How to check if a scale is calibrated

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Tutorial Overview

This tutorial is divided into six parts; they are:

  1. The Scale of Your Data Matters
  2. Numerical Data Scaling Methods
    1. Data Normalization
    2. Data Standardization
  3. Sonar Dataset
  4. MinMaxScaler Transform
  5. StandardScaler Transform
  6. Common Questions

Video

Paper size scales and magnification

We can now look at amending paper size scales and magnification. There are times when you may have a drawing on an A4 piece of paper, that you need to scale up to an A3 piece of paper for example. Let’s imagine you were needing to trace this drawing so would use a photocopier to scale the drawing up to the necessary size. 

How to convert paper sizes?

To convert the paper size you can use the percentages in the table below. Note that these percentages do not correspond to the scale factors. So, if you scale or magnify a paper size accurately, it does not mean that you will retain an accurate (or standard) scale of the drawing. So, if you want to increase the scale of a drawing using a photocopier, but want to increase it to a standard scale (1:10 for example) then you must use the percentage factors for converting scale. If it is just the paper size you wish to change, then you can use the paper size converter. I hope that makes sense. 

Table: Converting paper sizes and magnification fa

Table: Converting paper sizes and magnification factors

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Scaling Blocks

The scale commands explained above will work for block references too and you don’t need to use any special property to scale blocks. However, you can scale your blocks with different scale factors along the X, Y and Z axis which I will explain here.

I will use this drawing of a simple chair to explain this.

First, we need to convert this simple object into a block and for that, you can use B command. This article explains more about making a block in AutoCAD.

Just make sure that while creating the block you keep “Scale Uniformly” box unchecked as shown in the image here.

Once you have your block prepared you can change scales and then insert it in your drawing. To insert the block use I command.

Type I and press enter, the Insert window will show up. In this window, you can assign the scale of the block along X, Y and Z axis separately. If you keep the value of scale factor same along X, Y and Z axis then the overall size of the block will change.

Let’s use the scale factor of 1 along the X-axis and 1.5 along the Y-axis, I will keep scale along the Z axis as 1.

Now click OK and click at a point to insert the block. The block will look like image B shown below where image A is the original block at the overall scale of 1.

As you can clearly see the block, in this case, will be scaled to a factor of 1.5 only in the Y direction and not in any other direction.

StandardScaler Transform

We can apply the StandardScaler to the Sonar dataset directly to standardize the input variables.

We will use the default configuration and scale values to subtract the mean to center them on 0.0 and divide by the standard deviation to give the standard deviation of 1.0. First, a StandardScaler instance is defined with default hyperparameters.

Once defined, we can call the fit_transform() function and pass it to our dataset to create a transformed version of our dataset.

1234 . . . # perform a robust scaler transform of the dataset trans = StandardScaler ( ) data = trans . fit_transform ( data )

Let’s try it on our sonar dataset.

The complete example of creating a StandardScaler transform of the sonar dataset and plotting histograms of the results is listed below.

123456789101112131415161718192021 # visualize a standard scaler transform of the sonar dataset from pandas import read_csv from pandas import DataFrame from pandas . plotting import scatter_matrix from sklearn . preprocessing import StandardScaler from matplotlib import pyplot # load dataset url = “https://raw.githubusercontent.com/jbrownlee/Datasets/master/sonar.csv” dataset = read_csv ( url , header = None ) # retrieve just the numeric input values data = dataset . values [ : , : – 1 ] # perform a robust scaler transform of the dataset trans = StandardScaler ( ) data = trans . fit_transform ( data ) # convert the array back to a dataframe dataset = DataFrame ( data ) # summarize print ( dataset . describe ( ) ) # histograms of the variables dataset . hist ( ) pyplot . show ( )

Running the example first reports a summary of each input variable.

We can see that the distributions have been adjusted and that the mean is a very small number close to zero and the standard deviation is very close to 1.0 for each variable.

