using DNA-Based Computing

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20 Οκτ 2013 (πριν από 3 χρόνια και 7 μήνες)

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Recognition of Control Chart
Patterns
using

DNA
-
Based Computing


AMM Sharif Ullah


Kitami Institute of Technology, Hokkaido, Japan


and Masahiro Higuchi



Kansai University, Osaka, Japan


24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

1

The Flow:

Introduction

Control Chart Pattern Recognition

DNA
-
Based Computing

DNA
-
Based Molecular Activities

Proposed DNA
-
Based Computing

Results

Conclusions

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

2

Introduction

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

3

Bio
-
inspired Computing

(Genetic Algorithms, Neural
Network, Emergent
Synthesis,…)

Analytical Approach

Manufacturing
Problems

+

DNA
-
Based Computing

Recognition of
Patterns in
Control Charts

Control Chart Pattern Recognition

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

4

Normal Pattern

When Window
size is small the
normal pattern
might appear to
be an abnormal
pattern.

Control Chart Pattern Recognition

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

5

Pattern Recognition

Normal pattern

Very small window size

Pattern Recognition System

Result: Normal Pattern

Control Chart Pattern Recognition

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

6

Abnormal Pattern

When Window
size is small
cyclical pattern
appears to be
abnormal patters
(cyclical, shift,
mix,…)

Control Chart Pattern Recognition

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

7

Pattern Recognition

Abnormal Pattern

Very small window size

Pattern Recognition System

Result: Abnormal Normal Pattern

Control Chart Pattern Recognition

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

8

1.
Pham, D.T., Sahran, S., 2006, Control Chart Pattern Recognition Using
Spiking Neural Networks
, in Intelligent Production Machines and Systems,
Pham, D.T., Eldukhri, E.E., Soroka, A.J. (Eds.), Elsevier Science Ltd, Oxford:
319
-
325.

2.
Wang, C.
-
H., Kuo, W., 2007, Identification of control chart patterns using
wavelet filtering
and robust fuzzy clustering, Journal of Intelligent
Manufacturing, 18(3), 343
-
350.

3.
Purintrapiban, U., Kachitvichyanukul, V., 2003, Detecting patterns in process
data with
fractal dimension
, Computers & Industrial Engineering, 45/4: 653
-
667.

4.
Gauri, S.K., Chakraborty, S., 2009, Recognition of control chart patterns
using
improved selection of features
, Computers & Industrial Engineering,
56/4: 1577
-
1588.

5.
Yang, J.
-
H., Yang, M.
-
S., 2005, A control chart pattern recognition system
using a
statistical correlation coefficient
method, Computers & Industrial
Engineering, 48/2: 205
-
221.

6.
Chen, Z., Lu, S., Lam, S., 2007, A
hybrid system
for SPC concurrent pattern
recognition, Advanced Engineering Informatics, 21/3: 303
-
310.

7.
Jagnjic, Z., Bogunovic, N., Pizeta, I., Jovic, F., 2009, Time series classification
based on
qualitative space fragmentation
, Advanced Engineering Informatics,
23/1: 116
-
129.

DNA
-
Based
Computing


24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

9

…ACGCCCCCTTTTTTAAAAAGACGAAAAAAAACCCTACCCGGGG…

{A,C,G,T}

DNA

In Vitro

Small segments of
DNA sequence

Solution of Complex
Combinatorial
Problems (Travelling
Salesman)

Adleman, L.M., 1994, Molecular computation of solutions to
combinatorial problems,
Science
, 266/5187: 1021
-
1024.

DNA
-
Based
Computing


24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

10

Lipton, R.J., 1995, DNA solution of hard computational problems,
Science
, 268/5210: 542
-
545.



Ran, T., Kaplan, S., Shapiro, E., 2009, Molecular implementation of
simple logic programs,
Nature Nanotechnology
, 4/10: 642
-
648.

Processes:

Detection

Amplification

Synthesis

Cutting



Information density: 1 bit/nm
3

Highly speedy/parallel process:
10
18

processes/second

Energy Efficiency: 10
19

operations/Joule …

DNA
-
Based
Computing


24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

11

Ullah,

A
.
M
.
M
.
S
.
,

2003
,

Different

facets

of

a

computational

equivalent

of

genetic

addition,

BioSystems
,

68
/
1
:

31
-
41
.


Ullah,

A
.
M
.
M
.
S
.
,

Yano,

A
.
,

and

Higuchi,

M
.
,

1997
,

Protein

Synthesis

Algorithm

and

a

New

Metaphor

for

Selecting

Optimum

Tools,

JSME

international

journal,

Series

C,

Mechanical

systems,

machine

elements

and

manufacturing
,

40
(
3
),

540
-
546

DNA
-
Based Molecular Activities

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

12

A

A

A

T

A

G

T

A

C

T

G

C

G

C

G

U

U

U

U

Free

base
-
pairs

…AAAAGGGCTTTTTAAAACCCCCCGGGGGG…

…TTTTCCCGAAAAATTTTGGGGGGCCCCCC…

DNA

In vivo

…CUUUUUAAAACCCCCCGGGGG…

mRNA

A

>
U

T

>
A

C

>
G

G

>
C

DNA Transcription

DNA
-
Based Molecular Activities

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

13


Free

tRNAs

Free

Amino Acids (AAs)

AAC

GAA

CCA

UAC

Leu

In vivo

tRNA
-
Amino Acid Compound

GAA (CUU > GAA)

