Volume 11, No 1, 2017 - Universitas Pertamina · 2019. 11. 20. · Volume 11, No 1, 2017 Editorial Dear Respected Readers! INKOM Journal Vol 11 No 1 2017 is nally published. In this

Volume 11, No 1, 2017

Daftar Isi:

Optimizing the Gains of PD controller Using Artificial Bee Colony for Controlling theRigid Gantry Crane System

1-8

Edwar Yazid

Newtons Method for Distance Optimization in Firefly Algorithm in DeterminingOptimum Nutrition for Laying Hens

9-14

M.Shochibul Burhan, Fitri Utami Ningrum

Optimization of Robot Telemonitoring System Software using multi-thread method 15-24Midriem Mirdanies

Build and Design of Voyage Account Applications Using C#, WPF, and SQL Server2012 (Case Study Company X)

25-32

Desi Dyah Sulistyarini, Reza Kamaluddin Isman, Hata Maulana

Knowledge Discovery : Preprocessing Technique in Web Server Data Log 33-40Supriyadi

1/2017Pusat Penelitian Informatika - LIPI

Jurnal INKOM Vol. 11 No. 1 Hal. 1-40 Bandung, p-ISSN 1979-8059Mei 2017 e-ISSN 2302-6146

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Volume 11, No 1, Mei 2017

Penanggung JawabKepala Pusat Penelitian Informatika - LIPI

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Mitra BestariDr.Muhammad Agni Catur Bhakti, Dr.Edi Kurniawan, Dr.Martha Arbayani Zaidan, Dr.

Irwan Purnama, Dr.Oyas Wahyuanggoro, Dr.Hermawan Nugroho, Dr.Heru Susanto,Ms.Iftitahu Ni’mah, M.I.T

SekretariatAsri Rizki Yuliani, MBA Puslit Informatika LIPI

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Jurnal INKOM

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2

Volume 11, No 1, 2017

Content

Titles Pages

Optimizing the Gains of PD controller Using Artificial Bee Colony for Controllingthe Rigid Gantry Crane System

1-8

Edwar Yazid

Newton’s Method for Distance Optimization in Firefly Algorithm in DeterminingOptimum Nutrition for Laying Hens

9-14

M. Shochibul Burhan, Fitri Utami Ningrum

Optimization of Robot Telemonitoring System Software using multi-thread method 15-24Midriem Mirdanies

Build and Design of Voyage Account Applications Using C#, WPF, and SQLServer 2012 (Case Study Company X)

25-32

Desi Dyah Sulistyarini, Reza Kamaluddin Isman, Hata Maulana

Knowledge Discovery : Preprocessing Technique in Web Server Data Log 33-40Supriyadi

• i

Volume 11, No 1, 2017

Editorial

Dear Respected Readers! INKOM Journal Vol 11 No 1 2017 is finally published. In this edition,we publish five selected articles. The articles have been blind-reviewed by expert reviewers frominformatics and computer areas. The first paper entitled Optimizing the Gains of PD controller UsingArtificial Bee Colony for Controlling the Rigid Gantry Crane System. This paper proposes artificialbee colony algorithm to optimize the gains of the proportional-derivative controller. The papershows that the proposed method can improve the controlling performance. The second paper entitledNewton’s Method for Distance Optimization in Firefly Algorithm in Determining Optimum Nutritionfor Laying Hens. The paper describes implementation of firefly algorithm to optimize calculationof optimum nutrition for laying hens. The third author presents a paper entitled Optimization ofRobot Telemonitoring System Software using multi-thread method. The paper describes multi-threadmethod that can be used to speed up the data processing time. Here, the method is applied to handlereal-time thermal and color cameras of the telemonitoring system. The paper is then followed bythe fourth paper entitled Build and Design of Voyage Account Applications Using C#, WPF, andSQL Server 2012 (Case Study Company X). The paper reports development of a voyage account torecord consumption data on each voyage. The data is then used to calculate the profit and the loss inevery single voyage. The last paper entitled Knowledge Discovery: Preprocessing Technique in WebServer Data log, explains preprocessing method to provide filtered data for data mining analysis. Datacleaning method is proposed in the paper. The method uses parser algorithm and query processingwhich are written by using PHP programming. In the end, the Journal Editor would like to thankeditorial board and secretariat team members, reviewers and authors for their firm commitment andcooperation. We hope the published papers in this edition could give benefit and new insight for all ofthe journal readers.

INKOM JournalEditor-in-Chief

Esa Prakasa

ii •

Volume 11, No 1, 2017

The descriptor given are free terms. This abstract sheet may be reproduced without permission or charge.DDC 621.39Edwar Yazid (Research Center for Electrical Power and Mechatronics, Indonesian Institute of Sciences (LIPI)Optimizing the Gains of PD controller Using Artificial Bee Colony for Controlling the Rigid GantryCrane SystemINKOM, 11(1) 2017: 01-08

Control position and reduction of swinging of the payload of a rigid gantry crane system is a challengingwork because of under-actuated system. This paper addresses challenges by proposing the artificial bee colony(ABC) algorithm to optimize the gains of the PD controller to form what the so-called the artificial beecolony (ABC)-PD controller. The effectiveness of the proposed control algorithm is tested under constant stepfunctions and compared with Ziegler-Nichols (ZN)-PD controller. Simulation results show that the proposedcontroller produces slower rise time and peak time, but faster settling time than the ZN-PD controller as wellas no overshoot under the predefined trajectories.

(Author)Keywords : Gantry crane system, swing angle, PD gains, ABCDDC 621.38M. Shochibul Burhan, Fitri Utami Ningrum (Fakultas Ilmu Komputer Universitas Brawijaya Malang-JawaTimur)Newton’s Method for Distance Optimization in Firefly Algorithm in Determining Optimum Nutritionfor Laying HensINKOM, 10(1) 2017: 09-14

An accurate calculation of feed nutrition and more affordable price is an extremely complex. Fireflyalgorithm is an algorithm designed for optimization calculation whose output is highly dependent on lightintensity (β), which is influenced by distance (r). Therefore, in order to produce maximum output values,an optimization of firefly distance should be done. The most appropriate method is Newton’s Method as ithas the capability of solving roots of equations accurately. From the testing of distance optimization in fireflyalgorithm, a fairly good increase in the fitness value was obtained.

(Author)Keywords: Newton Method, Firefly Algorithm

• iii

Volume 11, No 1, 2017

The descriptor given are free terms. This abstract sheet may be reproduced without permission or charge.DDC 621.39Midriem Mirdanies (Research Center for Electrical Power and Mechatronics, Indonesian Institute of Sciences(LIPI)Optimization of Robot Telemonitoring System Software using multi-thread methodINKOM, 11(1) 2017: 15-24

The processor development today is on multi-core and multi-processor which can be used to a speedupof data processing time compared with one processor core only. One of the main ways that can be used to speedup the data processing time is by using multi-thread. Multi-thread method has been implemented on the robottelemonitoring system based on Graphical User Interface (GUI) which has been developed in Research Centerfor Electrical Power and Mechatronics, Indonesian Institute of Sciences (LIPI). A part of that requires highprocessing time and dynamic at the telemonitoring systems are the display of real-time thermal cameras andcolor camera along with tracking algorithm used, it can be seen from the camera display which less smoothespecially on the thermal camera, and some of the process is zero which means the buffer is unprocessed whichcausing lag. Two threads have been added to process each of the cameras separately. C programming languagewith the OpenCV library and the Integrated Development Environment (IDE) Qt Creator, has been used toimplement this method into an application program. Based on experiments, it can be seen that the display ofboth cameras could run smoothly, the average processing time faster than sequential, and the absence of a zeroprocess time. The average fps generated is more stable and is at the higher level that is 7.5 fps on the thermalcamera and 7.5 - 8 fps on the color camera.

(Author)Keywords: multi-thread, telemonitoring, GUI, Qt creator, C languageDDC 621.32Desi Dyah Sulistyarini, Reza Kamaluddin Isman, Hata Maulana (Politeknik Negeri Jakarta)Build and Design of Voyage Account Applications Using C#, WPF, and SQL Server 2012 (Case StudyCompany X)INKOM, 11(1) 2017: 25-32

Voyage Account is an application that record consumption data on each voyage, then the data isused to calculate the profit or loss of each voyage. The application interface was created using WindowsPresentation Foundation (WPF). Prototyping development methods was used to create these applications, C#as the programming language, and SQL Server 2012 as the database. This study discusses the making of theinterface, backend, and database of application. WPF has been selected, because WPF is the latest technologydeveloped by Microsoft after WinForms. WPF presents a display that can be customized to user needs. Thetest results showed that the Voyage Account application is already functioning in accordance with wishes ofthe user, the test is done using sql query by entering the appropriate username and password and match thequery returns results with what is displayed by the application. As for testing the interface shows if WPF canbe flexibly adapted to the screen resolution of 1366 x 768, 1920 x 1080 and 1280 x 720.

