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Title: Computing with Students from LIS


1
Computing with Students from LIS

ECE 1001

  • Prof. Marian S. Stachowicz
  • Laboratory for Intelligent Systems
  • ECE Department, University of Minnesota, USA
  • November 13, 2008




2
Outline
  • LIS
  • Computing with Words
  • Fuzzy Logic - Mathematica Package
  • Color Mining

3
LIS
  • LABORATORY FOR INTELLIGENT SYSTEMS
  • http//www.d.umn.edu/ece/lis

4
Laboratory for Intelligent Systems
5
Research
  • Soft computing
  • Computing with words
  • Colors in computer vision

6
Undergraduate and graduate students concentrate
on methods and algorithms for soft computing and
their applications in - image processing, -
multi-objective optimization, - color
recognition.
RESEARCH
7
Courses
  • ECE 3151 Control Systems
  • ECE 5831 Fuzzy Sets Theory
  • ECE 8831 Soft Computing

8
ECE 3151- Spring 2008
9
ECE 5831-F-2006
10
Study Abroad - Advisor
1
  • AGH University of Science and Technology-Poland
  • University of Canterbury - New Zealand
  • Sydney University - Australia
  • Linkoping University - Sweden
  • East Kazakhstan State Technical University -
    Kazakhstan

11
AGH Krakow - Poland, June 2008
12
AGH Krakow - Poland, June 2008
13
Ust-Kamenogorsk-Kazakhstan - 9/2008
14
LIS has been founded in cooperation with
Minnesota Power and 3M.
15
Computing with Words
  • Computing with Words (CW) is a methodology in
    which words are used in place of numbers for
    computing and reasoning.

16
LEXICAL IMPRECISION
  • STAR TRIBUNE-BUSINESS, MAY 12, 2008
  • STOCK PRICES FELL SHARPLY AND BROADLY THURSDAY,
    AS INVESTORS WORIED THAT RISING OIL PRICES WOULD
    QUICKEN THE PACE OF INFLATION AND FORCE
    POLICYMAKERS TO RAISE INTEREST RATES FURTHER.
  • THE ABRUPT DROP, THE LARGEST SINCE JANUARY, CAME
    SEVERAL DAYS OF MODEST BUT STEADY INCREASES IN
    DOW JONES INDUSTRIAL AVERAGE.

17
Computing with Words
  • CW is a necessity when the available information
    is too imprecise to justify the use of numbers.
  • When there is tolerance for imprecision which can
    be exploited to achieve tractability, robustness,
    low solution cost, and better rapport with
    reality.

18
A key aspect of CW is that it involves a fusion
of natural languages and computation with
linguistic variables.
19
A linguistic variable AGE
  • T(AGE) YOUNG, NOT YOUNG, VERY YOUNG, NOT VERY
    YOUNG, , OLD, NOT OLD, VERY OLD, NOT VERY OLD,
    , MIDDLE AGED, NOT MIDDLE AGED,, NOT OLD AND
    NOT MIDDLE AGED,, EXTREMELY OLD,

20
Fuzzy Partition
  • Fuzzy partitions formed by the linguistic values
    young, middle aged, and old

21
What are Fuzzy Sets?
22
Problem 1 Given the set U 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12,describe the set of prime
numbers.
A u in U u is a prime number
The elements of the set are defined unequivocally
as A 2, 3, 5, 7, 11
23
Problem 2 Now using the same set U,
suppose we want to describe the set of small
numbers.
M u in U u is a small number
Now, it is not so easy to define the set. We
can use a sharp transition like the following,
24
An alternative way to define the set would be to
use a smooth transition.
25
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26
Fuzzy Sets
  • A fuzzy set A defined in the universal space U is
    a function defined in U which assumes values in
    the range 0,1 .
  • A U ? 0, 1

27
Characteristic Function

A U ? 0, 1

Membership Function
M U ? 0, 1
28
Areas of Applications 1
  • Approximate Reasoning
  • Fuzzy Decision Making
  • Fuzzy Arithmetic
  • Fuzzy Modeling
  • Fuzzy Logic Control

29
Fuzzy Modeling
30
Fuzzy Sets
  • The human brain interprets imprecise and
    incomplete sensory information provided by
    perceptive organs.
  • Fuzzy sets theory provides a systematic calculus
    to deal with such information linguistically, and
    it performs numerical computation by using
    linguistic labels stipulated by membership
    functions.

