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WA11-1

SICE02 0090

Classifying

Smokes

Using an Electronic Nose

and Neural Networks

Bancha Charumpom, Sigeru Omatu

Department of Computer and Systems Sciences,

School of Engineering, Osaka Prefecture U niversity,

Sakai, Osaka

599-8351,

Japan

bancha@sig.cs.osakah-u.ac.jp, omatu@cs.osakah-u.ac.jp 

Abstract: We have created an electronic nose using the metal oxide sensors from two comm ercial brands, FIS and

FIGARO’.

In

this DaDer. we use this electron ic nose to classif y the s mell fro m 3 twes of burning materials and then

1.

Introduction

The inspiration after reading several papers I-’’ about the

development

of

artificial olfactory sensing system

encourages us to make our

own

artificial nose by

combining metal oxide gas sensors from two commercial

brand names,

FIS

and FIGA RO’, which are widely used in

the variety of electrical appliances, such as microwave

oven and air cleaner. Our electronic nose is designed as

simple as possible by using the normal air at room

temperature to provide enough oxygen for the oxidation

process of gas sensors. If the metal oxide element on he

surface of the sensa

is

heated at a certain high

temperature in air, the oxygen

is

absorbed on the crystal

surface with the negative charge. The change in the

negative charge of the metal oxide surface, which causes

by absorbing the deoxidizing

gas,

makes the grain

boundary potential bame r changed. When the o xidation of

deoxidizing gas changes the grain boundary potential

banier of the sensor, the resistance of the sensor also

varies as the partial of pressure of oxygen changes ”.

Based on the characteristics of the metal oxide sensors we

choose the proper sensors that can detect the deoxidizing

gas from the burning material to be the sensing devices

of

our machine.

We will not provide the concept of a human olfactory

system that can be read in some papers ’.) or any medical

books about an olfactoty system in this paper. However,

we will explain the patts of our machine, as shown in

Fig.1, by comparing with the olfactory process. First

process of the olfactory system is to sniff the smell and

flow the molecule to the olfactory receptor part, so we

make

a

sampling box providing with an electric fan to

flow the smell to the sensor box that contains several

..

we apply the standaid back propagation and recurrent back propagation neural n etworks to train and classify those

bum ing smell. In the experiment, we test 3 kinds ofjo ss stick,2 brands o f cigarette, and

a

mosquito coil. Moreover,

we also measure the difference of concentration o f smoke by varying the num ber of burning j o s s stick. The results

show that it is able to classify the smoke correctly. The idea of this research would be able to apply for making a

smart smoke detector in order to be able to detect a harmful burning material precisely before it is too late to stop the

fire.

Keyw ords: Electronic nose Recurrent Back hopa gati on, Pearson Correlation, Smoke

SlCE

2wz

Aug 5-7.2002.

Osaka 2661 PROwl WWOOWOl M 002SlCE

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’

FIS INC and

F’IGARO ENGINEERING

INC

are

Japancw

companies

mat produce

metal

oxide

gas YOSOPS

for

v a i c r y of eleceicalappliances.

metal oxide sensors which act like olfactory receptors.

Secondly, the olfactory receptor responses information to the

limbic system and then transfers to the cortex brain, so we

use a data logger to record the electrical voltage from sensors,

and then use these data to be the information for Artificial

Neural Network (ANN ), which acts like the human brain and

in this step human can recognize the smell, as the sam e that

we try to classify smell by ANN. The third step is the

cleaning process before human can detect the other smell

precisely by breathing a fresh air, so we add additional

electrical fans inside the sensor box to suck out the

deoxidizing gas from the machine to speed up the sensors to

remm to the normal condition. We

start

our first experiment

on his machine by

trying

o classify

3

kinds of simple smoke.

Fig.1.

The electronic nose when we are measuring the

cigarette smoke. 1 is the sampling box with an electrical fan

to flow the smell through the tube in to the sensor box,

2

is

the sensor box that

looks

like a personal com puter, but inside

contains two arrays of metal oxide sensors, 3 is the data

logger that is used to transfer the voltage signal from the

sensors to the computer and 4

is

the computer with the

software that controls the time interval for recording the

voltage signal.

mailto:bancha@sig.cs.osakah-u.ac.jpmailto:omatu@cs.osakah-u.ac.jpmailto:omatu@cs.osakah-u.ac.jpmailto:bancha@sig.cs.osakah-u.ac.jp

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2. Experiments

In

the experiment, we choose three kinds

of

buming

materials that are easily found

in

the market. The fimt kind

is three types of joss stick. Second kind is two types

of

cigarette and the third kind

is

mosquito coil. We also vary

the concentration

of

the smoke by buming

1

2 and

3

sticks of each type ofjoss stick, which is the least

h;irmful

smoke. The list of burning materials

in

this experiment is

shown in Table 1 .  

Table 1 List of burning materials in this experiment,

Joss

stick Brown

Inside this electronic nose, there ar e two sensors arrays,

Sensors Array1 (SAI) and Sensors Array2

SAZ).

