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By CarPartAU
Carpart.com.au has made it easier for people to locate car parts sellers in Brisbane. You only have to pay for the parts, not for finding suppliers. Our customer-centric ‘Request a Part’ tool has helped thousands locate cheap auto parts that may not be available in your local stores.
This free requesting tool connects interested buyers to local Brisbane car wreckers. Sellers who have the part will then send their quotes to you directly, saving you time and shipping costs by finding suppliers near you.
| Visit us at - https://carpart.com.au/blog/carpart/locate-wreckers-cheap-car-parts-in-brisbane-using-this-free-tool
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By CarPartAU
2010 MAZDA CX9 ENGINE / MOTOR FOR SALE WITH WARRANTY
OTHER MAZDA PARTS PLEASE INQUIRE BELOW OR CALL
Date Listed:27/03/2020 Last Edited:27/03/2020 Make:Mazda Warranty:available Condition:useD Visit us @ CAR PART.
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By CarPartAU
Date Listed:27/03/2020 Last Edited:27/03/2020 Make:Mazda Warranty:available Condition:used Visit us at CAR PART
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By Erica Zhu Feilong Jiangli
Farreo has developed an end-to-end learning system for speed control. The system adopts the function of neuron network and long-term memory (LSTM), which is a recurrent neural network (RNN), and can learn long-term dependencies.
The new system was developed by Wirbel and her colleagues, who also deployed an artificial neural network (ANN), which relies on in-depth learning technology. The network has been provided with a large number of demonstrations of human drivers'vehicle operation, which are recorded by forward-looking cameras and can be closely related to the driver's visual field when driving.
The neuron network will also be trained to imitate the driver's behavior, especially focusing on reproducing the current speed of the vehicle. For example, when the input image contains a speed-limiting panel of 50 km/h, the neuron network will ensure that the current speed of the vehicle is not higher than 50 km/h.
Perhaps in the near future, the neuron network system will be deployed in the automatic driving vehicle to improve the efficiency of speed control and achieve more intuitive driving control. Researchers plan to apply this proof-of-concept to more complex driving situations, adding a variety of complex driving manipulations, such as diversion, turning at intersections or vehicle navigation at roundabouts.
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