Computing with Students from LIS

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Computing with Students from LIS

ECE 1001

• Prof. Marian S. Stachowicz
• Laboratory for Intelligent Systems
• ECE Department, University of Minnesota, USA
• November 8, 2007

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• Dr. Marian S. Stachowicz
• Professor and Jack Rowe Chair
• 273 MWAH
• M and W from 1400 to 1530
• http//www.d.umn.edu/ece/lis

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Courses

• ECE 5831 Fuzzy Sets Theory
• ECE 3151 Control Systems
• ECE 8831 Soft Computing (Fall 2008)

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Outline

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

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LIS

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

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Laboratory for Intelligent Systems
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LIS has been founded in cooperation with
Minnesota Power and 3M.
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Undergraduate and graduate students concentrate
on methods and algorithms for soft computing and
their applications in - image processing, -
multi-objective optimization, - color
recognition.
RESEARCH
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LIS members
Matt Verraux J.D. Hoverman Dinesh Baniya
Kshatri Calvin Behling
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Computing with Words

• Computing with Words (CW) is a methodology in
  which words are used in place of numbers for
  computing and reasoning.

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LEXICAL IMPRECISION

• DALLAS STAR
• PRICESS OF CRUDE OIL, WHICH HAVE EDGED HIGHER IN
  RECENT WEEKS AFTER BEING REMARKABLY STABLE
  THROUGH MUCH OF THE YEAR, MAY FLUCTUATE AS MUCH
  AS A DOLLAR A BARREL IN THE MONTHS AHEAD,
• BUT ABRUPT CHANGES ARE NOT LIKELY, MANY ANALYSTS
  BELIEVE.

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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.

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A key aspect of CW is that it involves a fusion
of natural languages and computation with
linguistic variables.
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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,

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Fuzzy Partition

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

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What are Fuzzy Sets?
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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
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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,
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An alternative way to define the set would be to
use a smooth transition.
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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

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Characteristic Function

A U ? 0, 1

Membership Function
M U ? 0, 1
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Areas of Applications 1

• Approximate Reasoning
• Fuzzy Decision Making
• Fuzzy Arithmetic
• Fuzzy Modeling
• Fuzzy Logic Control

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Fuzzy Modeling
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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.

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Fuzzy sets theory provides a strict mathematical
framework in which vague conceptual
phenomena can be precisely and rigorously studied.
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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

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Where is Fuzzy System Used?

• Sensor Fusion, Risk Analysis
• Financial Systems
• Information Systems
• Data Base Management
• Information Retrieval
• Data Analysis
• Meteorology
• Art and Music

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Fuzzy Systems

• Why fuzzy systems?
• What are fuzzy systems?
• Where are fuzzy systems used and how?

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Fuzzy Systems

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

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A fuzzy IF-THEN rule is statement in which some
words are characterized by membership function
(MF).
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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.

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Example 1. 2

• Problem
• We want to design a controller to automatically
  control the speed of a car.

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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.

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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).

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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

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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.

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Mitsubishi Heating/Cooling

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

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Maytag Dishwasher

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

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Sony Palmtop

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

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Acknowledgments

• Jonathan Andersh
• Lance Beall
• Cheng Tong
• Chaohui Yang
• Dan Yao

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Purpose of research
Color and Computer
To explore the ways how color can be used in
computer.
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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

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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

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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.

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The spatial resolution

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

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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.

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COLOR MINING

• 256 x 256 x 256 16 777 216 colors per one pixel
  

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Color Recognition Method

• - Using only color information
• - Two main steps

Feature Extraction
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COLOR CUBE
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T(COLOR)RED, GREEN, BLUE, CYAN, MAGENTA,
YELLOW, WHITE, BLACK
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National Flags Identification
A system which can identify national flags by
comparing an input flag to a known database.
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The intensity resolution

• The intensity resolution describes how many
  different intensities or colors are possible for
  a particular pixel.

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Stamps Identification
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Grab.exe
I-35 near 4th Ave. West, Duluth, MN
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Heart Murmur Classification
normal
pathology
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Acknowledgments

• Sonny Zhan
• David Lemke
• Lucas May
• David Olsen
• Nicholas Andrisevic
• Adilbek Karaguishiyev
• Glenn Nordehn, M.D.

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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/

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Laboratory for Intelligent Systems
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AG-H Krakow - Poland 22-26 May, 2006
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Palma De Mallorca - Spain, 30 August, 2006
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ECE 5831-F-2006
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AGH Krakow - Poland,June 2007
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Professor Lotfi Zadeh and Professor Marian S.
Stachowicz Vienna, Austria, 28
November 2005
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HOMEWORK.

• 1. Find five WEB sites that contain valuable
  information about soft computing and color
  recognition. 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 Thursday, November 14, 2007
• 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

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THANK YOU.