Imeset’19 Malaysia Book Of Abstracts · 2020-01-17 · Dr. Yuriy Alyeksyeyenkov THK university THK university Electrical & Electronics department Associated Professor İbrahim Mahariq

International Conference on Multidisciplinary, Science,

Engineering and Technology (IMESET’19 Malaysia)

Sept 20-21, 2019,

Malaysia

IMESET’19 MALAYSIA

International Conference on

Multidisciplinary, Engineering,

Science, Education and Technology

(IMESET’19 Kuala Lumpur, Malaysia)

Hosted by University Malaysia Sabah,

September 20-21, 2019, Kuala Lumpur,

Malaysia

Imeset’19 Malaysia Book Of Abstracts

e - ISBN: 978-605-82480-6-9

Kuala Lumpur, Malaysia, 2019



Sept 20-21, 2019,

Malaysia


International Conference on Multidisciplinary, Engineering, Science,

Education and Technology (IMESET’19 Malaysia)

Hosted by Universiti Malaysia Sabah

September 20-21, 2019, Kuala Lumpur, Malaysia

BOOK OF PROCEEDINGS

IMESET’19 Malaysia



Sept 20-21, 2019,

Malaysia




Sept 20-21, 2019,

Malaysia


IV. INTERNATIONAL CONFERENCE ON MULTIDISCIPLINARY,

ENGINEERING, SCIENCE, EDUCATION AND TECHNOLOGY

(IMESET’19 MALAYSIA)

Hosted by Universiti Malaysia Sabah

Septemberber 20 -21, 2019, Kuala Lumpur, Malaysia

e - ISBN: 978-605-82480-6-9

EDITOR

Dr. Ali Farzamnia (University Maleysia Sabah University, Maleysia)

All papers have been peer reviewed.



Sept 20-21, 2019,

Malaysia


COMMITTEES

CONFEAdvisory Board / Danışma kurulu

• Prof. Dr. Murat Arı (Çankırı Karatekin University,Vice Rector)

• Prof. Dr. Abdul Karim Bin Mirasa(Universiti Malaysia Sabah,Dean of Engineering Faculty)

• Prof. Dr. M. Cengiz Taplamacıoğlu(Gazi University, Turkey)

Conference Chairs / Konferans Başkanları

• Dr. Javad Rahebi (Chair,THK University, Turkey)

(Responsible for Acceptence Letter and Attendance Certificate)

• Dr. Ali Farzamnia(Co Chair, Universiti Malaysia Sabah ,Malaysia)

(Responsible for organisaiton of malaysia side.)

• Dr. Mehmet Sait Cengiz(Co Chair, Bitlis Eren University,Turkey)

Organizing Board / Organizasyon kurulu

• Dr. Abdulrezzak Bakis (Bitlis Eren University)

• Dr. Cihan Onen (Bitlis Eren University)

Assoc.Prof. Dr. Mirvari Agayeva (Azerbaijan State University, Azerbaijan)



Sept 20-21, 2019,

Malaysia


SCIENTIFIC BOARD

Prof. Dr. Vanita Jain

Professor and Head of Information Technology Department

Bharati Vidyapeeth's College of Engineering

New Delhi, India.

K.Premkumar

Associate Professor,

Department of EEE

Rajalakshmi Engineering College

Chennai, India

P.Sivakumar

Associate Professor

Department of EEE


Chennai, India

S. B. Ron Carter

Assistant Professor,

Department of Electrical and Electronics Engineering,


Chennai, India.

Kusum Tharani

Associate Professor & Head of EEE Department

Bharati Vidyapeeth's College of Engineering

Delhi, India

Dr Toufique Ahmed Soomro

Assistant Professor

Electronics Engineering Department

Quaid-e-Awam University of Engineering and Science Technology Sindh Pakistan

Ahmed J. Afifi

Computer Vision and Remote Sensing

Technische University Berlin

Berlin, Germany

Uçman Ergün, Associated Professor

Biomedical Engineering

Afyon Kocatepe University

Mustafa Poyraz, Professor Doctor

Firat University



Sept 20-21, 2019,

Malaysia


Hüseyin Polat, Assistant Professor

Technology Faculty of Gazi University

Firat Hardalac Professor

Gazi University

Ali Farzamnia

Assistant Professor

Electrical & Electronic Engineering Program

Faculty of Engineering,

Universiti Malaysia Sabah

Dr. Ayhan Akbaş

THK university

Computer engineering department

Dr. Yuriy Alyeksyeyenkov

THK university

THK university

Electrical & Electronics department

Associated Professor İbrahim Mahariq

THK university

Dr. Esra Karaoğlu

THK university

Dr. Tayfun Okan

Mechanic Engineering

Dr. Mohammad Rafighi

THK university

Dr. Reza Aghazadeh



Sept 20-21, 2019,

Malaysia


Dear colleagues and young researchers!

First of all, I would like welcome and greet you for taking part in the second ‘International

Conference Engineering, Science, Education and Technology (IMESET’19 Malaysia. I believe that

this event, which on Multidisciplinary, Engineering, Science, Education and Technology (IMESET’19

Malaysia)’ held by Universiti Malaysia Sabah.

On behalf of the Organizing Committee, I am very happy to open International Conference on

Multidisciplinary, is the fruit of an intensive and devoted teamwork, will have an invaluable

contribution to the scientific world.

Today, universities need to get their power of existence from their own studies by setting strong

relationships with economic, social and cultural resources of their territory as access to information

has been simplified, education has become a lifelong activity and rivalry has become dominant. One

of the basic features of universities is to produce information, science and technology to serve to next

generation and to the people of the region as well as the country, since the responsibilities of

universities are not restricted to equip their students with occupation.

We wish everyone a fruitful conference and pleasant memories in Kuala Lumpur, Malaysia.

Assoc. Prof. Dr. Mirvari AGAYEVA

Chair of IMESET’19 MALAYSIA



Sept 20-21, 2019,

Malaysia


Dear Friends and Colleagues,

Welcome to the in the first ‘International Conference Engineering, Science, Education and

Technology (IMESET’19 Malaysia).

This book containts all abstracts presented in the second ‘International Conference Engineering,

Science, Education and Technology (IMESET’19 Malaysia. 18 papers will be presented at the

Conference. Peer reviewed papers will be considered for publication in a spesial issue.

The program for this Conference required the dedicated efforts are here with recorded. Secondly, we

thank the members of the Program Committe and additional rewiewers for their dligent and

professional rewiewing. Last but not least, we thank the invited speakers for their invaluable

contribution. We would also like to take this opportunity to thank Universiti Malaysia Sabah for

supporting and motivating us in every respect. Special thanks are also due to the organizing committee

members, for completing all preparations that are necessary to organize this conference. I express my

gratitude to the members of technical committee of the conference for the design and proofreading of

the articles.

A successful conference involves more than paper presentations; it is also a meeting place, where

ideas about new research projets and other ventures are discussed and debated. Therefore some social

events have been arranged in order to promote this kind of social networking.

We wish you have an exciting Conference and an unforgettable stay in the city of Kuala Lumpur.

On behalf of Organizing Committee



Sept 20-21, 2019,

Malaysia


Contents Mean Time To Repair Determinations For Subsystems Of A Military Vehicle According

To Availability Destination ............................................................................................................ 1 BER Perfomance Analysis on Modulation Techniques of WCDMA .................................. 2

A Revision in Tower Earthing Design for High-Voltage Transmission Network during

Lightning Condition ....................................................................................................................... 9 Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns

and Gaussian Low-Pass Filter ...................................................................................................... 22 New approaches for breast cancer nuclei detection in histological images ........................ 28 DESIGN OF IOT-BASED VERTICAL SYSTEM AQUAPONIC WITH FUZZY LOGIC

METHOD ..................................................................................................................................... 29

Equation Chapter 1 Section 1 Architecture and future prospect of 5G Technology .......... 42 Improved Dynamic Performance of Wind Energy Conversion System by STATCOM .... 47 Globalization and Employee Mobility: Interfusion for Customer Satisfaction in Selected

Multi-national Companies in Nigeria. Lagos Reviews ................................................................ 48

PROMOTING RENEWABLE ENERGY TECHNOLOGIES FOR RURAL

DEVELOPMENT IN NIGERIA .................................................................................................. 52 Increasing Efficiency of Gas Injection into Gas Cap of a Sandstone Oil Reservoir to Improve

Oil Recovery ................................................................................................................................ 53

International Conference on Multidisciplinary, Science, Engineering and

Technology (IMESET’19 Malaysia)

Sept 20-21, 2019, Malaysia

1 | P a g e O R A L P R E S E N T A T I O N

https://imeset.org/proceedings/


MEAN TIME TO REPAIR

DETERMINATIONS FOR

SUBSYSTEMS OF A MILITARY

VEHICLE ACCORDING TO

AVAILABILITY DESTINATION

Bayram KALENDER*

Gazi University Graduate School of Natural and

Applied Sciences, Mechanical Engineering Department,

Ankara, Turkey

Abstract

It is aimed to eliminate the need to develop a

method to ensure that the availability of the military

vehicles in the contracting stage, which is determined

for the needs of the army, can be provided in a cost-

effective manner at the beginning of the project.

A prototype fabricated military vehicle will be

used as a subject. The average Mean Time To Repair

(MTTR) for the vehicle systems will be determined

according to the expected availability target at the

contracting stage. In this study, real MTTR values and

availability values will be calculated by working on the

prototype vehicle manufactured and comparison will be

made. Since the actual readiness value is not suitable, a

histogram graph will be created with MTTR data to

determine which systems should be intervened.

Since a military vehicle with prototype

manufacturing was used in the study, due to

confidentiality, the numerical data cannot be shared

one-to-one and will be shared over a certain amount of

modified data to accurately reflect the results of the

calculations. The vehicle consists of 60 main systems.

The availability of the vehicle under the contract is

expected to be 80%. This value is distributed according

to previous experience, according to the main systems,

the number of subsystems and the mission critical

levels. Mean time between failures (MTBF) and MTTR

values of the systems used in the projects implemented

are taken into consideration. Although MTTR values of

the systems on the prototype vehicle were calculated at

this stage, MTBF values were obtained from past

projects using the same or very similar systems. The

availability of the prototype vehicle increased to

approximately 82%.

As a result of the studies, it was seen that the

availability of a vehicle in the preliminary design stage

was achieved thanks to its practical knowledge.

However, one of the major factors in the consistency of

calculations is the ability to make accurate estimates due

to experience. Considering this situation, it is aimed to

develop a theoretical calculation method.

Keywords: ILS, Maintainability, Availability, MTTR,

MTBF







BER PERFOMANCE ANALYSIS ON

MODULATION TECHNIQUES OF

WCDMA

Ali Farzamnia

Faculty of Engineering


Kota Kinabalu, Sabah, Malaysia

[email protected]

Navinn A/L Mohanaraj


Universiti Malaysia SabahKota Kinabalu, Sabah, Malaysia

[email protected]

Ngu War Hlaing



Kota Kinabalu, Sabah, Malaysia

[email protected]

Abstract

Wideband Code Division Multiple Access (W-

CDMA) is a 3rd Generation or known as 3G

standard that provides a high speed and also high

capacity services in the most of the area nowadays.

It uses the Direct Sequence-Code Division

Multiple Access (DS-CDMA), channel access

method and also the Frequency Division Duplex

(FDD). In this paper, the analysis is done on the W-

DCMA modulation technique which was subjected

to Additive White Gaussian Noise (AWGN) and

Rayleigh fading channel. The performance analysis

of Bit Error Rate (BER) has been carried out by the

help of MATLAB. The simulation of BER and

Signal to Noise Ratio (SNR) was done for W-

CDMA systems in this paper. By doing this, we

could find the most suitable modulation technique

to deliver the efficient and optimum data rate to

mobile terminal.

Keywords: Wideband Code Division Multiple Access,

Bit Error Rate, Signal to Nosie ratio, Direct Sequence-

Code Division Multiple Access, Additive White

Gaussian Noise.

INTRODUCTION

It is fascinating that high rates of the data signal

can be transmitted effectively through the air by the

use of W-CDMA system. It also allows the

transferring of applications that are rich in

multimedia such as videos, documents and pictures

which have a significantly high resolution with a

speed data rate. Therefore, this article emphasizes

the importance of an appropriate modulation

method and mechanism for error correction for the

use in Wideband Code Division Multiple Channel

(W-CDMA). It is also clear that the 2G networks,

specifically the Gaussian Minimum Shift Keying

modulation scheme is broadly used in Global

System for Mobile GSM communication.

The transmission of data through this 2G

modulation is just 1 bit per symbol thus it is without

a doubt that this modulation scheme is not suited

for the future generations of communication

systems. This paper provides useful insight about

the differences between the two modulations of

Quadrature Phase Shift Keying (QPSK) and 16-

Quadrature Amplitude Modulation (16-QAM) are

wide apart in the speed of transmission of data.

Notably, there is an emphasis that the

demodulators, modulators, transmission path, and

filter need to be perfect; a situation which is not

easy to attain [1].

Enhance Data Rate (EDGE) which linked to

the GSM evolution, is thought to be a good way of

switching to 3G which is a New Time Division

Multiple Access. It makes use of the frequency

bands namely 800, 900, 1800, and also 1900 MHz.

