When the excellent students gradually began to read a doctoral degree, after the helpless pain of academic circles, suddenly realized in a panic one day: our academic circles may also have their own "sweeping monk".
These people will learn both ancient and modern Chinese and Western, arts and sciences and repair agricultural and industrial storage, can keep up with the forefront of The Times hit the scientific research hot spots, and can insight into the format of paper bugs.
Even when you have academic materials that you can't find, we can usually find them in our "cabinet of Sutras".
Who are they, you ask? Is it the senior professor, the brilliant green pepper, or the legendary senior in the lab who has never been seen but has gained fame? No,We.
ASML has reassured officials about its ability to remotely disable its chipmaking machines, Bloomberg reports.
At the end of April, Musk made a large layoff of about 500 people in Tesla's supercharge department, including Rebecca, a senior director of global supercharge who has served for six years. Tinucci. But it wasn't long before he brought back some of those who had been fired.
Is the hypercharged network a chicken rib?
The so-called supercharge can be understood as "fast charging", and there are three main ways now:
One is the large voltage, is now the more mainstream 800v high voltage fast charge, Huawei, BYD, ideal, Xiaopeng and the Great Wall and other 11 car companies have also made a supercharge alliance, take the large voltage route.
The second is to change electricity, NiO is taking this way, and now also with Chery, Changan, Gillette, pull on the State Grid as a whole power change alliance.
The third is the high current, only Tesla and BYD are engaged in this, because the heat of the high current is very high, so the charging equipment is very demanding. Tesla is engaged in the charging line liquid cooling scheme, BYD is pushing the two-gun fast charging scheme.
Rafavi 120KW 750V 366A DC charging pile
一、什么是卡尔曼滤波?
你可以在任何含有不确定信息的动态系统中使用卡尔曼滤波，对系统下一步的走向做出有根据的预测，即使伴随着各种干扰，卡尔曼滤波总是能指出真实发生的情况。
在连续变化的系统中使用卡尔曼滤波是非常理想的，它具有占用内存小的优点(除了前一个状态量外，不需要保留其它历史数据)，并且速度很快，很适合应用于实时问题和嵌入式系统。
在Google上找到的大多数关于实现卡尔曼滤波的数学公式看起来有点晦涩难懂，这个状况有点糟糕。实际上，如果以正确的方式看待它，卡尔曼滤波是非常简单和容易理解的，下面我将用漂亮的图片和色彩清晰的阐述它，你只需要懂一些基本的概率和矩阵的知识就可以了.
