Showing posts with the label industrial iot

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The Quick and Easy Way to Analyze Numpy Arrays

The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Industrial IoT what GE says to improve Productivity

GE is once a top company in Heavy Engineering. This is to say items related to Thermal Power plants, Turbines, and maintenance. GE had always believed that since it knew the materials and the physics of its jet engines and medical scanners, no one could best it in understanding those machines. GE Industrial Internet  The aim is it should not share its data to third parties.    GE sets up its own IoT center.    GE is in IoT mood.    GE can improve operational efficiency by studying data from its machines like situated India and Russia. This is just an example.  GE is Targetting for Predictive Maintenace Improves industrial productivity Based on criticality productivity will zoom if maintenance carried in-time.