Flow Measurement and Instrumentation
Smart Device For Monitoring River Sediment Based On Adaptive Least Mean Square (LMS)
--Manuscript Draft--
Manuscript Number:
Article Type: Research Paper
Keywords: sediment; arduino; adjustable infrared sensor switch; adaptive Least Mean Square;
river maintenance; predictive Corresponding Author: Mauridhi Hery Purnomo
Institut Teknologi Sepuluh Nopember Surabaya, Jawa Timur INDONESIA First Author: Sri Arttini Dwi Prasetyowati, Dr.
Order of Authors: Sri Arttini Dwi Prasetyowati, Dr.
Bustanul Arifin Junido Ardalli Munaf Ismail
Mauridhi Hery Purnomo
Abstract: River maintenance is very important to monitor the sediment that causes very serious problems for the environment around the river as an examples flooding and water pollution. This research will revive river mantenance continuously and focuses on developing the device that can detect the sediment in the river using ultrasonic sensor.
The prototype of small boat with Arduino is made connected with sensor. The pipe in the prototype that can go up and down with adjustable infrared sensor switch are also prepared to measure points at certain depths.
This research shows that it can find the location and measure the volume of sediment.
Measurement error can be corrected using predictive adaptive Least Mean Square with the step size around 0,00002. The size of the object is inversely proportional to the step size $\mu$. The real device can be determined based on the comparison of real and artificial river.
Suggested Reviewers: Karsten Tawackolian, Dr.
Physikalisch-Technische Bundesanstalt [email protected]
Dr. Tawackolian has similar research topic with the proposed article Andreas Weissenbrunner, Dr.
Physikalisch-Technische Bundesanstalt [email protected]
Dr. Weissenbrunner has a similar research topic as the proposed article Bernt Lie, Dr.
University of South-Eastern Norway [email protected]
Dr. Lie has a similar paper related to the proposed article
Sri Arttini Dwi Prasetyowati Industrial Technology Faculty, Universitas Islam Sultan Agung, Indonesia Jl. Kaligawe Raya No.KM. 4, Terboyo Kulon, Kec. Genuk, Kota Semarang, Jawa Tengah 50112
May 7, 2020
Dear Journal of the Flow Measurement and Instrumentation Editor, Professor J. Delsing
We wish to submit an original research article entitled “Smart Device for Monitoring River Sediment
Based On Adaptive Least Mean Square (LMS)” for consideration by Journal of the Flow Measurementand Instrumentation.
We confirm that this work is original and has not been published elsewhere. In the past, we’ve tried to submit this article to Journal of the International Measurement Confederation (IMEKO), ISSN: 0263-2241 (https://www.journals.elsevier.com/measurement/). However, the IMEKO editor refused to review the article since its not part of the IMEKO research topic. Indeed, the IMEKO editor suggest to submit to the Journal of the Flow Measurement and Instrumentation Editor.
In this paper, we report show the method of sediment detection in the river that help to monitors the river environment condition. We believe that this manuscript is appropriate for publication by Journal of the Flow Measurement and Instrumentation.
We have no conflicts of interest to disclose.
Please address all correspondence concerning this manuscript to [email protected] Thank you for your consideration of this manuscript.
Sincerely,
Sri Arttini Dwi Prasetyowati
Cover LetterHIGHLIGHTS
Title:
Smart Device For Monitoring River Sediment Based On Adaptive Least Mean Square (LMS) List of Author:
Sri Arttini Dwi Prasetyowati, Bustanul Arifin, Junido Ardalli, Munaf Ismail, Mauridhi Hery Purnomo*
Corresponding Email:
[email protected] Affiliation:
Industrial Technology Faculty, Universitas Islam Sultan Agung, Indonesia
(*) Computer Engineering Department, Institut Teknologi Sepuluh Nopember, Indonesia Highlights:
1. The device is a prototype of small boat with Arduino in it, connected with Adjustable Infrared Sensor mounted on the pipe that can go up and down to measure points at certain depths.
2. Make sure that the differences voltage between the left and the right DC motors are not significant when they are given a varying PWM value. Because differences voltage will make differences in speed and will affect to the reading of the adjustable infrared sensor.
3. The movement of the device follows the rules of translation and rotation matrix according to the flowchart for calculating length, width and height
4. Adaptive LMS Prediction will minimize the Calculation error for length, width and height until one thousandth level of accuracy.
