Extended Abstract : STEM and Innovation

12th SCiUS Forum

Figure 1 : Soil-cement column Figure 2 : Installation of soil-cement column
[https://docs.lib.purdue.edu/cgi/viewcontent. [https://www.trevikuwait.co m/ media/imma gi ni/4

cgi?article=3993&context=roadschool] 353_deep_soil_mixing.jpg]

Nowadays, SCC is widely used in the pile foundation to strengthen the bearing capacity of the structure .

The strength of SCC known as unconfined compressive strength (UCS) mainly depends on cement content, curing

time, water content, and depth of the soil layer. However, to evaluate the UCS before the actual construction, many

trial UCS tests with various factors have been required in the laboratory. The UCS equation was limited to

determining the relationship between the strength developments with the factors mentioned above.

Methodology

The relationship between cement content and UCS value was created by assembling data from previous
investigations and separating them into 7, 14, and 28 curing days then plotting graphs for each curing day. The
cement content was plotted on the x-axis and the UCS on the y-axis then propose the UCS equation. The unrelated
collected data was filtered out to improve the equation's reliability. The equation's underlying behavior was truly
linear, with the R2 values more than 0. 7. Then, the qualified data was used in regression analysis to indicate the
accuracy of statistical results by the Multiple R-value approach 1 and the Significance F. value lower than 0.005.
After that, the most appropriate UCS equation under the condition above was chosen. To validate the equation,
the SCC samples were prepared and tested in the laboratory to insist on the reliability of the UCS equation.

SCC preparation in the laboratory is shown in Figure 3. The SCC samples were prepared from the SC at
the Ladkrabang construction area having the liquid limit ( LL) values of about 88.30 % . The SC was thoroughly
mixed with ordinary Portland cement ( OPC) in the ratios 10, 15, and 20 wt% of dry soil samples. Each sample,
SC was oven-dried at 110 °C for 4 h. 88.30 % of tap water was added into the oven-dried SC samples and mixed

approximately 30 min until the color and texture were uniforms, and then OPC was added into the SC samples and
mixed for 5 min. The mixtures were transferred into cylindrical molds to form the soil- cement column or SCC.

The SCC samples were covered by using plastic wrap and cured for 7, 14, and 28 days before the UCS test
following ASTM D2166.

(a) (b) (c) (d) (e)

Figure 3 : SCC preparation in the laboratory (a) soft clay sample (b) 88.30 % of tap water mixed with soil sample
(c) mixed soil samples with OPC (d) Molding SCC samples (e) UCS testing of SCC samples.
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Results, Discussion and Conclusion

The relationship between cement contents and the UCS of SC at 7, 14, and 28 curing days which have
been collected from the previous studies was observed that the proposed UCS equations have R2 values less than
0.7 for all curing days which are less reliable to predict the UCS. R2 is a statistical measurement of the data with
the best fit of the trend line. The R2 values of more than 0.7 is generally considered a strong effect size.

Figure 4 : Equation for evaluating the relationship between Figure 5 : Residual plot of
UCS data and cement contents of 7, 14 and 28 curing days. 7, 14 and 28 curing days

To increase the reliability of the proposed UCS equations, the liquid limit value of SC was considered in
the range of 47% - 103%, while the cement content was higher than 5 wt%. The UCS values were achieved in the
SCC samples from the dry-mixing method of the soil layer within 15 meters depth below the ground surface, and
containing water content over 60%. The non-alignment data of liquid limit and cement contents from the previous
researches have been ignored. The R2 values of UCS equations in all curing periods were improved to over 0.7 as
shown in Figure 4. In Figure 5, the residual plot presents the distance between the point of cement content data
and the point of predicted UCS data. The residuals aligned with the whole range of cement content values, and
therefore, the UCS equations can be used to predict the UCS of SCC on the SC by varying the cement contents.

Table 1 UCS Equations and statistical values of 7, 14 and 28 days.