In this paper, a study is conducted to examine data

rate with a high speed and its effect on the

modulations of Direct-sequence Spread Spectrum

W-CDMA and how they affect the channels. The

two QPSK and QAM techniques of modulation are

selected since they are the best choices for the

delivery of higher rates of data for High Speed

Packet Access (HSDPA), which is a component of

the 3G network. The distribution of data is assumed

to be taking place through Bernoulli Effect [2].







Simulations that depend on computers are the best

option that available now for the real-time

scenarios of radio systems on mobiles. The study

made use of 3 W-CDMA models each of them is

wireless. They included Multipath Rayleigh

Fading, W-CDMA system present within AWGN,

and W-CDMA system present within the AWGN

channel. The Cross-Sectional Function (CCF) and

Auto-Correlation Function (ACF) are the most

intuitive ways of characterizing CDMA codes [3].

In cell framework, diverse clients have diverse

channel characteristics in terms of SNR because of

the distance from the base station. The data

protection helps to control the quality of connection

according to the quality of channel so that an

excellent bit rate is obtained. By using the 8-Phase

Shift Keying (8PSK) and M-ary Quadrature

Amplitude Modulation (M-QAM) in W-CDMA,

the rate of data transmission can be higher. The

sequence that generated by Data that has undergone

modulation is spread using the code, known as M-

sequence. The transmission of data to users is done

simultaneously. There is a detection of each user’s

information data by the mobile user through a

correlation between the allocated code sequence

and the received signal.

The study of the W-CDMA’s system

performance is founded on 16-QAM and QPSK

techniques of modulation. From the generation of

data through the simulated W-CDMA codes on the

computer, we can obtain the link between models

QAM and QPSK modulations between the Bit

Error Rate [4]. In [5], BPSK and QPSK

modulations are investigated in terms of capacity

with Nakagami-m fading channel with the known

channel state information at the receiver.

Modulation techniques are discussed with the

knowledge of fiber communication [6]. Orthogonal

Frequency Division Multiplexing (OFDM)

systems are investigated with different arrays of

modulation techniques under the Rayleigh AWGN

channels [7].

The Multi-Input Multi-Output (MIMO)

architecture shown that the performance can be

better than Multi-Input Single-Output (MISO) used

with 32-PSK modulation, and it can be better with

efficient modulation technique like QAM instead

of PSK [8]. The finding of the study is that 16-

QAM and QPSK modulations in AWGN perform

better in comparison to the Multipath Rayleigh

channel. Additionally, there is a reduced

performance of the 16-QAM and QPSK after a

subjection to the Multipath fading accompanied by

an increased value of Doppler shift (Hz). In

comparison with the QPSK, it is clear that 16-QAM

has a poor performance in each of the AWGN

channels [9].

M-QAM modulation has the advantageous of

effectiveness for band limited channels because of

ability to transmit more than one bit at a time [10].

The QAM is a technique of adding two amplitude

modulated signals or known as AM signals into a

channel. Thus it can double up the bandwidth. The

other modulation technique is QPSK.

It transmits 2 bits per symbol such as 00, 10, 01

or 11. So the four quadrature phase shift keying’s

phase are 315, 225, 135, and also 45 degrees. If we

compare, the modulation schemes that can transmit

1 bit per symbol, this quadrature phase shift keying

is an advantageous in term of bandwidth. This

system can use same frequency for a baseband

signal, and thus the efficiency of bandwidth is

higher compare to BPSK and others.

In this paper, the performance

comparison carried out by differentiating the rate

of chip of pseudo-noise or known as pseudo noise

generator. The fading channel’s performance in

wideband code division multiple accesses is based

on the BER and also SNR. This paper mainly

focused on the Bit Error Rate performance analysis

of the modulation of WCDMA which is applied to

Rayleigh Fading Channels and also AWGN. The

modulation that has been used in this paper is 16-

ary QAM and also QPSK.







SYSTEM MODEL

The most powerful, efficient and also

fitting method for this paper has to support for the

real time situations of mobile radio function. The

simulation was done for wideband code division

multiple access models by using the theoretical

formulae and parameters. In this paper, three

wideband code division multiple access (W-

DCMA) system was used. The WCDMA systems

models are in AWGN channel, Multipath Rayleigh

Fading and AWGN channels.

Modulation Schemes that will be studied are 16-ary

QAM and QPSK in AWGN. AWGN is produced due to thermal

motion of electrons in electrical materials. Thermal noise can be

expressed with a zero-mean Gaussian random process. The

transmitted signal can be obtained by summation of the random

signal and Gaussian noise random variable which is expressed in

equation (1).

Z a n= + (1)

Probability distribution function (pdf) for Gaussian

noise can be represented in equation (2) where σ2 is the variance of

AWGN.

( )2

1

212

aZ

P Z e

− −

= (2)

This noise can cause distortion on the

received signal which can give the multipath

fading.

QPSK is M-ray PSK modulation technique where

the value of M is four. QPSK can transmit 2 bits per symbol. The

phase match on one of four equally spaced values which is 0, π/2, π

and 3π/2. Each phase follows a unique pair of message bits. The signal of QPSK can be described in equation (3).

( ) ( ) ( ) ( ) ( )1 1cos 1 sin 12 2

QPSK s sS t E i t E i t

= − −

(

3)

Where; 1,2,3,4.i = The advantage of

QPSK over BPSK is that QPSK can send the data

twice than BPSK in the same bandwidth and QPSK

has same bit error probability with BPSK. Moreover, QPSK can do

differential encoding for detecting non-coherent method.

For QAM, the amplitude varies with the phase. It is

the unification of Amplitude Shift Keying (ASK) and PSK. The

general form of M-ary signal can be expressed in equation (4).

( ) ( ) ( )min min2 2cos 2 sin 2i c c

s s

E ES t f t f t

T T = + (

4)

0 , 1,2,...t T i M =

Where; Emin means the minimum amplitude of the

signal. The chip rate of pseudo noise (PN) generator will be varied

to investigate the performance of the system. Although there are so

many methods to differentiate CDMA codes, the most effective

method is the auto-correlation function (ACF) and cross-correlation

function (CCF).

The process of chip-wise convolution, correlation or

matched-filtering give ACF. ACF is a measure of correlation

between two time shifting in the same code. If two consecutive bits

are the same, it can be called periodic and if they are not the same,

it can be called aperiodic. The binary data of ‘+1’ and ‘-1’ with equal

probability comes out equally likely. The convolution of two

functions can be called CCF.

The operation of a chip-wise convolution between

two different spreading codes in a same family of codes can result

CCF. CCF also has periodic and aperiodic CCF. To measure the

value of CCF between two certain codes such as X(t) and Y(t), the

argument of the function is shown in equation (5).

( ) ( ) ( )0

1T

xxR t X t Y t dT

= + (5)

In this paper, for producing sequences of the code in

W-CDMA, linear feedback shift registers (LFSR) will be used in

which a number of cells (1 to r) are included in each shift register.

Each cell acts like a storage unit. By controlling a clock pulse,

each cell moves the contents to its output while reading its new contents from the input. Fig. 1 shows a single LFSR, which can generate a sequence from generation polynomial h(x) = x5 + x2 + 1.

Fig. 1. Three-Stage M-Sequences

A generator polynomial can represent with ‘n’

number of linear binary shift register. It means ‘n’ degree of binary

polynomial.

( ) 1 1

1 1... 1; 0,1n n

n n iH x h X h X h X h−

−= + + + +

(

6)







In this paper, three-stage M-sequence and a random

sequence with a code length of 7 will be applied. A linear-feedback

shift register produces M-sequence with periodic auto-correlation.

According to the synchronous W-CDMA system, the data is spread

by each user, then they are modulated with M-sequence. Then, it will

be transmitted to the network users simultaneously. The mobile user

can detect the data with the correlating the received signal with a

code sequence corresponds to each user. The flowchart for the

simulation of W-CDMA system in this paper is shown in Fig. 2.

Fig. 2. Simulation Flowchart for WCDMA System

RESULTS AND DISCUSSION

The relationship of QPSK and QAM between

BER as a function are obtained by using the

generated data from a simulation of W-CDMA

models. The table 1 below shows the result for BER

versus SNR evaluation for the bit rate of 100Kbps

and table 2 shows for the 384Kbps. In this

simulation the value that obtained for BER are by

differentiating the SNR values from the range of 0

to 10.

TABLE 1. BER VERSUS SNR EVALUATION RESULT FOR

THE BIT RATE OF 100KBPS

SNR (dB) Errors BER

0 18392 0.18392

1 14307 0.14307

2 10349 0.10349

3 6820 0.06820

4 4069 0.04069

5 2154 0.02154

6 934 0.0093402

7 306 0.0030601

8 93 0.00093002

9 15 0.00015

TABLE 2. BER VERSUS SNR EVALUATION RESULT FOR

THE BIT RATE OF 384KBPS

SNR (dB) Errors BER

0 70673 0.18405

1 54670 0.14237

2 39605 0.10314

3 26332 0.068573

4 15573 0.040555

5 7909 0.020596

6 3611 0.0094097

7 1296 0.003375

8 326 0.00084896

9 66 0.00017188

The performances of QPSK and QAM

were degrading when it was subjected to the

Rayleigh multipath fading channels meanwhile, the

QPSK and QAM performed very well in AWGN

channel.

Fig. 3 shows the performance analysis

of W-CDMA with different channel using the

varied modulation techniques. In this simulation,

we can see that the performance of the connection

works very badly when the mobile terminal’s speed

increases.







Fig. 3. Additive White Gaussian Noise channels in Wideband Code Division Multiple Access

The simulated graph is decreasing in

the form of exponentially. It shows that when the

users are increased the system is working poorly.

Then, the 16-QAM and QPSK compared. From this

simulation, we could see that the quadrature

amplitude modulation gives a bad performance in

both of the AWGN channel and also in Multipath

Fading channel. Fig. 4 and Fig. 5 show the

simulation results for the W-CDMA performance

under Rayleigh Fading and comparison results

between the Empirical and Theoretical Symbol

Error Rate (SER) respectively.

Fig. 4. Binary DPSK over Rayleigh Fading channel.

Fig. 5. The comparison between the empirical and theoretical

value of SER.

Next, the simulation results for 16-

QAM of WCDMA system is obtained. The

performance of it was poor and the reason was due

to the obstruction between the carriers’ phase of 16

QAM. The technique of modulation cannot be done

and it is suspected because the differences of

amplitude in the constellation of the signal. Fig. 6

shows the graph for the performance of BER and

SER for the QAM. Fig. 7 shows the graph of semi

analytic BER compared with the Theoretical BER

for 16-QAM.

Fig. 8 shows the comparison graph

between the QPSK, 16-QAM and also with 8-PSK

modulation. For PSK symbols the detection was

done by comparing with the phase of the received

code. Meanwhile for the QAM, it detects by

comparing both the amplitude and also the phase of

the code. If we compare the QPSK and QAM, we

can see that the QPSK’s symbols all lies on the unit

circle meanwhile the QAM’s symbols all lies on a

straight line that form a square.

Fig. 6. Symbol and Bit Error probability for the 16-QAM







Fig. 7. Semi analytic BER compared with Theoretical BER for 16

Quadrature Amplitude Modulation

Fig. 8. Graph of comparing the 8-PSK,16-QAM and also with the

QPSK modulation techniques.

From the simulation, we can see that

the number of multiuser are limited to several

users. Thus the performance of the WCDMA is a

bit poor. The system needs to be improved so that

it can be simulate more users and it may help the

studies of the W-CDMA performances.

CONCLUSION

Overall, the main challenge encountered in

the field of telecommunication is the transmission

of information in the most efficient manner using

limited bandwidth. This is despite the fact that bits

of information get lost in the process and there will

be fading of the signal which had been sent

initially. This paper helps us to identify which

modulation technique is the best way to solve the

loss rate of information. The two modulation

methods that have been used in this paper are

QPSK and also the 16 QAM under the Rayleigh

Fading channels in the AWGN. The study has

provided enough indication that 16-QAM

technique is inferior in performance to QPSK when

considering W-CDMA system’s performance in

the AWGN channel. Lastly, we can see that as the

amount of mobile user increases, the modulation of

QPSK performs badly. Meanwhile, the 16-QAM

modulation technique failed to portrait the results

that expected as theoretical. This can be improved

by increasing the user for the simulation for QAM

modulation technique.

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Electrical, Electronics, and Optimization Techniques

(ICEEOT), pp. 3081-3086. IEEE, 2016.







A REVISION IN TOWER EARTHING DESIGN FOR HIGH-VOLTAGE

TRANSMISSION NETWORK DURING LIGHTNING CONDITION

Dr Azwadi Mohamad*

Lightning and Earthing Group, TNB Research Sdn Bhd, 43000 Kajang, Selangor, Malaysia.

ABSTRACT

The earth electrode of the transmission tower is designed in such a way to protect both the working personnel and

the equipment during a fault. The existing earthing system for transmission towers in TNB’s system is purposely

designed for low-frequency (normal power frequency) fault conditions that takes into account the step and touch

voltages. This earthing design is found to be inapt for lightning (transient) condition to a certain extent, which

involves a high-frequency domain. The current earthing practice of laying the electrodes radially in straight 60m

horizontal lines under the ground, in order to achieve the specified impedance value of less than 10 Ω, was deemed

ineffective in reducing the high-frequency impedance. This paper introduces a new earthing design that produces

low impedance value at the high-frequency domain, without compromising the performance of low-frequency

impedance. The performances of this new earthing design, as well as the existing design, were simulated for various

soil resistivity (SR) values at varying frequency. The simulated tower earthing design that gives small impedance

values at both low and high-frequencies was selected for actual field installation. From the simulation and field

measurement results, it is obvious that good earthing design should have a fine balance between compact and radial

electrodes under the ground.

