19岁华裔少女立志拯救宇宙!她研发的太空垃圾追踪系统比NASA都牛!(附视频&专访稿)
点击上方“精彩英语演讲”,选择“设为星标”
英语演讲视频,第一时间观看
My name is Amber Yang, I am 19 years old, And I am a physics major college student at Stanford University, I'm also the founder of Seer Tracking, We take the data of current satellites and spacecraft and try to predict if there will be any collisions with space debris.
So space debris is any defunct man-made space part or spacecraft that is orbiting in low-earth orbit right now, They can be as small as a paint fleck to as big as a completely dead satellite, And they often travel as fast as 17,500 miles per hour
and you can only imagine with something traveling that quickly that its impact on another orbiting object will be extremely large and will cause lots of damage.
This space debris could have such a big impact on essentially the success of the American space program and mankind's advancement into space technologies, I watched this series of videos that astronaut Scott Kelly published.
He would have to duck into the Soyuz capsule which is an adjacent capsule to the International Space Station,because there was fear that the spacecraft that he was in might be hit by space debris.
We're just inside one minute from the time of closest approach, It first became like an issue around the 1950's, and really in the past few decades, that number has only skyrocketed.
There is a concept that as the space debris collide with other space debris and other objects that fragmentation will cause even more space debris to occur, Currently the method for tracking space debris is extremely inaccurate at times because the orbit of space debris changes so quickly.
The method that I have introduced is using artificial neural networks and artificial intelligence to track space debris, An artifical neural network is essentially a computer's version of the human mind. It will provide a prediction, it will say I think this is where the space debris will be in this future point in time, And if we find the actual point in time in the future where the space debris is and there is an error metric between the artificial neural network and where the space debris actually is.
we can tell the neural network that oh you're wrong by this amount, I am basically allowing the artificial intelligence to learn the patterns of how the space debris positions are changing over time, and it will keep training itself until its predictions are actually very very accurate.
The primary concern would be for things already orbiting in space and trying to allow these things orbiting in space to have enough time to move out of a collision's way, If my software predicts that there would be a future collision.
Another thing that it could be readily used for is predicting when is the best optimal launch time for things like NASA and SpaceX, when they're trying to find a time window where to launch their rockets and other space cargo.
They want to look at a window where there isn't a lot of space debris, Right now a lot of people are putting space debris on the back burner, they're saying it could be potentially very dangerous someday, but right now it's not that big of a concern because we haven't really had any casualties yet, But the very terrifying version of the Kessler syndrome would be a space atmosphere so trashed that you couldn't launch anything up without it being hit by space debris.
The whole goal is that we're trying to preserve our Earth, our atmosphere for future generations in front of us and in order to keep developing technologies that will succeed and go to space, We need to make sure that there is enough space in space to not be hit by space debris.
防止未来失联
请长按识别二维码关注备用号
想第一时间接收英语演讲文章&视频?把精彩英语演讲设置为星标就对了!操作办法就是:进入公众号——点击右上角的●●●——找到“设为星标”点击即可。
最新评论
推荐文章
作者最新文章
你可能感兴趣的文章
Copyright Disclaimer: The copyright of contents (including texts, images, videos and audios) posted above belong to the User who shared or the third-party website which the User shared from. If you found your copyright have been infringed, please send a DMCA takedown notice to [email protected]. For more detail of the source, please click on the button "Read Original Post" below. For other communications, please send to [email protected].
版权声明:以上内容为用户推荐收藏至CareerEngine平台,其内容(含文字、图片、视频、音频等)及知识版权均属用户或用户转发自的第三方网站,如涉嫌侵权,请通知[email protected]进行信息删除。如需查看信息来源,请点击“查看原文”。如需洽谈其它事宜,请联系[email protected]。
版权声明:以上内容为用户推荐收藏至CareerEngine平台,其内容(含文字、图片、视频、音频等)及知识版权均属用户或用户转发自的第三方网站,如涉嫌侵权,请通知[email protected]进行信息删除。如需查看信息来源,请点击“查看原文”。如需洽谈其它事宜,请联系[email protected]。