5. To determine the size of the device for real conditions, by compared the surface area of artificial river to the surface area of the real river.
Highlights
Smart Device For Monitoring River Sediment Based On Adaptive Least Mean Square (LMS)
Sri Arttini Dwi Prasetyowatia, Bustanul Arifina, Junido Ardallia, Munaf Ismaila, Mauridhi Hery Purnomob,∗
aIndustrial Technology Faculty, Universitas Islam Sultan Agung, Indonesia
bComputer Engineering Department, Institut Teknologi Sepuluh Nopember, Indonesia
Abstract
River maintenance is very important to monitor the sediment that causes very serious problems for the environment around the river as an examples flooding and water pollution. This research will revive river mantenance continuously and focuses on developing the device that can detect the sediment in the river using ultrasonic sensor. The prototype of small boat with Arduino is made connected with sensor. The pipe in the prototype that can go up and down with adjustable infrared sensor switch are also prepared to measure points at certain depths. This research shows that it can find the location and measure the volume of sediment. Measurement error can be corrected using predictive adaptive Least Mean Square with the step size around 0,00002. The size of the object is inversely proportional to the step sizeµ. The real device can be determined based on the comparison of real and artificial river .
Keywords:
sediment, arduino, adjustable infrared sensor switch, adaptive Least Mean Square
∗Corresponding author
Email address: [email protected](Mauridhi Hery Purnomo)
Manuscript File Click here to view linked References
1. Introduction
Sediment causes a very serious water problems including flooding, water pollution, and problems related to water ecosystem. Hence, river maintenance is very important to administer, especially to monitor the existence of river sediment.
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This research aims at making a prototype to help maintaining river conditions in which good river conditions are regarded as clean and clear. Nowdays, the current river conditions have a lot of sediment. It makes the river depth become shallow affecting the volume of water that can be accommodated. River conditions will get worse if sediment problems are not immediately resolved. The
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prototype will look for the location of the sediment, calculate sediment volume, and send warning message to the operator by Internet of Things if the condition is unsafe. The Prototype is similar to a small boat with Arduino Mega 2560 RS in it, connected with sensor and also pipes that can go up and down to measure points at certain depths. Besides arduino, the components needed in making
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the prototype are Rotary Encoder, Adjustable Infrared Sensor Switch, Motor DC, driver motor, and Liquid Crystal Display (LCD). The followings are the previously published literature about sensor and methods for detecting object in the water, a journal entitled A Statistically-Based Method for the Detection of Underwater Objects in Sonar Imagery, explains algorithm that is suitable for all
20
sonar detection applications as well as suitable for all sonar types for detecting objects in water. The algorithm does not require knowledge about the shape and the size of the target [1]. This Journal is different from this research because this research actually intended to look for the shape of the target, even the volume of the target. Modeling and Analysis of Coverage Degree and Target Detection
25
for Autonomous
2. State of The Art Related to Sensor and Methods For detecting object in the water
The followings are the previously published literature about sensor and methods for detecting object in the water.
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A journal entitled A Statistically-Based Method for the Detection of Underwater Objects in Sonar Imagery, explains algorithm that is suitable for all sonar detection applications as well as suitable for all sonar types for detecting objects in water. The algorithm does not require knowledge about the shape and the size of the target [1]. This Journal is different from this research because this
35
research actually intended to look for the shape of the target, even the volume of the target.
Modeling and Analysis of Coverage Degree and Target Detection for Autonomous Underwater Vehicle-Based System, is article that theoretically investigates the dynamic aspects of the coverage degree and target detection in the underwater
40
environment resulting from the given moving scenarios of the autonomous underwater vehicles (AUVs). With the help of the continuous moving AUVs, the underwater targets, that cannot be detected by the stationary underwater acoustic sensor network can be detected with an expected probability, which is determined on the basis of the selected moving scenario of the AUVs. It indicates that, for
45
an AUV with randomly selected initial starting point and initial direction, the straight trajectory is the optimal route to achieve the maximum coverage and target detection probability [2].
Another paper focuses on automated recognition of underwater objects by means of light detection and ranging (LIDAR) systems. The paper was entitled Automatic
50
Underwater Object Recognation by means of Fluorescense LIDAR. Different from the common works involving LIDAR as means of underwater object recognition, where objects are recognized by their shape, here the interest is distinguishing objects on the basis of physical/chemical properties of object materials [3].