(Author)Keyword: Voyage Account, WPF, prototyping, C#, SQL Server 2012.

iv •

Volume 11, No 1, 2017

The descriptor given are free terms. This abstract sheet may be reproduced without permission or charge.DDC 621.38Supriyadi (Informatics, STMIK Kharisma Karawang)Knowledge Discovery: Preprocessing Technique in Web Server Data LogINKOM, 11(1) 2017: 33-40

Preprocessing is a process to avail a clean and ready data for data mining analysis. Four stages are carriedout in this process i.e. data selection, data cleaning, data integration and data transformation. The numeroussemi structured pattern of web server log data accumulation has made the data cleaning-up to select the reallyneeded data difficult. Hence, in this research a technique to carry out data cleaning using parser algorithmand query processing is proposed. The parser algorithm is written using PHP programming as web basedprogramming, while the query processing is implemented using relationship database management system(RDBMS) MySQL. The computation system is tested using twenty six different sized trial data, originatedfrom six web server. The final result concluded that in general the data being tested have 80 percent decreasingaverage with average processing velocity of 9.28 mbps.

(Author)Keywords: query processing, parser algorithm, semi structured data, web log data

• v

INKOM, Vol 11 No. 1, Mei 2017: 01-08

1

Optimizing the Gains of PD Controller Using Artificial Bee

Colony for Controlling the Rigid Gantry Crane System

Edwar Yazid Research Center for Electrical Power and Mechatronics

Indonesian Institute of Sciences

Email:[email protected]

_______________________________________________________________________________________

Abstract

Control position and reduction of swinging of the payload of a rigid gantry crane system is a challenging work

because of under-actuated system. This paper addresses challenges by proposing the artificial bee colony (ABC)

algorithm to optimize the gains of the PD controller to form what the so-called the artificial bee colony (ABC)-PD

controller. The effectiveness of the proposed control algorithm is tested under constant step functions and compared

with Ziegler-Nichols (ZN)-PD controller. Simulation results show that the proposed controller produces slower rise

time and peak time, but faster settling time than the ZN-PD controller as well as no overshoot under the predefined

trajectories.

Keywords: Gantry crane system, swing angle, PD gains, ABC

_______________________________________________________________________________________

1. Introduction

1 Gantry crane system, a non-slewing-luffing

crane system is most widely used in several work

places such as ports, factories, construction sites.

This type of crane is designed for repeat motions

such as hoisting, transporting which includes

longitudinal, transverse motion, and lowering

heavy payload, as well as combination of each

motion. Schematic of gantry crane system is shown

in Figure 1. Gantry crane system can be divided

into two subsystems, namely gantry crane and

stationary crane framework. Gantry crane

incorporates interaction among trolley, wire rope

as hoist cable and payload which is manipulated by

trolley and hoist mechanism. The payload is

grabbed using hook system, which is then hoisted

from trolley by means of cable.

The function of cable drum is to wind up or

unwind the cable, raises or lowers the payload

attached to the hook. The trolley and hoist work

simultaneously to perform the task of gantry crane.

In general, the task performed by a gantry crane

is to pick the payload, raise it, move it to target

position and lower it down on the crane

framework.

Because the traverse motion of trolley during

transport operations, the payload has the tendency

to swing naturally due to traverse motion of

trolley. The swinging motion reduces the speed,

accuracy and safety requirements of crane

1Received: 14 March 2017; Revised: 17 May 2017;

Accepted: 26 May 2017; Published Online: 3 May 2018

@2017 INKOM 2017/18-NO527

DOI:http://dx.doi.org/10.14203/j.inkom.527

operations. It lowers the speed of crane operations

because the payload swing must be avoided before

the payload can be safely lowered into specified

position.

Framework

Wheels

Rails

Trolley

Cable

Payload

Hook

Top beam

Figure 1. Gantry crane system [1]

The swings make it difficult to perform

alignment, fine position, or other accuracy driven

task. Swing effect also causes safety problems to

the crane framework. That’s why control systems

are needed to suppress the effects. If the cable and

stationary crane framework are considered as rigid

bodies, then the gantry crane system is regarded as

a rigid system.

Much attention has been placed on the

modeling of dynamics and control of gantry crane

system. Khalid et al. [2] have controlled the bridge

and gantry cranes by proposing the PD and input

shaping controllers and succeeded to achieve good

2 INKOM, Vol 11 No. 1, Mei 2017: 01-08 2

positioning accuracy and significant sway

reduction. Hazriq et al. [3]-[4] have studied

numerically the dynamic behaviour of a nonlinear

gantry crane system by varying the input voltage,

cable length, payload mass and trolley mass and

controlled the system. They modeled the dynamics

using Lagrange’s approach and simulated the

system through the simulink of Matlab. The rest

can be referred to references [5]-[10].

The major contribution of this study is to

improve the control capacity of the PD controller

(PDC) from the aforementioned previous works.

There are several PD tuning methods in the

literature (e.g. Ziegler-Nichols (ZN), Cohen-Coon,

lambda tuning. However, they do not always lead

to an acceptable performance and they may also

suffer from some from robustness issues [11].

An optimizer, namely artificial bee colony

(ABC) algorithm is proposed to optimize the gains

of PDC. That is because this algorithm is a

powerful optimization technique, good numerical convergence and multi-dimensional

search space compared to other soft computing

techniques such as genetic algorithm and particle

swarm optimization [12] as well as fuzzy logic and

neural network. The combination between the

ABC algorithm and PDC is called the artificial bee

colony (ABC)-PD controller. This proposed

controller is applied to control the position and to

reduce the swinging of the payload of rigid gantry

crane system. Ziegler-Nichols (ZN) based PD

controller is chosen as a benchmark because of its

common practice.

The remainder of this paper is organized as

follows. Section 2 derives the modeling of rigid

gantry crane system. Section 3 describes a typical

structure of a PDC with proposed optimizer.

Section 4 presents the concept of optimization of

PDC via ABC algorithm. Section 5 discusses the

control and optimization. Finally, Section 6

concludes this paper.

2. Modeling of Rigid Gantry Crane System

For simplicity of the characteristics of the physical gantry crane system, several assumptions

are put forward to the dynamical model. Mass of

trolley Tm and payload Pm are modeled as

lumped mass which is connected by inextensible

hoist cable P . Payload and its cable behave as

pendulum model as depicted in Fig. 2. The payload has one swing angle with respect

to the inference frame: is denoted as angle

between the Tx -axis and TT yx -plane. The payload

swings either small or large swing angles. Friction

between trolley and the top beam of crane

framework and dynamics of hoist cable and drum

in hoist system and hoist drive mechanism are not

considered. The structure is treated as a rigid body.

Z

Y

X

Tm

Pm

P

xf

Figure 2. Model of rigid gantry crane system

The equations of motion of rigid gantry crane system can be derived by Lagrange’s equations,

with the following form,

.,xq,xq,fq

F

q

L

q

L

dt

dT,Ti

(1)

All terms of ,xq T are defined as the generalized coordinates to describe the trolley and

payload motion. General velocity of the system is

.,xq T The Lagrangian L is defined as ,PKL where K is kinetics energy and P is

potential energy of system. Generalized force is

denoted as if , where they are yx f,f and zf applied

input force for the x, y and z motions respectively.

Total kinetics energy of the system K in terms

of generalized coordinates and velocities are the

kinetics energy of the trolley and payload,

.cosxxmK,xmK

,rmrmKKK

PPTTpP

TTT

ppTTPT

222

2

22

22

1

21

21

21

(2)

Total potential energy of the system is the potential

energy of the trolley and the potential energy of the

payload,

.cosgmPP PpP (3)

The energy of damping is expressed as,

.xcF Tx 2

1 (5)

By differentiating the Lagrangian operator L and F with respect to the generalized coordinates q and q ,

Optimizing the Gains of PD Controller Using Artificial Bee Colony ........ : Edwar Yazid 3

,fx

F

x

L

x

L

dt

dx

TTT

(6)

.FLL

dt

d0

(7)

The nonlinear equation of motion of gantry

crane system can be written in Eqs. (8)-(9),

,fsincosmxcxmm xPpTxTpT 2 (8)

.sing

cosx

PP

T 0

(9)

Equations (8)-(9) are dynamics of gantry crane

coupled with dynamics of crane framework and

call for some remarks.

1. Equation (8) presents dynamics of trolley motion with the input force, while Eq. (9) is

dynamics of payload.

2. Term xf is input force or driving force for the

trolley motion while PT mm is mass total from trolley and payload.

3. Term Tx is the acceleration of trolley which

appears as the forcing term in the payload

dynamics if the input force for gantry crane is

set up to be zero.

