Fuzzli Arab Makale

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Arab J Sci Eng (2014) 39:30953099

DOI 10.1007/s13369-014-0953-6

R E S E AR C H A R T I CL E - E L E C TR I C A L E N G I N E ER I N G

Fuzzy-Based Assessment of Health Hazards of a ReferenceAntenna

Selcuk Comlekci Ozlem Coskun Mesud Kahriman

Received: 13 June 2012 / Accepted: 31 December 2012 / Published online: 15 February 2014

King Fahd University of Petroleum and Minerals 2014

Abstract This paperdiscusses briefly a fuzzy-based assess-

ment of health hazard due to electromagnetic radiation. TheRF electromagnetic fields, out of the measurement points,

were calculated by the developed software based on fuzzy

logic.The electric andmagnetic field components of RF radi-

ation value at any point can be compared with national/inter-

national standardsand limitseasily usingthis software. There

is currentlya general consensus in thescientific andstandards

community that the most significant parameter, in terms of

biologically relevant effects of human exposure to radiofre-

quency electromagnetic fields, specific absorption rate is the

specific energy absorption rate in tissue, a quantity properly

averaged in time and space and expressed in watts per kilo-

gram. The Institute of Electrical and Electronics Engineers

recognizes that there is public concern regarding the safety of

exposure to the radio frequency and microwave fields from

hand-held, portable, and mobile cellular telephones. Interna-

tional organizations have established guidelines for human

exposure to radio frequency energy. While these guidelines

differ in some respects, their limits in the frequency range

used by wireless communications devices are broadly simi-

lar. The consensus of the scientific community, as reflected

in these exposure guidelines, is that exposure to RF energy

within the recommended limits stated in these guidelines is

safe. However, there is a scientific discontinuity in view of

health hazards. In this study, a fuzzification/defuzzification

method of thediscontinuity problem makes thesoft bound-

aries between hazardous regions and non-hazardous regions.

In future studies, more sophisticated fuzzy methods should

be tested for more realistic solutions.

S. Comlekci O. Coskun (B) M. KahrimanDepartment of Electronics and Communication Engineering, Faculty

of Engineering, Suleyman Demirel University, 32260 Isparta, Turkey

e-mail: [email protected]

Keywords Fuzzy logic

Safety standard

Health hazard

1 Introduction

The utilization of electromagnetic (EM) energy has increased

rapidly since the late 1990s. A number of organizations have

established limits for human exposure to EM fields.The stan-

dards vary somewhat in their exposure limits and in other

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3096 Arab J Sci Eng (2014) 39:30953099

particulars. However, the frequencies used for wireless com-

munications systems are broadly similar for (in) these dif-

ferent guidelines. All of these guidelines include provisions

for different exposure situations. These include limits for

whole-body exposure or partial body exposure that are more

relevant to the users of wireless communications. The stan-

dards also require that the exposure be averaged over time

periods ranging 630 min [1].The International Commission on Non-Ionizing Radia-

tion Protection (ICNIRP) guidelines [2], and the Institute

of Electrical and Electronics Engineers (IEEE) standard [1],

specify occupational and general public (ICNIRP)/controlled

environment and uncontrolled environment (IEEE) thresh-

old levels for whole body and local rates of electromagnetic

energy absorption, expressed in terms of the specific absorp-

tion rate (SAR), measured in wattsper kilogram.For a human

body radiated by a base station antenna, the electromagnetic

energy absorption depends in part on the antenna body dis-

tance, so that, for a given exposure threshold level, there

is a corresponding minimum distance required between theantenna and the body.

International organizationshave established guidelinesfor

human exposure to radio frequency energy. These include

the IEEE C95.1 standard and The ICNIRP guidelines [2].

Despite a considerable amount of speculation in the scientific

literature, no mechanism has established a standard such that

electromagnetic fields at levels below recommended limits

can produce biological damage of clinical consequence [3

8].

Mousa [9] studied the electromagnetic radiation emitting

from some cellular base stations around the city of Nablus.