1234567891011                  0             1   …            58            59count  2.080000e+02  2.080000e+02  …  2.080000e+02  2.080000e+02mean  -4.190024e-17  1.663333e-16  …  1.283695e-16  3.149190e-17std    1.002413e+00  1.002413e+00  …  1.002413e+00  1.002413e+00min   -1.206158e+00 -1.150725e+00  … -1.271603e+00 -1.176985e+0025%   -6.894939e-01 -6.686781e-01  … -6.918580e-01 -6.788714e-0150%   -2.774703e-01 -2.322506e-01  … -2.499546e-01 -2.405314e-0175%    2.784345e-01  2.893335e-01  …  3.865486e-01  4.020352e-01max    4.706053e+00  5.944643e+00  …  4.615037e+00  7.450343e+00[8 rows x 60 columns]

Histogram plots of the variables are created, although the distributions don’t look much different from their original distributions seen in the previous section other than their scale on the x-axis.

Histogram Plots of StandardScaler Transformed Input Variables for the Sonar Dataset

Next, let’s evaluate the same KNN model as the previous section, but in this case, on a StandardScaler transform of the dataset.

The complete example is listed below.

1234567891011121314151617181920212223242526272829 # evaluate knn on the sonar dataset with standard scaler transform from numpy import mean from numpy import std from pandas import read_csv from sklearn . model_selection import cross_val_score from sklearn . model_selection import RepeatedStratifiedKFold from sklearn . neighbors import KNeighborsClassifier from sklearn . preprocessing import LabelEncoder from sklearn . preprocessing import StandardScaler from sklearn . pipeline import Pipeline from matplotlib import pyplot # load dataset url = “https://raw.githubusercontent.com/jbrownlee/Datasets/master/sonar.csv” dataset = read_csv ( url , header = None ) data = dataset . values # separate into input and output columns X , y = data [ : , : – 1 ] , data [ : , – 1 ] # ensure inputs are floats and output is an integer label X = X . astype ( ‘float32’ ) y = LabelEncoder ( ) . fit_transform ( y . astype ( ‘str’ ) ) # define the pipeline trans = StandardScaler ( ) model = KNeighborsClassifier ( ) pipeline = Pipeline ( steps = [ ( ‘t’ , trans ) , ( ‘m’ , model ) ] ) # evaluate the pipeline cv = RepeatedStratifiedKFold ( n_splits = 10 , n_repeats = 3 , random_state = 1 ) n_scores = cross_val_score ( pipeline , X , y , scoring = ‘accuracy’ , cv = cv , n_jobs = – 1 , error_score = ‘raise’ ) # report pipeline performance print ( ‘Accuracy: %.3f (%.3f)’ % ( mean ( n_scores ) , std ( n_scores ) ) )

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example, we can see that the StandardScaler transform results in a lift in performance from 79.7 percent accuracy without the transform to about 81.0 percent with the transform, although slightly lower than the result using the MinMaxScaler.

1 Accuracy: 0.810 (0.080)

C#

 // C# Program to check whether an item// can be measured using some weight and// a weighing scale.using System;public class GFG { // Variable to denote that answer has// been foundstatic int found = 0; static void solve(int idx, int itemWt, int []wts, int N) {if (found == 1) {return;} // Item has been measuredif (itemWt == 0) {found = 1;return;} // If the index of current weight// is greater than totalWtsif (idx > N) {return;} // Current weight is not included// on either sidesolve(idx + 1, itemWt, wts, N); // Current weight is included on the// side containing itemsolve(idx + 1, itemWt + wts[idx], wts,N); // Current weight is included on the// side opposite to the side// containing itemsolve(idx + 1, itemWt – wts[idx], wts,N);} // This function computes the required array// of weights using astatic bool checkItem(int a, int W) {// If the a is 2 or 3, answer always// existsif (a == 2 || a == 3) {return true;} int []wts = new int[100]; // weights arrayint totalWts = 0; // feasible weightswts[0] = 1;for (int i = 1;; i++) {wts[i] = wts[i – 1] * a;totalWts++; // if the current weight// becomes greater than 1e9// break from the loopif (wts[i] > 1e9) {break;}}solve(0, W, wts, totalWts);if (found == 1) {return true;} // Item can’t be measuredreturn false;} // Driver Code to test above functionspublic static void Main() { int a = 2, W = 5;if (checkItem(a, W)) {Console.WriteLine(“YES”);} else {Console.WriteLine(“NO”);} a = 4; W = 11;found = 0;if (checkItem(a, W)) {Console.WriteLine(“YES”);} else {Console.WriteLine(“NO”);} a = 4; W = 7; found = 0;if (checkItem(a, W)) {Console.WriteLine(“YES”);} else {Console.WriteLine(“NO”);}}} //this code contributed by Rajput-Ji

2 Types of Adjustments in Digital Weighing Scale (DWS) Calibration

Most of the electronic or digital weighing scales today are programmable where an auto-calibration can be performed. This means that it can be adjusted. Once the calibration menu is unlocked or open, adjustment can be done by using the standard weights.

