intuitionistic fuzzy multilayer perceptron as a part of integrated systems for early forest-fire...

INTUITIONISTIC FUZZY MULTILAYER PERCEPTRON AS INTUITIONISTIC FUZZY MULTILAYER PERCEPTRON AS A PART OF INTEGRATED SYSTEMS FOR EARLY FOREST-A PART OF INTEGRATED SYSTEMS FOR EARLY FOREST-

FIRE DETECTIONFIRE DETECTION

Sotir SotirovProf. Asen Zlatarov University,

Burgas-8000, [email protected]

Ivelina VardevaProf. Asen Zlatarov University,

Burgas-8000, [email protected]

Maciej Krawczak Higher School of Applied Informatics and Management,

Warsaw, Poland, e-mail: [email protected]

IntroductionIntroduction

Forest-fire detection is a real problem. Early fire detection should be carried out in few seconds or minutes at large. The location of fire with enough resolution is very important.


Forest-fire detection involves heterogeneous knowledge We will propose intuitionistic fuzzy multilayer perceptron as a part of an integrated system for early forest-fire detectionObtained information from different sources: infrared images, visual images, satellite images, different sensors, Geographic information systems.

Introduction Introduction

Information sources:Images from infrared cameras and TV camerasReal-time meteorological information from a meteorological station can be used to decrease or to increase the possibility of alarmsInformation about the terrain slope from the Geographic information systems


Very often the trigger to fire are fake, so the system is unworkable in the real world;The proposed here intuitionistic fuzzy multilayer perceptron for integrated system for early forest-fire detection uses intuitionistic fuzzy estimation for decreasing of fakes alarms and for increasing of the systems' performance.

Recognition of the fire with IFMLPRecognition of the fire with IFMLP

A flame is a mixture of reacting gases and solids emitting visible, infrared, and sometimes ultraviolet light, the frequency spectrum of which depends on the chemical composition of the burning material and intermediate reaction products.

Recognition of the fire with IFMLPRecognition of the fire with IFMLP

Visual systems encode pixel color values by devoting eight bits to each of the R, G, and B components (we can take this information from framebuffer in computer). RGB information can be either carried directly by the pixel bits themselves, or provided by a separate color.

Recognition of the fire with IFMLPRecognition of the fire with IFMLPThe transformation of the RGB and XYZ:

B

G

R

Z

Y

X

*

950227.0119193.0019334.0

72169.0715160.0212671.0

180423.0357580.0412453.0

XYZ color space

INTUITIONISTIC FUZZY MLPINTUITIONISTIC FUZZY MLP


We put on the inputs of the IFMLP values of the XYZ color space that represent different colors. On the inputs p1, … pn of the neural network there are values from the XYZ color space.Outputs a , a and a obtain intuitionistic fuzzy evaluations. The first output gives the degree of the affiliations of the fire - ; the second - degree of a non affiliations of the fire - , and the third - degree of uncertainty = 1--. The estimation reflect of the possibilities to have a fire.

Recognition of the fire with IFMLPRecognition of the fire with IFMLPThe MLP neural network is trained with the XYZ color space values of the pixels that belong to fire regions.The MLP is tested in Matlab. It has a structure 3:15:3 (3 inputs; 15 neuron in hidden layer; 3 output neuron in the output layer).The purpose of verification is to protect the neural network from overfitting. In this case we use for training 90% of the input vector, 5 % for verifications and 5 % for testing.


Initially when still no information has been obtained, all estimations are given initial values of <0, 0>. When k 0, the current (k+1)-st estimation is calculated on the basis of the previous estimations according to the recurrence relation

where

kk , where is the previous estimation, and <, > is the estimation of the latest measurement, for m, n [0, 1] and m + n 1.

GN-ModelGN-Model

GN-ModelGN-Model

Z1 – Work of the image devices;

Z2 – Work of the local meteorological devices;

Z3 – Work of the GPS system;

Z4 – Image processing;

Z5 – Meteorological processing;

Z6 – Work of the Geo Information system;

Z7 – Work of the decision making system.

GN-ModelGN-Model

token in place S1A:

= “Current image devices”,

token in place S2A:

= “Current local meteorological devices”,

token in place S3A:

= “GPS system”,

token in place SIP:

= “Algorithms and systems for image processing”,

token in place SMS:

= “Local meteorological station”,

token in place SGIS:

= “Geo Information system”,

token in place SIS:

= “Decision makers system”.

