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Oduvanchick [21]
3 years ago
6

A horizontal angle was measured by repetition six times with a total station. If the initial display reading was 21o33'18" and t

he final reading was 129o20'04", determine the value of the angle to the nearest second.
Engineering
1 answer:
KIM [24]3 years ago
4 0

Answer:

The value of the angle is  107° 46' 46''

Explanation:

we know that

To find out the value of the angle , subtract the initial display reading from the final reading

Remember that

1°=60'

1'=60''

1°=3,600''

we have

Initial display reading=21° 33' 18''

Convert to seconds

21(3,600)+33(60)+18=77,598''

Final display reading=129° 20' 04''

Convert to seconds

129(3,600)+20(60)+4=465,604''

Find the difference

465,604''-77,598''=388,006''

Convert 388,006'' to degrees

388,006''/3,600=107.7794°

Convert 0.7794° to minutes

0.7794(60)=46.764'

Convert 0.764' to seconds

0.764(60)=45.84''=46''

therefore

The value of the angle is

107° 46' 46''

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e-lub [12.9K]

Answer:

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Optimal skew = 26.455 ≈ 27

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Explanation:

given data

heads = 16

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cylinder zones = 100

each sector contains = 512 bytes

average seek time = 1 msec

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solution

first we get total number of sectors that is  

total number of sector = number of zones × (number of sectors in different zones

total number of sector = 100 × (160+200+240+280)

total number of sector = 88000

so

Disk Capacity = total number of sectors  ×  size of each sector

Disk Capacity =  88000 × 512

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Answer:

In[7] def pegasos(feature_matrix, labels, T, L):

   """

   .

   let learning rate = 1/sqrt(t),

   where t is a counter for the number of updates performed so far       (between 1   and nT inclusive).

Args:

       feature_matrix - A numpy matrix describing the given data. Each row

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       labels - A numpy array where the kth element of the array is the

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   (nsamples, nfeatures) = feature_matrix.shape

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In[7] (np.array([1-1/np.sqrt(2), 1-1/np.sqrt(2)]), 1)

Out[7] (array([0.29289322, 0.29289322]), 1)

In[8] feature_matrix = np.array([[1, 1], [1, 1]])

   labels = np.array([1, 1])

   T = 1

   L = 1

   exp_res = (np.array([1-1/np.sqrt(2), 1-1/np.sqrt(2)]), 1)

   

   pegasos(feature_matrix, labels, T, L)

Out[8] (array([0.29289322, 0.29289322]), 1.0)

Explanation:

In[7] def pegasos(feature_matrix, labels, T, L):

   """

   .

   let learning rate = 1/sqrt(t),

   where t is a counter for the number of updates performed so far       (between 1   and nT inclusive).

Args:

       feature_matrix - A numpy matrix describing the given data. Each row

           represents a single data point.

       labels - A numpy array where the kth element of the array is the

           correct classification of the kth row of the feature matrix.

       T -  the maximum number of times that you should iterate through the feature matrix before terminating the algorithm.

       L - The lamba valueto update the pegasos

   Returns: Is defined as a  tuple in which the first element is the final value of θ and the second element is the value of θ0

   """

   (nsamples, nfeatures) = feature_matrix.shape

   theta = np.zeros(nfeatures)

   theta_0 = 0

   count = 0

   for t in range(T):

       for i in get_order(nsamples):

           count += 1

           eta = 1.0 / np.sqrt(count)

           (theta, theta_0) = pegasos_single_step_update(

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   return (theta, theta_0)

In[7] (np.array([1-1/np.sqrt(2), 1-1/np.sqrt(2)]), 1)

Out[7] (array([0.29289322, 0.29289322]), 1)

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   L = 1

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