The principle is that the unit will require or search for an exact weight as a reference to be programmed internally.  This procedure depends on the model of the weighing scale so be sure to have a manual while performing a calibration.

Digital Weighing Scale Adjustment Procedure
Digital Weighing Scale Adjustment Procedure

For me, the most full filling instruments to calibrate are the digital weighing scale. Why? As per experience, you can see the actual adjustments and removal of the error. You can return back the instrument in its most accurate range to almost no error at all (visually).

There are 2 ways where we can perform an adjustment, either by

  1. internal adjustment
  2. external adjustment

This type of adjustment in balance calibration is also known as ‘internal calibration’ and ‘external calibration’. But in a real sense, it is a method of a weighing scale adjustment.

Remember that during the execution of any adjustment (internal or external), the adjustment factor that is stored in the system will be changed once you pushed the accept button.

Before performing balance adjustment, it is important that you know the tolerance of your weighing scale. If you do not know your tolerance limit, visit my other post here for a calculation guide >> determining the tolerance limit of a weighing scale

Internal Calibration

example of internal adjustment for a Mettler Toled
example of internal adjustment for a Mettler Toledo digital balance

Internal calibration also known as the internal adjustment is an adjustment using the internal weights installed inside the weighing scale. There is no need to load standard weights. It is an automated adjustment where the DWS is adjusted just by a press of a button.

The digital weighing scale will detect the zero load of the pan then it will use the internal weights installed to perform an adjustment or calibration. Once executed, there are no other steps to be done, but to wait for the final settings to be saved.

When to use internal calibration?

Internal calibration is usually used every time a DWS is transferred from one place to another,  a big change in environmental conditions or a suspected deviation in readings.

But be sure to have standard weights with you every time you perform this to verify that errors are removed or within an acceptable range. If you do not need a set of weights, at least use standard weights that are equal or near to the range that you are using.

The disadvantages of this method of adjustment are:

> the adjusted range is limited and therefore the accuracy

> since the internal weight is not certified, there is no traceability of the performed calibration

> once you have executed this adjustment in the balance, it will delete the past calibration settings and therefore invalidate the performed past calibration.

Advantages of Internal Calibration of a Weighing Scale.

  • Easy to execute.
  • No need for a physical or standard weights
  • Can be executed anytime.

External Calibration

External adjustment on a Mettler Toledo digital ba
External adjustment on a Mettler Toledo digital balance

In External calibration or external adjustment of a weighing scale,  we will need a set of weights, depending on the range that is required or programmed in the DWS.

We will load the weights manually on the pan of the DWS to perform the adjustment. The DWS will prompt you what range of weights you need to put in its pan (or depending on what range you have programmed).

External calibration of balance is used during scheduled calibration (full-range or what is required as per user) and to ensure a traceable calibration.

A simple demonstration of an analytical scale external adjustment

Advantages of External Calibration or Adjustment

  • More accurate adjustments
  • Multiple ranges can be adjusted.
  • Traceability is ensured as long as the standard weights are calibrated
  • Full range calibration based to an International Standard procedure

Remember that every time an adjustment is executed, do not forget to perform balance verification.

Take note that not all electronic weighing scales or balances are adjusted successfully even if you performed the above scale adjustment method. One reason is that the load cells are already defective where a scale calibration service by a 3rd party is needed, usually the OEM for repair or replacement of load cells.

Why Use a Likert Scale? Advantages and Disadvantages

There are Likert scale advantages and disadvantages. However, there are many more pros than cons!

Likert scales are easy for people to understand and complete. Because questions using the Likert method follow a scale, respondents don’t have to answer yes or no, or either-or. Instead, they can choose to be neutral. All of this means that they wind up delivering better response rates! In addition, Likert scale questions are easier to analyze and report on than open-ended or fill-in-the-blank questions, which are more difficult to analyze because the answers haven’t been configured in advance. 