The input force xf is generated from the torque of

trolley motor. Dynamic of trolley motor can be

written as,

.xrR

zKu

rR

zKz

r

Tf

pT

TT

pT

T

p

xx

2

2

(10)

Equations (8) and (10) are combined to yield,

.xrR

zKu

rR

zK

sincosmxcxmm

T

pT

TT

pT

T

PpTxTpT

2

2

2

(11)

Terms in Eq. (11) can be explained as: TTT u,R,K

are torque constant, motor resistance and input

voltage, respectively while pr,z are gear ratio and

radius of motor pulley.

3. Closed-Loop Control System with PD Controller (PDC)

A typical structure of a PDC for controlling the

rigid gantry crane system is shown in Fig. 3. It is

seen that the system is classified as a double input

single output system (under-actuated system). The

first input for the PDC is error and error derivative

)n(e),n(e TT of trolley position while the second input is error and error derivative )n(e),n(e of payload swing. The errors and error derivatives are

multiplied by the gains of PDC, namely

proportional pK and derivative dK and then combined to form the control signal of PDC as

follows,

.)n(e)n(eK)n(e)n(eKnu PTdPTp (12)

Terms in Eq. (12) are as follows:

)n(e),n(e),n(e),n(e PTPT are errors of trolley

position and payload swing as well as their

error derivatives, respectively. The value of

neT nxd

Rigid

Gantry

Crane

System

nxT

ABC

neT

PDC

Figure 3. Diagram block of PD controller for rigid gantry crane system system

n nuT

ne

ne

Motor

Dynamics

nu

4 INKOM, Vol 11 No. 1, Mei 2017: 01-08 4

nu is limited by using saturation function before it is sent to the motor. The control action nu is then applied to the gantry crane system.

Under any types of system input, the gains

in Eq. (12) significantly affect the closed-loop

response. Suboptimal gains lead to the system

becomes unstable, high overshoot and large

steady-state error. The gains must be designed to match the system input (set-point) and the system

output by giving the corrective action in terms of

control action. Hence, ABC algorithm is

employed to optimize those gains. In addition,

Eq. (12) also reflects that gain for the errors of

trolley position and swinging of the payload

are set up similarly and so do the gain of error

derivatives. Both gains can be set up

separately, but this is intended to make the

optimization process efficient. It is worthy to note that integral action is not

required due to the presence of integral (reset)

windup leading to significant amount of

overshoots. Hence, PD controller is an appropriate

choice for controlling the system.

4. Optimization of PD Controller via ABC Algorithm

Parameters dP K,K are modeled as foods in artificial bee colony (ABC) algorithm. For the

sake of clarity, optimizer for the parameters of

PDC using ABC algorithm is called ABC-PD.

PDC parameters recalled as dP K,K are initialized randomly in ABC-PD controller. The

major phases of this algorithm has been outlined

[12], but it is revisited here for a new cost function,

1. Initialization The initial candidate solutions for x are

produced for employed bees by using Eq. (12),

.D,,j,S,,i,xxxx minjmaxjminjj,i 11 (12)

Terms in Eq. (812 can be explained as

follows: j,ix is j-th dimension of i-th

employed bee, minj

x and maxj

x are lower and

upper bounds of j-th parameters, respectively,

is a random number in range of 1,0 , S is the number of food sources and D is the

number of gains of PDC. The cost function

values of i-th the initial solution are calculated

using Eq. (13),

.nxnxSSEN

n

T

^

T

1

(13)

Terms in Eq. (13) are as follows: N is the

number of data, nxT is the actual trolley

position while nx T^

is the calculated trolley

position. The fitness value of Eq. (13) is

calculated by using following formula,

.

otherwiseCF1

0fifCF1

1

f

i

iii

(14)

2. Employed bee phase

In this phase, employed bees update the initial

candidate solutions using Eq. (15),

.D,,j,S,,k,i

,xxxg j,kj,ij,ij,ij,i

11

(15)

Terms in Eq. (15) can be explained as follows:

j,ig is j-th dimension of i-th updated candidate

solution, j,ix is j-th dimension of i-th

employed bee, j,kx is j-th dimension of k-th

employed bee, term is a random number in

range of 1,1 . Also, j and k indices are randomly selected among initial solutions so

that ki to assure that the initial candidate solutions can be updated. After the solutions

are updated, the cost function and fitness

values of i-th updated candidate solutions are

calculated using Eqs. (14)-(15). If the fitness

value of updated candidate solutions is better

than fitness value of initial candidate

solutions, then the initial candidate solutions

are replaced with updated candidate solutions.

3. Onlooker bee phase In this phase, the employed bees communicate

with the onlooker bees by calculating the

probability of each fitness value as follows,

.

f

fp

S

i

i

ii

1

(16)

Based on the probability value, the onlooker

bees also randomly improve the candidate

solutions in the employed bee phase by using

Eq. (16), as well as calculate the cost function

and fitness values of i-th updated candidate

solutions using Eqs. (15)-(16). If the fitness

value of the updated candidate solutions found

in the onlooker bee phase is better than fitness

value of the solutions in the employed bee

phase, the onlooker bee is replaced with the

employed bee.

Optimizing the Gains of PD Controller Using Artificial Bee Colony ........ : Edwar Yazid 5

4. Scout bee phase If the candidate solutions during the employed

bee phase cannot be updated until a predefined

number of iterations reaches to the limit, the

employed bee becomes scout bee and new

candidate solutions are produced by using Eq.

(12).

Step by step of optimization process above is

depicted in Fig. 4.

5. Results and Discussions

In this section, performance of proposed controller

is investigated under constant step function. Other

functions such as varying step function and

sinusoidal functions are not elaborated in this

study. Basic parameters of gantry crane system and

parameters of ABC algorithm are shown in Table 1

and Table 2, respectively. Interval for the

searching space is 100x0 for DP K,K . Since this paper works on model based control, then Eqs.

(9) and (11) are solved using fourth-order Rung-

Kutta with sampling time of 0.01 s. Control and

optimization processes are performed

simultaneously using Matlab.

Table 1. Gantry crane parameters

Parameters

Trolley mass, Tm 50 kg

Payload mass, pm 200 kg

Cable length, P

Gravitational acceleration, g

1 m 2s/m81.9

Initial conditions, 00o ,, 0,0,0o

Table 2. Parameters of ABC algorithm

ABC

Number of iterations 550 Onlooker number 50%

Number of foods 550 Employed bee number 50%

Number of optimized

parameters

2 Scout number 1

Time domain responses obtained from ZN-PD

and ABC-PD controllers are compared one to

another. Control performances in time domain are

then assessed in terms of rise time, settling time,

overshoot and peak time.

Crane is commanded to track a position in

mx 12 by giving a constant step function. The

system response for duration 180 s is shown in Fig.

5. The figure depicts that the crane is able to track

the commanded position and both controllers have

successfully stabilized it with respect to time. This

is confirmed by Fig. 6 where the error of both

controllers decays to zero.

However, each controller has different

performances during the tracking process. ZN-PD

controller has faster rise time and peak time than

the ABC-PD controller. However, fast rise time

and peak time lead to overshoot until it reaches its

settling time. In the other side, slow rise time and

peak time of ABC-PD controller lead to no

overshoot so that settling time can be achieved

faster than ZN-PD controller. Details are

elaborated in Table 3.

Onlooker Bee

Phase

Scout Bee

Phase

e

Cost Function, Eq. (13)

Foods

Gains

PK

DK

New Foods

Employed Bee

Phase

Figure 4. Gains optimization process in ABC-PD controller

system

e

6 INKOM, Vol 11 No. 1, Mei 2017: 01-08 6

Figure 5. Trolley position under step function

Figure 6. Error of trolley position under step function

Table 3. Controller’s performances under step function

Performance ZN-PD ABC-PD

Rise time

8.31 26.23

Settling time 58.44 46.85

Overshoot 4.58 0

Peak time 24.3 177.2

Optimal gains of PD controller are tabulated in

Table 4. It is worthy to note that the initial gains

are set randomly and it will lead to the change of

the optimized gains accordingly. After 50

realizations, it is found that the optimized PK and

dK of ABC-PD controller are lower than the

optimized gains of ZN-PD controller. It explains

why the ABC-PD controller has the slow rise time

and peak time, fast settling time as well as no

overshoot as the function of gain PK is to increase

the rise time of the system response and the

function of dK is to reduce the oscillation.

Table 4. Optimal gains of PD controller under step

function

Gains ZN-PD ABC-PD

Optimized Optimized

pK 1.36 0.01

dK 56.85 20.92

(a)

(b)

Figure 7. Payload swing angle under for step function

(a) time window 0-180 s (b) time window 0-25 s

Control performance in Figs. 5-6 is elaborated

by Fig. 7. The figure shows the consequent of fast

rise time and peak time of ZN-PD controller.

Faster the crane reaches the target position, bigger

the swing angle of payload occurs. Large swing

angle of payload of ZN-PD controller in Fig. 7 is

the consequent of using full nonlinear dynamic

model in Eq. 9 and Eq. 11. At this point, control

designer can choose whether the trolley moves fast

with large swing angle as expense or reasonable

speed of trolley with no overshoot. The latter is

favorable since it is required for safety in crane

operation. Hence, all results confirm that the ABC-PD controller outperforms the ZN-PD controller.