31
Fuzzy sets theory provides a strict mathematical
framework in which vague conceptual
phenomena can be precisely and rigorously studied.
32
Where is Fuzzy System Used?
  • Linear and Nonlinear Process Control
  • Robotics, Automation, Tracking
  • Consumer Electronics
  • VCRs, Digital High Definition Television,
    Microwave Ovens, Cameras, etc.
  • Pattern Recognition
  • Image Processing, Machine Vision
  • Decision Making

33
Where is Fuzzy System Used?
  • Sensor Fusion, Risk Analysis
  • Financial Systems
  • Information Systems
  • Data Base Management
  • Information Retrieval
  • Data Analysis
  • Meteorology
  • Art and Music

34
Fuzzy Systems
  • Why fuzzy systems?
  • What are fuzzy systems?
  • Where are fuzzy systems used and how?

35
Fuzzy Systems
  • Fuzzy systems are knowledge-based or
  • rules-based systems.
  • A fuzzy systems is constructed from a collection
    of fuzzy IF-THEN rules.

36
A fuzzy IF-THEN rule is statement in which some
words are characterized by membership function
(MF).
37
Two kinds of justification for fuzzy system
theory
  • We need a theory to formulate human knowledge in
    a systematic manner and put it into engineering
    systems.
  • The real world is too complicated for precise
    descriptions to be obtained.

38
Example 1. 2
  • Problem
  • We want to design a controller to automatically
    control the speed of a car.

39
Two approaches to designing such a controller
  • use conventional control theory,
  • for example, designing a PID controller.
  • to emulate human drivers, that is, converting the
    rules used by human drivers into an automatic
    controller.

40
Knowledge-based or rules-based.
  • IF speed is low, THEN apply more force to the
    accelerator,
  • IF speed is medium, THEN apply normal force to
    the accelerator,
  • IF speed is high, THEN apply less force to the
    accelerator.
  • Where the words low, medium, high and more,
    normal, less
  • are characterized by membership functions (MF).

41
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42
Where Are Fuzzy Systems Used ?
  • Fuzzy washing machine
  • Digital image stabilizer
  • Fuzzy systems in cars
  • Fuzzy control of a cement kiln
  • Fuzzy control of subway train

43
Cannon Camera
  • Screen is divided into 6 parts
  • 2 Inputs per part
  • 13 Rules
  • 1.1 Kbytes of Memory
  • Rules are used to bring the picture into focus
    using less memory that traditional methods.

4
44
Digital image stabilizer.
  • IF all the points in the picture are moving in
    the same direction, THEN the hand is shaking.
  • IF only some points in the picture are moving,
    THEN the hand is not shaking.

45
Matsushita Vacuum
  • Dust sensors are used to adjust air-flow
  • System is closed-loop
  • Fuzzy rules are applied to reduce power
    consumption
  • Example of rule
  • If there is little dust,
  • then reduce power.

3
46
Mitsubishi Heating/Cooling
  • 25 Heating Rules
  • 25 Cooling Rules
  • Heats/Cools 5x faster
  • Reduces power consumption by 24

5
47
Maytag Dishwasher
  • Measures soil in water, adjusts wash accordingly
  • Adjusts for dried-on foods
  • Determines optimum wash cycle

6
48
Sony Palmtop
  • Used directly for character recognition
  • Each person writes letters slightly differently
  • Fuzzy rules account for these differences

8
49
Professor Lotfi Zadeh and Professor Marian S.
Stachowicz Vienna, Austria, 28
November 2005
50
Acknowledgments
  • Jonathan Andersh
  • Lance Beall
  • Cheng Tong
  • Chaohui Yang
  • Dan Yao

51
Purpose of research
Color and Computer
To explore the ways how color can be used in
computer.
52
Color and Computer Images
  • Three main color schemes used with computers
  • - CMYK cyan, magenta, yellow, black
  • used by printers
  • - HSB hue, saturation, brightness
  • similar to human vision
  • - RGB red, green, blue
  • most common system
  • computer images are generally stored in this
    format
  • used in this research

53
Color and Computer Images
  • The Basic Image Element Pixel
  • Pixels are described by two features
  • Location in the x-y plane
  • Color - in the from R, G, B,
  • where R, G, B 0 to 255

54
Spatial and Intensity Resolutions
  • An image with M pixels can be represented by a
    spatial-chromatic hybrid vector
  • Xi (xi, yi, Ri, Gi, Bi )T (i 1, 2,
    , M)
  • where
  • xi, yi are the spatial
    coordinates
  • Ri, Gi, Bi are the color components.