Each

array contains 8 different senson and detail of each

sensors array is shown in Table AI. n this paper, we use

only the data from SA1 to analyze. Generally, metal

oxide sensor has some effects from the humidity and

temperature. In order to prove the robustness of our

machine

on

these effects, we collect

20

sets

of data

in

different condition. For the temperature effect, w e have

measured the data during the winter and spring season,

which have a high difference in temperature. For hum idity,

we have measured the data in the rainy day and shiny day.

We

have also changed the room for measuring the data

in

this

experiment to provide the

normal

air in different

environment. Each data set contains 12 signals of huming

materials in Ta blel.

Before measuring the voltage signal of each smoke, we

must measure the voltage signal of the normal air at that

time

every second for a period of 1 minute and find the

average value (v.") to use as an air reference point. Then

the voltage signals of the sensors when absorbing with

smoke, v -.

,

re collected every 2 seconds for a period

of 2 minutes

on

each type of smoke sample. Then the total

change in signal on each period, Vmoketis calculated by

-

mwk.t = rmke.t

-

(1)

where f period I lo 60.

Some of the signals

of

this experiment are shown in

F i g .

In

,we used these data to train and test by ANN

directly, but the training time was

so

slow, so in this paper

we normalize these data by

where

v s

the mean of all data in each data set and

O

s

the standard deviation

of

all data in each data set. After that

we use the normalized data to train and test with Standard

Back-Propagation (SBP) and Recurrent Error Back-

Propagation (RBP).

~~~ ~~~ ~~

Purple Joss Stick 3 sticks

3.5 I

10 2

30

40 50 60

-

eriod [Zsec.]

- ensor1- e n s o d ensoR . .- Sensor

ensor5- ensorb

ensor7

- msor

-

Marlboro Light Cigarette

3 5

- 3

2 5

0 2

2

1.5

= 1

p

0.5

n

I

0

10

20

30 40 50

60

-0.5

Period [Zsec.]

4ensor1- ensorz c senson ...-...SUIS

- ensor5 sensol8 enson

ensor8

Jl

Mosquito Coil

3.5 I

- 3

2.5

2

9 1.5

+ I 1

g

0.;

- ensor5- ensol8 enson

-

earor8

Fig.2. Sample data from sen sor array1

(SAI)

of th i s

experiment.

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3. Neural Networks Analysis

Before analyzing these data with the neu ral network, we

calculate the

Pearson

Correlation Value, r in order

to

check the similarity of these data by

where x and

y

are the data that we want to compare and n

is the number of da ta

If the correlation value, r

is

closer to

1

or -1, it means

that the comparison data has a highly positive and negative

similarity respectively. In the opposite way, if

r

is closer to

0,

it means that the comparison data

is

totally different.

When the number of data increases, it is not easy to select

the training data randomly and get a good result especially

in the case that data are so fluctuated. So, we use this

correlation value to be the gu ide to select a prop er training

set instead of choosing the training set randomly.

3.1 Standard Back- Propagation

(SBP)

Method

Firstly, we

try

to analyze the data from each sensors

array by using the SBP method. Since sensor 1 of SA1

does not response to any smoke as can be seen on the

sample data in

Fig.2,

so we take the data from this sensor

out

of

the analytical data. Then we use all 60 periods data

of remaining

7

sensors

to

be an input of the SBP. The

parameters

for

training by SB P are shown in Table

2. 

Table

2

SB P training parameters.

r Value

raining Parameters

I 420 I

Input Node (sensor quantity x 60

periods)

Hidden Node

Output Node (joss stick, cigarette

and mos uito coil node)

Minimum Mean Square Error

(MSE) constraint

0.0008

Momentum Rate, 3 0.0DI

We select

3

to

5

data from 20 data sets to be the training

sets and use the other

data

sets as test sets. We

use

the

Pearson correlation value a s a guide to select the training

set. For example, the correlation value between

joss

stick

data is higher than

0.9,

so we select only 3 data that have a

very high correlation, i.e. higher than

0.95

with the other

data to be the training sets. In the case of mosquito coil,

the correlation values

are

quite fluctuated from

data

to data,

so

we need to provide 5 data of mosquito coil in the

training set.

In

this paper, we use the normalized data to train and test.

The training time by the parameters in Table

2 

is nearly 7

hours, but if we use the raw data without normalize

to

train

like we have done previously”, it take longer than

14

hours

with the sam e initial weight. However, 7 hours for training is

still

too slow, so

we use the RBP as an altemative method to

decrease the training time.

With the proper selected training sets, we can get a perfect

result as shown in Table 4 .

3.2

Recurrent Back Propagation (RBP)

Method

The structu re of SBP case is very huge, so the training time

is very slow. We use RBP as an altemative way to decrease

the training structure.

In

case of RBP, the output from hidden

node layer of last cycle will be fed as the input

of

next cycle.

So we decrease the number of input node from 420 in case

of

SBP to 70 in case of RBP by feeding IO periods of each

data

for each cycle. The training parameters of RBP

are

shown in Table 3. 

Table

3.

RBP training parameters.

Training Parameters

Value

Input Node (sensor quantity

x

10 70

I

periods)

I

I

Hidden Node

Output Node (joss stick, cigarette

and m osquito coil node)

3

In

this case, we need to find the correlation value

of

each

data set for every

1

periods in order to see the clear

relationship between the data

sets in

each timing interval.