Leu

Table 1 in
proceedings paper

DNA
-
Based Molecular Activities

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

14


CUU
UUUAAAACCCCCCGGGGG…

mRNA

Thr

Lys

Growing

Protein

Protein

GAA

Leu

GAA

Leu


Anti
-
codon


Codon

RNA Translation

DNA
-
Based Molecular Activities

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

15

DNA

DNA Replication

DNA

DNA Transcription

mRNA

RNA Translation

Protein

Genetic Rules among DNA Base
-
Pairs {A,C,G,T} and
Protein Base
-
Pairs {AA
1
,…,AA
20
}

No

Amino Acid
(single
-
letter
symbol)

Codon in term of DNA
base
-
pairs

1

Isoleucine (I)

ATT, ATC, ATA

2

Leucine (L)

CTT, CTC, CTA, CTG, TTA,
TTG

3

Valine (V)

GTT, GTC, GTA, GTG

4

Phenylalanine
(F)

TTT, TTC

Table 1 in the Proceedings

Proposed DNA
-
Based
Computing


24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

16

x(t)

R

diff(t) = x(t)
-
R

a

b


c


d

T


C


A


G


T

…ACGGGCCCTTTAAACCC…

DNA
R

Proposed DNA
-
Based
Computing


24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

17

x(t)

R1

R2

ACCCCTTTAACG DNA
R1


ATTTTCATCCCA DNA
R2

A
A
C
T
C
T… mRNA


mRNA(i)=DNA
R1
(j)

浒乁
⡩⬱⤠㴠䑎=
R2
(j)

Proposed DNA
-
Based
Computing


24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

18

A
A
C
T
C
T… mRNA

codon

codon(k) = mRNA(i)

m剎A⡩⬱(

mRN䄨A⬲+




codon(k) = TTT

呔䌠


偲潴敩渨焩㴠=

捯摯渨欩㴠呔T

T呇




Protein(q) = L



…NSFLQRP… Protein

Results

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

19



= 80,


= 5,
A

= [7.5,12.5], and
T

= {8,9,…,30}

R1

R2

R3

x(t) t=1,…,10

a = [12.5]

b = [7.5]

c = [
-
7.5]

d = [
-
12.5]

80

100

60

100

100

60

Results

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

20



No

80,100,60

100,100,60

1

IIIIVIIIII

FFFFFFFFFF

2

IIIRIIVIII

FFFGFFFFFF

3

IYVIIIIIRR

FNFFFFFFGG

4

ILIVIVIIRI

FLFFFFFFGF

5

IIVIIIIIII

FFFFFFFFFF

6

VIIIRIIIII

FFFFGFFFFF

7

IIIIIIIVIR

FFFFFFFFFG

8

IIIIIIIIII

FFFFFFFFFF

9

IIIRRVIRII

FFFGGFFGFF

10

IRIIRIRIII

FGFFGFGFFF

11

VIIIIIIVII

FFFFFFFFFF

12

RIIIIIIIII

GFFFFFFFFF

13

IIIIIIIIII

FFFFFFFFFF

14

IIIIIIIIII

FFFFFFFFFF

15

IIIVIIIIIR

FFFFFFFFFG

16

IIIIIIIIII

FFFFFFFFFF

17

IIIIIIIIII

FFFFFFFFFF

18

IIIIIIRIRI

FFFFFFGFGF

19

IIIRIIIIII

FFFGFFFFFF

20

IIIIVIIIIV

FFFFFFFFFF

Normal Patterns



No

80,100,60

100,100,60

1

RRIYIRRIVL

GGFNFGGFFL

2

IYYRRIRIVV

FNNGGFGFFF

3

IIRIRIIILV

FFGFGFFFLF

4

YIYIYIRIII

NFNFNFGFFF

5

IIRIRYRIII

FFGFGNGFFF

6

RYRYRYYRRI

GNGNGNNGGF

7

RYYRIIIVVI

GNNGFFFFFF

8

IRRRIIRIRI

FGGGFFGFGF

9

IRRYRRYIRI

FGGNGGNFGF

10

RRRRYIRIII

GGGGNFGFFF

11

IRRIVIILVI

FGGFFFFLFF

12

IYRIIVIVVV

FNGFFFFFFF

13

IIYYRRILIV

FFNNGGFLFF

14

IIRIIIIVII

FFGFFFFFFF

15

IRIYYIRYRI

FGFNNFGNGF

16

IIRRIIIIIV

FFGGFFFFFF

17

IYIIRIIIIV

FNFFGFFFFF

18

IIRIIIVIII

FFGFFFFFFF

19

IIIRYIIIII

FFFGNFFFFF

20

IIRRRRYRVI

FFGGGGNGFF

Abnormal

Results

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

21

Protein of Normal Pattern and Protein of
Abnormal Pattern are different from each other.

An pattern generated by the equation of
abnormal pattern, but does not look like an
abnormal pattern, may exhibit a Protein of
normal pattern.

Conclusions

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

22

If the window size is kept very small (i.e.,
number of observation is kept around 10 or so),
then it is difficult to distinguish a normal pattern
from an abnormal pattern.


To solve the aforementioned problem, a DNA
-
Based Computing method is developed.


Using examples, the effectiveness of the method
is demonstrated.


This opens a scope for solving other
manufacturing problems by using the developed
DNA
-
Based Computing. This issue remains open
for further investigation.

24 June 2010

CIRP ICME’10 23
-
25 June 2010 Capri, Naples, Italy

23

Thank you!