0
FUTURE OF OLED
AS
LIGHTING SOLUTION

12th Nov, 2012

SHAMEER P.H.
m.Tech - technology management
ABSTRACT
In this market trend of using eco-friendly
products, an exciting technology has been available
in many small devi...
ABSTRACT


Objective:
 Forecast

the potential of OLEDs in the
field of Lighting and Displays using
Growth Curve models ...
“My interest is in the future, Because I’m
going to spend the rest of my life there.”
C.F. Kettering
TECHNOLOGY MANAGEMENT





TECHNOLOGY
MANAGEMENT
&
TECHNOLOGY
FORECASTING
TECHNOLOGY MANAGEMENT
“TM is an interdisciplinary field concerned
with the planning, development and
implementation of tec...
TECHNOLOGY MANAGEMENT


Aims:
 using technology as a source of
competitive advantage.



Deals with:
 Developing techn...
TECHNOLOGY MANAGEMENT..
Industries seek to manage the technology they control,
use or produce to contribute to corporate g...
TM & TF
In short, technology management draws on historical and
future perspectives.

Forecasting is intended to bring inf...
TECHNOLOGY FORECASTING
A tool for technology management..


WHAT?
 “Prediction

of the future characteristics of useful
...
TECHNOLOGY FORECASTING


HOW?
Technology lifecycle
The technology‟s
performance improvement
follows the S-Curve with
1) Embryonic Phase
2) Growth Phase
...
FUTURE OF OLEDS AS
LIGHTING SLOUTION
1.

2.
3.

4.

OLED TECHNOLOGY- A
REVIEW
METHODOLOGY
FORECASTING RESULTS
AND DISCUSSI...
1)OLED TECHNOLOGY- A REVIEW
This Section deals with
 Basics of Luminescence
 Evolution and Types of Light
Bulbs
 OLED t...
Basics of Luminescence





Light is a form of Energy.
To create light, another form of energy
must be supplied
There a...
INCANDESCENT






LIGHT from HEAT.
If you heat something to a high enough
temperature, it will begin to glow.
Sun and...
LUMINESCENCE







COOL LIGHT
Caused by movement of electrons from
more energetic state to less energetic state.
Chem...
FLUORESCENCE



The luminescence caused by
absorption of some form of radiant
energy, and ceases as soon as the
radiation...
TYPES &EVOLUTION OF LAMPS
TYPES OF LAMPS
 INCANDESCENT LAMPS
 HALOGEN LAMPS
 FLUORESCENT LAMPS
 COMPACT FLLORESCENT LAMPS
 HIGH INTENSITY DISCH...
EVOLUTION OF LAMPS
IN TERMS OF LUMINOUS EFFICACY
OLED TECHNOLOGY
DEALS WITH
what is an OLED?
structure of OLED,
working principle of OLED
its applications and advantages.
What is an OLED ?






OLEDs are energy conversion devices
based on ELECTROLUMINESCENCE.
OLEDs are organic because the...
background


The first observations of electroluminescence in
organic materials were in the early 1950s by A.
Bernanose a...
However, the scene changed when..


Chin Tang and Van Slyke introduced
the first light emitting diodes from
thin organic ...
then..


2000 - Alan G. MacDiarmid, Alan J.
Heeger, and Hideki Shirakawa of University of
Pennsylvania received Nobel Pri...
STRUCTURE OF OLED
OLED is a solid-state semiconductor device that is 100 to 500
nanometres thick or about 200 times smalle...
structure


Substrate (clear plastic, glass, foil) The substrate supports the OLED.



Anode (transparent) - The anode r...
structure


Organic layers:


Conducting layer- made of organic plastic
molecules that transport "holes" from the anode....
HOW IT WORKS..


The battery or power supply of the device
containing the OLED applies a voltage
across the OLED.



An ...
TYPES OF OLEDS
AMOLED

PMOLED

WHITE OLED




SMALL
DISPLAYS



LARGE

LIGHTING
SOLUTIONS
APPLICATIONS
OLED DISPLAYS
The essential requirements of present generation displays are
reproduction of good light
quality, brightness...
Applications







Televisions
Cell Phone screens
Watches
Computer Screens
Digital Camera
Portable Device displays
OLED DISPLAYS


Thinner, lighter and flexible.
OLED DISPLAYS


BRIGHTER!!