KEYWORDS – Tower Earthing design, High-frequency, Lightning, TFR







OPTIMIZATION OF QUICKLIME PRODUCTION FR

OM EGGSHELL USING RESPONSE SURFACE METHODOLOGY

Salisu Nuhu a Department of Polymer Technology, College of Science And Technology

Hussaini Adamu Federal Polytechnic Kazaure, Jigawa State

Abstract:- This study developed empirical response surface models for optimizing the quicklime characteristics.

The calcination process parameters evaluated were calcination temperature, calcination time, and eggshell particle

size. Two process models were successfully developed and validated for RSM models. The modeling validation

runs were within the 95% prediction interval of the developed models and their residual errors compared to the

predicted values were less than 5%. Results from this study shows that the significant parameters that influenced

the quicklime yield and reactivity are calcination temperature, calcination time and eggshell particle size. The RSM

approach shows that a compromised setting of calcination temperature of 945.91oC and calcination time of 180.82

min will produce quicklime of optimal yield of 99.6608 % and optimum level of calcination time of 210 min and

calcination temperature of 895.03oC produced optimum quicklime reactivity of 0.467835oC/s. The RSM models

developed in this study can be used in the quicklime production industries to find the settings of the calcination

process that will maximize quicklime quantity and quality. This will reduce the downtime encountered by industries

having problems caused by variation in the quality of purchased quicklime.

Key: Eggshell, 0ptimization, Calcination, Respones, Surface Methodology RSM

Introduction

Disposal and treatment of eggshell waste in order to free the environment and the society of the

menace constituted by accumulated solid waste have been issues of serious concern to individual

countries and the entire world (Gallala et al., 2007). The composition of the eggshells similar to that

of limestone lends the effects of it as an alternative to limestone for production of quicklime. It is

scientifically known that the Eggshell is mainly composed of compounds of calcium. Akande (2015)

presented eggshell as being composed of 93.70% calcium carbonate, 4.20% organic matter, 1.30%

magnesium carbonate, and 0.8% calcium phosphate. As previously mentioned, calcium carbonate

(CaCO3) is the major composition of the eggshell, accounting for 93.70% of the total composition of

the Eggshell. Akande (2015) also presented calcium carbonate, as an important constituent of eggshell.

Similarly, calcium carbonate is the primary raw material in the production of quicklime (Yusuf (2015);

Bello (2015), Akande (2015). It is, therefore, indicated that eggshell and limestone have the same

primary composition in calcium compounds. The calcium trioxocarbonate (IV) which has been

established to be common to eggshell and limestone is a naturally abundantly available mineral.

According to Akande (2015), calcium carbonate occurs abundantly in earth's crust as lime stone,

marble, and chalk, and as well as in natural ores like calcite, dolomite, and Iceland spar. Literature

search did not show any previous research on optimal production of quicklime from eggshell.

Aim and Objectives of the reseach

To applies response optimization to production of quicklime from eggshell using design of

experiment.

The objectives are:

to determine the settings of calcination temperature, time and particle size that will

maximize quicklime yield during quicklime production from eggshell in a laboratory kiln.







to determine the settings of calcination temperature, time and particle size that will

maximize quicklime reactivity during quicklime production from eggshell in a laboratory

kiln.

Materials and methods

The major equipment used are briefly described, glasses ware such as measuring cylinder,

thermometer, thermocouple, beaker, mortar and pestle. Tyler mesh, Desiccators, Tong, crucible and

spatula were used for this study

Sample Collection

Eggshell used in this study was collected from dumpsite of Kaduna South metropolis located in

Kaduna state, Nigeria. The sample was carefully selected to avoid difference in characteristics.

1. Sample Preparation

Prior to the experiment, the eggshell sample collected was washed clean and dried to remove external

impurities i.e. silica. The eggshell sample was crushed using jaw crusher. The sample was collected and

grinded using Ball Mill grinding machine. The sample was collected and sieved using mechanical agitator

with different Tyler mesh arranged to obtain a particle sizes of 450microns.

REULT AND DISCUSSION

Response Surface Methodology Results

The RSM was implemented using Design Expert release 9.0. Two models were developed to

correlate the effect of the output response factors (quicklime yield and reactivity). The model result show

how the calcination process parameters (temperature, time and particle size) can be used to maximize the

output responses. The model result shows how the calcination process controlled parameters can be used

to maximize the calcination output responses (quicklime yield and reactivity) shown in table 1. Therefore,

the objective of the model is to determine the calcination time, temperature and particle size settings that

will maximize quicklime yield and reactivity.The quicklime yield empirical equations for the selected

model were obtained.

Table 1: Response Surface Methodology Experimental Run and Results of Quicklime Yield.

Exp

Run

Calcination

Temperature (oC)

Calcination

Time (min)

Eggshell

Particle Size

(mm) Quicklime

Yield [(%)]

Reactivity oC/s

1 890 210 0.3 87.4479 0.42444

2 980 210 0.575 91.6492 0.26444

3 980 165 0.3 95.2268 0.29111

4 890 165 0.575 90.83541 0.4200

5 890 165 0.575 89.1377 0.4200

6 890 210 0.85 94.2021 0.46222







7 890 120 0.3 87.5556 0.415556

8 800 120 0.575 39.5429 0.108889

9 890 165 0.575 90.8728 0.415556

10 890 165 0.575 91.2621 0.42

11 800 165 0.3 42.6701 0.13111

12 980 165 0.85 93.0412 0.3

13 980 120 0.575 82.6721 0.29111

14 800 210 0.575 40.5121 0.197778

15 890 165 0.575 89.0268 0.422222

16 890 120 0.85 85.3757 0.402222

17 800 165 0.85 46.1234 0.193333

Response surface modelling of quicklime yield with respect to calcination process parameters

The results of the mean and variance models correlating calcination temperature and time to quicklime

yield and reactivity are presented in this section shown in table 2. Also presented are the results of model

selection and model terms significance. The mean and variance models developed provide the basis for

optimization to determine the settings of calcination temperature and time that will maximize quicklime

yield and reactivity.

Table 2: Sequential Model Sum of Squares (SMSS) Analysis for Quicklime Yield Model

Source

Sum of

Squares

Df

Mean

Square

F-

Value

p-

value Prob>

F

Mean vs Total

105175

.33 1

105175

.33 Linear vs

Mean

4739.7

5 3

1579.9

2 8.62

0.002

1

2FI vs Linear 43.94 3 14.65 0.06

0.978

4 Quadratic vs

2FI

2328.7

6 3 776.25

576.

13

<

0.0001

Sugges

ted

Cubic vs

Quadratic 4.95 3 1.65 1.47

0.349

4

Aliase

d

Residual 4.49 4 1.12

Total

112297

.21

1

7

6605.7

2







Table 3: Lack of Fit Test for Quicklime Yield Model

Source p-value Prob> F Linear < 0.0001 2FI < 0.0001 Quadratic 0.3494 Suggested

Cubic Aliased

Pure Error

Table 4: Model Summary Statistics for Quicklime Yield Model

Sour

ce

Standard

Deviation R2

Adjust

ed R2

Predict

ed R2

PRES

S

Line

ar 13.5366

0.6

655 0.5883 0.3911

4336.

5243

2FI 15.2912

0.6

717 0.4747

-

0.3075

9311.

5157 Quad

ratic 1.1608

0.9

987 0.9970 0.9879

86.14

08

Sugg

ested

Cubi

c 1.0590

0.9

994 0.9975 +

Alias

ed

Quicklime yield model

The experimental data of Table 5. was used to obtain the second order empirical model coefficients

of Equations 1. The empirical model equations for the quicklime yield as a function of one noise variable

(eggshell particle size) and two controllable process variables (calcination temperature and calcination

time) was developed using experimental data of Table 5 and are represented as Equations 1 below. The

quadratic equation model terms excluding insignificant ones as shown in Table 6.

Table 5: Model Coefficient Table for Quicklime Yield Response Surface Reduced Quadratic

Model

Factor

Coefficient

Estimate

Intercept 90.2270

A-Temperature 24.2176

B-Time 2.3331

C-Particle Size 0.7303

AB 2.0020

AC -1.4097

BC 2.2335

A^2 -23.0064







B^2 -3.6265

C^2 2.0448

The coded quicklime yield empirical model is shown in Equation 1.

4.1

.

Table 6: Analysis of Variance (ANOVA) Table for Quicklime Yield Response Surface Reduced

Quadratic Model

Source

p-value

Prob> F Model < 0.0001 Significant

A-Temperature < 0.0001 B-Time 0.0007 C-Particle Size 0.1184 AB 0.0107 AC 0.0455 BC 0.0063 A^2 < 0.0001 B^2 0.0004 C^2 0.0086 Residual Lack of Fit 0.3494 not significant

Pure Error Cor Total

Effects of process variables on quicklime yield using the optimized reactivity mean model

Figures 1 and 2 show the contour and surface plot of the quicklime yield model respectively. The

experimental levels of each factor and the relationship between them was graphically represented using

contour and response surface plots.The plot shows the effect of varying two process parameters while

holding the particle size at zero point. Figures 1 and 2 show influence of quadratic function of

calcination temperature and significant influence of calcination time on quicklime yield while neglecting

the particle size and keeping it constant at zero. The plots show that increased quicklime yield was

observed with increasing calcination time and temperature values. Figures 1 and 2 show increased

quicklime yield was observed with increasing calcination temperature and time values.







Figure 1. Model Contour Plot Showing the Effect of Calcination Temperature and Time on

Quicklime yield for Response Optimization of Quicklime Yield Model.







Figure 2. Model Response Surface Plot Showing the Effect of Calcination Temperature and Time

on Quicklime Yield for Response Optimization of Quicklime Yield Model.

Response Surface Modelling of Quicklime Reactivity with Respect to Calcination Process

Parameters

The results of the mean and variance models correlating calcination temperature and time to quicklime

reactivity are presented in this section. Also presented are the results of model selection and model terms

significance. The mean and variance models developed provide the basis for optimization to determine

the settings of calcination temperature and time that will maximize quicklime reactivity.

Table 7: Sequential Model Sum of Squares (SMSS) Analysis for Quicklime Reactivity Model

Source p-value Prob> F Model < 0.0001 Significant

A-Temperature < 0.0001 B-Time 0.0003 C-Particle Size 0.0019 AB < 0.0001 AC 0.0065 BC 0.0081 A^2 < 0.0001 B^2 0.3523 C^2 0.0222 Residual Lack of Fit 0.0088 Not significant

Table 8: Lack of Fit Test for Quicklime Reactivity Model

Source p-value Prob> F Linear < 0.0001

2FI < 0.0001 Quadratic 0.0088 Suggested

Cubic Aliased

Pure Error

Table 9: Model Summary Statistics for Quicklime Reactivity Model

Sour

ce

Standard

Deviation R2

Adjust

ed R2

Predict

ed R2

PRE

SS

Linear 0.116132

0.17

2369

-

0.01862

-

0.54252

0.32

6767







2FI 0.130623

0.19

4568

-

0.28869

-

2.3486

0.70

9367 Quad

ratic 0.006979

0.99

839

0.9963

21

0.9758

6

0.00

5114

Sugg

ested

Cubic 0.002434

0.99

9888

0.9995

53 +

Alias

ed

Quicklime reactivity model

The experimental data of Table 10 was used to obtain the second order empirical model coefficients

of Equations 2. The empirical model equations for the quicklime reactivity as a function of one noise

variable (eggshell particle size) and two controllable process variables (calcination temperature and

calcination time) was developed using experimental data of Table 10 and are represented as Equations 2

below. The quadratic equation model terms excluding insignificant ones as shown in Table 11.

Table 10: Model Coefficient Table for Quicklime Reactivity Response Surface Reduced Quadratic

Model

Factor Coefficient Estimate

Intercept 0.4195556

A-Temperature 0.064444375

B-Time 0.01638875

C-Particle Size 0.011944375

AB -0.028889

AC -0.01333325

BC 0.012778

A^2 -0.200611175

B^2 -0.003388925

C^2 0.009944325

The coded quicklime reactivity empirical model is shown in Equation 2.

Table 11: Analysis of Variance (ANOVA) Table for Quicklime Reactivity Response Surface

Reduced Quadratic Model

Source

p-value

Prob> F Model < 0.0001 Significant

A-Temperature < 0.0001 B-Time 0.0003 C-Particle Size 0.0019 AB < 0.0001 AC 0.0065 BC 0.0081 A^2 < 0.0001







B^2 0.3523 C^2 0.0222 Residual Lack of Fit 0.0088 not significant

Pure Error < 0.0001 Significant

Cor Total < 0.0001

Effects of process variables on quicklime reactivity using the optimized reactivity mean model

Figures 3 and 4 show the contour and surface plot of the quicklime reactivity model respectively. The

experimental levels of each factor and the relationship between them was graphically represented using

contour and response surface plots.The plot shows the effect of varying two process parameters while

holding the particle size at zero point.

Figure 3. Model Contour Plot Showing the Effect of Calcination Temperature and Time on

Quicklime reactivity for Response Optimization of Quicklime Reactivity Model.







