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comments i
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yes the additive also use all the place
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so
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uh so you have a well established means of getting your p.
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you're on your d. you know you have a model
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um during your tuning uh and then you verify in flight but the
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thing is stable your mobile and the holes ability to once you
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have it and you have your mobile you can push stability really option of i think the whole the option of analysis was invented
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is it but also impact is so there's that you so you have this controller you put it on the test then she
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knew subjected to all kinds of uh of normal conditions you prove that it remains stable that's how these things work
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even
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so that's that that that goes along the line of would you say that why would
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i replace a well understood good working p. i. d. controller with a neural network
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to certify the network yeah but not to solve
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yeah
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long term process not sure i mean it it could be it could be good um
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so the this paper from a nice and honeywell um on that the systems
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talks about gain scheduling which is where you have so you're you're the controls really relies system
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um that operates in the specific range you prunes billy there but then maybe go
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to a completely different operating range you know you could put in different games
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so this is what i figured oh you're gonna change your gaze n.
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flights no now so then we're already at the boundary of um
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what is a set that rocket science uh but not a good way i'm sol
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it would be hard to outperform
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no no actually would be very thing um
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to have a instead of a bit controller have neural network outperform it completely that actually very good
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thing
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ha uh yeah let me think well that's more
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okay anything about it some more
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more questions suggestions and
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i i i
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that was exactly my question
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here
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so you have to use the show some form so our first hunch was i can give you some thoughts
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about working here but i don't wanna be a sport i'll be a creative to you too much
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so our first thought was you look at the entropy distributions of colours this abuse of
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bigger features rather than if you for that have the relevant features are really
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what gets triggered in a network so if we can compare what goes on in the networks and we can say given the network
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these data sets trigger have the same kind of figure patterns on the inside
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therefore for the purpose of the network the we are the same because they have the same sed statistic
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not just uh the traitor but also the code for if you get all the pairs of nodes
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and you make a that you get the distribution of a three ring together or not and they
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are roughly the same for all players you can think of or a large sample of those
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maybe these data sets are the same for does not work in any
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meaningful way and that could be way to put back on that
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oh
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uh_huh
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okay
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well
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well that would be very elegant
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okay
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yeah
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yeah
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ooh
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yeah i would yeah i would hand craft this so that it looks like a accent 'cause one oh
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it's actually very hard to get realistic lighting so we've been but so if it looks like plastic
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i'm doing real time that we don't have to put some whatever that so and usually do
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this would be to localise might have the to maximise local contrast and post rise and
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oh
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but we we could still that do that on top of that because we because we need to do
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so i'm mega pixels are luxury um so we're going to use uh in in the lower levels
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you have to um do some yeah get rid of the pixels that's the big one
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i'm a reduced resolution of it so we have to do that anyway but then what
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you say we can still do it the five twelve or five twelve later
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and that that's what i propose i just to make the input space more regular lies them about it
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or suggestions

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Conference program

Airworthy AI; challenges of certification, part one
Dr. Luuk van Dijk, Founder and CEO of Daedalean
12 Oct. 2018 · 2:05 p.m.
Airworthy AI; challenges of certification, part two
Dr. Luuk van Dijk, Founder and CEO of Daedalean
12 Oct. 2018 · 2:30 p.m.
Airworthy AI; challenges of certification, Q&A
Dr. Luuk van Dijk, Founder and CEO of Daedalean
12 Oct. 2018 · 2:56 p.m.

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