The organic layers of an OLED
are much thinner than the
corresponding inorganic crystal
la...
OLED DISPLAYS


LARGE FIELD OF VIEW.
OLED DISPLAYS


FAST RESPONSE TIME

OLED (10µs)

LCD (200ms)
OLED lighting SOLUTIONS
OLEDs are an entirely new way for
architects, designers, system integrators, planners
and luminair...
applications





Mood Lighting
Object Illumination
General Illumination
advantages










Non-glaring area light source.
High quality white light. (CRI 80)
Requires less power (Low volt...
OLED AND OTHERS ….
WHO ALL ARE
IN THE FIELD..

Source:
HENDY
The key players








GE
PHILIPS
OSRAM
KONICA MINOLTA
MOSER BAER
LUMIOTEC









VERBATIM OLED
LEDON OLED
...
WHY OLEDS…


Lighting
Incandescent bulbs are inefficient !
 Fluorescent bulbs give off ugly light !!
 Ordinary LEDs are...
2)METHODOLOGY
1.

DATA COLLECTION

2.

FORECASTING BY NON-LINEAR
REGRESSION.

3.

FORECASTING BY GA BASED N GREY
BERNOULLI...
DATA COLLECTION
Patent Data from LexisNexis Database is used
to forecast.
PATENT DATA






A patent is an exclusive right to an invention over a limited
period of within the country where the ...
PATENTS & TLC








Patent growth generally follows a similar trend that
can resemble S-Curve.
In early stages of a ...
PATENT DATA
collection


The appropriate keywords were used to
determine the number of patents for a given
year globally....
forecasting using

growth curve MODELS
forecasting using

growth curves


Technology life cycles are used for
modelling technological growth by
using either Gom...
Growth Curves


Assumptions



The upper limit to the growth curve is known.
The chosen growth curve to be fitted to the...
Gompertz Model

Logistic Model

Where,
‘Yt’ is the measure of interest tagged by time ‘t’,
‘a’ is the Location Coefficient...
Finding

the coefficients
Gompertz Model

Logistic Model

Linear transformation of these equations using natural
logarithm...
Selection between

gompertz & pearl curves


The choice between these curves is performed by using a
regression model (de...
Selection between

gompertz & pearl curves






The regression model for the Gompertz
curve is linear in t and the exp...
GENETIC ALGORITHM BASED
GREY MODELING
Includes
a) Grey Systems theory
b) Non-linear Grey Bernoulli method
c) Genetic Algor...
Grey Systems theory







Introduced by Deng (1982).
In systems theory, a system can be defined in terms of a color
t...
Grey modeling


Grey models require only a limited amount of data to estimate
the behaviour of unknown systems.



Funda...
Grey System based prediction


Grey models predict the future values of a
time series based only on a set of the most
rec...
Generation of grey sequences




Main task of GS theory is to extract the governing laws of the
system.
If the randomnes...
GM(N,M) model

GM(1,1) model






“Grey Model First Order One Variable”.

„n‟ is the order of
the difference
equation ...
Non-linear

Grey Bernoulli method



Developed by Liu, Dong, and Fang (2004).
Model is,
 Step 1: original data sequence...
Non-linear

Grey Bernoulli method


Step 3: The NGBM(1,1) model of the first-order
differential equation
 Fit

the data ...


Step 4 : Objective is to minimize the error function



The software Evolver 5.5 (Palisade) is used in this study to f...


Step 6 : Take the IAGO on
, the
corresponding IAGO is defined as
where k = 2, 3, . . . , n.
 This is our predicted val...
Genetic Algorithm

John Holland, University of Michigan (1970‟s)
Biological evolution
Organisms produce a number of
offspring similar to themselves but
can have variations due to:


Muta...


Some offspring survive, and
produce next generations, and
some don‟t:



The organisms adapted to the
environment bett...
Genetic Algorithm


Genetic Algorithms are optimization
techniques based on the mechanics of
biological evolution.



A ...
Stochastic operators
Selection

replicates the most
successful
solutions found in
a population at a
rate proportional
to t...
softwares


IBM Inc.‟s SPSS



Microsoft Excel



Palisade Evolver
3) FORECASTING
A.

B.