Figure 4. Model Response Surface Plot Showing the Effect of Calcination Temperature and Time

on Quicklime Reactivity for Response Optimization of Quicklime Reactivity Model.

CONCLUSION AND RECOMMENDATIONS

Conclusion

This study has demonstrated the application of single response optimization in seeking optimum

conditions for calcination experiments. Differently maximization of quicklime yield and reactivity was

investigated using single response optimization.

The empirical models developed were presented as contours and surface plots in order to explore the

model region of operability and have better understanding of the process.

The conclusions drawn from this study are summarized below.

• Calcination temperature of 945.91 oC, calcination time of 180.82 min and particle size of 0.84 mm

produced optimum quicklime yield of 99.6608% for single response optimization of quicklime

production from thermal decomposition of eggshell.

• Calcination temperature of 895.03 oC, calcination time of 210 min and particle size of 0.84 mm

produced optimum quicklime reactivityof 0.467835 oC/s for single response optimization of

quicklime production from thermal decomposition of eggshell.

Recommendations

This study has applied single response optimization to production of quicklime from eggshell in a

laboratory kiln. Through this study, it has been found that there are some areas that would benefit from

additional study. These areas include:







• Application of multiple response optimization to production of quicklime from eggshell in a pilot

plant rotary kiln should be investigated.

• There are different categories eggs depending on the bird types and sample from each bird should

be used as the uncontrolled factor in the multiple response surface models.

REFERENCES

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conditions in hard turning of AISI P-20 tool steel using response surface methodology and

desirability graphs, International Journal of Machining & Machinability of Materials, 4(1), 95-

110.

Akande, H. F. (2015). Multiple Response Optimization of Quicklime Production from Limestone Using

Design of Experiment. Minna: PhD Thesis Federal University of Technology, Minna, Nigeria

Antonia, M., Bakolas, A., & Aggelakopoulou, E. (2000). The Effects Of Limestone Characteristics and

Calcination Temperature to The Reactivity of the Quicklime. Cement and Concrete Research, 3-

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Arias. J.L, Fink, D.J., Xiao, S. Q., Heuer, A.H., Caplan, A.I. (1993) Biomineralisation of eggshells: cell-

mediated acellular compartments of mineralised extracellular matrix. lnt.Rev.Cytol. Vol. 145: 217-

250 .

Baziotis, I., Leontakianakos, G., Ekonomou, G., Delagrammatikas, G., Galbenis, C. T., & Tsimas, S.

(2010). A Case Study of Different Limestones during Quicklime and Slaked-Lime Production.

Geological Society of Greece, 3(4), 2-5.

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Production.Minna: Unpublished MEng Thesis Federal University of Technology, Minna, Nigeria.

Bhattacharya, T.K., Ghosh, A. & Das, S.K. (2001). Densification of Reactive Lime from Limestone.

CeramicsInternational, 27(3), 455-459.

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Journal, 31 (1), 103-111.

Borkowski, J. J., & Lucas, J. M. (1997). Designs of Mixed Resolution for Process Robustness Studies.

Technometrics, 39(4), 63-70.

Borror, C. M., & Montgomery, D. C. (2000). Mixed Resolution Designs as Alternatives to Taguchi

Inner/Outer Array Designs for Robust Design Problems. Quality and Reliability Engineering

International, 16(3), 117-127.

Box, G.E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of

the Royal Statistical Society, Series B,13(1), 1–45.

Boynton, R. S. (1980). Chemistry and Technology of Lime and Limestone. New York: John Wiley& Sons

Publishing, USA.







Dogu, G. & Mine. (2002). Calcination Kinetics of High Purity Limestones. Chemical Engineering Journal,

5, 1-2.

Foraminifera (2012). Hydrated Lime Production from Limestone in Nigeria How Viable. Retrieved from

http://www.foramfera.com/index.php/investment-opportunities-in-nigeria/item/420.

http://www.foramfera.com/index.php/investment-opportunities-in-nigeria/item/420







FACE RECOGNITION WITH SYMMETRICAL FACE TRAINING SAMPLES

BASED ON LOCAL BINARY PATTERNS AND GAUSSIAN LOW-PASS FILTER

Saad Omran Elhashmi Allagwail

Department of Electrical & Computer Engineering

Osman Serdar Gedik

Department of Electrical & Computer Engineering

University of Ankara Yildirim Beyazit

Ankara, Turkey

Abstract— In practical face recognition applications, the

human face can have a limited number of training images.

However, it is known that in general an increasing number of

training images also increases the performance of face

recognition system. In this case, a new set of training samples

can be generated from the original samples using the symmetry

property of the face. Although many face recognition methods

have been proposed, a robust face recognition system is still a

challenging task. In this paper, recognition performance is

improved by using the symmetry property of the face.

Moreover, the effects of illumination and pose variations are

reduced. A Local Binary Pattern based on Gaussian Low Pass

Filter, which is a new approach for face recognition using

symmetry, is presented. The method has three main stages,

Preprocessing, Feature Extraction and Classification. The

Gaussian Low Pass Filter is used for preprocessing. The Local

Binary Pattern is used for feature extraction, and the Euclidean

Distance is used for classification. The proposed method is

implemented and evaluated using the ORL dataset. This study

also examines the importance of the preprocessing stage in a

face recognition system. The experimental results show that the

proposed method has higher recognition rates than the methods

in the literature.

Keywords— Face recognition, Symmetry, Gaussian Low Pass

Filter, Local Binary Pattern

I. INTRODUCTION

Robust and accurate face recognition (FR) is

one of the most important problems in computer

vision applications. In the literature, there are

several methods used for FR, including holistic,

local and hybrid methods [1] [2]. On the other

hand, recent research has revealed that a symmetry-

based dataset for FR is a useful method to solve the

FR problem; thus, it is possible to realize FR using

the symmetry property of the face [3].

In this paper, a new method is proposed to

solve two main problems in FR that are still open.

First is the limited number of face training samples,

and second, the variations in poses and facial

expressions in addition to lighting conditions. The

proposed method uses the symmetry property of

the face to reduce the effect of those two problems.

In this paper, the Local Binary Pattern (LBP)

is used for feature extraction since this method

perform well for texture feature extraction that can

be used for FR [4] . The images of the face are

enhanced before extracting their features. This

enhancement operation is accomplished by a

preprocessing step using well-known techniques,

namely the Gaussian low-pass filter (GLPF) [5].

The proposed method is analyzed using a

benchmark facial dataset, namely the ORL [6].

There are many studies related to FR, such as

the authors of [7] presenting a robust method for

FR using sparse representation (SR). Although the

results are good, the method has a high

computational cost. FR with LBP is proposed by

Ahonen et al. [8] in which the algorithm is not

sensitive to light and accordingly this point is

considered the robustness of their study. The

authors of [9] use discriminative dictionary

learning and SR along with the Gabor filter bank

and the LBP for feature extraction and reduce the

influences of illumination changes. One of the

milestones for FR under variations can be stated as

the Fisherfaces and Eigenfaces [10] technique,

which is insensitive to illumination variations. Pose

variation is also studied in [11] by using view based

eigenfaces. The authors of [12] combine the







generalized photometric stereo and eigen light field

concept to generate a generic method which is also

insensitive to illumination changes. The authors of

[13] present a method to arrange the variation of

poses and illumination, including shadows and

reflections; however, the computational cost in

their method decreases the efficiency of the

recognition system since they generate 3D models

from 2D images. Shashua et al. propose a method

in [14] based on the illumination invariant

signature image, since they show that it is possible,

even in bad conditions, to use a small dataset to

generate more images with varying illumination.

However, their method is not appropriate when the

images include some shadows. Georghiades et al.

show that in [15], when the pose is fixed, all

possibilities of illumination in the image space

have a convex cone. In addition, they use their

method to reconstruct the shape and albedo of the

face by training the system using only a few images

with different directions of light. The authors of

[16] introduce a modified asymmetric model to

overcome pose variations. However, the

performance of their method is affected by the

discretization resolution of the pose space.

Fig. 1- Face Recognition System

One of the most important properties in

nature, and particularly in human faces, is the

symmetry property. Many authors have noticed its

role [17]. It has been observed that the human face

is almost symmetrical, so the use of this property

for face detection (FD) and FR has been previously

studied, as in [18], where the authors develop a

technique to compute bilateral symmetry axis

automatically and use it in their research. Zhao and

Chellappa in [19] use the symmetry of the face to

reduce the effects of illumination in FD. It has been

shown that symmetry is also useful for extracting

the facial profile in facial recognition techniques,

as described in [20]. The authors of [21]

successfully apply the symmetry property to FD,

and they conclude that the expressions of the face

are also symmetrical. The authors of [3] improve

Classific

ation

Euclidean

Distance

Gaussian Low-Pass

filter

Prepr

ocessing

Local Binary Pattern Feature Extraction

Test Image

Original Dataset

Recognition

Result

Gene

rating more

images







the rate of FR recognition accuracy by using the

symmetry property of the face to design a

collaborative representation-based classification

method (SCRC).

II. METHOD STAGES

A) Preprocessing

The Gaussian Low-Pass filter (GLPF) is used

for the preprocessing stage. The GLPF is the result

of smoothing an image using a Gaussian function.

It is used to filter images and reduce image noise

[22].

Fig. 2- Gaussian Low-Pass Filter

This unit allows only the lower-frequency

components of the image to pass [5]. The equation

of a Gaussian function in two dimensions is given

by the following formula:

𝐺(𝑥, 𝑦) =1

2𝜋𝜎2𝑒

−𝑥2+𝑦2

2𝜎2 (1)

Where, x and y refers to a two-dimensional

coordinate, and 𝜎 represents the standard deviation

of the Gaussian distribution in the spatial domain.

B) Feature Extraction

The Local Binary Pattern (LBP) method is

used for the feature extraction stage since it is one

of the most widely used methods to analyze and

model texture [4]. It can be basically described as

a 3 × 3 square operator. In this square, the

8-neighborhood pixels are compared with the one

in the center. If the pixel values of the neighbors

are greater than or equal to the pixel value at the

center, they are replaced by 1. If not, then their

values are replaced by 0. Then the new binary

values of the neighbors are concatenated to

produce one decimal value that is considered a new

value for the pixel in the center. The window is

passed to the next pixel and the same operation is

repeated. These new decimal values represent the

histogram of the input texture. Equation (2)

describes the algorithm of the LBP operation:

𝐿𝐵𝑃𝑃,𝑅(𝑥,𝑦) = ∑ 𝑠(𝑔𝑝 − 𝑔𝑐)2𝑃𝑃−1𝑃=0 (2)

Where 𝑠 is the sign function, 𝑔𝑝 represents the gray level

value of the neighboring pixels, and 𝑔𝑐 represents the gray

level value of the central pixels. 2𝑃is required to produce

decimal values.

The traditional LBP [8] analyzes the texture of the image

and thresholds a 3 × 3 square neighborhood with the center

pixel value. It only uses the sign information to produce the

LBP, as illustrated in Fig. 3 [4]

Fig. 3- LBP architecture

In a newer implementation, the LBP operation is upgraded

to deal with any size of neighborhood by replacing the square

with a circle [23]. This can be described by (P, R), where P is

the number of interested pixels on the circle, while R is the

radius of the used circle. Fig. 4 illustrates an (8, 2)

neighborhood. Additionally, there are a number of other

modifications to LBP [4].

Fig. 4- Circular (8, 2) neighborhood

The term 𝐿𝐵𝑃𝑃,𝑅𝑢2

is used to describe the LBP operation,

where u2 denotes the use of a uniform pattern. The resulting

histogram results in the necessary information distributed in

the image, such as edges, corners, uniform areas, etc. The







effective operation must take care of the spatial information in

the image during the representation. One strategy to

accomplish this is to partition the image into a number of small

areas 𝑅0, 𝑅1, … , 𝑅𝑚−1 [8], where 𝑚 is the number of areas. If

the size of the histogram is B, then the length of the feature

vector is mB. It is obvious from this relation that the number

of areas m determines the length of the feature vector, which

means selecting small areas resulting in long feature vectors

leading to extreme use of memory and slow classification

processing. Selecting large areas causes loss of spatial

information. An example of a preprocessed face image

partitioned into 36 windows and the resulting face feature

histogram are illustrated in Fig. 5 [24].

Fig. 5- Example of a preprocessed face image partitioned into 36

windows and a feature histogram

C) Classification

The Euclidean Minimum Distance classifier is used for the

classification stage since it is considered to be one of the most

popular classifiers that can be easily designed and widely

used [25]. In general, it is used to examine the similarities

between objects.

The Euclidean distance d between two points i and j, where

i = (i1, i2,..., in) and j = (j1, j2,..., jn), in Cartesian coordinates, is

the length of the straightest line between them. This distance

is given by the formula:

𝑑(𝑖, 𝑗) = √(𝑖1 − 𝑗1)2 + (𝑖2 − 𝑗2)2 + ⋯ + (𝑖𝑛 − 𝑗𝑛)2 (3)

Therefore, if the two points are close to each other, then

the value of d is small; otherwise, it is large. The Euclidean

vector is the location of a point in a Euclidean n-space, where

the length of this vector is measured by the formula of the

Euclidean norm, given by:

‖𝐼‖ = √𝑖12 + 𝑖2

2+. . . +𝑖𝑛2 (4)

This tool is used to test how similar one object (face) is to

another by testing the similarities between their respective

feature vectors.

