Explanation:
Dempwolf created by John Augustus, Among the most prominent innovative solutions in Southern California Pennsylvania was established by Dempwolf with brother Reinhardt or uncle's son Frederick entered the company of J.A. Dozens of structures in 10 states were engineered by Dempwolf.
The brakes are being bled on a passenger vehicle with a disc/drum brake system is described in the following
Explanation:
1.Risk: Continued operation at or below Rotor Minimum Thickness can lead to Brake system failure. As the rotor reaches its minimum thickness, the braking distance increases, sometimes up to 4 meters. A brake system is designed to take kinetic energy and transfer it into heat energy.
2.Since the piston needs to be pushed back into the caliper in order to fit over the new pads, I do open the bleeder screw when pushing the piston back in. This does help prevent debris from traveling back through the system and contaminating the ABS sensors
3.There are three methods of bleeding brakes: Vacuum pumping. Pressure pumping. Pump and hold.
4,Brake drag is caused by the brake pads or shoes not releasing completely when the brake pedal is released. ... A worn or corroded master cylinder bore causes excess pedal effort resulting in dragging brakes. Brake Lines and Hoses: There may be pressure trapped in the brake line or hose after the pedal has been released.
Answer:
I dont really know, I am sorry, but I am going to ask my teacher
Answer:
import numpy as np
import time
def matrixMul(m1,m2):
if m1.shape[1] == m2.shape[0]:
t1 = time.time()
r1 = np.zeros((m1.shape[0],m2.shape[1]))
for i in range(m1.shape[0]):
for j in range(m2.shape[1]):
r1[i,j] = (m1[i]*m2.transpose()[j]).sum()
t2 = time.time()
print("Native implementation: ",r1)
print("Time: ",t2-t1)
t1 = time.time()
r2 = m1.dot(m2)
t2 = time.time()
print("\nEfficient implementation: ",r2)
print("Time: ",t2-t1)
else:
print("Wrong dimensions!")
Explanation:
We define a function (matrixMul) that receive two arrays representing the two matrices to be multiplied, then we verify is the dimensions are appropriated for matrix multiplication if so we proceed with the native implementation consisting of two for-loops and prints the result of the operation and the execution time, then we proceed with the efficient implementation using .dot method then we return the result with the operation time. As you can see from the image the execution time is appreciable just for large matrices, in such a case the execution time of the efficient implementation can be 1000 times faster than the native implementation.