A reseach entitled Imaging of Compact Objects Buried in Underwater Sediments
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Using Electrical Impedance Tomography was undertaken to investigate the
performance and phenomenology of electrical impedance tomography for underwater applications. Experiments were performed in an aquarium tank filled with water and a sediment layer. A 64-electrode square array, appropriately scaled down in size and a previously developed data acquisition system are used.
60
An evaluation was conducted of the ability to detect compact objects buried at various depth in the sediment, with different horizontal separations, and at various vertical separations between the electrode array and the sediment layer. The objects included metallic and nonmetallic mine-like objects and inert ammunition projectiles, all appropriately scaled down in size. The effects of a
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number of other physical factors are studied, including sediment type, water turbidity and salinity, and object coating integrity and rusting [4].
The Journal entitled Wireless sensor network-based solution for environmental monitoring: water quality assessment case study presents a wireless sensor network architecture that combines low cost sensing nodes and a low cost multi-
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parameters sensing probe for reliable monitoring of water quality parameters of surface waters (lakes, estuaries and rivers) in urban areas [5].
Soetjie Poernama Sari (2009) in the research entitled Detection and Interpretation of Targets on the Seabed Using Side Scan Sonar Instruments, was conducted using a local fishing boat that has been installed towfish. Inside this towfish
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there are two sonar mounted on the side of the bottom. The sonar used is the C-Max Side Scan Sonar which has a frequency between 100 kHz to 500 kHz.
But it was not calculate the volume of the targets [6].
3. Electronic Prototype Design
Arduino Mega 2560, as a processor and controller would be programmed
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using the Arduino IDE. Push button located near the LCD to make easy for setting up or testing the prototype. Lipo socket is a place where the lipo battery is connected. Header pin is used to link lipo battery source to the electronic part of the motor driver and the Arduino Mega 2560.
Adjustable Infrared sensor is the most important part in this prototype,
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(PWM) Pin 12 (PWM) Pin 13
12V
5IN1 7IN2
6ENA
OUT12
OUT23
11ENB OUT313
OUT414
10IN3 12IN4
SENSA 1
SENSB
15 GND
8 VS
4 VCC
9 U1
L298 SIDE MOTOR 1
12V
SIDE MOTOR 2 12V
12V
IN15
IN27
ENA6
2OUT1 3OUT2
ENB11
13OUT3 14OUT4
IN310
IN412
SENSA1
SENSB15
GND 8 VS
4 VCC
9 U2
L298
PWM COMUNICATION
DIGITAL
ANALOG IN ATMEGA2560
16AU 1126
TX0 TX3 TX2 TX1 SDASCL
RX0 RX3 RX2 RX1
Reset BTN
www.TheEngineeringProjects.com ON
ON ON
Arduino Mega 2560
PD0/SCL/INT021PD1/SDA/INT120PD2/RXD1/INT219PD3/TXD1/INT318PH0/RXD217PH1/TXD216PJ0/RXD3/PCINT915PJ1/TXD3/PCINT1014
PE0/RXD0/PCINT80PE1/TXD0/PDO1PE4/OC3B/INT42PE5/OC3C/INT53PG5/OC0B4PE3/OC3A/AIN15PH3/OC4A6PH4/OC4B7
PH5/OC4C8PH6/OC2B9PB4/OC2A/PCINT410PB5/OC1A/PCINT511PB6/OC1B/PCINT612PB7/OC0A/OC1C/PCINT713
AREF PK7/ADC15/PCINT23A15PK6/ADC14/PCINT22A14PK5/ADC13/PCINT21A13PK4/ADC12/PCINT20A12PK3/ADC11/PCINT19A11PK2/ADC10/PCINT18A10PK1/ADC9/PCINT17A9PK0/ADC8/PCINT16A8
PF7/ADC7/TDIA7PF6/ADC6/TDOA6PF5/ADC5/TMSA5PF4/ADC4/TCKA4
PF3/ADC3A3PF2/ADC2A2PF1/ADC1A1PF0/ADC0A0
RESET VCCGND
PA0/AD022
PA1/AD123
PA2/AD224
PA3/AD325
PA4/AD426
PA5/AD527
PA6/AD628
PA7/AD729
PC6/A1431
PC5/A1332
PC4/A1233
PC3/A1134
PC2/A1035
PC1/A936
PC0/A837
PD7/T038
PG2/ALE39
PG1/RD40
PG0/WR41
PL742
PL643
PL5/OC5C44
PL4/OC5B45
PL3/OC5A46
PL2/T547
PL1/ICP548
PL0/ICP449
PB3/MISO/PCINT350
PB2/MOSI/PCINT251
PB1/SCK/PCINT152
PB0/SS/PCINT053
PC7/A1530
ARD1
ARDUINO MEGA 2560
+88.8
FRONT ROTARY MOTOR 1
+88.8
FRONT ROTARY MOTOR 2 PIN 2PIN 32GND PIN 3PIN 30GND
D714D613D512D411D310D29D18D07
E6RW5RS4
VSS1VDD2VEE3 LCD
5V
PUSH BUTTON MOTOR DRIVER 2
MOTOR DRIVER 1
www.TheEngineeringProjects.comIn
fra red