D) Dataset

The ORL (Olivetti Research Laboratory) is a well-known

face dataset that is used to test FR algorithms. It has 400

images of 40 distinct persons, 10 images for each person. The

dataset is variational in many aspects. First, the images were

taken at different times during the lives of the people. Second,

the images include different variations and different facial

expressions, such as closed or open eyes. Some of the people

are smiling, others are not. In addition, there are a number of

people wearing spectacles while others are not wearing

spectacles. Furthermore, a number of the images include up to

twenty degrees of tilting and rotation of the face [3]. The size

of each image is 112x92 pixel. A number of face images from

the ORL dataset are illustrated in Fig. 6.

Fig. 6- Sample images from the ORL Dataset

III. EXPEREMENTS AND RESULTS

This section shows some results obtained using

MATLAB. The experiments were implemented on images

from the ORL dataset.

The FR system consists of three stages. The first stage is

the Preprocessing Stage, in which the GLPF is used. The

second stage is the Feature Extraction Stage, where the LBP

is examined. In the final stage (the Classification Stage), the

Euclidean distance is used as a classifier.

The procedure is carried out and tested, first, using only

the Original Training Samples (OTS) and the second time

using the Original with Symmetrical Training Samples

(OSTS). Also, the experiment is implemented one time

without a preprocessing stage and the second time using a

preprocessing stage. The performance is compared in the four

experiments along with the performance of SCRC [3].

A) Generating new images

In order to increase the size of the training data, new

training images are generated using the symmetry property of

the face, since those images reflect some appearance of the

face that is not shown by the original images as illustrated in

Fig. 7.

Face features

histogram

LBP Transformation

g) f) e) d) c) b) a)







Fig. 7- a) Original image, b) left side, c) right side, d) mirror of left side, e) mirror of right side, f) integrating left side with mirror, g) integrating right

side with mirror

B) Experiment on original ORL face dataset

In the experiment, one, two up to nine face images of each

person with size 112x92 were taken respectively as the

training samples and the rest of images were taken as testing

samples. The features of the training and testing images are

extracted using the LBP method. Each image has one feature

vector, 𝑓 = [𝑓1, 𝑓2 ⋯ 𝑓𝑚], where 𝑚 is the number of one image

features.

The feature vector of the test image is compared with the

feature vectors of the training images using the Euclidean

distance classifier. The person who has a training image

feature vector with a minimum Euclidean distance is

considered as the recognition result.

The recognition rate is calculated as the average of the

recognition results for each number of training samples.

C) Experiment on symmetrical ORL face dataset

The procedure is repeated as in B, the only difference is

that the original and the generated symmetrical images are

used together to train the system, the results show that the use

of symmetrical images improves the recognition rate from

92.5% to 95% for 9-training samples as shown in Table 1.

D) Experiment using a preprocessing stage

The procedure is repeated as in C and D but in this case

using the GLPF as a preprocessing stage, the GLPF being used

with a standard deviation of σ = 1 and a window size of

5 pixels. With 9-training images, the results show that adding

GLPF as a preprocessing stage improves the recognition rate

from 92.5% to 95% for OTS and from 95% to 100% for

OSTS. The results of the experiments for training samples

6,7,8 and 9 are summarized in Table 1.

TABLE I. The recognition rates of different methods on ORL dataset

P

reprocessi

ng

Method

F

eature

Extract

ion

Metho

d

No. of Training Images

6 7 8 9

Recognition Rate %

N

o

L

BP

OTS

90

92.5

92.5

92.5

O

STS

9

5

9

5

9

2.5

9

5

G

LPF

L

BP

OTS

92.5

97.5

95

95

O

STS

9

5

9

7.5

1

00

1

00

N

o

S

CRC

O

STS

9

5

9

6

9

6

9

5

Fig. 8 shows the recognition rates versus the variation of

training sample size for OSTS. From the figure, we can see

the proposed method (using original and symmetrical images

for training along with a preprocessing stage) outperforms

other four methods.

Fig. 8- Recognition rates using LBP, proposed method and SCRC

methods versus size of training set

IV. CONCLUSION

This paper presents an effective method to

overcome the restricted number of the training sets

using the symmetry property of the face. The use

of this property also reduces the effect of

illumination and pose variations. First, a new set of

face images is generated using the left and right

halves of each face. Second, the original and

generated samples are preprocessed using the

GLPF; then the features of these samples are

extracted using LBP method. Finally, the

Euclidean classifier is used to obtain the results of

the recognition. This paper also shows that adding

a preprocessing stage to the recognition system has

a significant impact on the improvement of

recognition rates since it increases the recognition

rate from 92.5% to 100% (for 8-training images)

and from 95% to 100% (for 9-training images).

References







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single image per person: A survey," Pattern Recognition, vol. 39, pp.

1725-1745, 2006/09/01/ 2006. [3] Z. Liu, J. Pu, Q. Wu, and X. Zhao, "Using the original and symmetrical

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469-481.

[9] Z. Lu and L. Zhang, "Face recognition algorithm based on discriminative dictionary learning and sparse representation,"

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fisherfaces: Recognition using class specific linear projection," Pattern

Analysis and Machine Intelligence, IEEE Transactions on, vol. 19, pp. 711-720, 1997.

[11] A. Pentland, B. Moghaddam, and T. Starner, "View-based and modular

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[12] S. K. Zhou and R. Chellappa, "Illuminating light field: Image-based face recognition across illuminations and poses," in Automatic Face

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morphable model," Pattern Analysis and Machine Intelligence, IEEE

Transactions on, vol. 25, pp. 1063-1074, 2003. [14] A. Shashua and T. Riklin-Raviv, "The quotient image: Class-based re-

rendering and recognition with varying illuminations," Pattern

Analysis and Machine Intelligence, IEEE Transactions on, vol. 23, pp. 129-139, 2001.

[15] A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, "From few

to many: Illumination cone models for face recognition under variable lighting and pose," Pattern Analysis and Machine Intelligence, IEEE

Transactions on, vol. 23, pp. 643-660, 2001.

[16] S. J. Prince, J. Warrell, J. H. Elder, and F. M. Felisberti, "Tied factor analysis for face recognition across large pose differences," Pattern

Analysis and Machine Intelligence, IEEE Transactions on, vol. 30, pp.

970-984, 2008. [17] T. A. Davis and C. Ramanujacharyulu, "Statistical analysis of bilateral

symmetry in plant organs," Sankhyā: The Indian Journal of Statistics,

Series B, pp. 259-290, 1971. [18] X. Chen, P. J. Flynn, and K. W. Bowyer, "Fully automated facial

symmetry axis detection in frontal color images," in Automatic

Identification Advanced Technologies, 2005. Fourth IEEE Workshop on, 2005, pp. 106-111.

[19] W. Y. Zhao and R. Chellappa, "Illumination-insensitive face

recognition using symmetric shape-from-shading," in Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on,

2000, pp. 286-293.

[20] G. Farin, J. Femiani, M. Bae, and C. Lockwood, "3D face authentication and recognition based on bilateral symmetry analysis,"

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Emerging Technologies, 2007, pp. 1-4.

[22] Gonzalez and Woods, Digital image processing, 2 ed.: Prentice Hall, 2002.

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and rotation invariant texture classification with local binary patterns," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.

24, pp. 971-987, 2002.

[24] O. Nikisins and M. Greitans, "Local binary patterns and neural network based technique for robust face detection and localization," in 2012

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Neural Networks," 2018.

International Conference on Multidisciplinary, Science, Engineering

and Technology (IMESET’19 Malaysia)

Sept 20-21, 2019,

Malaysia




NEW APPROACHES FOR BREAST CANCER NUCLEI DETECTION IN

HISTOLOGICAL IMAGES

Faozia Ali ALSARORI*, Hilal Kaya 2* *2 Department of Computer Engineering, Ankara Yıldırım Beyazıt University; Ankara, Turkey.

Abstract

Cancer is one of the leading causes of death worldwide and is known to infect a specific member.

If it is not detected early, its spread in cells may be difficult to control and treat. Breast cancer is the

sixth most common cause of death among other cancers.

The ability to automatically detect breast cancer nuclei in microscopy images is of significant

interest to a wide range of biomedical research and clinical practices. Cell detection methods have

evolved from employing hand-crafted features to deep learning-based techniques. The work proposed

in this paper is divided into two stages to provide a concept system for detection. The first stage is

using Convolutional Neural Network (CNN) followed by Fuzzy C-Mean and then combine these two

methods to reach a satisfactory performance. Based on a comprehensive study of the most studies done

so far, using such technique hasn’t been used for breast cancer nuclei detection. Furthermore, this

study has been carried out on histological dataset contains100 microscopic slides of H & E stained

samples of breast biopsy. The proposed method registered the shortest median distance which is 2.14

comparing with the latest studies.

Keywords: Convolutional Neural Network (CNN), Fuzzy C-Mean, Cell Detection.



Sept 20-21, 2019,

Malaysia




DESIGN OF IOT-BASED VERTICAL SYSTEM AQUAPONIC WITH

FUZZY LOGIC METHOD

Gerhard Imanuel Simare Mare, Haris Rachmat, Denny Sukma Eka Atmaja

Telkom University, Telkom University, Telkom University

Abstract— There are opportunities in the use of urban farming because the level of urban farming

community participation in Indonesia is 10%. One of the uses of urban farming is aquaponic. A system is

needed that makes it easy for humans to monitor and control the variables of water pH, EC water and water

level on aquaponic. Aquaponic is an integration of aquaculture and hydroponics. The application of the

Internet of Things (IoT) requires a connection between everyday objects and devices using networks. Fuzzy

logic method functions to evaluate water quality before automatic system with after automatic system. This

research aims to design a monitoring system for water pH, EC, water temperature, water level and

controlling water pH, water level and IoT-based fish feeding on an aquaponic vertical system, designing a

system to determine water quality using the fuzzy logic method. Vertical aquaponic system testing by

planting plants in the vertical system aquaponic and measured for the first 15 days obtained average growth

of plant height of 0.361 cm compared to plant growth in conventional systems to get average growth of

plant height of 0.282 cm. Based on data processing using fuzzy logic Sugeno obtained water quality before

the automatic system is bad and the water quality after the automatic system is good.

Index Terms—fuzzy logic method, Internet of Things (IoT), Raspberry Pi 3 B, Vertical system aquaponic

I. INTRODUCTION

Industrial revolution 4.0 is the integration and relationship between physical and digital in the

industrial world that depends on, digitalization and integration of value chains horizontally and

vertically, digitization of products and services and introduction of innovation business models

(Gilchrist, 2016). According to Gilchrist (2018) the industrial revolution 4.0 can be applied to

several sectors namely, the health sector, logistics, manufacturing and agriculture. From each

sector in the utilization of the industrial revolution the health sector has the highest readiness level

of 30%, followed by the logistics sector by 25%, manufacturing by 25%, and finally is agriculture

and other sectors by 20%. From these data the agricultural sector still has a low level of readiness

compared to other sectors so that there are still opportunities to develop the utilization of the

industrial revolution 4.0 in the agricultural sector. The development of agriculture and animal

husbandry that uses technology is called smart farming with plants, animals and soil media

receiving the proper care needed (Huawei, 2017). The characteristic of smart farming is treating

plants and animals as accurately as possible which requires several core elements, such as

automatic detection to determine variations in the behavior of soils, plants and animals. This can

be achieved by sensors. In addition to detecting automatically, smart farming also requires a

decision system, and a model that will translate measured variances into actions taken by



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considering the environment accurately. The use of some of these core elements requires

customized technology (De Wilde, 2016).

According to research conducted by Hallet et al (2017) it was found that starting from 10% the

level of community participation in urban farming activities in Indonesia and nearly 70% in

Vietnam. In the city of Bandung, urban farming is characterized by the existence of the Bandung

Berkebun community (Vidyana and Murad, 2016). The Bandung Berkebun community was

established in 2011, with activities in the form of gardening in some abandoned land in the city of

Bandung, increased by some collaboration with schools and other institutions (Vidyana and

Murad, 2016). Based on the application of urban farming in the city of Bnadung and data that the

level of participation of Indonesian people in urban farming by 10% can be concluded that there

are opportunities to develop urban farming in Indonesia. In addition, on average 15 to 20 percent

of world food production comes from urban farming (Hallet et al, 2017).

One of the uses of smart farming is aquaponic (Datta, 2018). As for the understanding of

aquaponic is the integration of aquaculture and hydroponics. This is considered to be sustainable

agriculture because there is a closed water circulation with fish droppings that will be useful for

plants as nutrients (Goddek et al, 2015). The aquaponic mechanism is based on the principle of

hydroponics in which chemical salts dissolved in water are a source of nutrition for plants grown

on growing media that grow above water. In aquaponic systems, organic aquaculture enriched

wastewater is a source of nutrients released and available to plants with a number of processes

carried out by a large number of microorganisms that live in water (Datta, 2018). In the application

of aquaponic, smart farming can be applied because there are several variables that must be

monitored and controlled regularly, namely, pH and EC levels in pond water (Nayyar and Vikram,

2016).

