target device

Processor family

Clock rate

| MHz

Max

Custom device name

Application budget

Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency for your specific application. Not sure yet? Start with the defaults and modify them later on.

RAM

| KB

Max

ROM

| KB

Max

Latency

| ms

Max

YEARLY

$400 /month

billed annually

SAVE 15%

MONTHLY

$475 /month

billed monthly

Test data

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Add data

Model testing output

Results

Accuracy

Confusion matrix

Feature explorer

Test data

Model variants

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Sample length (ms)

Sample frequency (Hz)

Add data

Model testing output

Results

Accuracy

Confusion matrix

Feature explorer