OLED DISPLAY
FORECAST
OLED LIGHTING
FORECAST
FORECASTING
OLED DISPLAY TECHNOLOGY





PATENT DATA
FORECAST
RESULT ANALYSIS
INFERENCE
PATENT DATA


Appropriate keywords
were used to determine the
number of patents on the
OLED technology for a
given year.
...
FORECAST
East

West

North
90

PATTERN
OBTAINED
FROM NONLINEAR
REGRESSIO
N MODEL
(LOGISTIC
CURVE)
FORECASTI
NG

46.9

45.9...
FORECAST
PATTERN
OBTAINED
FROM
GANGBM
MODEL
FORECASTI
NG
WIDESCREEN
PICTURES
S-CURVE OF OLED DISPLAY TECHNOLOGY
PHASES OF LIFE
EMEREGNT PHASE
up to 2004

:

GROWTH PHASE
2004-2015

:

MATURITY PHASE
2015-2020

:
INFERENCE:
R&D




The Technology is
currently in its end of
growth phase.
Will enter its mature
stage by 2015.

MARKET
...
OLED LIGHTING TECHNOLOGY






PATENT DATA
FORECAST
RESULT ANALYSIS
INFERENCE
PATENT DATA


Appropriate keywords
were used to determine the
number of patents on the
OLED technology for a
given year.
...
FORECAST
East

West

North
90

PATTERN
OBTAINED
FROM NONLINEAR
REGRESSIO
N MODEL
FORECASTI
NG

46.9

45.9
38.6
30.6

45

4...
FORECAST

PATTERN
OBTAINED
FROM
GANGBM
MODEL
FORECASTI
NG
S-CURVE OF OLED LIGHTING TECHNOLOGY
PHASES OF LIFE
EMEREGNT PHASE
up to 2015

:

GROWTH PHASE
2015-2025

:

MATURITY PHASE
2025-2034

:
INFERENCE:
R&D




OLED Lighting
technology is still in its
emergence phase.
Huge investments are
required.

MARKET


...
CONCLUSIONS
The OLED technology in Display Sector will enter its maturity
stage by 2015.
For small size displays OLED will...
CONCLUSIONS
The OLED Lighting technology is still in its emerging
phase.
There is an uncertainty about the market.
For new...
References
1.

J.P. Martino: A review of selected recent advances in technological forecasting, Technological Forecasting ...
References
12.

John K. Borchardt: Developments in organic displays; materials today September 2004.

13.

HowStuffworks.c...
References
24.
25.

26.
27.
28.

29.

30.

http://ledsmagazine.com/
N. Thejo Kalyani , S.J. Dhoble: Organic light emitting...
QUESTIONS
&
COMMENTS
FUTURE OF OLEDs as LIGHTING SOLUTIONS & DISPLAYS
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FUTURE OF OLEDs as LIGHTING SOLUTIONS & DISPLAYS

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STUDY ON OLED LIGHTS & DISPLAYS, FORECAST ON FUTURE OF OLEDs
METHODOLOGY: GENETIC ALGORITHM based
GREY MODELING

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  • All industries manage technology, even if their management plan is to have no plan. Managing technology is inextricably linked with time. Industries seek to manage the technology they control, use or produce to contribute to corporate goals today. They try to manage the development and implementation of technology to increase the realization of those goals tomorrow. To manage, they draw on the lessons of yesterday buttressed by management models developed from experience. In short, technology management draws on historical and future perspectives.
  • The following slides show several examples of timelines using SmartArt graphics.Include a timeline for the project, clearly marking milestones, important dates, and highlight where the project is now.
  • Prior to the OLEDs, many display technologies such as CRTs, LEDs, LCDs, plasma displays were leading in the market. All these displays have their own limitations including bulkiness, low viewing angle, colour tunability, etc. The essential requirements of present generation displays are reproduction of good light quality, brightness, contrast, improved colour variation, high resolution, low weight, reduction in thickness, reduction in cost, low power consumption. All these short comings are rectified in these OLED devices and a new flat panel display technology on these organic based devices commonly known as OLEDs emerged. OLEDs offer many advantages over both LCDs and LEDs:
  • Because the light-emitting layers of an OLED are lighter, the substrate of an OLED can be flexible instead of rigid. OLED substrates can be plastic rather than the glass used for LEDs and LCDs.
  • OLEDs have large fields of view, about 170 degrees. Because LCDs work by blocking light, they have an inherent viewing obstacle from certain angles. OLEDs produce their own light, so they have a much wider viewing range.
  • OLEDs have a faster response time than standard LCD displays. Whereas the normal LCD displays currently have a 200 ms response time, OLEDs can have less 10 micro sec.
  • Patent growth generally follows a similar trend that can resemble S-Curve. In early stages of a technology the number of patents issued is very limited. A fast-growing period then follows when the number of patents filed and issued increases and then a plateau is reached. Because the patent process is costly and can take several years, filing a patent generally means there is optimism in economic or technical contribution.
  • (*LexisNexis patent database includes patents from the United States Patent and Trademark Office (USPTO), the European Patent Office (EPO), the Japanese Patent Office (JPO), and the Patent Cooperation Treaty (PCT) of the World Intellectual Property Organization (WIPO).)
  • A=4.40B=5.77Gamma= 0.979
  • A=4.565B=5.598Gamma= 0.9846
  • Transcript of "FUTURE OF OLEDs as LIGHTING SOLUTIONS & DISPLAYS"