Research on the application of smart farming in aquaponic conducted by Kyaw, and Ng (2017)

that is, designed aquaponic can be monitored remotely using the web or mobile app. Components

used in the form of sensors, temperature sensors, pH sensors, ultrasonic sensors and light sensors

and other components used are fish feeders. This study uses Arduino as a microcontroller (Kyaw

and Ng, 2017). In addition, the research uses the goodness-of-fit-measure method to process the

results of the design. In this study the results in the form of plant growth data and fish weight were

measured by the relationship between the two. But in that study there was no monitoring and

controlling of water levels. Although the circulation of water in aquaponic can minimize the

reduction in water. But the water in aquaponic can still be reduced due to evaporation or

evaporation, and plant transpiration. The water level must be maintained (Goddek, 2015).

Whereas the research conducted by Shout and Scott (2017) designed the aquaponic control

system. In this study using the fuzzy logic method used to evaluate inputs and provide the right

output. In this study the use of fuzzy logic was chosen because the variables used namely pH and

water temperature have blurred values. In this study the variables monitored were water

temperature, and water pH. The research still has not monitored the variables needed for an



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aquaponic namely pH, EC, and water level and the variables that must be controlled are pH and

water level (Datta, 2018). Therefore we need a smart farming system in the form of an integrated

aquaponic vertical system IoT. In this study, aquaponics will be controlled and monitored for pH

and water level, EC (Electrical Conductivity) and feeding, and will be monitored for pH and EC.

As for the fuzzy logic method in this study to issue an output in the form of an assessment based

on existing variables, namely pH and EC.

II. LITERATURE REVIEW

A. Vertical System Aquaponic

The aquaponic mechanism is based on the principle of hydroponics in which chemical salts

dissolved in water are a source of nutrition for plants grown on growing media that grow above

water.

In this system, the place for the tank where the fish live and the reservoir is placed at the bottom.

Then the vegetables are planted in a hole in a pipe arranged vertically. Water that is rich in nutrients

is pulled to the top level with the help of a pump. Water is allowed to drip down through the roots

of plants. Because most systems are closed, little or no waste is produced, and there is no need for

fertilizer or pesticides. This is a system that will save water and storage space. Figure 2.1 is a

picture of the vertical aquaponic system.

Figure 2.1 Vertical System Aquaponic

B. Internet of Things (IoT)

The application of the Internet of Things (IoT) requires the connection between everyday objects

and devices with all types of networks such as corporate intranets, peer-to-peer networks and even

global internet networks (ITU, 2005). The application of the Internet of Things (IoT) depends on

technical innovations in a number of fields. First, in connecting everyday objects and devices to

the database and to the internet, it requires a system that is cost-effective and uncomplicated.

Second, data collection can have the ability to detect changes in the physical status of objects,

namely through sensor technology. Furthermore, progress in minaturation and nanotechnology

means that little things have the ability to interact and connect. The combination of all these

developments will form the Internet of Things (IoT) that connects objects through detection and

intelligence. The application of the Internet of Things (IoT) occurs in developments in several

fields, namely, smart materials, smart homes, smart vehicles, robots and smart



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C. Fuzzy Logic Sugeno

Mechanisms In general, in fuzzy logic systems there are four basic elements (Bede B, 2013),

namely:

1. Rule base (rule base), which contains linguistic rules that are sourced from existing

reference models in the Sugeno method using rules in the form of: IF x1 is A1 AND ...

AND xn is An THEN y = k

2. A decision making mechanism (inference engine), which demonstrates how experts make

decisions by applying knowledge;

3. Fuzzification process (fuzzyfication), which changes the firm quantity (crisp) to the

amount of fuzzy;Proses defuzzifikasi ( defuzzyfication ), yang mengubah besaran fuzzy

hasil dari inference engine , menjadi besaran tegas ( crisp )

Fuzzyfication. Input value is definite (crisp input) which is converted to fuzzy input form in the

form of linguistic value which is determined based on its membership function (Gottwald S, 1993).

Inference. The fuzzy rule is written as: IF antecendent THEN consequent. In a fuzzy rule-based

system, the inference process takes into account all the rules that have been made before. At this

stage there are two stages that must be carried out sequentially, namely, the impilcation process

and the composition process of rules (Gottwald S, 1993). According to Nguyen and Sugeno (1998),

the Sugeno method used in this process uses fuzzy set operations as in Table 2.1.

Table 2.1 Fuzzy set operations

OR (Union) AND

(intersection)

MAX 𝐌𝐚𝐱𝛍𝐀(𝐱), 𝛍𝐁(𝐱) MIN 𝐌𝐢𝐧𝛍𝐀(𝐱), 𝛍𝐁(𝐱)

After doing the implication process, the rule composition process will be carried out. According

to Nguyen and Sugeno (1998) the composition of the rules in the sugeno method uses the Max

function, the composition of the rules constitutes an overall conclusion by taking the maximum

membership level of each consequent function implication and combining all the conclusions of

each rule, so as to obtain a fuzzy solution area as follows:

μ(z) = maxμc(z), μc(z)

Deffuzification. At this stage the output value obtained is changed in the non-fuzzy form. The

Sugeno deffuzification method uses the center of single-ton method.

z∗ =∑ μ(z). z

∑ μ(z)

III. DATA PROCESSING

A. Design of Vertical System Aquaponic

In designing a vertical aquaponic system, the design is made first using software design. In the



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vertical aquaponic system, water is pumped from the fish pond at the bottom of the aquaponic to

the top through the inside of the pipe. After that the water will flow down from the pipe above.

The flow of water from above will flow down and hit the plants in the existing plant pots. Figure

3.1 is a vertical aquaponic system.

Figure 3.1 Vertical system aquaponic

B. Design of Automation System

At this stage the design of the automation system is carried out in the form of installing parts in the form of sensors,

ADS1115, water pumps, mini pumps, relays that lead to the output carried out, with Raspberry Pi. Figure 3.2 is a

schematic diagram.

Figure 3.2 Schematic diagram

According to Blinder (2009) block diagram is a picture of the whole system that is connected by an arrow line

consisting of input, process, and output parts. Figure 3.3 is a block diagram of an existing system

Relay

untuk

pH up

Relay

untuk

pH

down

Relay

untuk

pompa

air

Sensor

pH

Sensor

EC

ADS1115

Sensor

Ultrasonik

Raspberry Pi 3 B

DS18B20

(Sensor

Temperatur

e)

Relay

untuk

fish

feeder



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Malaysia




Figure 3.3 Block diagram

C. Design of Internet of Things System

In making the application in this study there are several features available to support the android

application that will be used. In the monitoring feature, the user can monitor the state of water

temperature, water pH, EC water and water level in aquaponic. In this feature there is a report

function that will send data on temperature, pH, EC and water level to the user's email every day.

In controlling feature, the user can monitor the existing output by looking at the indicators of each

output. In this feature the user can turn on the fish feeder. In the graphic data feature, users can see

the state of water temperature, water pH, EC and water level directly every minute, every 15

minutes and every hour. Figure 3.4 is a feature in the application

Figure 3.4 Application Feature

D. Fuzzy Logic

The membership function will be used at the fuzzyfication and deffuzzyfication stages. The

membership function functions to change the linguistic term on variables to quantitative. Figure

3.5 is a graph of the pH variable membership function.



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Figure 3.5 pH Membership Function graphic

The pH variable uses trapezoidal curves in acidic, normal and basic membership. This model

is used because the peak point value of each variable has a value of more than one particular range

of values. The EC variable uses trapezoidal curves in low, normal and high membership. This

model is used because the peak point value of each variable has a value of more than one particular

range of values. Figure 3.6 is a graph of the EC variable membership function.

Figure 3.6 EC Membership Function graphic

In the variable height of the water using the quadratic trapes at low and high membership.

This model is used because the peak point value of each variable has a value of more than one

particular range of values. Whereas the normal category uses a triangular curve because the peak

point value of the variable has a value of just one value. Figure 3.7 is a graph of the membership

function of the water level variable.

Figure 3.7 Water Level Membership Function graphic

At this stage, linguistic rules are derived from existing reference models in the Sugeno

method using rules in the form: IF x1 is A1 AND ... AND xn is An THEN y = k



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In this research there are 27 rules because there are 3 variables that become input variables.

In Table 3.3, the fuzzy rules are used . Table 3.3 fuzzy rules

Rules number

Input Output

pH EC Water

Level Water

quality

1 Acid Low Low Bad

2 Acid Low Normal Bad

3 Acid Low High Bad

4 Normal Low Low Bad

5 Normal Low Normal Bad

6 Normal Low High Bad

7 Base Low Low Bad

8 Base Low Normal Bad

9 Base Low High Bad

10 Acid Normal Low Bad

11 Acid Normal Normal Bad

12 Acid Normal High Bad

13 Normal Normal Low Bad

14 Normal Normal Normal Good

15 Normal Normal High Bad

16 Base Normal Low Bad

17 Base Normal Normal Bad

18 Base Normal High Bad

19 Acid High Low Bad

20 Acid High Normal Bad

21 Acid High High Bad

22 Normal High Low Bad

23 Normal High Normal Bad

24 Normal High High Bad

25 Base High Low Bad

26 Base High Normal Bad

27 Base High High Bad In the Sugeno output method that will be produced in the form of constants so for this study

there are two water quality categories namely Bad and Good. Whereas the assessment is as follows. Bad = 0 ≤ x ≤ 50

Good = 50 <x <100



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At this stage the calculation is done using the fuzzy method. Table 3.4 is the result of fuzzy

logic before the automatic system is applied. Table 3.5 shows the fuzzy results after the automatic

system was applied.

Table 3.4 result of fuzzy logic before the automatic system is applied

Sample Input Output

pH EC Water

level Defuzzyfication

result Category

1 8,55 2,81 14,97 0 Bad

2 8,58 2,76 15,06 0 Bad

3 8,56 2,82 15,07 0 Bad

4 8,51 2,79 14,96 0 Bad

5 8,50 2,82 15,06 0 Bad

6 8,47 2,82 14,92 0 Bad

7 8,49 2,81 14,95 0 Bad

8 8,46 2,85 14,96 0 Bad

9 8,50 2,76 14,96 0 Bad

10 8,49 2,79 14,91 0 Bad

11 8,47 2,83 14,97 0 Bad

Sample Input Output

pH EC Water

level Defuzzyfication

result Category

12 8,50 2,77 15,03 0 Bad

13 8,46 2,81 14,97 0 Bad

14 8,47 2,82 15,07 0 Bad

15 8,47 2,79 15,01 0 Bad

16 8,45 2,79 14,90 0 Bad

17 8,46 2,78 14,90 0 Bad

18 8,49 2,75 15,06 0 Bad

19 8,46 2,76 14,90 0 Bad

20 8,45 2,85 14,95 0 Bad

Average 0 Bad

Table 3.5 result of fuzzy logic after the automatic system is applied

Sample

Input Output

pH EC Water

level

Defuzzyfication

result

Category

1 6,90 2,84 18,19 81 Good



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2 6,95 2,82 17,82 82 Good

3 6,93 2,78 18,17 83 Good

4 6,93 2,81 18,04 96 Good

5 6,89 2,75 17,81 81 Good

6 6,91 2,8 18,07 93 Good

7 7,00 2,83 17,81 81 Good

8 6,99 2,79 17,82 82 Good

9 6,90 2,85 18,10 90 Good

10 6,96 2,81 18,14 86 Good

11 6,86 2,75 17,91 91 Good

12 6,87 2,81 18,12 88 Good

13 6,86 2,82 17,98 98 Good

14 6,99 2,78 18,20 80 Good

15 6,89 2,85 17,84 84 Good

16 6,90 2,78 17,93 93 Good

17 6,90 2,83 17,96 96 Good

18 6,91 2,83 17,88 88 Good

19 6,95 2,83 17,82 82 Good

20 6,96 2,82 17,99 99 Good

Average 87,7 Good

E. Calculation of Plant Growth

In processing plant growth data obtained from the average growth of 25 plants from conventional systems and vertical aquaponic systems in the first 15 days. After collecting the data, the processing of plant growth data for both systems is processed. Data processing of plant growth is done by calculating the average plant growth of both systems on each day. Table 3.6 is the data processing of the two systems.

Table 3.6 Data processing of plant growth

Day Conventional Vertical

system

1 0,5375 0,7

2 0,56 0,324

3 0,628 0,612

4 0,324 0,284

5 0,376 0,292

Day Conventional Vertical

system

6 0,136 0,332



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7 0,132 0,304

8 0,16 0,168

9 0,192 0,24

10 0,144 0,22

11 0,264 0,22

12 0,124 0,212

13 0,268 0,36

14 0,236 0,224

15 0,144 0,248

Average 0,282 0,316

IV. ANALYSIS

A. Analysis of Fuzzy Logic

According to Datta (2018), it was found that the variables affecting plant growth were pH,

and the level of water conductivity. As for the plants 'height in aquaponic can be reduced due to

the evaporation and absorption of water by plants so that the plants' height should be controlled to

avoid water that can run out (Datta, 2018). As for aquaponic EC levels are obtained from fish

droppings so that EC levels can be controlled from the amount of fish in the pond. In this study

the fuzzy logic method is used to evaluate inputs and provide the right output. In this study the use

of fuzzy logic was chosen because the variables used are pH and the level of water conductivity

has a blurred value. So to get the value of water quality in the form of constant use of the Sugeno

fuzzy logic method is considered appropriate because according to Nguyen and Sugeno (1998) the

Sugeno fuzzy logic method is a fuzzy inference method that gets the system output not in the form

of fuzzy sets, but in the form of constants.