In optimizing the gains, PD controller

optimized by ABC algorithm produces cost

function as shown in Fig. 8. It displays the cost

0 20 40 60 80 100 120 140 160 180 0

2

4

6

8

10

12 T

roll

ey p

osi

tio

n, x

(m

)

Time (s)

Reference

ZN-PD

ABC-PD

0 20 40 60 80 100 120 140 160 180 -2

0

2

4

6

8

10

12

Time (s)

Reference

ZN-PD PSO-PD

Tro

lley

po

siti

on

err

or,

ex (

m)

0 20 40 60 80 100 120 140 160 180 -50

50

Time (s)

Reference

ZN-PD ABC-PD

0

Pay

load

sw

ing

ang

le,

θ (

deg

)

0 5 10 15 20 25 -50

0

50

Time (s)

Reference

ZN-PD

ABC-PD

Pay

load

sw

ing

ang

le,

θ (

deg

)

Optimizing the Gains of PD Controller Using Artificial Bee Colony ........ : Edwar Yazid 7

function with respect to the number of iterations.

As observed, the cost exhibits a gradual

convergence and seems like a ladder function as

the number of iteration increases. However, the

cost function starts to converge after the-31st

iteration and is steady to a certain value.

Figure 8. Cost function for step function with respect to number of iterations

Figure 9. Trolley position under payload mass variation

To test the effectiveness the proposed controller

for a more challenging condition, payload mass is

varied from 200 kg to 400 kg. Under this

condition, the commanded position 4 is fixed and

the result is shown in Fig. 9. The crane, as

expected, is still able to track the commanded

position. However, the figure shows that as the

payload mass increases, the rise time and settling

time also increases with respect to time. With

increased payload mass, performance of ABC-PD

controller will deteriorates further.

6. Conclusions

In this paper, a controller namely the ABC-PD

is proposed for controlling the rigid gantry crane

system. Simulation results show that the proposed

controller can improve the performance of closed-

loop control system under constant step function.

Important conclusions and suggestions from this

work are derived below.

- The ABC-PD controller outperforms the ZN-PD controller in terms of fast settling time and

no overshoot.

- Control and optimization are still performed under simulation since the ABC-PD controller

is computationally expensive for real-time

implementation.

- The generated cost function seems like a ladder function.

- The proposed controllers can easily be applied

to PID controller, where the gain iK is

included.

- The proposed controllers can be applied to control other dynamic systems.

Acknowledgments

Research facility for this work from research

center for electrical power and mechatronics,

Indonesian Institute of Sciences is gratefully

appreciated.

References

[1] SPANCO. (1998). Installation and parts manual for SPANCO PF series gantry cranes, Available:

http://www.spanco.com/products/gantry

cranes.html

[2] Khalid, L.S., William, S. and Stephen, D., DA Controller Enabling Precise Positioning and Sway

Reduction in Bridge and Gantry Cranes, Control

Engineering Practices Vol. 15, 2007, pp. 825-833.

[3] Hazriq, et al., Dynamic Behaviour of a Nonlinear Gantry Crane System, Proceedings of the ICEEI

Vol. 11, 2013, pp. 419-425.

[4] Hazriq, et al., Performance analysis for a Nonlinear Gantry Crane System using Priority-

based Fitness Scheme in Binary PSO Algorithm,

Advanced Materials Research Vol. 903, 2014, pp.

285-290.

[5] Diep, D.V. and Khoa, V.V., PID-Controllers

Tuning Optimization with PSO Algorithm for

Nonlinear Gantry Crane System, International

Journal of Engineering and Computer Science Vol.

3 No. 6, 2014, pp. 6631-6635.

[6] Sandor, I., et al., Stabilizing Model Predictive

Control of a Gantry Crane Based on Flexible Set-

Membership Constraints, Proceedings of the IFAC

Vol. 48-23, 2015, pp. 248-253.

[7] Ahmad, B.A., et al., Optimal Analysis and Control

of 2D Nonlinear Gantry Crane System,

Proceedings of the ICSSA, 2015, pp. 30-35.

[8] Hussien, S.Y.S., Ghazali, R., Jaafar, H.I., and Soon

C.C., An Experimental of 3D Gantry Crane System

in Motion Control by PID and PD controller via

PFPSO, Journal of Telecommunication, Electronic

0 20 40 60 80 100 120 140 160 180 0

2

4

6

8

10

12

14

Time (s)

Reference 200 kg 300 kg 400 kg

Tro

lley

po

siti

on

, x

(m

)

0 10 20 30 40 50 3300

3500

3700

3900

4100

Number of iterations

Co

st f

un

ctio

n (

SS

E)

8 INKOM, Vol 11 No. 1, Mei 2017: 01-08 8

and Computer Engineering Vol. 8 No. 7, 2016, pp.

133-137.

[9] Hussien, S.Y.S., Ghazali, R., Jaafar, H.I., and Soon

C.C., Analysis of 3D Gantry Crane System by PID

and VSC for Positioning Trolley and Oscillation

Reduction, Journal of Telecommunication,

Electronic and Computer Engineering Vol. 8 No.

7, 2016, pp. 139-143.

[10] Wei, H. and Shuzi, S.G., Cooperative Control of a

Nonuniform Gantry Crane System with

Constrained Tension, Automatica Vol. 66, 2016,

pp. 146-154.

[11] Skoczowski, S., Domek, S., Pietrusewicz. K. and

Broel-Plater, B., A method for improving the

robustness of PID control, IEEE Transactions on

Industrial Electronics Vol. 52, 2005, pp. 1669-

1676.

[12] Yazid, E. Liew, M.S., Parman, S., and Kurian,

V.J., Improving the modelling capacity of Volterra

model using evolutionary computing methods

based on Kalman Smoother adaptive filter, Journal

of Applied Soft Computing, Vol. 35, 2015, pp.

695-707.

INKOM, Vol 11 No. 1, Mei 2017: 09-14

Newton’s Method for Distance Optimization in Firefly Algorithm in Determining Optimum Nutrition for Laying

Hens

M.Shochibul Burhan, Fitri Utami Ningrum Fakultas Ilmu Komputer, Universitas Brawijaya, Malang, Jawa TImur

Email:[email protected]

_______________________________________________________________________________________

Abstract

An accurate calculation of feed nutrition and more affordable price is an extremely complex. Firefly algorithm is an

algorithm designed for optimization calculation whose output is highly dependent on light intensity (β), which is influenced

by distance (r). Therefore, in order to produce maximum output values, an optimization of firefly distance should be done.

The most appropriate method is Newton’s Method as it has the capability of solving roots of equations accurately. From the

testing of distance optimization in firefly algorithm, a fairly good increase in the fitness value was obtained.

Keywords: Newton Method, Firefly Algorithm

_______________________________________________________________________________________

1. Introduction

1In determining the composition of the feed

material is very a complex calculation. Because it

must consider all the nutrient content in the feed

material in accordance with the age of animals,

especially laying hens. And also required fast and

accurate computation so that decisions can be

taken immediately. For that we need a

computational algorithm that is able to complete

the optimization calculation of the dosage of feed

ingredients, quickly and accurately.

The firefly algorithm is an algorithm created to

find the optimal solution [1]. The advantage of the

firefly algorithm is that the best generated

generation will be the reference to change the light

intensity or the quality of the solution, so that when

the changes in the solution is not far from the best

solution. So that the required computation iteration

more efficiently.

Setiawan, et al [2]. Using the firefly algorithm

to do the scheduling, and get a small enough

penalty of 0.0003. Hadhi, et al [3]. Use the firefly

algorithm for scheduling the power plant unit. By

obtaining a low enough cost below 2% the firefly

algorithm is considered reliable in terms of

optimization of power plant scheduling. The distance between firefly individuals is the most

vital element in the firefly algorithm. This is because the

firefly algorithm uses light intensity in changing the

value of each individual that is considered poor [1].

Meanwhile, the level of light intensity between fireflies

1Received: 20 Jan 2017; Revised: 13 April 2017;

Accepted: 29 May 2017; Published Online: 3 May 2018

@2017 INKOM 2017/18-NO509

DOI:http://dx.doi.org/10.14203/j.inkom.509

is highly influenced by distance. The shorter the

distance of the firefly, the stronger the level of light

intensity.

With regard to laying hen feed, firefly

algorithm is used for the optimization of the best

amount of feed in accordance to the required

nutrition and the minimum price. Ini this algorithm

process, 15-35 [1] initial solutions are given

randomly, which are also called the initial

inviduals, which represents a number of feed types.

Then, a change in every individual is made in order

to be closer to the ideal or fitness value.