S
en so
r VccDGNTOU
TestPin
LEFT IR IR OBSTACLE SENSOR
www.TheEngineeringProjects.comIn
fra red
S
en so
r VccDGNTOU
TestPin
RIGHT IR IR OBSTACLE SENSOR
www.TheEngineeringProjects.comIn
fra red
S
en so
r VccDGNTOU
TestPin
CENTER IR IR OBSTACLE SENSOR
Figure 1: Electrical Circuits
because this is a device that detects objects in water at a certain distance. It will be ”0” or ”LOW” if it detects solid objects and becomes ”1” or ”HIGH”
if it does not detect the existence of objects in the water. Figure 1 Shows the electrical circuits and Figure 2 shows the block diagrams of the prototype.
3.1 Prototype Program Design
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There are seven programs to run prototype: LCD and button program, front motor program, front motor Proportional Integral Derivative (PID) program, rotary encoder program, side motor program, adjustable infrared sensor switch program, and program for running the prototype.
Each of the programs has its own specific use. LCD and Button Program is
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used to display data on the LCD and process button data, Front Motor Program is used to control the front motor or to control motor that is used to lower and
Figure 2: Block Diagrams of Prototype.
raise the sensors under the prototype; the Rotary Encoder Program is used to process the input data from rotary encoder; and Side Motor Program is used to control the rotation speed of the motor on the side of the prototype.
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In addition, the Front Motor PID Program is used to process data from motor and rotary encoder. The function of the rotary encoder is to count the number of turns. Therefore this program processes the number of pulses of each rotation and the diameter of the rope winder. Then, the diameter is processed into a circumference. The circumference will be calculated with pulse data and
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will produce a distance for each pulse. With this program the distance data is processed using the PID system to be able to adjust the rotation speed of the motor so that both front motors can rotate at the same speed.
The Adjustable Infrared Sensor Switch Program is used to process the data from an adjustable infrared sensor switch. This sensor will be worth 0 or LOW
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if it detects an object at an adjusted distance and vice versa, if the sensor does not detect objects at a distance that has been adjusted, the data that come out will be worth 1 or HIGH. This sensor is used to detect underwater sediments.
The Program for running prototypes is used to measure sediment volume.
When the sensor has not detected an object, the two front motors will rotate to
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be able to lower the sensor. After the sensor goes down, the prototype moves
forward toward the sediment. The front motor rotates and the rotary encoder counts. The first count is saved.
Then when the front sensor detects an object, the front motor will rotate in the opposite direction to raise the sensor. After the three sensors do not detect
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objects, the prototype goes forward. The bottom sensor that detects objects starts counting to find out the length of the object. When the sensor rises, the second count has been saved. From the difference between the first count and the second count, the height of the object is obtained. After the bottom sensor does not detect an object, the front motor starts turning to lower the sensor.
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The prototype rotates 90°to the other side of the object. In the same way, the width of the object can be found. The programs could be described into two part of flowchart.
3.2 Flowchart Measurement of Sediment Volume
Figure 3 (first and second flowcharts) explain how to measure the length
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and height of the sediment whereas the third and fourth flowcharts show how to measure width of the sediment. It will be described into matrices.
