Auto news on Youtube Dec 16 2017

It's kids cooking and crafts time I'm Ava and I'm Corbin and today we'll be showing you how to make a DIY

cardboard box Christmas candy vending machine

Look at all these amazing

Candies that we have they're so awesome and I just want to eat them right now

We're gonna be using these plastic candy canes as part of our vending machine first

We have to get the candy out we're gonna take off these labels very

carefully because we're gonna use them as part of our vending machine, so

We got our label these ones are runts.

All right, there's our candy moving on to our cardboard box

We drew four squares on the bottom of our box one for each candy. Let's cut them out

Nice job

There we go hello, I'm a robot we have all of our boxes done

We need to cut some slits right over the top of those boxes

Now we're gonna get some cardstock and cut it so it fits in the slit and covers up the holes

I'm just marking how wide I need to cut it now?

I'm going to mark how tall it needs to be

Now let's cut it up

We have our slits of paper here now, let's slide them in

All we need to do is pull this tab and It will make it so all the candy comes out so it cut these

Rectangles out of some cardboard and now we're going to glue them inside of the box

So that they will be like dividers because we don't want our candy to be all mixed up.

Now we have four slots for our candy, let's dump it in.

There we go our candy is in the slots. Time to put our stickers back on let's do it.

Now we just got to stick it on our board

Yep yep yep

Is it crooked

Yeah

You guys might be thinking

We're done yay. Oh, no, not yet

We're gonna get our plastic candy canes

Take off the top and cut them in half

We gotta cut off the white piece at the bottom first

So just poke a hole at the bottom of the container and then start cutting all

the way around

Tada now we're gonna cut it in half.

We're cutting a strip out of the middle of the tube

These see how it's a little bit more open. This is going to be a wrapper candy to roll down

We're gonna put this on here, so it's really steep so the candy has somewhere to roll down to oh

Yeah, let's glue these other ramps on and let it zig zag down the board

Take Oh

Why

It's time to try it out give me some

Hershey's yeah, she's all right

three two one

We're gonna have to retry that

All right, we're gonna make some slight modifications

So what we need to do is put some walls at the end of these ramps

I said it with this one so that when they come down. They don't just like fly off.

We're gonna get some of the

Squares that we cut out earlier and kind of just use as like a block so then you don't go like flying everywhere

There we go

Nice all right, let's try it three two one

Okay, this time. Let's try this skittles three two one go

Hey

All right this time we're going to do the Reeses Ava you do the honors

Reeses

Hmm, I love reeses and the best part about this is that you can refill it

Because there's like the holes in the top time for the rest. Let's see if the bananas can actually roll

Three two one

The banana came all the way down, I love bananas

So cool, I liked how like the skittles like went around it like yeah

Because they like came in and then they went down

And then just like kind of spun around out of all it was really cool

Thank you guys so much for joining us today on kids cooking and crafts

And if you want to see more vending machines just comment down below

We'll make it happen. If you enjoyed this Christmas candy vending machine

You might also enjoy our other cardboard candy vending machine. It was so much fun

It was awesome you guys need to go and check that one out. See you guys next time

Bye

For more infomation >> How To Build Cardboard Candy Dispenser Vending Machine Christmas Edition - Duration: 8:15.

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B&W Tek - BWIQ - Building a Classification Model - Duration: 9:01.

BWIQ is B&W Tek's chemometric software

designed for both on and off -line quantitative and qualitative spectroscopy measurements

Today we are going to demonstrate how to build a classification model with Raman spectral data

In this video, we will import the spectra into the BWIQ software

define the class for each sample

classify the spectra using a Principal Component Analysis-Mahalanobis Distance algorithm

and finally use the model to classify new samples

In classification models, spectra of samples corresponding to specific groups are collected

and then modeled according to their similarity to form a class

For this example, we will create a model using the Raman spectra of amino acids l-alanine

l-aspartic acid and l-cysteine hydrochloride

that were collected on B&W Tek's NanoRam handheld Raman system

20 spectra were collected for each amino acid

To import the spectra into the BWIQ software, click File - Import

and select the file format you wish to use

Locate the data set and import the files into BWIQ

The data files now appear in the Spectra window along with the corresponding spectra

When developing classification models, we need to define the class for each spectrum in the software

Click the blue addition button in the upper left corner of the Spectra window

a third column will appear

You can rename the column as "Class"

by right-clicking the header and selecting "Rename" from the dropdown menu

Here, we will enter an integer value for each distinct class

so that all aspartic acid spectra are designated class 1

all alanine spectra are class 2

and all l-cysteine hydrochloride spectra are class 3

There are several ways to designate spectra files as calibration or validation samples in BWIQ