Newton‟s Method is one of the best methods

used for obtaining maximum optimization of

square roots [4]. Newton‟s Method uses approach

repetition to obtain the value that is the closet to

the real value. The distance between fireflies is

calculated by finding the square root of the

difference between the value of the best individual

and the value of the poorest individual. The

optimization process is done by obtaining the

optimum square root value by doing some iteration

so that the optimum result is obtained, then it is

used as the value of the distance between

individuals.

In the previous research, Ismail et al [5] used

Newton‟s Method for the optimization of genetic

algorithm in solving nonlinear equations, in which

new individuals are going to be selected and the

ones that have values close to nonlinear value will

be maintained. As for the individuals whose values

are far from the nonlinear value will be changed

using Newton‟s approach. The result shows a

better accuacy level compared to the previous

research. Farook and Raju [6] in their resarch

optimized individuals in firefly using Genetic

10 INKOM, Vol 11 No. 1, Mei 2017: 09-14

Algorithm, in which the best individual will be

crossed over to poorer individuals. The results

obtained are the global solution that have even

fitness and good fitness value.

In this research, only distance optimization is

going to be carried out as firefly algorithm has a

number of complex parameters for finding optimal

solutions. Thus, the merging of methods that have

complex parameters such as genetic algorithm will

add more computation load in the calculation. In

this research, the method of addition is going to be

done as efficient as possible to maximize the

computatin in firefly algorithm.

2. Newton’s method

Newton’s method is an approach method for

finding the valu of the roots of equations. It is

expressed in the following equation [7]:

Following linear algebra equation

(1)

From the equation above, the tangent point is

, ( ) = f ‘ ( ) the tangent equation is 0 - f( ) = f ‘ ( ) ( x - ) and they are intersecting on x axis when y=0. Thus, 0 – f( ) = f ‘ ( ) (x -

) and = x = - if = - . It is

started from if . Jika = . Therefore,

the following equation is obtained

(2)

If the equation above is used in solving the

equation f(x) = - 5 and f „(x) = 2x, the following

is obtained

= =

= =

thus, the simplest equation obtained from

Newton‟s method is

(3)

3. Firefly Algorithm

By adopting the behavior of fireflies and the

way fireflies flash, in 2007-2008, Xin-Sheyang

developed firefly algorithm under the following

rules [1]:

a. Fireflies have unisex nature so the

attraction to others is not based on the sex.

b. The attraction toward others is influenced

by the brightness level of fireflies‟ flash. In

a wide distance, the brightness decreases,

so the fireflies that have less brightness

will approach those brighter. The fireflies

will move to any direction or randomly if

there is no bighter light.

c. The light of the fireflies is determined by

the objective condiction and function to

determine the level of brightness.

The attraction of fireflies is determined by the

brightness perceived by the fireflies nearby. Thus,

the formula of attractiveness β can be expressed

(4)

β0 = attractiveness at r = 0.

γ = The coefficient of light absorption, which

controls the light decrease.

= || - || =

𝑘=1𝑑𝑥𝑖,𝑘−𝑥𝑗,𝑘2 (5)

= the distance between two fireflies

= component to from the spatial coordinate

from firefly .

( ) + (6)

= the initial position of firefly i.

= attractiveness.

= the firefly‟s random movement. α is a

random number from 0 to 1

( ) + . We

substitute the formula for finding the distance with

Newton‟s method formula.

= || - || = = ( ) (7)

4. Laying Hen Feed

The feed inside hen‟s body has several

functions as [8]:

a. Constituent substance (building substance)

is the constituent substance that builds the

body structure of hens, including protein,

mineral, fat and water.

b. Energy substance is the energy source

producing heat, work and/or accumulation

of fat such as carbohydrate, fat, protein.

c. Regulating substance as the substance

controling the function inside fowl body,

including vitamins, enzimes, hormones,

minerals, certain amino acids and certain

fat acids.

d. Additional function is a function used for

producing something such as egg and

milk.

Newton‟s Method for Distance Optimization .... : M.S. Burhan, F.U.Ningrum 11

Table 1. Nutritional needs of laying hen [9] (*ISA Nutrition Menegement Guide , 2010. ** SNI,1995).

NUTRITION

Age (Week) 0-4 >4-10 >10-16 >16 – Egg Production 2% Egg Production 2% - 28 28 - Afkir

Water Content (%)**

12 INKOM, Vol 11 No. 1, Mei 2017: 09-14

This is illustrated in the flowchart below:

Figure 2. The flowchart of Newton‟s method in determining the optimum distance in firefly algorithm.

Table 2. Analysis of the raw material of laying hen feed [10]

Raw Material ME

(Kcal/kg)

Crude

Protein (%)

Crude

Fat (%)

Crude

Figer (%)

Ash

(%)

Calcium

(%)

Phosphor

(%)

Water

Content

(%)

Yellow Corn 3.360 9,0 4,1 2,2 24 0,3 50 15

Buckwheat 3.040 11,0 1,9 3,4 1,65 0,028 0,28 16

Broken Rice 3.050 8,9 2,0 1,0 0 0 1,36

Mill Fine Bran 1.630 13,6 8,2 8,0 10,8 0 0 14,5

Traditional Fine Bran 2.240 41,7 4,0 6,2 10,8 0 0 14,5

Peanut Meal 2.200 43,9 6,0 12,6 0 0,2 0,65 6,6

Coconut Cake 1.540 20,5 6,7 12,0 6,36 0,08 0,52 0

Fish Flour 2.910 61,8 7,8 0,6 7,19 0 1,01 0

Meat Flour 2.150 57,80 10,2 1,7 0 0 0 0

Shrimp Flour 2.900 33,20 4,4 18,3 0 0 0 0

White Leadtree Leaf Flour 1.140 23,20 2,4 20,1 7,79 0 0 0

Vegetable Hummingbird Leaf Flour

1.230 31,7 1,9 22,4 0 0 0 0

Papaya Leaf Flour 1.230 23,50 9,1 11,3 0 0 0 0

Distance between

both fireflies

Distance optimization using

Newton‟s method

Difference of optimization values

Diference approach to

0 or, Diference= 0

The best distance

Taking the best firefly ( ) and

the firefly to be changed ( )

Start

NO

YES

End

Newton‟s Method for Distance Optimization .... : M.S. Burhan, F.U.Ningrum 13

6. Testing result

There were several phases used in this study.

The first phase was testing the number of optimum

fireflies, and the number of iteration = 100. After

the number of optimum fireflies was obtained, the

movement value (β), light coefficient (γ) and

random value (α) were obtained. The testing was

compared to the fitness. The higher the fitness, the

closer the solution to be optimum. Based on this

testing, the Newton’s method was then applied and

the best iteration was tested.

In the testing of the number of fireflies, 15 to

40 fireflies were tested. By using 6 feed

combinations, the best fitness was obtained when

the number of fireflies used was 35. This is shown

in the table below.

Table 3. Testing of the number of fireflies

Number of

Firefly

β γ α Fitness

15 1 0,2 -1 0,961 20 1 -0,2 -1 1 25 1 0,8 -1 0,985 30 1 1 -1 0,930 35 1 1 -1 0,960 40 1 -0,2 -1 1

Fitness = +

(8)

According to the results of the testing, it was

found out that the best numbers of fireflies based

on the highest fitness value were 20 and 40. The

number of fireflies of 20 was selected due to

lighter computation.

The next phase was testing the number of

iteration in the Newton‟s method. In the testing,

10-80 iterations were conducted by using 4 feed

combinations20 firefly algorithm iterations, β

value = 1. γ = -0,2. α = -1.

Table 4. Ptesting of the number of iterations in

Newton‟s method Jumlah

Iterasi

Best 1 Best 2 Deference

10 9,620 9,580 0,040

20 9.0142723769946 9.0000112987902 0,014

30 9 9 0

40 9 9 0

50 9 9 0

The optimum number of iterations is 30+

iterations. Therefore, the number of iterations in

the Newton‟s method in this research was 30.

Figure 3. Chart of the best iteration testing

Figure 4. Chart of iteration testing using Newton‟s

method

The next phase was testing Newton‟s method in

the application of firefly algorithm in distance

optimization.

In fitness testing some comparisons with other

individual-based optical algorithms are evolution

strategic (ES). with μ = number of feed

combinations or called parent (2,3,4,5) and ʎ = 3

for each parent. Then compared with firefly

algorithm which is not done distance optimization.

A number of input combination were

conducted, and the results are as follows:

Table 5. Fitness result

Jumlah

Kombinasi Pakan

Fitness

E.S Firefly Firefly+ Newton

2 0,43085 1,03319 1,03321 3 0,35616 0,94230 1,03381 4 0,24605 1,03097 1,03154 5 0,23220 0,25330 0,48198

14 INKOM, Vol 11 No. 1, Mei 2017: 09-14

Figure 5. Chart of the comparison of fitness of firefly

algorith, E.S (blue), FA (Red) and firefly algorithm

optimized using Newton‟s method, FA + NW (metro)

The results of the testing of fitness between

Evolution Strategic, normal firefly algorithm and

firefly algorithm optimized using Newton‟s

method showed an increase in the fitness. This

shows that there was an increase in the light

intensity which was influenced by distance, so the

optimum result could be obtained.

