Transforming Data into Information

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来自 University at Buffalo 的课程

Intelligent Machining

125 个评分

University at Buffalo

Intelligent Machining

125 个评分

课程 5（共 9 门，Specialization Digital Manufacturing & Design Technology ）

Manufacturers are increasingly utilizing machine tools that are self-aware – they perceive their own states and the state of the surrounding environment – and are able to make decisions related to machine activity processes. This is called intelligent machining, and through this course students will receive a primer on its background, tools and related terminology. Learn how the integration of smart sensors and controls are helping to improve productivity. You’ll be exposed to various sensors and sensing techniques, process control strategies, and open architecture systems that can be leveraged to enable intelligent machining. This course will prepare you to contribute to the implementation of intelligent machining projects. Main concepts of this course will be delivered through lectures, readings, discussions and various videos. This is the fifth course in the Digital Manufacturing & Design Technology specialization that explores the many facets of manufacturing’s “Fourth Revolution,” aka Industry 4.0, and features a culminating project involving creation of a roadmap to achieve a self-established DMD-related professional goal. To learn more about the Digital Manufacturing and Design Technology specialization, please watch the overview video by copying and pasting the following link into your web browser: https://youtu.be/wETK1O9c-CA

从本节课中

Sensors and Sensing Techniques

The purpose of this module is to introduce spectrum of sensors used to implement intelligent machining. The module will also discuss the basics of signal processing and analysis techniques that has brought intelligent machining paradigm closer to industrial realization. Following issues pertaining to sensors and sensing techniques will be elaborated up: (1) Which sensors are to be used in each application? (2) How to acquire and process sensor signals?

Transforming Data into Information4:49

与讲师见面

Rahul Rai
Associate Professor
Mechanical and Aerospace Engineering

This lesson is a preface to the following lesson

on machine learning and

artificial intelligence in sensing.

In this current lesson,

we will be introduced to

factors driving the need for machine learning to

transform large sets of data into

information that can be used to make smarter decisions.

In this lesson, we will learn

the meaning of terms, features,

and how features can help convert

raw data into prediction using machine learning.

Using a few day-to-day examples,

students will learn how

all our lives are being touched by fusion

of several exciting and emerging fields

of technologies such as machine learning,

artificial intelligence, and big data.

Let's consider the question, why machine learning?

As we discussed in the previous module,

that is module one,

we need three main components to

enable intelligent manufacturing framework.

First, sensors to collect data.

Second, a data acquisition and

management system to process, and store data.

Third, machine learning and knowledge discovery

component to process the stored data.

We spoke about components

number one and component number two,

which is sensors collecting data and

signal processing in the previous lessons.

Now, let's discuss how

the output of signal processing can be

used by the machine learning and

knowledge discovery component to make decisions.

In this context, we will introduce

the concept of machine learning.

To understand the concept of machine learning,

let's consider an example.

You may have used Netflix to watch your favorite movie.

You log in, search for a movie title,

then select a movie,

and you watch it halfway or in full.

Then you sign out and go about your daily life.

Once you log back again into Netflix system,

have you noticed that the system

gives you a list of recommended movies

in a singular genre to

the one you have watched previously?

How does that occur?

The system uses data from all previous system uses,

and learns about our likes and dislikes,

and makes prediction based on our past uses of Netflix.

This is an example of

machine learning working in our daily lives.

In the Netflix example,

some features used to make

predictions are the age of the user,

genre of the movie, and movie actors.

Features are salient way to abstract and

represent data that are useful to make predictions.

There are several other examples of

machine learning in action in our daily lives.

For example,

social networking profile recommendations features

of LinkedIn and Facebook uses machine learning.

The online shopping recommendations in

Amazon and eBay are enabled by machine learning.

The machine learning is also pivotal in implementation of

automated office assistants such as

Apple's Siri and Microsoft's Cortana.

In general terms, we can explain

the conceptual working model

of machine learning as follows.

Machine learning focuses on the development of

computational models that can teach themselves to

learn patterns or models from existing data and

utilize the learned model when exposed to new data.

In the advanced analysis course,

we spoke about big data,

high volume of data of wide variety

being generated at high velocity,

the three V's of big data.

Machine learning systems search through

big data to learn patterns in the data and

use these patterns to make predictions or

recommendation in light of new data.

For example, recommending movie genres

a user may like based on

pattern learned from the past users

is the use of machine learning.

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