The study was performed at several sites. The readings obtai-

ned werecomparedto someinternationalstandardsand guide-

lines. It has been noticed that the maximum measured value

was only 0.007 % of the ICNIRP and 0.005 % of the FCC

international limits. Furthermore, the values measured rep-

resented not only radiation emitted from the mobile base sta-

tions, but also that emitted from all other sources of radiation

in the range of 200 kHz to 3 GHz. The signals here can have

either destructive or instructive interference at some specific

points, so it is recommended that the radiation emitting from

the base stations should be investigated together with other

sources such as local TV, FM and WLAN transmitters. This

can be achieved using a suitable spectrum analyzer. Another

important issue is that the radiation exposure to the mobile

station itself should be measured since it may have a much

larger value being very close to the users [9].

In Kaluski and Stasierskis [10] work, a rough numerical

technique for the calculation of the near EM field distribution

in the vicinity of FM and TV antenna systems was presented.

Faraone et al. [11] investigated the character of the average

power density in the closeproximityof base-station antennas.

In 2003, a new measurement method for radiation emanating

from AM, FM, and TV antennas and mobile phone base sta-

tions was proposed by Shay et al. [12]. Cicchetti and Faraone

[13] proposed a prediction formula for estimating the peak

equivalent power density in the near-field of cellular base-

station array antennas.

Recently, Larcheveque et al. [14] studied the impact of

small-scale fading on the estimation of local average power

density for radiofrequency exposure assessment. Joseph et al.[15] studied a low-cost measurement method for the extrac-

tion of the relative phases of the fields of the base station and

broadcast antennas. In the end, Colak and Kocsalay worked

RF electromagnetic field distribution around a TV broadcast

antenna. They developed artificial neural network based soft-

ware to estimate RF EMFin a small area aroundTV broadcast

antennas[16].

2 Fuzzy Model and Study Design

The fuzzy logic method can be used to control processes thatare complex and nonlinear in the traditional control structure.

In fuzzy systems, effective results can be obtained based on

uncertain linguistic knowledge. Therefore, the fuzzy logic

method is convenient for cases where the system is complex,

and the result cannot be found using the traditional meth-

ods or cases where the information is infinite or uncertain.

Fuzzy logic is fit for soft computing in engineering prob-

lems. In particular, uncertainties on the boundary conditions

can be solved using the soft computing approach of fuzzy

logic[17,18].

A novel model has been used to find a realistic relation-

ship between health hazard (or SAR) and electromagneticradiation (measured and calculated). The main objective is

to overcome the problem of uncertainty regarding the eval-

uation and classification of hazardous regions in the vicinity

of antenna.

SAR is a unit of measurement used in the standard and it

measures the amount of radio frequency energy

SAR = E2m

(1)

where is effective incident electric field value, mass density

of tissue, conductivity of tissue. The commercial field probes

operating in the wireless communication bands are sensitiveto and the reading is usually expressed in. These instruments

are referred as isotropic E-field probes [19].

Exposure limits for radio frequency radiation have been

established by the Institute of Electricaland Electronics Engi-

neers (IEEE) and the International Commission on Non Ion-

ising Radiation Protection (ICNIRP). Safety distance (from

antenna to measuring point) can be found as

d=

30 P 10G/10E

(2)

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Arab J Sci Eng (2014) 39:30953099 3097

where is output power of antenna, is antenna gain (25 dBi for

commercial antenna), is maximum permissible electric field

intensity [1].

The main purpose of thispaper is to study the use of fuzzy

modeling for the analysis of health hazards due to electro-

magnetic radiation.

3 Measurements and Modeling

In this study, there were a total of 50 measuring points in the

center of City of Isparta, Turkey. The two service providers

had total 5 pylons of 900 MHz reference antennas in this res-

idential area. For system validation, our model was tested in

view of obtained and calculated electric fields. These mea-

surements have been conducted using the spectrum analyzer/

satellite receiver meter unit which was used to investigate the

reflections and background noises in the measuring media.

Repetition time, frequency, and amplitude of spectrum of RF

energy (900 MHz) were also investigated, observed, and ver-

ified by the satellite level meter that is PROMAX, MC-877C

(Barcelona/Spain). All the reflection and exposure measure-

ments were carried out by utilizing the Portable RF Survey

System, HOLADAY, HI-4417 (MN/USA) with its standard

probe aswell. The probe is able to select and obtain the vector

sum on the X, Y and Zaxis. In order to see if they matched,

themeasuredand calculated results were compared with each

other.

If one measures the field density value using the measured

or calculated electric field intensity in the above equation, the

safety distance can be calculated. The most common values

of parameters can be obtained as shown in Figs.1and2.

The normalized electric field can be expressed as.