    1. 1. FUTURE OF OLED AS LIGHTING SOLUTION 12th Nov, 2012 SHAMEER P.H. m.Tech - technology management
    2. 2. ABSTRACT In this market trend of using eco-friendly products, an exciting technology has been available in many small devices such as cell phones and digital camera displays for the last few years. It is claimed that this technology can cause a renaissance in the fields of lighting and display solutions. The technology is organic light emitting diode (OLED).
    3. 3. ABSTRACT  Objective:  Forecast the potential of OLEDs in the field of Lighting and Displays using Growth Curve models and GA based Grey Bernoulli Model.
    4. 4. “My interest is in the future, Because I’m going to spend the rest of my life there.” C.F. Kettering
    5. 5. TECHNOLOGY MANAGEMENT   TECHNOLOGY MANAGEMENT & TECHNOLOGY FORECASTING
    6. 6. TECHNOLOGY MANAGEMENT “TM is an interdisciplinary field concerned with the planning, development and implementation of technological capabilities to shape and accomplish the operational and strategic objectives of an organization” (National Research Council Report (USA), 1987)
    7. 7. TECHNOLOGY MANAGEMENT  Aims:  using technology as a source of competitive advantage.  Deals with:  Developing technology strategies  Developing/Acquiring technologies  Using Technologies.
    8. 8. TECHNOLOGY MANAGEMENT.. Industries seek to manage the technology they control, use or produce to contribute to corporate goals TODAY. They try to manage the development and implementation of technology to increase the realization of those goals TOMORROW. To manage, they draw on the lessons of YESTERDAY buttressed by management models developed from experience.
    9. 9. TM & TF In short, technology management draws on historical and future perspectives. Forecasting is intended to bring information to the technology management process by trying to predict possible future states of technology and/or conditions that affect its contribution to corporate goals.
    10. 10. TECHNOLOGY FORECASTING A tool for technology management..  WHAT?  “Prediction of the future characteristics of useful machines, procedures or techniques”  WHY? Many reasons, but mainly to Maximize the gain or minimize the loss from future conditions 
    11. 11. TECHNOLOGY FORECASTING  HOW?
    12. 12. Technology lifecycle The technology‟s performance improvement follows the S-Curve with 1) Embryonic Phase 2) Growth Phase 3) Maturity Phase 4) Saturation Phase 5) Declining Phase
    13. 13. FUTURE OF OLEDS AS LIGHTING SLOUTION 1. 2. 3. 4. OLED TECHNOLOGY- A REVIEW METHODOLOGY FORECASTING RESULTS AND DISCUSSIONS CONCLUSION
    14. 14. 1)OLED TECHNOLOGY- A REVIEW This Section deals with  Basics of Luminescence  Evolution and Types of Light Bulbs  OLED technology
    15. 15. Basics of Luminescence    Light is a form of Energy. To create light, another form of energy must be supplied There are two common ways for this to occur:  Incandescence  Luminescence
    16. 16. INCANDESCENT     LIGHT from HEAT. If you heat something to a high enough temperature, it will begin to glow. Sun and other Stars... Incandescent Bulbs, Halogen bulbs..
    17. 17. LUMINESCENCE     COOL LIGHT Caused by movement of electrons from more energetic state to less energetic state. Chemiluminescence, Electroluminescence, Bioluminescence…. Fluorescence &Phosphorescence
    18. 18. FLUORESCENCE  The luminescence caused by absorption of some form of radiant energy, and ceases as soon as the radiation causing it has stopped. PHOSPHORESCE NCE  The luminescence continues after the radiation causing it has stopped.
    19. 19. TYPES &EVOLUTION OF LAMPS
    20. 20. TYPES OF LAMPS  INCANDESCENT LAMPS  HALOGEN LAMPS  FLUORESCENT LAMPS  COMPACT FLLORESCENT LAMPS  HIGH INTENSITY DISCHARGE LAMPS  LOW PRESSURE SODIUM LAMPS  SOLID STATE LIGHTING
    21. 21. EVOLUTION OF LAMPS
    22. 22. IN TERMS OF LUMINOUS EFFICACY
    23. 23. OLED TECHNOLOGY DEALS WITH what is an OLED? structure of OLED, working principle of OLED its applications and advantages.
    24. 24. What is an OLED ?    OLEDs are energy conversion devices based on ELECTROLUMINESCENCE. OLEDs are organic because they are made from carbon and hydrogen. made by placing a series of organic thin films between two conductors.