55
The spatial resolution
  • The spatial resolution describes how many pixels
    are possible within a certain distance such as
    150 dots per inch (DPI).

56
24-bit color
  • Almost always, each of the R G B numbers is a
    single byte, so the red, green, and blue
    components can take on integer values from 0 to
    255.
  • 255, 255, 255 would represents white,
  • 0, 0, 0 would represent black,
  • 255, 0, 0 would represent red, and so on.

57
COLOR MINING
  • 256 x 256 x 256 16 777 216 colors per one pixel

58
Color Recognition Method
  • - Using only color information
  • - Two main steps

Feature Extraction
59
COLOR CUBE
60
T(COLOR)RED, GREEN, BLUE, CYAN, MAGENTA,
YELLOW, WHITE, BLACK
61
National Flags Identification
A system which can identify national flags by
comparing an input flag to a known database.
62
The intensity resolution
  • The intensity resolution describes how many
    different intensities or colors are possible for
    a particular pixel.

63
Stamps Identification
64
Grab.exe
I-35 near 4th Ave. West, Duluth, MN
65
Heart Murmur Classification
normal
pathology
66
Acknowledgments
  • Sonny Zhan
  • David Lemke
  • Lucas May
  • David Olsen
  • Nicholas Andrisevic
  • Adilbek Karaguishiyev
  • Glenn Nordehn, M.D.

67
Strange Attractors
68
CATHERINE MARIE CHARLTON
69
HOMEWORK.
  • 1. Find five WEB sites that contain valuable
    information about soft computing and biometrics.
    Provide the URL for each of the sites. Provide a
    brief description of what information is
    available on each site.
  • 2. Two page limit !!!
  • 3. On the top of the first page, provide name and
    e-mail address.
  • 4. Typed (Word Processor) is much preferred over
    a hand written submission.
  • 5. Due in class on Tuesday, November 25, 2008
  • 6. As an added suggestion, the Fuzzy Logic
    Package by Prof. M. S. Stachowicz and Lance Beall
    can be found in the WOLFRAM Research folder with
    some good tutorial information -http//www.wolfram
    .com/applications/fuzzylogic

70
References
  • 1 L.A. Zadeh, Fuzzy sets, Information and
    Control,
  • vol.8, pp. 338-353, 1965.
  • 2 George S. Klir and Bo Yuan, Fuzzy Sets,
    Uncertainty, and Information. Prentice Hall,
    Englewood Cliffs, New Jersey, 1995.
  • 3 M.S. Stachowicz and Lance E. Beall, Fuzzy
    Logic Package for use with Mathematica, Wolfram
    Research, Inc. Champaign, IL 61820, 2003
  • http//www.wolfram.com/fuzzylogic
  • 4 C.M. Charlton, Strange Attractor, CD Album
    of Piano Improvisations, Orange Moon Production,
    Inc. 19672 Stevens Creek Blvd., 178, Cupertino,
  • CA 95014, http//www.catherinemariecharlton.com/

71
Dr. Marian S. StachowiczProfessor and Jack Rowe
Chair
OFFICE MWAH 273 OFFICE HOURS Tu, Th 1315
1415, Th 1700 1800
  • mstachow_at_d.umn.edu

72
Laboratory for Intelligent Systems
73
LIS advice you
  • Schaum's Outline Series, McGraw-Hill offers in
    all engineering topics, including calculus, books
    with several hundreds examples with solutions
    step by step.
  • These books cost around 20 US .

74
ENJOY UMD AND GOOD LUCK!
  • LIS members

75
THANK YOU.
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