There are 2 (two) types of transformation matrices, translation and rotation matrix. Translational displacement of x-axis, y-axis, and z-axis in coordinate system.
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x0 y0 z0 1
=
1 0 0 tx 0 1 0 ty 0 0 1 tz
0 0 0 1
x y z 1
There are 3 (three) different matriks for rotation, rotations with respect to the x-axis, y-axis and z-axis, each can be shown in the following matrix: [7].
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Rotation matrix with respect to the X-axis:
Figure 3: Flowchart Prototype
x0 y0 z0 1
=
1 0 0 tx
0 cosa −sina ty 0 sina cosa tz
0 0 0 1
x y z 1
Rotation matriks with respect to Y-axis:
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x0 y0 z0 1
=
cosa 0 sina tx
0 1 0 ty
−sina 0 cosa tz
0 0 0 1
x y z 1
Rotation matriks with respect to Z-axis:
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x0 y0 z0 1
=
cosa −sina 0 tx sina cosa 0 ty
0 0 1 tz
0 0 0 1
x y z 1
The movement of the prototype follows the rules of translation and rotation.
Assumed that the position of the sensor on the water surface is the center (0,0,0).
DC motor lowers the sensor, and will try to detect object for the first detection.
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In this condition, if the bottom sensor detects an object, then the data from the rotary encoder will start to be counted.
The flowchart in Figure 3 explains that after the bottom sensor detects an object, the prototype will move forward until the front sensor detects the object in front of it. When moving forward, the translation matrix will represent the
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movement of the prototype. Suppose the prototype moves forward by m1 cm, then the transition matrix becomes:
Figure 4: The position after the front sensor detects the sediment, then the prototype will go up
1 0 0 0
0 1 0 m1
0 0 1 0
0 0 0 1
0 0 0 1
=
0 m1
0 1
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Furthermore, the prototype will go up and the front sensor detects the object in front of it until it does not detect the object. Figure 4 and Figure 5 show that condition. The rotary encoder will record the upward movement as a high of sediment h:
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1 0 0 0
0 1 0 0
0 0 1 h
0 0 0 1
0 m1
0 1
=
0 m1
h 1
The prototype begins to move forward (m2 cm) and moves as far as sediment length l or the position become m2 + l.
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Figure 5: The prototype go up and the front sensor detected the topmost sediment
1 0 0 0
0 1 0 m2 +l
0 0 1 0
0 0 0 1
0 m1
h 1
=
0 m1 +m2 +l
h 1
Figure 6 shows the movement of prototype as far as sediment lenght
After obtaining the sediment length, the prototype will search for the sediment width according to Figure 7. The prototype will go forward along 10 cm, then
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rotate 90°counterclockwise with the Y-axis to the center: (0, m1+m2+l, h)
1 0 0 0
0 1 0 10
0 0 1 0
0 0 0 1
0 m1 +m2 +l
h 1
=
0
m1 +m2 +l+ 10 h
1
Here is a rotation matrix where the prototype is rotated 90°. Figure 7 shows
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the rotation of the prototype 90°counterclockwise along the Y-axis.
Figure 6: Prototype moves as far as sediment length
Figure 7: The prototype is rotated 90°along the Y-axis, the center position of the ship does not change
cos90 0 sin90 0
0 1 0 0
−sin90 0 sin90 0
0 0 0 1
0 10
0 0
+
0 m1 +m2 +l
h 1
=
0 0 1 0
0 1 0 0
−1 0 0 0
0 0 0 1
0 10
0 0
+
0 m1 +m2 +l
h 1
=
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0 10
0 0
+
0 m1 +m2 +l
h 1
=
0
m1 +m2 +l+ 10 h
1
The image for the next process is analogous to the three images above, the
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difference only depends on the movement of the prototype according to its axis.
Next, it moves forward 5 cm along the X-axis
1 0 0 5
0 1 0 0
0 0 1 0
0 0 0 1
0
m1 +m2 +l+ 10 h
1
=
5
m1 +m2 +l+ 10 h
1
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Then, it rotates again 90° counterclockwise to the X-axis, with the center:
(0, m1+m2+l+10, h).