After the Sugeno fuzzy logic process is done, the average result of defuzzyfication before

the automatic system is applied is Bad, because before the automatic system is applied, the monitor

system is carried out manually with a pH meter to measure pH, EC meter to measure the level of

water conductivity and a ruler to measure the height. water. As for controlling before the IoT

system there is no controlling system. Meanwhile, after the automatic system was applied the

average defuzzification was obtained Good because it was included in the Good range which was

50 <x ≤ 100, because after the IoT system was applied to monitor user variables, it could be

monitored through an android application. As for the controlling system after the automatic system

is applied to the pH when the pH is below the normal limit of 6.5, the pump will run the pH up

while for the pH level above the normal limit of 7.0, the pump will be turned down. As for

controlling the water level, when the water level is below the normal limit of 18 cm, it will turn on

the water pump. On the controlling system, the user can see the pump indicator that lights up

through the android application. From the two results the average value of water quality there is



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an increase in the value of water quality from 0 to 85 so that the application of the IoT system can

increase the value of water quality in ponds.

B. Analysis of Plant Growth

From processing data on plant height growth on both systems, namely conventional and

vertical aquaponic systems, the results obtained on conventional from the first 15 days amounted

to 0.2817 cm and plant height growth on vertical aquaponic systems amounted to 0.316 cm. The

data is obtained from 25 plants from the two planting systems. In conventional planting systems

plants are planted in polybags using planting media in the form of soil. As for the vertical system,

the aquaponic system is planted in a pot arranged vertically on a pipe with the planting medium

used is nutritious water.

Water nutrition in the vertical aquaponic system is obtained from fish sludge in the pond that

is flowed by a water pump. From the average plant growth of the two systems, the plant growth in

the vertical aquaponic system is better than the conventional system of 12.17%. In addition to

better plant growth in the vertical aquaponic system, it can save land because plants are planted in

potted plants and arranged vertically. Because it does not require a large amount of land to eat

vertical aquaponic systems suitable for agriculture in the city. In the vertical aquaponic system,

nutrients do not need to be added anymore because nutrients are obtained from fish droppings. If

the conventional system of providing nutrients needs to be given routinely in the form of fertilizer.

V. CONCLUSION

Based on the results of the IoT-based aquaponic vertical system design that is integrated between

Raspberry Pi 3 B and the Android application, the design system is already running with Good. Due to the

growth of plants in the first 15 days on the aquaponic vertical system get an average plant height of 0.316

cm gives a positive impact of plant height growth on conventional systems with an average plant height

growth of 0.282 cm. Besides testing the monitoring system of water pH variables, water health, water

temperature and water EC content in 10 times the overall trial experiment with Good running and

controlling system testing on water pH, water health and fish feeding in 10 times the overall trial trial

running with Good.

Determination of water quality with variables namely, water pH, EC water content, and water health

with a normal pH limit of 6.5-7, normal EC limit of 2.5 - 3 ms / cm and a normal limit of water height of

18 cm . Water quality data processing using the fuzzy logic method using Matlab R2018a software before

the automatic system is applied, after the automatic system is applied the fuzzy logic processing results are

in the range of 0 ≤ x ≤ 50 while for water quality after the automatic system is applied it gets the results of

fuzzy logic 50 processing <x ≤ 100. Therefore, the water quality after the automatic system is applied is

better than before the automatic system is applied.

REFERENCES



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Malaysia




Binder, M. D., Hirokawa, N. dan Windhorst, U. (2009) ‘Block Diagram’, in Encyclopedia

of Neuroscience. Berlin, Heidelberg: Springer Berlin Heidelberg

CGI GROUP INC. (2017) ‘Making your business more competitive’, Industry 4.0, pp. 1–

24.

Datta, S. Et al. (2018) ‘Aquaponics: A green and sustainable eco-tech for environmental cum

economic benefits through integration of fish and edible crop cultivation’, Wastewater

Management Through Aquaculture,

De Wilde, S. (2016) ‘The future of technology in agriculture’, Stichting Toekomstbeeld der

Techniek, 81

Gilchrist, A., 2016. Industry 4.0: Th e Industrial Internet of Things. New York: Springer

Science Business Media

Goddek, S. et al. (2015) ‘Challenges of sustainable and commercial aquaponics’,

Sustainability (Switzerland), 7(4)

Hallett, S., Hoagland, L. and Toner, E. (2017) Urban Agriculture : Environmental.

Huawei (2015) ‘The Connected Farm - A Smart Agriculture M

Kyaw, T. Y. and Ng, A. K. (2017) ‘Smart Aquaponics System for Urban Farming’, Energy

Procedia. Elsevier B.V., 143arket Assessment’.

Nayyar, A. and Puri, V. (2016) ‘Smart farming’, in Communication and Computing Systems.

Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-

2742: CRC Press

Nguyen, H. T., & Sugeno, M. (Eds.). (1998). Fuzzy Systems.

Shaout, A. dan Scott, S. G. (2017) ‘IoT Fuzzy Logic Aquaponics Monitoring and Control

Hardware Real-Time System’.

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2018 2nd International Conference on Smart Grid and Smart Cities (ICSGSC). IEEE

Vidyana, C. and Murad, F. (2016) ‘Community Garden di Indonesia Kasus : Komunitas

Bandung Berkebun’, (1)



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ARCHITECTURE AND FUTURE PROSPECT OF 5G TECHNOLOGY

M. Sayrac1 M. Ari2 C. Taplamacioglu3 J. Rahebi4

1. Department of Physics, Cankiri Karatekin University, Cankiri, Turkey,

[email protected]

2. Electrical and Electronics Engineering Department, Cankiri Karatekin University, Cankiri, Turkey,

[email protected]

3. Electrical and Electronics Engineering Department, Gazi University, Ankara, Turkey, [email protected]

4. Electrical and Electronics Engineering Department, University of Turkish Aeronautical Association,

Ankara, Turkey, [email protected]

Abstract Fifth generation mobile communication technology that is freed of existing obstacles in the communication

system will be used in many countries after 2020. Researches are being conducted all over the world to develop new

technologies that have an important place for the widespread use of 5G technology. This new technological research

will bring solutions such as high speed, high capacity, spectral efficiency, energy efficiency to problems in data transfer

system. 5G technology offers the services in product engineering, documentation, electronic transactions, etc. The

main aim of 5G is to design seamless wireless communication world compared to earlier generations. This study

focuses the challenges, spectrum distribution and application areas of 5G technology. Also, it is mentioned about the

opportunities that will create usage areas of 5G technology in Turkey.

Keywords: Technology Route, 5G, 5G Spectrum, Wireless Communication, Data Transfer

I. INTRODUCTION

Generated and transmitted data worldwide are expected to be approximately zettabyte (1zettabay= 1021 bayt) size

after 2020. Currently used communication technologies would not support such large volumes of big data. For this

reason, next generation technology that can carry large amounts of data is required. This new technology is called the

5th generation mobile communication technology. Researches are being carried to prepare 5G wireless technology for

worldwide use. 5G technology is expected to be used commercially worldwide in the near future. There is also an

increasing demand from industries such as health and education sectors to take advantage of 5G technology. The

advantagement of 5G technology over current communication technologies can be given as follows [1].

5G technology provides:

• Thousands times higher data rate than 4G,

• Faster connections,

• Lower latency,

• Data rate greater than 1 Gb / s,

• Reduction of the delay time compared to 4G technology by a factor of five times,

• Devices with 5G enabled artificial intelligence (AI) capabilities,

One of the most challenging issues in the wireless signal communication is to provide a better wireless connection

in rural areas. This topic is still the most discussed topic among industries and researchers because of the unstable

environmental factor. Fast and reliable wireless data communication will be provided by overcoming the obstacles

causing wireless signal communication as a result of the improvement of 5G technology.

The fifth generation mobile communication system is one of the ambitious technologies that could change the

current communication technologies. For this reason, it is important to investigate the requirements, challenges,

mailto:[email protected]






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benefits, advantages, and disadvantages of 5G technology for successful implementation. The challenges of 5G

technology are described in related references [2-4]. Many innovation studies are being carried out to make this

technology widely used in the future. One of these studies is a millimeter wavelength equipment developed for 28GHz

and 38GHz operation frequencies [5]. Another study describes the role of cloud computing for achieving 5G network,

and that is expected to be difficult due to the increment in the number of wireless devices [6]. Important technologies

in 5G technology are explained in several articles. These important technologies are communication technologies such

as device-centric, architecture, millimeter wavelength and smart device, and they can have a big impact on design of

5G technology. Evolution of 1G to 5G technology is shown in Figure 1. This technological development started with

voice distribution with 1G technology. Then it was continued with multimedia services with 3G technology. 5G

technology will be used in many areas with the broadband of networks. 5G technology will find application in many

different areas in addition to high-speed data transfer. This new technology will be used in a wide range of applications

such as agricultural sensors, autonomous vehicles, and remote health screening [8].

II. FEATURES OF 5G

The data transfer speed of 4G technology is approximately 100 Mbps. This data transfer is fast, but it is not enough

to meet the data transfer rate in the near future. The data transfer rate for users after 2020 is expected to be 1Gbps or

even more [9].

In addition to massive data transfer speed, 5G technology will ensure safe data transfer in crowded and

electronically noisy areas. For example, such crowded areas as concert hall or stadium, users are exposed to delays or

interruptions in signal transmission due to overloading the network. The benefits of 5G technology are aimed at

providing better data transfer in crowded areas [9]. In addition, 5G technology will be definitely used in potential

Figure 1. The development of communication technology over the years, Ref [7].

Figure 2. Advantages of 5G technology, Ref.

[16].



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applications such reading invoices at long distances, e-health service, smart cities and video surveillance system. It is

aimed to develop such services and to facilitate daily life at long distances with the 5G technology [10]. Data transfer

traffic in indoor areas is greater than data transfer traffic in external areas. Also, the data transfer rate in the internal

areas is not very good for the speed of data transmission in the external areas compared to this excess data flow. Data

usage traffic is expected to increase by approximately 30% in the near future. For this reason, 5G technology is

intended to meet the large amount of data rate with the best service quality. High-quality communication technology

will find use in every aspect of life in the future. Therefore, it is very important that this communication technology

is to make efficient, safe and high data transfer. With the development of the communication technologies, it will find

a wide range of applications in radars, unmanned aircraft systems and security screening imaging and biological

detection. The advantage of this technology is that it offers a wide range of applications by providing significant

benefits to the community since it is user-friendly and safe to use.

III. MACHINE TO MACHINE COMMUNICATIION IN 5G TECHNOLOGY

Machine-Machine (M2M) communications will be used very widely in all areas of life with the development of

communication technologies. The main task of the M2M device is to collect and to send data such as temperature,

humidity level, and fuel consumption for other devices or infrastructure. Developed 5G technology networks are

expected to use and benefit in every aspect of life with features such as increased productivity of future M2M

communication and reduced data latency between devices. Internet usage in the world increased by approximately

250 million users and more than 4 billion people used the internet in 2018 reports [12]. The vast majority of internet

users believe that the internet does more than harm. This will lead to increase the number of internet and mobile device

users each year and so an increase the data traffic. Therefore, 5G technology is a must to meet the increasing data

flow. The increasing use of digital data stream and the penetration rate in Turkey are increasing every year. Thus, new

communication technology is an important requirement for Turkey for catching up the leader of this technologies.

According to the BTK report the use of mobile penetration rates in Turkey is in a good place among other countries.

Turkey as having the mobile penetration rate of 91.46% between the mobile device and other countries in the use of

digital data has a good position [13].

5G technology will bring in features and innovations that cannot be compared with previous communication

technologies. These innovations will enable broadband ranges to be used in areas such as automotive, health, and

security as well as rapid data transmission, increasing the need for broadband ranges and meeting the need for

communication of many devices. 5G technology will allow thousands of sensors to communicate with improved

mobile bandwidth and machine-machine communication. Moreover, 5G will have an important place for critical

machine-machine communication because of safe and fast communication service, and it will be used in autonomous

cars and health applications [14]. With the further development of 5G technology, Virtual Reality (VR) technology

can reduce the use of bank branches, and may even make it completely unnecessary in 10 years [14]. With the use of

internet of things and communication smart cities, smart cars, smart homes, information retrieval, billing and payment

would be possible [14, 15]. With the improvement of 5G technology, it will provide convenience in many areas, Figure

2.

IV. REQUİREMENTS FOR 5G TECHNOLOGY

The available frequency ranges that can be used in the electromagnetic spectrum have reached the saturation point

because of the high data transfer and increased number of devices connected to each other. The current mobile

communications systems use frequencies below 6GHz, and these frequencies have already used because these

frequencies have also been used in other wireless technologies. Therefore, new spectrum ranges should be defined for

next generation technology. High frequencies will be used to transport signals with the introduction of 5G technology,

and antenna structures will be designed to contain many antennas. This new antenna designs would bring a large

number of radio frequency (RF) bandwidth requirements. The possible frequency ranges that can be used for 5G

technology under 6GHz are proposed as 3.4-3.8GHz. The frequency ranges that can be used for frequencies above

6GHz operate in the 28GHz band in the US and in the Far East countries. This frequency range overlaps with satellite



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connections in Europe and the 26GHz band could be used as an alternative, Figure 3. It is thought that these frequency

ranges will satisfy the large amounts of data needed in the future [14]. There is signal loss in current cellular

communication networks. For preventing the signal loss, cells size will be reduced so that number of cells will be

increased. Larger frequency band will be required to provide data capacity in 5G technology. Products that can operate

at frequencies above 90GHz will be of great importance in this respect [14]. In addition, 5G technology is aimed to

reach the white spectrum that provides wide frequency band [11].