Then we use these correlation data

to

select proper training

sets. Th e training time by the parameters in Table 3 is

around

3.5

hours that

is

much faster than the case o f SB P that

took almost 7 hours, however; the accuracy of output value

of RBP is not as perfect as the case of SBP in some case

especially in case

of

cigarette and mosquito coil data as

shown in Table 5 .  

4. Experiment Results

4.1

SBP Case

The result in Table

4

shows the number of test data

sets

that can be classified by the SBP after training with the

parameters shown in Table 2.  The perfect classification,

partial classification, and miss classification case are the case

that the output value show the correct output value above

80 , between 50-80 , and less than 50 , respectively.

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Perfect Partial Miss

Dat a Classification Classification C lassification

P3 17

P2 17

From the result in Table 4, all of the tested data are

perfectly classified with the output value above 99% in

case of

j o s s

stick and most data of cigarette and mosquito

coil, but in some data of cigarette and mosquito coil, the

output value show correctly output around 91%. The total

number of tested data set that is less than 20, like P3 data

set, a re the selected data that we use in the training set. So,

in the case

of

mosquito coil, we need to select 5 data in to

the training set because the data

of

mosquito coil from

data to data are so noisy. In the other way, the data from

j o s s stick is less noisy,

so

we can feed less data of joss

stick into the training set.

Total

17

17

4.2

RBP

Case

D a ta

P3

W e use the same judgme nt category as SBP case in that

if the average output value shows the correct output value

above 80 , we call it perfect classification. The result of

RBP case is shown in Tab le 5.

Ta b le

5.

RBP results show the number of classified data

Perfect Partial Miss

Classification Classifleation Classiflcatiou Total

17 17

In

the case of RBP, the data from last cycle will effect

Lo

the output

of

next cycle.

So,

in the case of noisy data like the

case of cigarette and mosquito coil that data are very similar

in some period of time and vary from data to data.

So

it is

not so easy to provide a proper training set until we get a

good result. All

of

the results in the Table

5 

are perfectly

classified with the output value above 97 in case of joss

stick data and most data of cigarette and mosquito coil, but

some data o f cigarette and mosquito coil only have the output

value above 83 % which is less accuracy than the SBP case .

P1 17

5.

Conclusion

17

This electronic nose does not have a mechanism to reduce

the effect from humidity and temperature, so we have just

used the ANN to handle the noisy data directly because we

want to imitate the human nose that can classify variety of

smell in a different environment. In this experiment, the

burning materials also effect the value of the smoke signal

because the density of burning material like cigarette may

vary from one cigarette to another cigarette. The burning

material itself also easy to absorb the moisture in the air and

has some effect to the output signal. We norm alize the data

before feeding in to the ANN in or der to reduce the baining

time and red uce the effect of

noisy data.

SBP method is able to handle the noisy data quite precisoly

however; the training time for SB P is

so

slow compare w ith

the RBP method. In this paper, we use the Peartion

Correlation Value to be the guidance to select a proper

training set instead of choosing the training set randomly.

With the carefully selecting the training set using Pearson

correlation value as guidance, we ca n achieve the level that

does not need to provide such a huge data for training. So

correlation value is quite a useful tool to select a proper

data

into the training set.

Pearson correlation value is also ab le to measure the noisy

of

data, so

in this experiment we do not intend to classify the

smoke

in

more detail, like classify MA RL from CAST or P3

from PI because the correlation values o f these data are too

fluctuate to handle this case precisely.

18

I I

17

I

17

References

[I] Bancha Charumpom, Sigeru Omam, Classifying Smokes

using Electronic Nose and Neural Network, Proceeding

of the 46' Annual Conference of the Institute of Systems,

Co ntro l a nd Info rm ation Eng inee rs, pp. 513- 814.

Presented at International Conference Center Kobe,

Kobe, Japan on May 17,2002

121

The Basis of FIGARO

Gas

Sensor, FIGARO

M a r l

C a s t 16 16

M a s

..

Engineering Inc.

131 Cosimo Distante. Pietro Siciliano. Lorenzo Vasan~slli.

~,

Odor Discrimination Using Adaptive Resonance Thetxy,

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Dong Hyun

Yun,

Kyuchung Lee, Yung Kwon Sung,

Portable Electronic Nose System wt

Gas

S en so r A m y

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and A rtificial Neural N etwork, Sensors and Actuators

B

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M.

Penza, G. Cassano, F. Tortorella, G Zaccaria,

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WO3

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[7] P. Keller, L. Kang as, L. Liden,

S.

Hashem, R. Kouzes,

"Electronic Noses and Their Applications," World

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on

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Julian

W. Gardner, Fuzzy

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Appendix

Table

A l .

Detail of sensors in each sensor array that use in the electronic nose

ensor Array Sensor No. Sensor Model

I

Manufacturer

ENGINEERIN1

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