7. Conclusion

For a series of test above, be obtained 20 the

number of fireflies as the best solution for a total

amount tertimggi Gym numbered 1 (scala 0-1).

With the value of β (light intensity). With the

movement kuang-kunangterbaik nilaiγ = -0.2 dan

α = -1.

From the test results on the algorithm of

fireflies and then test methods newton on the

number of iteration, iteration 30 best obtained by

the difference amounted to 0.

Optimization was then performed at a distance

(r) the firefly algorithm, generating kenaiikan

fitness value. In the second combination of feed

has increased by 0,002. In the third combined gain

of 0.1 feed. At 4 combinations of feed rose 0,001

and the five combinations of feed increased by 0.2.

This shows the optimization method will

increase the accuracy of calculations newon feed in

determining optimal nutrition in chickens petelor.

Reference

[1] X. S. Yang, “Firefly algorithms for

multimodal optimization,” Lect. Notes

Comput. Sci. (including Subser. Lect. Notes

Artif. Intell. Lect. Notes Bioinformatics),

vol. 5792 LNCS, 2009, pp. 169–178.

[2] H. Setiawan, L. H. Hanafi, and K. R.

Prilianti, “Implementasi Algoritma

Kunang-Kunang,” J. Buana Inform., vol. 6,

2015, pp. 269–278.

[3] B. P. Hadhi, R. S. Wibowo, and I. Robandi,

“Mempertimbangkan Fungsi Biaya Tidak

Mulus Dengan Firefly Algorithm,” J. TENI

POMITS, vol. 3, no. 1, 2014.

[4] S. Raskin, “Newton‟s method,” Dep. Math.

Univ. Chicago, vol. 2, 2006, pp. 1–4.

[5] A. A. Mohd Arfian Ismail, Safaai Deris,

Mohd Saberi Mohamad, “A Hybrid of

Newton Method and Genetic Algorithm for

Constrained Optimization method of the

Production of Metabolic Pathway,” Life Sci.

J., vol. 11, no. 1, 2014, pp. 409–414.

[6] S. Farook and P. S. Raju, “Evolutionary

Hybrid Genetic-Firefly Algorithm for

Global Optimization,” IJCEM Int. J.

Comput. Eng. Manag., vol. 16, no. 3, 2013.

[7] M. Allame and N. Azad, “On Modified

Newton Method for Solving a Nonlinear

Algebraic Equations by Mid-Point,” vol.

17, no. 12, 2012.

[8] Sudarmono, Guidelines for the maintenance

of laying hens (Panduan Beternak Ayam

Petelor). Yogyakarta: KANISUS, 2003.

[9] Info Medion, “Studying Patterns Giving

Rations Laying Hens (Mempelajari Pola

Pemberian Ransum Ayam Petelur),” info

medion, 2014.

[10] J. Servatius, Successful breeding laying

hens (Sukses Beternak Ayam Petelor), PT.

Agro Media Pustaka, Depok, 2005.

INKOM, Vol 11 No. 1, Mei 2017: 15-24 1

Optimization of Robot Telemonitoring System Software using

multi-thread method

Midriem Mirdanies Research Center for Electrical Power and Mechatronics, Indonesian Institute of Sciences (LIPI), Kompleks LIPI,

Komp LIPI Bandung, Jl. Sangkuriang, Gd. 20. Lt. 2, Bandung 40135, Indonesia

Email: [email protected]

_______________________________________________________________________________________

Abstract

The processor development today is on multi-core and multi-processor which can be used to a speedup of data

processing time compared with one processor core only. One of the main ways that can be used to speed up the data

processing time is by using multi-thread. Multi-thread method has been implemented on the robot telemonitoring

system based on Graphical User Interface (GUI) which has been developed in Research Center for Electrical Power

and Mechatronics, Indonesian Institute of Sciences (LIPI). A part of that requires high processing time and dynamic at

the telemonitoring systems are the display of real-time thermal cameras and color camera along with tracking

algorithm used, it can be seen from the camera display which less smooth especially on the thermal camera, and some

of the process is zero which means the buffer is unprocessed which causing lag. Two threads have been added to

process each of the cameras separately. C programming language with the OpenCV library and the Integrated

Development Environment (IDE) Qt Creator, has been used to implement this method into an application program.

Based on experiments, it can be seen that the display of both cameras could run smoothly, the average processing time

faster than sequential, and the absence of a zero process time. The average fps generated is more stable and is at the

higher level that is 7.5 fps on the thermal camera and 7.5 - 8 fps on the color camera.

Keywords: multi-thread, telemonitoring, GUI, Qt creator, C language

______________________________________________________________________________

1. Introduction

1Processor development today is on a multi-

processor, and multi-core processors from the two

cores and above, as well as the hyper-threading

technology. On desktop processor today, it has

been available processor with 10 cores with a total

of 20 threads with hyper-threading technology that

is Intel CoreTM i7-6950X Processor Extreme

Edition [1], while for server processor, has been

available processor with 24 cores with a total of 48

threads such as Intel Xeon Processor E7-8890 v4

[2] and Intel Xeon Processor E7-8894 v4 [3]. An

example of a quad-core processor architecture can

be seen in Figure 1.

In Figure 1 it can be seen that each processor

can consist of more than one processor core which

each core has individual memory, and can connect

to one another using shared memory. This multi-

core processor can be used to run multiple

programs or threads simultaneously to speed up

data processing performance. Techniques to

optimizing multi-core processors to speed up data

processing on a program are parallel programming

1 Received : 3 May 2017; Revised : 4 Oct 2017;

Accepted : 15 Dec 2017; Published Online: 3 May 2018

@2017 INKOM 2017/18-NO550

DOI:http://dx.doi.org/10.14203/j.inkom.550

or multi-thread. In multi-thread, a program is

divided into two or more parts and processed on a

different thread.

Some publications have been reported using

multi-core processors for specific applications. In

image processing, cabaret et al. have implemented

the Connected Component Labeling (CCL)

algorithm for labeling objects [5], and Mirdanies

et al. have also conducted research that utilizes

multicore processors using shared memory to

speed up the multi-object identification process by

the SIFT and SURF methods [6]. Huynh et al. have

also used multi-core processors in data mining to

speed up mining data process on large datasets

using a method called Parallel Dynamic Bit Vector

Sequential Pattern Mining (pDBV-SPM) [4].

Publications that used multi-threading methods

on multi-core processors i.e. rui et al. to hide the

deduplication of I/O latency by using multi-thread

deduplication (Multi-Dedup) [7]. Sikora et al. have

used multi-thread method in the audio signal field

to speed up the beam-tracing simulation [8].

16 INKOM, Vol 11 No. 1, Mei 2017: 15-24

1

6

The telemonitoring system has been widely

implemented in a variety of areas including the

health field that Gyllensten has done for the

treatment of patients with chronic heart failure [9].

Azimi et al. also had discussed a monitoring

system for the elderly using the concept of the

Internet of Things (IoT) [10]. Both of the

telemonitoring systems above are more emphasis

on the concept and how the telemonitoring system

mechanism used but did not discuss the

optimization to speed up the processing time,

especially by using multi-core processors.

This paper describes the implementation of

multi-thread method to optimize the robot

telemonitoring system program. This system has

been created using c programming language,

OpenCV library, and Integrated Development

Environment (IDE) Qt Creator.

2. Methods

Figure 2 is two Degrees Of Freedom (DOF) of

robotic arms which have been developed in the

Research Center for Electrical Power and

Mechatronics, Indonesian Institute of Sciences

(LIPI).

Microcontrollers are used to drive two DOF

mechanisms, while mini-pc is used for complex

data processing and requires fast processing times

such as for image processing, stabilization

systems, ballistics, and telemonitoring or

telecontrol systems. Mini-pc specification used in

this research is intel core processor i3-4160T, 8GB

DDR3, 500GB HDD, Intel HD Graphics VGA,

GbE NIC, and windows 7 professional.

Figure 1. An example of a quad-core processor architecture [4].

Figure 2. Two DOF of robotic arms.

Figure 3. Telemonitoring system diagram of the two DOF robot arm

Optimization of Robot Telemonitoring System Software ..... : M.Mirdanies 17

Telemonitoring system diagram that has been

created to monitor and control the robotic arm can

be seen in Figure 3.

Mini-PCs read data from thermal cameras, color

cameras, and LRF that are displayed to the user

through a GUI-based touchscreen monitor. In

addition, the user can send commands to the mini-

pc through the GUI. The Mini-PC is connected to a

microcontroller using a serial cable to transmit the

azimuth and elevation angle coordinates and read

the current azimuth and elevation positions.

A microcontroller is an intermediary between a

mini-pc and a motor driver that sends commands

from a mini-pc to a motor driver and reads the

current angle of the azimuth and elevation

mechanisms.