1 0 0 0
0 cos90 −sin90 0 0 sin90 cos90 0
0 0 0 1
5−0
0 h−h
1−1
+
0
m1 +m2 +l+ 10 h
1
=
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1 0 0 0
0 0 −1 0
0 1 0 0
0 0 0 1
5 0 0 0
+
0
m1 +m2 +l+ 10 h
1
=
5
m1 +m2 +l+ 10 h
1
It moves forward 15 cm along the Y-axis,
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1 0 0 0
0 1 0 15
0 0 1 0
0 0 0 1
5
m1 +m2 +l+ 10 h
1
=
5
m1 +m2 +l+ 25 h
1
Then turn 90°counterclockwise with the center: (5, m1+m2+l+10, h) ti the Y-axis.
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cos90 0 sin90 0
0 1 0 0
−sin90 0 cos90 0
0 0 0 1
5−5
15 h−h
l−l
+
5
m1 +m2 +l+ 10 h
1
=
0 0 1 0
0 1 0 0
−1 0 0 0
0 0 0 1
0 15
0 0
+
5
m1 +m2 +l+ 10 h
1
=
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Figure 8: Predictive Adaptive LMS Block Diagram
5
m1 +m2 +l+ 25 h
1
Next, repeat steps like the beginning to find the length of the sediment, and find the width of the sediment w cm.
3.3 Least Mean Square Adaptive Predictive
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The length, width and height measurements obtained from the adjustable infrared sensor must have measurement errors. The efforts to minimize errors occurred are by repeating the measurements several times. Furthermore, the data is processed in order to obtain accurate data. The process carried out is a predictive process to predict the most accurate data.
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Predictive process is performed using the Adaptive Predictive Least Mean Square (LMS) Algorithm. The predictive Adaptive LMS block diagram is given in Figure 8. [8][9][10]
The values of d and x are targets and inputs, respectively. Both are the same data, but input x is the target that is delayed by one sample. The Adaptive
LMS algorithm with the Linear Combiner scheme is as follows:
yk=
L
X
0
WlkXk−l
k =dk−yk
Wk+1=Wk+ 2µXkk
k =error resulting f rom adaptation during k−iteration dk =target (desiredoutput)during k−iteration
µ=step size
yk=output during k−iteration Xk= [Xk Xk−1 Xk−2...Xk−L]T Wk = [W0 W1W2...Wk]T xk=input during k−iteration wk =weight during k−iteration
(1)
4. Experiments and Results
4.1 DC motor, Rotary Encoder and Adjustable Infrared Sensor
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There are differences in the voltages of the left and the right front DC motors when they are given a varying PWM value. Due to the voltage difference, it will cause the difference in the speed of the right motor and left motor. The difference in the speed will affect to the reading of the adjustable infrared sensor, therefore a rotary encoder is needed to stabilize the speed. Figure 9 shows the
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differences between left and right front DC motor voltages.
Figure 10 shows a graph of the difference in left and right side DC motor voltages.
Because of the difference in voltage on the left DC motor and the right DC motor, it is necessary to check the results of the rotary distance on the left and
245
right motors.
It is necessary to do research on the rope wheel that has a diameter of 1.5 cm, circumference of 4.71 cm. The pulse at one rotary encoder rotation is 1700
Figure 9: The differences between left and right front DC motor voltages
Figure 10: The differences between left and right Side DC motor voltages
Figure 11: Graph of rotary encoder distance testing results
pulses. Then it is calculated in the Arduino program to get the distance in each centimeter. Figure 11 shows the testing data of rotary distance on the left and
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right motors.
The measurement error of the left rotary and right rotary compared the actual value is very small. The error average is 0.9.
4.2 Measurement of Sediment Volume
The length of the sediment is obtained through the motion of the prototype
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with the sensor as the detector. The prototype is driven using the right and left side motors. This movement is carried out starting from the beginning of the sediment until the end of the sediment in the direction of the sediment length as shows in Figure 12 Testing of sediment width is the same with testing sediment length reading. The difference is the movement made from the beginning to the
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end of the sediment in the direction of the width of the sediment.
For testing the height of the sediment, it was done by processing the rotary encoder data. Figure 13 shows the sediment width detection. When sediment has not been detected by an adjustable infrared sensor switch, the motor will rotate the rope to lower the sensor to the bottom. Then when the sensor detects
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Figure 12: Length detection
Figure 13: Width detection
the sediment, the motor will rotate the rope to make the sensor rise until the sensor does not hit the sediment. From the motor rotation, rotary encoder take the data for computing the height of sedimen. Figure 14 shows the sediment height detection.