    25. 25. background  The first observations of electroluminescence in organic materials were in the early 1950s by A. Bernanose and co-workers at the NancyUniversité, France.  M. Pope and co-workers discovered electroluminescence in organic semiconductors in 1963. Unfortunately, their high operating voltages (>1000V) prohibited them from becoming practical devices.
    26. 26. However, the scene changed when..  Chin Tang and Van Slyke introduced the first light emitting diodes from thin organic layers at Eastman Kodak in 1987.  In 1990 electroluminescence in polymers was discovered at Cavendish Laboratory, Cambridge University by Friend and coworkers.
    27. 27. then..  2000 - Alan G. MacDiarmid, Alan J. Heeger, and Hideki Shirakawa of University of Pennsylvania received Nobel Prize in chemistry for “The discovery and development of conductive organic polymer”.  1999- The First OLED display on market. 2008- The first OLED lighting fixture was introduced by OSRAM. 
    28. 28. STRUCTURE OF OLED OLED is a solid-state semiconductor device that is 100 to 500 nanometres thick or about 200 times smaller than a human hair. OLEDs can have either two layers or three layers of organic material.
    29. 29. structure  Substrate (clear plastic, glass, foil) The substrate supports the OLED.  Anode (transparent) - The anode removes electrons (adds electron "holes") when a current flows through the device.  Cathode (may or may not be transparent depending on the type of OLED) - The cathode injects electrons when a current flows through the device.
    30. 30. structure  Organic layers:  Conducting layer- made of organic plastic molecules that transport "holes" from the anode. One conducting polymer used in OLEDs is polyaniline.  Emissive layer - made of organic plastic molecules (different ones from the conducting layer) that transport electrons from the cathode; this is where light is made. One polymer used in the emissive layer is polyfluorene.
    31. 31. HOW IT WORKS..  The battery or power supply of the device containing the OLED applies a voltage across the OLED.  An electrical current flows from the cathode to the anode through the organic layers  At the boundary between the emissive and the conductive layers, electrons find electron holes.  The OLED emits light
    32. 32. TYPES OF OLEDS AMOLED PMOLED WHITE OLED   SMALL DISPLAYS  LARGE LIGHTING SOLUTIONS
    33. 33. APPLICATIONS
    34. 34. OLED DISPLAYS The essential requirements of present generation displays are reproduction of good light quality, brightness, contrast, improved colour variation, high resolution, low weight, reduction in thickness, reduction in cost, low power consumption. All these features can be seen in the OLED devices. OLEDs offer many advantages over both LCDs and LEDs
    35. 35. Applications       Televisions Cell Phone screens Watches Computer Screens Digital Camera Portable Device displays
    36. 36. OLED DISPLAYS  Thinner, lighter and flexible.
    37. 37. OLED DISPLAYS  BRIGHTER!!  The organic layers of an OLED are much thinner than the corresponding inorganic crystal layers of an LED.  Also, LEDs and LCDs require glass for support, and glass absorbs some light. OLEDs do not require glass.
    38. 38. OLED DISPLAYS  LARGE FIELD OF VIEW.
    39. 39. OLED DISPLAYS  FAST RESPONSE TIME OLED (10µs) LCD (200ms)
    40. 40. OLED lighting SOLUTIONS OLEDs are an entirely new way for architects, designers, system integrators, planners and luminaire makers to create with light. OLED devices are ultra-flat and emit very homogeneous light. The OLED grants a high degree of design freedom to users. By combining colour with shape OLEDs offer an exciting new way of decorating and personalizing surroundings with light.
    41. 41. applications    Mood Lighting Object Illumination General Illumination
    42. 42. advantages       Non-glaring area light source. High quality white light. (CRI 80) Requires less power (Low voltage DC(2-10 v)) Mercury free, RoHS conform. High luminous efficacy. Light weight (˜ 24 gm)
    43. 43. OLED AND OTHERS ….
    44. 44. WHO ALL ARE IN THE FIELD.. Source: HENDY
    45. 45. The key players       GE PHILIPS OSRAM KONICA MINOLTA MOSER BAER LUMIOTEC       VERBATIM OLED LEDON OLED PANASONIC IDEMITSU OLED LG CHEM. SMD NEC LIGHTING
    46. 46. WHY OLEDS…  Lighting Incandescent bulbs are inefficient !  Fluorescent bulbs give off ugly light !!  Ordinary LEDs are bright points; not versatile !!!   Displays: Significant advantages over liquid crystals Faster!  Brighter!!  Lower power!!!  OLEDs may be better on all counts 