The application program of the telemonitoring

system based Graphical User Interface (GUI) can

be seen in Figure 4. The data displayed on the GUI

i.e. thermal camera, color camera (RGB), azimuth

and elevation angle, object distance, the choice of

a manual or automatic mode in the movement of

the robot arm, button to movements robot

manually with speed setting which can be used in

manual mode and "Tembak" button to shoot the

object. If the automatic mode is selected, the robot

arm will move to follow the direction of the

selected object using tracking algorithms.

Figure 4. The telemonitoring system program based Graphical User Interface (GUI)

18 INKOM, Vol 11 No. 1, Mei 2017: 15-24

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Telemonitoring system flowchart can be seen in

Figure 5. The thermal camera used in this research

is Flir A65 (f = 25mm, 7.5 Hz) which connected

using Local Area Network (LAN) cable with an

RJ45 connection, and the color camera is DFK

72AUC02-F connected via USB. The both of

camera can be seen in Figure 6 and Figure 7

Figure 5. Telemonitoring system flowchart

Optimization of Robot Telemonitoring System Software ..... : M.Mirdanies 19

One part that requires dynamic and high

processing time on this telemonitoring system is

the display of two cameras i.e. thermal camera and

color camera with 640 x 480 pixel resolution with

tracking algorithm used in real-time, it can be seen

primarily in thermal camera display which less

smooth, it can be seen in Figure 8. The computing

process of thermal cameras and color cameras is

done sequentially at any given interval using a

timer. The object tracking algorithm used in this

telemonitoring system is Tracking Learning

Detection (TLD) [11].

The GUI display in Figure 8a is taken shortly

after the bottle is moved to the front of the camera,

while in Figure 8b is three seconds after it. In

Figure 8a we can see the delay in the thermal

camera shown by the absence of the bottle,

whereas in Figure 8b has appeared. On the other

hand, the color camera has a bottle showing, both

in Figure 8a and Figure 8b, although there is still a

little delay occurs.

The sequential timing of sequential processing

has been done at several time intervals to ensure

real-time processing that can be seen in section

three. From the test results can be seen that there is

still a lag that causes the appearance of the camera

on the GUI has not been smooth.

Based on this, two new threads have been

added, first to process thermal camera, and second

to process the color camera. Each thread is

processed in parallel and separate, so it can handle

the needs of a fast and dynamic processing. Two

new threads implemented can be seen in Figure 9.

Illustrated of thread execution on this system on

the processor cores can be seen in Figure 9. The

first thread is used to process raw thermal camera

data until display it in the GUI and the second

thread is used to process raw color camera data

until display it in the GUI. Core processor was

used to executing this thread is set automatically,

so no need to specify the program.

Flowchart of the new proposed system can be

seen in Figure 10. In Figure 10 it can be seen that

after the 'Mulai' button is pressed it will form two

new threads that process color camera and thermal

camera data continuously until the 'Stop' button is

pressed. So the total thread was used is three.

If the user chooses automatic mode and chooses

the object to be tracked either by the color camera

or thermal camera, the tracking algorithm will

work and send commands to the microcontroller to

move the azimuth and elevation mechanisms to

always point to the object, whereas if manual

mode is selected, the tracking algorithm does not

work, the system will only move the azimuth and

elevation mechanisms according to the direction

keys pressed by the user.

Thread had been implemented into an

application program using Qt Creator IDE with

Figure 6. Thermal camera

Figure 7. Color camera

20 INKOM, Vol 11 No. 1, Mei 2017: 15-24

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QtConcurrent header. Source code had been

made using c language is as follows.

//Header

#include

// variables declaration QFuture thread_RGB;

QFuture thread_Thermal;

//running thread

thread_RGB = run(this,

&Dialog::proses_RGB);

thread_Thermal = run(this,

&Dialog::proses_Thermal);

// Procedures to be executed on thread void Dialog::proses_RGB() {

// Source code of color camera

// processing

}

void Dialog::proses_Thermal() {

// Source code of thermal camera

// processing

}

Two procedures have been created to process

each of these cameras, and each procedure is

processed on a different thread,

Dialog::proses_RGB() procedure is processed

(a)

(b)

Figure 8. GUI when running the program sequentially; (a) shortly after the bottle is moved to the front of the camera;

(b) three seconds later.

Figure 9. The implementation of multi-thread on telemonitoring system

Optimization of Robot Telemonitoring System Software ..... : M.Mirdanies 21

on thread_RGB, while the

Dialog::proses_Thermal() procedure is

processed on thread_Thermal.

The processing time of a sequential program

and with added two threads is calculated using

std::chrono::high_resolution_clock [12]

to calculate the actual processing time in a

microsecond and QTime [13] to calculate Frame

Per Second (FPS) of each process. Source code in

c language using

std::chrono::high_resolution_clock is as

follows. //Header

#include

//Start calculation

auto start =

std::chrono::high_resolution_clock::n

ow();

//Read buffer of thermal or color

//camera & process the tracking

//algorithm

//Calculate elepsed time in us

auto elapsed =

std::chrono::high_resolution_clock::n

ow() - start;

long long microseconds =

std::chrono::duration_cast(elapsed).count();

The source code in c language using QTime is

as follows.

//Header

#include

//variables declaration

QTime myTimer;

double fps;

double second;

int jml_counter = 0;

//Start calculation

myTimer.start();

//Source code of camera processing

//Calculate FPS

jml_counter++;

if (jml_counter >= 120) {

second = myTimer.elapsed() / 1000;

Figure 10. Flowchart telemonitoring system with added two threads

22 INKOM, Vol 11 No. 1, Mei 2017: 15-24

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fps = jml_counter / second;

myTimer.restart();

}

3. Results and Discussion

The sequential processing time was performed

at 100 ms, 134 ms, 159 ms, and 300 ms time

intervals as shown in Figure 11.

In Figure 11 it can be seen that the processing

time of the thermal camera and color camera at

100 ms, 134 ms, 159 ms, and 300 ms intervals

vary. This is related to the timer interval used and

loads the process done, whether it is just reading

the image buffer and displaying it, or accompanied

by the processing of the tracking algorithm. Some

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Figure 11. The sequential processing time of: (a) color camera 100 ms; (b) thermal camera 100 ms; (c) color camera

134 ms; (d) thermal camera 134 ms; (e) color camera 159 ms; (f) thermal camera 159 ms; (g) color camera 300 ms; (h)

thermal camera 300 ms

Optimization of Robot Telemonitoring System Software ..... : M.Mirdanies 23

value of the processing time of the thermal camera

is 0 which means that the buffer is not processed at

that time and causing the lag. Unlike the thermal

camera, almost all value of the color cameras are

more than 0, except at 300 ms intervals, it means

that the amount of lag of the color camera is less

than thermal camera. Based on the experimental

results, it can be seen that all the timer intervals

have not been able to process both cameras smooth

and stable without any lag.

Experiments have also been performed on PCs

with Intel Core i7-4790, 8GB DDR3, 1 TB, Nvidia

GeForce GTX 745, and Windows 8.1 as

comparators. Based on experimental results,

obtained the same conclusion, there are still lag on

several experiments.

Fps generated from the sequential time of

process at both 100 ms, 134 ms, 159 ms, and 300

ms intervals can be seen in Figure 12. In Figure 12

it can be seen that the highest fps time is obtained

at intervals of 134 ms ie 7.5 fps, but it can not be

used as a solution because the appearance is still

not smooth because of the lag as shown in Figure

11.

The GUI display after added two threads can be

seen in Figure 13. The GUI in Figure 13a is taken

shortly after the bottle is moved to the front of the

camera, while Figure 13b is three seconds later. In

Figure 13a and Figure 13b, it can be seen that

there is no delay like Figure 8, that means the

display of thermal camera and the color camera

has been running smooth.

The processing time of the thermal camera and

color camera after each run parallel on a different

thread can be seen in Figure 14. In Figure 14 it can

be seen that the processing time of the color

camera and thermal camera data is more stable

than using a certain timer interval. In this way it

can also accommodate dynamic processing, it can

be seen from the absence of a zero processing time

so that no lag occurs. The resultant fps on parallel

processing can be seen in Figure 15. In Figure 15 it

can be seen that the average fps generated on each

camera's processing is more stable and is at the top

level of 7.5 fps on the thermal camera and 7.5 - 8

fps in the color camera.

Figure 12. The sequential processing time at each

interval in fps

(a)

(b)

Figure 13. GUI with added two threads; (a) shortly

after the bottle is moved to the front of the camera; (b)

three seconds later.