4.3.Prediction Results with Adaptive LMS
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The results of measurements done in Section 4.2, still contain measurement errors. The existence of measurement error is shown in Figure 15. There are no precise 40 cm measurements made by the adjustable infrared sensor which is recorded by a rotary encoder.
Figure 14: Height detection
Figure 15: Measurement of 40 cm sediment
Table 1: Optimum Step Size and Error
SIZE OPT STEP SIZE ERROR
H6 0,000861 0.0014
W10 0.00029615 0.0001
H12 0.000208 0.0046
L20 0.0000778 0.0074
W20 0.00007328 0.0003
L40 0.00002059 0.0063
Adaptive LMS Prediction can minimize the error untill one thousandth level
275
of accuracy. Theµparameter is a parameter that greatly determines the speed and accuracy of adaptation. With various values ofµbeing tested, the accuracy of measurement of length, width and height can be shown in Figure 16.
Figure 16 shows that all of the data can be achieved the right value in five iterations. Table 1 shows that the size of the object is inversely proportional
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to the parameter value mu. This is due to the more careful adaptive systems in maintaining the occurrence of high oscillations. Step sizeµhas values range from 0,00002 until 0,0003, depending on the size of the object. Table 1 would be presented as a graphic in Figure 17.
By conditioning the error, the predictive adaptive process will get a value
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that close to accurate.
4.4 Comparison Between Prototype and Actual Device
Figure 18 shows the size of the prototype compared to the artificial river and could be the basis for measuring the actual device when applied to the real river.
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Figure 19 shows the prototype with the dimention that could be the basic for measuring the actual device when applied to the real river.
To determine the size of the device for real conditions, it is necessary to calculate the surface area of artificial river compared to the surface area of the
Figure 16: Prediction for ”Length” , ”Height” and ”Width” in Centimeter (Cm)
Figure 17: Optimum value of step size and Error prediction
Figure 18: Prototype and the artificial river
Figure 19: Prototype with sensors
real river. The calculation results show the prototype is (15 cm x 25 cm) = 375
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cm2, while the area of the artificial river to be checked is (100 cm x 200 cm)
= 20.000 cm2. The river is examined per section with an area of each section is 600 m2. Thus, when applying a real device in the real river, the size of the device does not have to be adjusted to the length of the river, but only adjusted to a certain river area, that is 6.000.000 cm2 . The device will move every 10
300
meters to measure area of 600 m2. Thus, for a real river with area 600 m2, must have the device with surface area: ((6.000.000) (375)) / 20.000 = 112.500 cm2 and must be broken down in length and width.
From Figure 19, the ratio between length and width is length : width = l : w = 5:3, so that, the dimension of actual boat must be
305
l× w= 112.500 l= (112.500/w) l/w= (112.500/w)/w l/w= 5÷3
so that
(112.500/w)/w= 5÷3 3(112.500/w) = 5w
w= 259.8cm dan l= 433.02cm
(2)
The device to be made has dimensions l=433.02 cm and w=259.8 or if rounded l = 433 and w = 260.
5. Conclusions The conclutions are:
1. The measurement error of the left and right rotary compared to the actual
310
value is 0,9 percent.
2. Side DC motor voltages are more stable than front DC motor voltages.
3. The predictive LMS adaptive algorithm will get a measurement value that is close to accurate value with the step size between 0,00002 and 0,0003 depend on the size of the object. The size of the object is inversely proportional
315
to the step sizeµ.
4. The predictive LMS adaptive algorithm can minimize the error until one thousandth level of accuracy.
5. The dimension of actual device close to 433 cm long and 260 cm wide.
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320
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Declaration of interests
Title: Smart Device For Monitoring River Sediment Based On Adaptive Least Mean Square (LMS) List of Author:
Sri Arttini Dwi Prasetyowati, Bustanul Arifin, Junido Ardalli, Munaf Ismail, Mauridhi Hery Purnomo*
Corresponding Email:
[email protected] Affiliation:
Industrial Technology Faculty, Universitas Islam Sultan Agung, Indonesia
(*) Computer Engineering Department, Institut Teknologi Sepuluh Nopember, Indonesia
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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