    47. 47. 2)METHODOLOGY 1. DATA COLLECTION 2. FORECASTING BY NON-LINEAR REGRESSION. 3. FORECASTING BY GA BASED N GREY BERNOULLI METHOD. 4. RESULTS AND DISCUSSIONS 5. INFERENCE.
    48. 48. DATA COLLECTION Patent Data from LexisNexis Database is used to forecast.
    49. 49. PATENT DATA    A patent is an exclusive right to an invention over a limited period of within the country where the application is made. Patents are granted for inventions which are novel, inventive and have an industrial application. Patents measure inventive output and may be used as measure for innovation and the growth of that corresponding technology.
    50. 50. PATENTS & TLC     Patent growth generally follows a similar trend that can resemble S-Curve. In early stages of a technology the number of patents issued is very limited. A fast-growing period then follows when the number of patents filed and issued increases and then a plateau is reached. Because the patent process is costly and can take several years, filing a patent generally means there is optimism in economic or technical contribution.
    51. 51. PATENT DATA collection  The appropriate keywords were used to determine the number of patents for a given year globally.  The Scirus search tool was used to scan for the majority of world patents through the LexisNexis database*. (*LexisNexis patent database includes patents from the United States Patent and Trademark Office (USPTO), the European Patent Office (EPO), the Japanese Patent Office (JPO), and the Patent Cooperation Treaty (PCT) of the World Intellectual Property Organization (WIPO).)
    52. 52. forecasting using growth curve MODELS
    53. 53. forecasting using growth curves  Technology life cycles are used for modelling technological growth by using either Gompertz or logistics curves.  The methodology starts first with choosing between the logistic and Gompertz curves, and continues with forecasting different emerging technologies for the coming years. The lower asymptote is the starting level. The upper asymptote is the mature level. The point of inflexion is the point of maximum growth.
    54. 54. Growth Curves  Assumptions  The upper limit to the growth curve is known. The chosen growth curve to be fitted to the historical data is the correct one. The historical data gives the coefficients of the chosen growth curve formula correctly.    1) 2) The growth curves most frequently used by technological forecasters are the Pearl curve the Gompertz curve
    55. 55. Gompertz Model Logistic Model Where, ‘Yt’ is the measure of interest tagged by time ‘t’, ‘a’ is the Location Coefficient of the Curve, ‘b’ is the Shape Coefficient of the Curve and ‘L’ is the asymptotic maximum value of Yt Both the Gompertz curve and the logistic curve range from ‘zero’ to ‘L’ as ‘t’ varies
    56. 56. Finding the coefficients Gompertz Model Logistic Model Linear transformation of these equations using natural logarithm will lead to:
    57. 57. Selection between gompertz & pearl curves  The choice between these curves is performed by using a regression model (developed by P.H. Franses) that tests for non-linearity between the dependent variable (to be forecasted) and time.  As dependent variable, we will use the number of patents for the OLED technology under investigation
    58. 58. Selection between gompertz & pearl curves    The regression model for the Gompertz curve is linear in t and the expression for the logistic curve is nonlinear in t . Taking ∆ as the first difference operator, the regression model is represented as In the case when γ is significantly different from zero, the forecasting method to be used will be based on logistic curve rather than Gompertz curve.
    59. 59. GENETIC ALGORITHM BASED GREY MODELING Includes a) Grey Systems theory b) Non-linear Grey Bernoulli method c) Genetic Algorithm
    60. 60. Grey Systems theory     Introduced by Deng (1982). In systems theory, a system can be defined in terms of a color that represents the amount of clear information about that system. A system whose internal characteristics are unknown= a black box. If everything is clear= white system. Then, Grey System?
    61. 61. Grey modeling  Grey models require only a limited amount of data to estimate the behaviour of unknown systems.  Fundamental concepts of grey system theory  Grey system based prediction  Generations of grey sequences  GM(n,m) model  GM(1,1) model