Spectra files can be manually designated as calibration, validation, or ignored files

by clicking the drop-down button from the "Usage" column

Files designated "Ignored" will not be included in the final analysis

There are also several sampling algorithms available in the BWIQ software

which can be selected under "Sampling"

The parameters for the algorithms selected will appear in the algorithm properties panel

In the algorithm properties panel, you can change the ratio of calibration files to validation files

for instance, designating 60% of the files as calibration files, and 40% as validation files

To apply the sampling algorithm to the data

click the blue "execute" triangle in the algorithm properties window

the usage column in the Spectra window

will show which spectra are designated as calibration samples and which are validation samples

For this type of qualitative analysis, we designate all files as calibration files

Next, we can add pre-processing steps to remove variation in the data set

that is not related to chemical differences

but instead may result from scattering, instrumental variation, spectral noise, or background differences

The Raman data presented were collected using an automatic integration time feature

so the integration times for each spectrum are noticeably different

To normalize the data intensities, we will use a standard normal variate normalization algorithm

Click Pre-Process, then Standard Normal Variate

The normalization now appears in the algorithms window

When building classification models, an important step is to mean center the data

as we are interested in the difference of the data from a centered point

not how far the samples are from that center

Click "Pre-processing" then "Center"

The step is now added to the algorithms window

Manual variable selection enables us to use the entire spectrum for analysis

or alternatively to restrict the analysis to selected regions of the data

Using the whole spectrum for analysis will allow the model to be more sensitive to contaminants

or changes in the samples that introduce signal in other spectral regions

However, it can be helpful to remove non-informative or noisy regions of the data from the analysis

For example, in this data we can see that above 1800 wave numbers

there is little Raman signal

Under Variable, choose Manual Selection

A Manual Variables selection is automatically added to the selected algorithms window

Now that we have finished adding pre-processing steps

we can add a classification algorithm

For this data, we will select a Principal Component Analysis Mahalanobis Distance classification method

Principal Component Analysis, or PCA

is an excellent exploratory chemometric tool

that uses a reduced variable space to define the greatest variance within a data set

With PCA, model scores and loadings are computed

and the 2D scores plot provides a visualization of natural groupings of samples

based on the similarity of the data

With each sample represented by a single point in the new Principal Component space

To classify new samples with the model, we'll use Principal Component Analysis

in combination with the parameter known as the Mahalanobis Distance

which measures the distance of a new sample to the center of each class

The Mahalanobis Distance for each new sample is calculated for every specified class

The sample is classified by the software as the group that corresponds to the shortest calculated Mahalanobis Distance

Choose Classification, and then PCA-MD

Click the blue addition button in the Algorithm Properties window

to add the PCA-MD algorithm to the model

The algorithms that are listed in the selected algorithms window

now make up the steps of our classification model

To save this training file with the extension .train

Go to File, then Save As, and save the file

Training files can be edited and saved at any time in Method Development

To execute the model, click the blue triangle at the top of the window

This will perform the steps in sequence

Because our model includes a Manual Variable Selection

The manual variables window appears upon execution of the model

In this window, we can type in the the spectral regions of interest

or use the cursor to define the analysis regions

Because there is no significant Raman signal above 1800 wave numbers

for any of these amino acids in this data set

we will limit our analysis from 200-1800 wave numbers in these spectra

The data shown have been pre-processed and are now mean centered

with peaks in both directions around the zero line

Upon execution of the method, a new model file is created

The model file is a .cmml file that can be saved and used to classify new samples on-line

while connected to a B&W Tek i-Raman series instrument

or offline using previously collected data

To view the scores plots, click the Scores icon in the toolbar

The scores plot shows the clusters of the samples in principal component space

In this plot, we observe three distinct, well separated clusters

These three clusters correspond to the three different classes of amino acids

Based on how close they fall to these three clusters

we can classify new spectra as belonging to one of these groups

There are several other plots available to view data

including loadings and variance

To classify unknown samples from previously acquired data

click Predict, and load the prediction files in the correct format

The prediction results list the individual spectral file names

along with the calculated Mahalanobis distances from each class in the model

The column to the right labeled Class shows the final classification for the sample

In this case, either one, two, or three

The class showing the lowest calculated Mahalanobis distance

is the class chosen by the BWIQ software to represent that sample

Under Generate PDF Report, you can also save a PDF report

which details the method parameters and the classification predictions

For more information on using the BWIQ software, please refer to the user manual

as well as our other support videos

You can also reach us at www.bwtek.com/support

Thank you for watching!