Initially, the tokens , , , , , and stay in places S1A, S2A, S3A, SIP, SMS, SGIS and SIS with

initial and current characteristics:

cux

cux

cux

cux

cux

cux

cux

GN-ModelGN-ModelZ1 =<{S1A, S71}, { IR, TV, Sat, S1A}, R1,

(S1A, S71)>,

R1=

The 1-, 2- and 3-tokens that enter places IR, TV and Sat obtain characteristic respectively:

= “Information from infrared camera” in place IR,

= “Information from TV camera” in place TV,

= “Information from satellite” in place Sat.

TrueFalseFalseFalseS

TrueWWWS

SSatTVIR

Sat,ATV,AIR,AA

A

71

1111

1

1cux

2cux

3cux

GN-ModelGN-ModelZ2 =<{S2A, S41}, { t, Hum, Plu-W, S2A}, R2,

(S2A, S41)>,

R2=

The 1-, 2- and 3-tokens that enter places t, Hum and Plu obtain characteristic respectively:

= “Information from thermometer” in place t, = “Information from humidity sensor” in

place Hum,

= “Information from pluviometer” in place Plu.


TrueWWWS

SWPluHumt

Plu,AHum,At,AA

A

41

2222

2

1cux

2cux

3cux

GN-ModelGN-ModelZ3 =<{S3A, S42}, { GPS, S3A}, R3,

(S3A, S42)>,

R3=

The 1-token that enters place GPS obtain characteristic:

= “Information from satellite” in place GPS.

TrueFalseS

TrueWS

SGPS

GPS,AA

A

42

33

3

1cux

GN-ModelGN-ModelZ4 =<{ IR, TV, Sat, SIP, SSTR}, { S41, S42, S43, SIP },

R4, ((IR, TV, Sat), SIP, SSTR)>,,

R4=

The 4-, 5- and 6-tokens that enter places S41, S42 and S43 obtain characteristic respectively:

= “Query for information from local meteorological devices” in place S41,

= “Query for information from satellite” in place S42,

= “Information from image devices” in place S43.


TrueWWWS

TrueFalseFalseFalseSat

TrueFalseFalseFalseTV

TrueFalseFalseFalseIR

SSSS

STR

,IP,IP,IPIP

IP

434241

434241

4cux

5cux

6cux


TrueWWWS

TrueFalseFalseFalseSat

TrueFalseFalseFalseTV

TrueFalseFalseFalseIR

SSSS

STR

IPIPIPIP

IP

43,42,41,

434241

GN-ModelGN-ModelZ5 =<{ t, Hum, Plu-W, SMS}, { S51, SMS }, R5,

((t, Hum, Plu), SMS)>,

R5=

The 4-token that enters place S51 obtain characteristic:

= “Information from local meteorological devices”.

TrueWS

TrueFalsePlu

TrueFalseWHum

TrueFalset

SS

,MSMS

MS

51

51

3cux

GN-ModelGN-ModelZ6 =<{ GPS, GIS}, { S61, GIS }, R6,

(GPS, GIS)>,

R6=

The 2-token that enters place S61 obtain characteristic:

= “Information from satellite”.

TrueWGIS

TrueFalseGPS

GISS

,GIS 61

61

2cux

GN-ModelGN-ModelZ7 =<{ S43, S51, S61, SIS}, { S71, S72, S73, SIS }, R7,

((S43, S51, S61), SIS)>,

R7=

From place Sint 0-token enters the net with characteristic

= “New decision making system”.

The 1-, 2- and 3-tokens that enter places S71, S72 and S73 obtain characteristic respectively:

= “Query for information from local meteorological devices” in place S71,

= “Query for information from satellite” in place S72

= “Information from image devices” in place S73.

TrueWWWS




SSSS

,IS,IS,ISIS

IS

737271

61

51

43

737271

0cux

1cux

2cux

3cux

ConclusionConclusion

In this paper was presented intuitionistic fuzzy neural network as a part of generalized net model of multi-sensorial integrated systems for early detection of forest fires. On the inputs of the IFMLP we put intuitionistic fuzzy values taken from the YXZ color space. On the outputs we obtain intuitionistic fuzzy estimations of the possibilities for the real fire. The NN is learned with data that are in spectrum of the fire of XYZ color space

ConclusionConclusion

The IFMLP is a part from one integrated early forest fire detection system that use many information and data sources- infrared images, visual images, sensors data, and geographic data bases. One of the main purpose is using of the intelligent methods for decision when must alarm starts.

AcknowledgmentAcknowledgment

The authors are grateful for the support provided by the project "Simulation of wild-land fire behavior" funded by the

National Science Fund, Bulgarian Ministry of Education,

Youth and Science.

Thank you for your attention!Thank you for your attention!

intuitionistic fuzzy multilayer perceptron as a part of integrated systems for early forest-fire...

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