%E=E

|E| 1 100 (3)

|E|: Obtained electric field value analytically,

Fig. 1 Electric field versus distance from antenna with limit value

in 900 MHz communication system. This limit is recommended by

ICNIRP as the safety distance[20]

Safety distance vs power

4,05,0

6,0

7,0

8,0

9,0

10,0

11,0

12,0

13,0

14,0

0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0

Output power (W)

Safetydistance(m)

Fig. 2 Safety distance (m) from antenna can vary by means power

Table 1 Measured and calculated electric field values versus distance

Distance from

antenna (m)

Calculated

electric field

(V/m)

Measured

electric field

(V/m)

SAR related

hazard grade

(%) 1,000

1 435.4 400 1,600

5 87 90 8.1

10 43.5 45 2

10.6 41 40 1.6

50 8.7 10 0.1

100 4.4 5 0

500 0.9 1.4 0

Measured electric field determines health hazard dealing with SAR

Pt(Watt)

Distance(m)

18 Rules

MamdaniPercentage(%)

Fig. 3 Proposed fuzzy model to the system

|E|: Permissible electric field value to provide safety (42V/m). So, the normalized percentage canbe used as a hazard

grade.

The electric field results obtained from these measure-

ments were used to establish a fuzzy model. This model

requires someresults obtained fromopen areameasurements.

The model was used for the prediction ofEfield values. So

one needs only a validated model without any measurement

process. The SAR defines the local Efield and the energy

absorbed into tissue. Our model predicts the Efield value in

tissue, or SAR. The predicted values from the model were

tested and validated. According to the basic electromagnet-

ics, we had to use some rules. These electric field measure-

ments are tabulated in Table1.

The Fuzzy Logic Toolbox of MATLABv6 was utilized

to establish our model at the Suleyman Demirel University

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3098 Arab J Sci Eng (2014) 39:30953099

Table 2 FAM table and rule

base of the system Pt (W) Distance (m)

Very very near Very near Near Mid Far Very far

Low Very harmful Mid Harmless Harmless Very harmless Very harmless

Mid Very very harmful Low harmful Mid Harmless Harmless Harmless

High Very very harmful Harmful Low harmful Mid Harmless Harmless

Fig. 4 Comparison of fuzzified

harm zones between directional

and omni directional antennas.

Fuzzified zones. Boldzones

represent more hazards

Fig. 5 Evaluation of harmful can be obtained analytically. SAR is

known directly proportional to electric field. Health hazard is defined

as dealing with SAR

Engineering Faculty Lab. The fuzzy model consists of two

inputs (transmitter power of base station antenna, distance

between base station antenna and measuring point) and one

output as a percentage for the expected health hazard. This

fuzzy inference system (FIS) model is shown in Fig.3 and

the fuzzy associative memory (FAM) table is provided in

Table2.

Fig. 6 Outputs of the model. Fuzzified zones can be classified as soft

transition among the areas

4 Conclusion

After the defuzzification of the system, the crisp values are

utilized to compare the analytical results for the calculated

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Arab J Sci Eng (2014) 39:30953099 3099

safety distance results. Graphical representations are pro-

vided in the form of graphs in Figs. 4 and 5.Figure6 rep-

resents the fuzzified solutions to the regional health hazards

using the fuzzy model presented. There have been a suffi-

cient number of matches, between our results from the model

and our measurements that were mentioned in the Sect. 5.

Figure5shows a close agreement between the measured and

calculated fields, especially in the near field.

5 Discussion

In this study, a new approach to obtain a risk assessment for

the energy radiated by a reference antenna is presented. For

instance, the output power of the antenna varied between 5

and 20 W in 900 MHz. According to the basic electromag-

netic, electric field intensity decreases by distance in steps so

that the most effective criterion is a field at a certain point. In

this respect, it is not easy to establish certain limits or bound-

aries among harmful or harmless regions. Using the pro-posed method, one can classify (in view of hazards) some

points in the vicinity of an antenna. It can be seen in Figs.

4,5;Table1 that the relative risk calculated from the fuzzy

method and from the analytical solution matches each other.

MATLAB-FIS gives acceptable linguistic outputs. Due to the

variable traffic condition, adaptive or proper models should

be created. Moreover, 3D solutions are always an essential

requirement for real-time geographicconditions. In the future

studies, more agreeable fuzzy models will be developed for

more reliable risk assessment mapping of directional anten-

nas.

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