(a)

(b)

Figure 14. The parallel processing time of; (a) color

camera; (b) thermal camera

Figure 15. The parallel processing time in fps

24 INKOM, Vol 11 No. 1, Mei 2017: 15-24

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4. Conclusions

The multi-threaded method has been successfully

implemented on two DOF robotic arm

telemonitoring systems. The experimental results

show that both the camera display with tracking

algorithm used inside it after the process is

separated using two threads can work smooth

which can be seen visually, no lag in the

processing time, and the average fps is more stable

and is on higher level that is 7.5 fps on the thermal

color and 7.5 - 8 fps on color camera. Based on

this, it can be seen that the two threads added can

improve the speed, and make it stable to make the

display to be smooth without any delay like a

sequential program.

Acknowledgments

Author would like to thanks to Research Center for

Electrical Power and Mechatronics, Indonesian

Institute of Sciences (LIPI) for the support of

research funds, Mechatronics research group,

especially to Aditya Sukma Nugraha, Hendri Maja

Saputra, and Rifa Rahmayanti that have supported

this research, and all those who have helped

conducting this research.

References

[1] Intel Corporation, “Intel® Core™ i7-6950X

Processor Extreme Edition”, [Online]. Available:

https://ark.intel.com/products/94456/Intel-Core-

i7-6950X-Processor-Extreme-Edition-25M-

Cache-up-to-3_50-GHz. [Accesed: 03 04 2017].

[2] Intel Corporation, “Intel® Xeon® Processor E7-

8890 v4”, [Online]. Available:

https://ark.intel.com/products/93790/Intel-Xeon-

Processor-E7-8890-v4-60M-Cache-2_20-GHz.

[Accesed: 03 04 2017].

[3] Intel Corporation, “Intel® Xeon® Processor E7-

8894 v4”, [Online]. Available:

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Processor-E7-8894-v4-60M-Cache-2_40-GHz.

[Accesed: 03 04 2017].

[4] B. Huynh, B. Vo dan V. Snasel, “An efficient

method for mining frequent sequential patterns

using multi-Core processors”, Applied

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[5] L. Cabaret, L. Lacassagne dan D. Etiemble,

“Parallel Light Speed Labeling: an efficient

connected component algorithm for labeling and

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[6] M. Mirdanies, A. S. Prihatmanto dan E. Rijanto,

“Object Recognition System in Remote

Controlled Weapon Station using SIFT and SURF

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Vehicular Technology, Vol. 4, No. 2, 2013, pp.

99-108.

[7] R. Zhu, L.-h. Qin, J.-l. Zhou dan H. Zheng,

“Using multi-threads to hide deduplication I/O

latency with low synchronization overhead”,

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6, 2013, pp. 1582-1591.

[8] M. Sikora dan I. Mateljan, “A method for

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[9] I. C. Gyllensten, A. Crudall-Goode, R. M. Aarts

dan K. M. Goode, “Simulated case management

of home telemonitoring to assess the impact of

different alert algorithms on work-load and

clinical decisions”, BMC Medical Informatics and

Decision Making, Vol. 17, 2017, pp. 1-11.

[10] I. Azimi, A. M. Rahmani, P. Liljeberg dan H.

Tenhunen, “Internet of things for remote elderly

monitoring: a study from user-centered

perspective”, Journal of Ambient Intelligence and

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273-289.

[11] Z. Kalal, K. Mikolajczyk dan J. Matas, “Tracking-

learning-detection”, IEEE transactions on pattern

analysis and machine intelligence, Vol. 34, No. 7,

2012, pp. 1409-1422.

[12] cppreference.com, “C++ reference”, 2017.

[Online]. Available: http://en.cppreference.com/ w/cpp/chrono/high_resolution_clock. [Accesed:

13 04 2017].

[13] The Qt Company, “Qt”, 2017. [Online]. Available:

http://doc.qt.io/qt-5/qtime.html. [Accesed: 13 04

2017].

INKOM, Vol 11 No. 1, Mei 2017: 25-32

Build and Design of Voyage Account Applications Using C#, WPF,

and SQL Server 2012

(Case Study Company X)

Desi Dyah Sulistyarini, Reza Kamaluddin Isman, Hata Maulana Politeknik Negeri Jakarta

Jl. Prof. G.A. Siwabessy, Kampus Baru UI, Kota Depok Jawa Barat, Indonesia 16424

Email: [email protected]

Abstract

Voyage Account is an application that record consumption data on each voyage, then the data is used to calculate

the profit or loss of each voyage. The application interface was created using Windows Presentation Foundation (WPF).

Prototyping development methods was used to create these applications, C# as the programming language, and SQL

Server 2012 as the database. This study discusses the making of the interface, backend, and database of application.

WPF has been selected, because WPF is the latest technology developed by Microsoft after WinForms. WPF presents a

display that can be customized to user needs. The test results showed that the Voyage Account application is already

functioning in accordance with wishes of the user, the test is done using sql query by entering the appropriate username

and password and match the query returns results with what is displayed by the application. As for testing the interface

shows if WPF can be flexibly adapted to the screen resolution of 1366 x 768, 1920 x 1080 and 1280 x 720.

Keyword:Voyage Account, WPF, prototyping, C#, SQL Server 2012.

1. Preliminary

1The main business of Company X is focused

on the coal transportation to supply PLN power

plants, a subsidiary of PLN, and Independent

Power Producers (IPP). Consumption from every

voyage captured and recorded to quantify the

costs incurred by the company. Calculation from

recorded data is made to be used as an evaluation

for the Operation Department and Fleet

Department.

Each voyage which is run by fleet has the

income and cost. Costs to be charged in one

voyage, among others MFO (Marine Fuel Oil),

MDO (Marine Diesel Oil), CO (Crude Oil), FW

(Fresh Water), and HSD (High Speed Diesel).

When fleet do not travel in accordance with a

predetermined time, the operational costs of fleet

will increase. There are many factors that cause

the fleet did not arrive at their destination on time,

one of which is the weather. Because of that

factor, Voyage Account application is made so

that can be used to record data obtained during the

voyage and this application can calculate profit or

loss that is earned by the company in one voyage.

Voyage Account application can shorten the time

of calculation of profits and losses which

1Received : 10 April 2017; Revised : 28 Sept 2017;

Accepted : 15 Dec 2017; Published Online: 3 May

2018 @2017 INKOM 2017/18-NO541

DOI:http://dx.doi.org/10.14203/j.inkom.541

originally was done manually. In addition, the

information generated by the application can be

used as an evaluation of each voyage.

2. Result

(Soni, 2012) [1] argued that WPF is a reliable

technology in the manufacture of a GUI. When

combined with the method MVVM, processing

applications can be more focussed, because the

method of MVVM that divides an application into

three parts, namely Model (back end Voyage

Account), ViewModel (back end Window Voyage

Account), and View (front end or Window)

Technology WPF and MVVM pattern be applied

in applications Voyage account.

According Akiki, Pierre A. Arosha K.

Bandara, and Yijun Yu [2] applications design

that created already adapted to the whim of the

client. However, sometimes users who use these

applications do not necessarily have the same

opinion on the application design that has been

made, because of the experience and knowledge

of each user is different.Thus, if the user desires

varied met, factors such as performance, price,

and time will increase, impacting applications and

users themselves. This problem can be solved

with adaptive design, in which the method of this

design, the varied desires can be met. Because,

applied adaptive design is the simplest design

application and each user can add their own

functionality according to the needs and desires of

each. For example, the default view Adobe

26 INKOM, Vol 11 No. 1, Mei 2017: 25-32

Illustrator CS6, functions basic functions such as

to draw a square or circle that has been provided

on the toolbox left of the application, while using

a function to draw the kind of brush-shaped arrow

in the application, the user must add it via the

Window menu in the menubar section on the

application and then choose a brush library and

choose a new arrows then the user can use the

function to draw the arrow.This is done in Adobe

for drawing basic shapes such as square or circle

is the basic need of every user applications, both

from a user-level amateur to professional, while

the functions of drawing with the brush-shaped

arrow usually used a specific user only, so with an

adaptive design applied to Adobe, users with

amateur level does not interfere with the choices

necessary functions that are not shown in the

default view of the application.Adaptive design is

a method that can be applied to a wide variety of

programming languages in accordance with the

ability of the developer. As with the Java

language, C # (WinForms and WPF), Python, or

Objective C and Swift. But when referring to the

second article, WPF than the other advantages is

rendered WPF display with 2 threads. Thread 1 to

render and thread 2 to adjust the responsiveness of

the user interface, so that the display of adaptive

design can be better and more responsive.

3. The Making of Application

Voyage Account application created by using

Microsoft Visual Studio 2015. Microsoft [3]

stated that Microsoft Visual Studio 2015 is a set

of software tools to create, from the planning

phase through User Interface design, coding,

testing, debugging, analyzing code quality and

performance, deploy to customers (publishing),

and collect telemetry (metered variable) on use. In

making the Voyage Account, the method used in

analysis of needs (requirements analysis) is to

interview the users from Operation and Fleet

Department. After the interviews obtained several

requirements, including:

• Using an integrated database. • Add and edit data ship, barge, and ports. • Add and edit fixed cost of each fleet. • Calculate the profit / loss of each voyage by

the fleet.

• Can view v