    62. 62. Grey System based prediction  Grey models predict the future values of a time series based only on a set of the most recent data.  Assumptions    all data values to be used in grey models are positive The sampling frequency of the time series is fixed Can be viewed as curve fitting approaches.
    63. 63. Generation of grey sequences   Main task of GS theory is to extract the governing laws of the system. If the randomness of data is smoothed, the process will be easier.
    64. 64. GM(N,M) model GM(1,1) model    “Grey Model First Order One Variable”. „n‟ is the order of the difference equation and „m‟ is the number of variables.   The solution is an exponential curve. The model fails when there lies a saturation level for the data.
    65. 65. Non-linear Grey Bernoulli method   Developed by Liu, Dong, and Fang (2004). Model is,  Step 1: original data sequence,  Step 2: new sequence generated by AGO,
    66. 66. Non-linear Grey Bernoulli method  Step 3: The NGBM(1,1) model of the first-order differential equation  Fit the data in to the equation.  Find out the values of a and b using least square method.  Use genetic algorithm to improve the accuracy by optimizing the value of γ.
    67. 67.  Step 4 : Objective is to minimize the error function  The software Evolver 5.5 (Palisade) is used in this study to find the optimal value of „gamma‟ using Genetic Algorithm.  Step 5 : Substitute the values of a, b and γ into the following whitening equation
    68. 68.  Step 6 : Take the IAGO on , the corresponding IAGO is defined as where k = 2, 3, . . . , n.  This is our predicted value.
    69. 69. Genetic Algorithm John Holland, University of Michigan (1970‟s)
    70. 70. Biological evolution Organisms produce a number of offspring similar to themselves but can have variations due to:  Mutations   (random changes) Sexual Reproduction  (offspring have combinations of features inherited from each parent)
    71. 71.  Some offspring survive, and produce next generations, and some don‟t:  The organisms adapted to the environment better have higher chance to survive  Over time, the generations become more and more adapted because the fittest organisms survive
    72. 72. Genetic Algorithm  Genetic Algorithms are optimization techniques based on the mechanics of biological evolution.  A genetic algorithm maintains a population of candidate solutions for the problem at hand, and makes it evolve by iteratively applying a set of stochastic operators
    73. 73. Stochastic operators Selection replicates the most successful solutions found in a population at a rate proportional to their relative quality Recombination decomposes two distinct solutions and then randomly mixes their parts to form novel solutions Mutation randomly perturbs a candidate solution
    74. 74. softwares  IBM Inc.‟s SPSS  Microsoft Excel  Palisade Evolver
    75. 75. 3) FORECASTING A. B. OLED DISPLAY FORECAST OLED LIGHTING FORECAST
    76. 76. FORECASTING OLED DISPLAY TECHNOLOGY     PATENT DATA FORECAST RESULT ANALYSIS INFERENCE
    77. 77. PATENT DATA  Appropriate keywords were used to determine the number of patents on the OLED technology for a given year.  A 16 year span (1994– 2009) has been studied with this method
    78. 78. FORECAST East West North 90 PATTERN OBTAINED FROM NONLINEAR REGRESSIO N MODEL (LOGISTIC CURVE) FORECASTI NG 46.9 45.9 38.6 30.6 45 43.9 34.6 31.6 27.4 20.4 20.4 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
    79. 79. FORECAST PATTERN OBTAINED FROM GANGBM MODEL FORECASTI NG
    80. 80. WIDESCREEN PICTURES S-CURVE OF OLED DISPLAY TECHNOLOGY
    81. 81. PHASES OF LIFE EMEREGNT PHASE up to 2004 : GROWTH PHASE 2004-2015 : MATURITY PHASE 2015-2020 :
    82. 82. INFERENCE: R&D   The Technology is currently in its end of growth phase. Will enter its mature stage by 2015. MARKET     Uncertainty is reduced. High Competition . The mainstream technology in small screen displays. Best time for the Industry players to enter the market.
    83. 83. OLED LIGHTING TECHNOLOGY     PATENT DATA FORECAST RESULT ANALYSIS INFERENCE
    84. 84. PATENT DATA  Appropriate keywords were used to determine the number of patents on the OLED technology for a given year.  A 12 year span (1998– 2009) has been studied with this method