V. OPPORTUNITIES OF 5G TECHNOLOGY IN TURKEY

It is expected that the application areas of 5G technology will reach different levels as compared to previous

generation technologies. Researches about 5G technology conducted in Turkey in parallel with the researches carried

out in the world will provide great opportunities for Turkey. 5G technology will find applications in many areas such

as high data rate, low latency, more data carrying capacity, automotive industry, and health [14]. According to the

BTK report penetration rates in developed cities in Turkey are higher than in the developing cities in Turkey, and there

is a noticeable difference among both [13]. 5G technology in Turkey will create equal opportunity to reach the signal

and the data transfer rates. It is expected to create nearly 22 million jobs worldwide thanks to 5G technology. In

addition, it will have the opportunity to use 5G technology in the new industrial areas such as fast data transfer, e-

health and autonomous vehicles.

VI. CONCLUSIONS

In present days, wireless signal traffic is increasing due to increasing demand for communication areas. New

methods are being developed to meet increasing data rate. One of these methods is 5G technology. In this paper,

discussion and evaluation of 5G technology are studied. This study aims to emphasize the importance of 5G

technology. Advantages of 5G technology against 4G technology have been mentioned. Features, data transfer rate,

and data security of 5G technology have been mentioned. The frequency ranges required for the efficient use of 5G

technology are mentioned. The aims of 5G technology in machine-machine communication are discussed. Digital data

usage in the world and Turkey is mentioned. Benefits and opportunities of new generation technologies to Turkey are

specified. In general, the main purpose of 5G technology is to provide fast and reliable data transfer free of obstacles.

This will be possible by integrating current technologies into 5G technology. There will be a new era in the area of

wireless signal communication by widespread use of 5G technology after 2020.

REFERENES

[1] Pekka Pirinen, A brief overview of 5G research activity, ICST, 17–22, 2014.

[2] Shanzhi Chen, Jian Zhao, The requirements, challenges, and technologies for 5G of terrestrial mobile

telecommunication, IEEE Commun. Mag. 52, 36–43, 2014.

Figure 3. Spectrum of 5G technology, Ref.

[16].



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[3] J. Thompson, X. Ge, H.C. Wu, R. Irmer, H. Jiang, G. Fettweis, S. Alamouti, 5G wireless communication systems:

prospects and challenges, IEEE Commun. Mag. 52, 62–64, 2014.

[4] S. Patil, V. Patil, P. Bhat, A review on 5G technology, Int. J. Eng. Innovative Technol. 1, 1, 2012.

[5] T.S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, F. Gutierrez, Millimeter wave mobile

communications for 5G cellular: it will work!, IEEE Access 1, 335–349, 2013.

[6] P. Rost, C. Bernardos, A. Domenico, M. Girolamo, M. Lalam, A. Maeder, D. Sabella, Cloud technologies for

flexible 5G radio access networks, IEEE Commun. Mag. 52, 68–76, 2014.

[7] Evolution, converge, and innovation 5G white paper, White Paper, Datang Wireless Mobile Innovation Center,

2013.

[8] T. Nakamura, S. Nagata, 5G Standards Frontline Report – High Speeds and High Capacity or Ultra High

Confidence and Low Latency, Which Should Come First: Two Sides of a Debate at the 3GPP, Fifth Generation

Mobile Communication Promotion Forum, 2016.

[9] METIS Project, Scenarios, Requirement and KPIs for 5G Mobile and Wireless Communication System, 2013.

[10] 5G Radio Access Ericsson White Paper, 2013.

[11] METIS Project: Intermediate description of spectrum needs and usages principles, 2013.

[12] İmdat Türkay, “Digital in 2018" Raporunda Dünyada ve Türkiye’de Durum”, https://vergialgi.net/, Erişim, 10-

02-2019.

[13] BTK, Türkiye Elektronik Haberleşme Sektörü Penetrasyon Oranlarının Diğer Ülkelerle Kıyaslanması ve İl

Bazında Analizi, Bilgi Teknolojileri ve İletişim Kurumu Ankara, Nisan 2014.

[14] BTK, 5G ve Ötesi, Bilgi Teknolojileri ve İletişim Kurumu

[15] O. Dikmen, S. Kulaç, “5G Teknolojisine Genel Bir Bakış”, 4th International Symposium on Innovative

Technologies in Engineering and Science, 2016

[16] www.emfexplained.info/, “EMF Explained Series”, March, 2018

https://vergialgi.net/

http://www.emfexplained.info/



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IMPROVED DYNAMIC PERFORMANCE OF WIND ENERGY

CONVERSION SYSTEM BY STATCOM

ABDELOUAHED Touhami

Intelligent Control and Electrical Power System Laboratory, University of Sidi Bel-Abbes,

Algeria

Zidi Sid AHMED, FADI M. ALBATSHB

Electrical Engineering Section, University Kuala Lumpur, International College, Malaysia

Abstract

The renewable energy plays an important role to provide electrical energy other than

conventional sources. Wind power is one of the renewable energy sources used to minimize the

environmental impact on conventional plant; it is one of the fastest growing sources of energy in the world.

However, when the wind power is integrated to an electric grid may cause problems important in terms of

power quality such as active / reactive power variation, variation of voltage, flicker, harmonics, and

electrical behavior of switching operations. The paper study demonstrates the power quality problem

due to installation of wind turbine with the grid, and regarding to this problem the STATCOM is

connected at a point of common coupling to mitigate the power quality issues. Simulation studies are

carried out in the MATLAB/Simulink environment to examine the performance of the wind farm with and

without STATCOM for improving Power Quality of wind farms connected to electrical network.

Keywords: renewable energy, Wind farm, Power system, STATCOM, Power Quality



Sept 20-21, 2019,

Malaysia




GLOBALIZATION AND EMPLOYEE MOBILITY: INTERFUSION FOR

CUSTOMER SATISFACTION IN SELECTED MULTI-NATIONAL

COMPANIES IN NIGERIA. LAGOS REVIEWS

Haruna Abdullahi

Chrisland University Abeokuta, Nigeria

Abstract

Globalization is impacting positively and negatively on every aspects of human resources

administration, employee mobility is not an exception, in the event of daunting challenges of

satisfying customer with the state-of-the-earth products. Modern employees have to be globally

mobile in a bid to experience and acquire the necessary modes of operation in the overall interest

of their customers. Hence, this paper investigates globalization and employee mobility as

interfusion for customer satisfaction in selected multi-national companies in Nigeria. Lagos

Reviews. This paper utilizes modernization, scientific management by Taylor and contingency

theories. The study employed cross sectional survey design and was descriptive, combining both

qualitative and quantitative techniques of study; data for the study was sourced secondarily through

textbooks, online sources and journals, and primary data through administration of 550

questionnaires on employees of the three multinational companies; Nestle Nigeria Plc, Total

Nigeria and Mobil Nigeria Unlimited, the sample size was selected through purposive and simple

random. However, 533 questionnaires were returned from the field. The quantitative data was

analyzed with frequency table. The mean age of the respondents was 43.7 years, 70.2%(374) were

married,(67.3%)(358.7) had Bachelor Degree, a significant percentage(58.8%)(313) have

experience off and on shore business exposure and the mean number of years spent in service by

the respondents was 25.3 years. Sizeable number of the respondents (56.7%)(303) was of the views

that mobility exposed them to new experiences and better customer satisfaction. The paper

recommends global exposure of employees in order to improve their services and ensure

organizational sustainability and continual global training and retraining for operational resilience

in view of ever-increasing global business competitiveness. This study will be beneficial to human

resource practitioners and scholars in the field of human resource and allied disciplines would

initiate research from this study.

Keywords: Globalization, Employee Mobility, Customer Satisfaction, Multi-National

Companies Business Competitiveness, Nigeria, Lagos



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Solving the Combined Emission and Economic Dispatch of

Power System Including Solar Photo Voltaic Generation with

Optimization Method

Abdurazaq Elbaz, Muhammed Tahir Güneşer

Department of Electrical & Electronics Engineering, Karabuk University, Karabuk, Turkey

Abstract

The economic dispatch of power system is an integral part of power system and major role

and purpose of using it are for achieving a reliable and efficient operation out of power system

generation networks, and this operation should be obtained by minimizing the generator fuel cost.

Getting optimal solutions for economic dispatch problem requires efficient optimization

algorithms. Hybrid Bat-Crow Search Algorithm is one of the most recent methods and it is already

proved its efficiency and reliability for solving the economic dispatch problem. In this paper we

proposed Hybrid Bat-Crow Search Algorithm in order to solve the economic dispatch problem

based on large scale power system including solar photo voltaic generation. Hybrid Bat-Crow

Search Algorithm is one of the most recent methods and it is already proving its efficiency and

reliability for solving the economic dispatch problem. This study proposes Hybrid Bat-Crow

Search Algorithm in order to solve the economic dispatch problem based on the large scale power

system including solar photo voltaic generation. We proved that the Hybrid Bat-Crow Search

Algorithm for efficiency and take a perfect performance for small-scale systems comparing with

the Hybrid Bat-Crow Search Algorithm. To test the performance of Hybrid Bat-Crow Search

Algorithm during small and large scale power system, we applied it to Combined Economic

Emission Dispatch problem.



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Keywords: CEED, Solar Photo Voltaic, Optimization Method, BAT algorithm, Crow search

Algorithm

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Systems, vol. 11, pp. 52-58, 1996.

[8] J.-Y. Fan and L. Zhang, "Real-time economic dispatch with line flow and emission

constraints using quadratic programming," IEEE Transactions on Power Systems, vol. 13, pp. 320-

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constraints using a classical technique," IEE Proceedings-Generation, Transmission and

Distribution, vol. 141, pp. 1-10, 1994.

[10] A. El-Keib, H. Ma, and J. Hart, "Environmentally constrained economic dispatch using

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[11] Y. Brar, J. S. Dhillon, and D. Kothari, "Multiobjective load dispatch by fuzzy logic

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dispatch using fuzzy decision trees," Electric Power Systems Research, vol. 66, pp. 115-122, 2003.

[13] P. Attaviriyanupap, H. Kita, E. Tanaka, and J. Hasegawa, "A fuzzy-optimization

approach to dynamic economic dispatch considering uncertainties," IEEE Transactions on Power

Systems, vol. 19, pp. 1299-1307, 2004.



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Generators Using Valve Point Effects and Multiple Fuel Options."



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Malaysia




PROMOTING RENEWABLE ENERGY TECHNOLOGIES FOR RURAL

DEVELOPMENT IN NIGERIA

BASHIR AHMED DANZOMO

Departmet Of Mechanical Engineering, Hussaini Adamu Federal Polytechnic, Kazaure,

Jigawa State

ABUBAKAR AHMED

Department Of Electrical Engineering, Hussaini Adamu Federal Polytechnic, Kazaure,

Jigawa State

ABSTRACT

Currently a high proportion of the world’s total energy output is generated from fossil fuels

such as oil and coal. In general, the quest for an option to conventional power schemes for

extension to remote and rural locations of developing countries like Nigeria arises from the high

costs associated with the extension, as well as maintenance, of the power grid system to rural areas.

It is universally accepted that fossil fuels are finite and it is only a matter of time before their

reserves become exhausted. The need for supplementary or even alternatives that ideally will be

non-depletable energy sources have since been recognized. These non-depletable energy sources

are replenishable and are also referred to as renewable energy sources as they are available in

cyclic or periodic basis. These include: Solar Energy which has estimated world wide average

power potentials of 24 W /m2 of the earth’s surface; Hydropower, major sources which are still

under developed, has an estimated potential of the range of 2-3 TW. Available also in limited areas

of the world are Wind energy and Biomass. This paper reviews the availabity of renewable

energies and their current level of usage in rural communities of Nigeria with a view to put forward

necessary policy measures that are essential in order to promote the use of these technologies.

Keywords: rural communities; fossil fuels; biomass; energy consumption; man-hour



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Malaysia




INCREASING EFFICIENCY OF GAS INJECTION INTO GAS CAP OF A

SANDSTONE OIL RESERVOIR TO IMPROVE OIL RECOVERY

Abdollah Esmaeili

Cyprus International University – Cyprus

Abstract

In this paper, according to actual condition of this oil field, we investigated about gas

injection to find an optimized gas injection process for this oil field. Recovery efficiency of this

EOR method was tested experimentally using several cores of this sandstone reservoir. Reservoir

rock and fluid properties changing during this process were investigated. For this purpose, a set of

experimental tests based on core flooding tests on sandstones was designed. Totally, this research

was done in two sections. In phase 1, called problem statement, we tried to get enough data and

information about this field to know the problems in this field related to this research topic. In

phase 2, called finding solution methods, solution methods for solving these problems were

investigated.

The author wishes to express his sincere thanks and gratitude to Cyprus International

University (CIU) for their helps and encouragements in connection with this study. I am grateful

for all their assistance.

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Documents

Imeset’19 Malaysia Book Of Abstracts · 2020-01-17 · Dr. Yuriy Alyeksyeyenkov THK university THK university Electrical & Electronics department Associated Professor İbrahim Mahariq