The one drawback is that you don’t always get in-depth feedback. Consider a restaurant. Sure, you may know someone is dissatisfied with your restaurant because they only gave you one star. But, you won’t know why they were dissatisfied (Was it the food quality? The service? Cleanliness?).  To solve for this, it’s important to ask multiple questions about different aspects of the customer’s visit. After that, ask for their overall satisfaction level.

Scaling Viewports

Drawings are often made in model space but text, title blocks and other required information related to drawing are generally added in the layout or paper space. In this section, I will explain methods of creating viewports in paper space and changing their scale to fit the drawing.

I am using this simple elevation drawing to explain this feature.

This drawing is in model space but we will insert this one in viewports of different scale in the layout. Start by selecting Layout 1 from the bottom left tabs, you can also hit the + icon to make a new layout tab.

The layout space will open up with a default viewport and your model space drawing will be automatically added in it. Delete the existing viewport and then click on the Page Setup option from the Layout tab as shown in the image here.

Click on Modify button in the page setup manager window and change settings of the layout like printer/plotter and page size. I have selected DWG to PDF.pc3 in the plotter and ANSI A as the paper size.

After making these changes click OK to exit all open windows. Now we are all set to make our viewports.

Type -VPORTS and press enter (don’t forget to add the dash before vports). Now click at a point close to the top left of the layout well within plotter margin and then click at another point to make a rectangular viewport. Repeat the command and make two more rectangular viewports in the same layout view.

After making all the viewports your layout space should look like this.

Currently, the scale of drawing inside the viewport is automatically set by AutoCAD but you can change it manually. To change the scale select the viewport boundary then change the scale from scale option of the status bar as shown in the image here.

If the scale which you want to use is not available in this list of viewport scales then you can add your own custom scale too. To add the custom scale select any viewport then click on scale option from the status bar as shown in the image above and select the Custom option from the list of scales.

“Edit Drawing Scales” window will open up. Let’s assume that we want to add a scale of 1:6 in this list, for that click on add button.

In the name field of the next window add 1:6 and then add 1 in the paper unit and 6 in the drawing unit field. Click OK to accept the changes and close all open windows.

You will notice that your new scale is now added to the list and you can now use it in your viewports. You can also watch the video shown below to know more about scaling drawings in paper space or layouts.

Java

// Java Program to check whether an item// can be measured using some weight and// a weighing scale.class GFG {// Variable to denote that answer has// been foundstatic int found = ;static void solve(int idx, int itemWt, int wts[], int N) {if (found == 1) {return;}// Item has been measuredif (itemWt == ) {found = 1;return;}// If the index of current weight// is greater than totalWtsif (idx > N) {return;}// Current weight is not included// on either sidesolve(idx + 1, itemWt, wts, N);// Current weight is included on the// side containing itemsolve(idx + 1, itemWt + wts[idx], wts,N);// Current weight is included on the// side opposite to the side// containing itemsolve(idx + 1, itemWt – wts[idx], wts,N);}// This function computes the required array// of weights using astatic boolean checkItem(int a, int W) {// If the a is 2 or 3, answer always// existsif (a == 2 || a == 3) {return true;}int wts[] = new int[100]; // weights arrayint totalWts = ; // feasible weightswts[] = 1;for (int i = 1;; i++) {wts[i] = wts[i – 1] * a;totalWts++;// if the current weight// becomes greater than 1e9// break from the loopif (wts[i] > 1e9) {break;}}solve(, W, wts, totalWts);if (found == 1) {return true;}// Item can’t be measuredreturn false;}// Driver Code to test above functionspublic static void main(String[] args) {int a = 2, W = 5;if (checkItem(a, W)) {System.out.println(“YES”);} else {System.out.println(“NO”);}a = 4; W = 11;found = ;if (checkItem(a, W)) {System.out.println(“YES”);} else {System.out.println(“NO”);}a = 4; W = 7; found = ;if (checkItem(a, W)) {System.out.println(“YES”);} else {System.out.println(“NO”);}}}//this code contributed by Rajput-Ji

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