    85. 85. FORECAST East West North 90 PATTERN OBTAINED FROM NONLINEAR REGRESSIO N MODEL FORECASTI NG 46.9 45.9 38.6 30.6 45 43.9 34.6 31.6 27.4 20.4 20.4 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
    86. 86. FORECAST PATTERN OBTAINED FROM GANGBM MODEL FORECASTI NG
    87. 87. S-CURVE OF OLED LIGHTING TECHNOLOGY
    88. 88. PHASES OF LIFE EMEREGNT PHASE up to 2015 : GROWTH PHASE 2015-2025 : MATURITY PHASE 2025-2034 :
    89. 89. INFERENCE: R&D   OLED Lighting technology is still in its emergence phase. Huge investments are required. MARKET    Less Competition. High Opportunities. For Newcomers, this is the best (sometimes the only ) phase to enter the market.
    90. 90. CONCLUSIONS The OLED technology in Display Sector will enter its maturity stage by 2015. For small size displays OLED will be the mainstream technology. The competition will be in an increasing mode. For companies already present in the industry this may be a good phase to enter the market. But, for the newcomers it will be almost impossible.
    91. 91. CONCLUSIONS The OLED Lighting technology is still in its emerging phase. There is an uncertainty about the market. For newcomers this phase is often the only phase to enter the new market. The concerned industrial players can opt for investing in research and development activities. If the companies are still in confusion to invest in this field, its better for them to go for joint ventures.
    92. 92. References 1. J.P. Martino: A review of selected recent advances in technological forecasting, Technological Forecasting and Social Change 70 (2003); 719–733. 2. NPTEL course on Management Science 3. Franses, P.H.: A method to select between Gompertz and logistic trend curves, Technological Forecasting and Social Change 46 (1994) ;45-49 4. Li - Chang Hsu: A genetic algorithm based nonlinear grey Bernoulli model for output forecasting in integrated circuit industry, Expert Systems with Applications 37(2010); 4318—4323 5. D. E. Goldberg , Genetic Algorithm In Search, Optimization and Machine Learning, 1989 6. E. Kayacan, B. Ulutas , O. Kaynak: Grey system theory-based models in time series pre-diction, Expert Systems with Applications 37 (2010) ; 1784–1789 7. S.N.Sivanandam , S.N.Deepa: Introduction To Genetic Algorithms, Springer Publishing Company 2007 8. Genetic Algorithm ware house www.geneticalgorithms.ai-depot.com/Tutorial/Overview 9. 10. 11. obitco.com www.obitko.com/tutorials/genetic-algorithms/ www.geneticprogramming.com en.wikipedia.org/wiki/Genetic-algorithm
    93. 93. References 12. John K. Borchardt: Developments in organic displays; materials today September 2004. 13. HowStuffworks.com http://electronics.howstuffworks.com/oled.htm 14. novaled.com http://www.novaled.com/oleds/oled-in-lighting/ 15. 16. 17. Zill, D. G. and Cullen, M. R.:Advanced Engineering Mathematics 2nd ed. Massachusetts: Jones and Bartlett, 2000 Chun-I Chen, Pei-Han Hsin, Chin-Shun Wu: Forecasting Taiwan’s Major Stock Indices by the nash Nonlinear Grey Bernoulli Model; Expert Systems with Applications 37(2010); 7557–7562 US Department of Energy http://www.doe.gov/ 18. Display Search Report http://www.displaysearch.com/ 19. Cintelliq Report http://www.cintelliq.com/ 20. IEEE Spectrum on OLEDs http://spectrum.ieee.org/tag/OLED 21. Research and Market.com http://www.researchandmarkets.com/ 22. NanoMarket’s Report on OLEDs http://nanomarkets.net/ 23. http://www.oled-display.net/
    94. 94. References 24. 25. 26. 27. 28. 29. 30. http://ledsmagazine.com/ N. Thejo Kalyani , S.J. Dhoble: Organic light emitting diodes: Energy saving lighting technology—A review, Renewable and Sustainable Energy Reviews 16 (2012) 2696– 2723. About.com http://inventors.about.com/od/lstartinventions/a/lighting.htm megavolt.co.il http://www.megavolt.co.il/Tipsandinfo/types ofbulbs.html Tugrul U. Daim, Guillermo Rueda, Hilary Martin, Pisek Gerdsri:Forecasting emerging technologies: Use of bibliometrics and patent analysis,Technological Forecasting& Social Change 73 (2006) 981 – 1012. Yu-Heng Chen, Chia- Yon Chen, Shun- Chung Lee:Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies,international journal of hydrogen energy 36 (2011) 6957 - 6969. OLED Display.net Report on OLED Revenue http://www.oled-display.net/oled-revenuesforecast-to-reach-55b-by-2015-says- displaysearch/
    95. 95. QUESTIONS & COMMENTS
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