Curing time (days) UCS Equation (Eq.) R2 Regression analysis

7 y = 39.063x + 169.66 (Eq. 1) 0.8522 Multiple R Significance F
14 y = 46.599x + 240.66 (Eq. 2) 0.8867 0.9161 1.319×10-10
28 y = 53.241x + 321.08 (Eq. 3) 0.9159 0.9420 1.869×10-10
Where; x is cement contents (wt%) and y is UCS (kPa) 0.9568 4.223×10-11

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From Table 1, the multiple R value which indicates how closely two variables move in tandem with each
other was approach to 1 in all curing days. Moreover, the significance F value indicates the hypothesis in our
regression model cannot be rejected. The significance F values which were below 0. 005, therefore, confirm the
statistically significant relation between cement contents and the collected UCS data in this work.

The UCS values of SC samples were plotted as a function of cement content. To verify the UCS equations,
cement contents in range of 10, 15, and 20 wt% were replaced in the x value on the Eq. (1)-(3).

Figure 6 : Comparing graph between Predicted UCS and Practical UCS
Therefore, The UCS equations can estimate the strength and predict the optimized ratio between cement

and SC from the Ladkrabang area for construction of the SCC under specific conditions. Laboratory samples of
SCC with different cement content and curing time were prepared and the UCS were measured and used to validate
the UCS equation. These findings can help to reduce the cost and time applying at the actual construction site.

Acknowledgement
This project was supported by Science Classroom in University-Affiliated School (SCiUS) under

Suranaree University of Technology and Rajsima Wittayalai School. The funding of SCiUS is provided by the
Ministry of Higher Education, Science, Research and Innovation. This extended abstract is not for citation.

References
1. C. Teerawattanasuk, P. Voottipruex, S. Horpibulsuk. Mix design charts for lightweight cellular cemented

Bangkok clay. Applied Clay Science. 2014; 104:319.
2. S. Horpibulsk, R. Rachan, A. Suddeepong, A. Chinkulkijniwat. STRENGTH DEVELOPMENT IN CEMENT

ADMIXED BANGKOK CLAY: LABORATORY AND FIELD INVESTIGATIONS. SOILS AND
FOUNDATIONS. 2011; 51(2):245.
3. S. Apimeteetamrong, J. Sunitsakul, A.Sawatparnich. The engineering behavior of highway embankments on
soft clay during construction of highway number 3117 Klongdan – BangBor. Bureau of Road Research and
development. 2006; 221:91.
4. GraduateTutor.com. Interpreting Regression Output-Without all the Statistics Theory [Internet]. New Jersey:
[publisher unknown]; 2021 [cited 2021 12 20]. Available from: https://www.graduatetutor.com/statistics-
tutor/interpreti ng-regression-outp ut/

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Title: Design and manufacturing a prototype OS2_09_04
of the garbage collecting water bike
Field:
Author: STEM and innovation
Miss Chanchita Meemoongtham
School: Mr. Nattapong Rattanaseangsuang
Advisor: Miss Nathamon Phonimthai
Darunsikkhalai School, King Mongkut's University of Technology Thonburi
Assoc. Prof. Dr. Yossapong Laoonual, Faculty of Engineering, King Mongkut's University
of Technology Thonburi
Mr. Supakit Rongam, Darunsikkhalai School, King Mongkut's University of Technology
Thonburi

Abstract:
Garbage pollution, especially on water sources, is one of the main problems that happened in urban

cities, especially Bangkok. Therefore, the objective of this project is to design and build a prototype of the
garbage collecting water bike model to test before building the real size water bike in the future. The design
method depends on the fluid statics theory as main and designed a scaled-down model for a 1:6 scale prototype
to its actual size by SOLIDWORKS 2020 application. The water bike prototype has the main components
following: the belt-conveyor mechanism for collecting garbage and a pair of waterwheels for movement. All
of this is controlled by circuits of radio-controlled boat through DC motors to transmit mechanical power to
all part of mechanism. Additionally, the model also has the operation simulation by SOLIDWORKS 2020 that
describe working process in each part of this model and the location of Centre of mass. However, due to the
limitation of time, this work was able to be done all only by designing and creating simulations in
SOLIDWORKS 2020. This work has been continued for opportunity to prepare building an actual water bike
in the future.

Keywords: Belt-conveyor system / Garbage collecting water bike / Garbage pollution

Introduction
Garbage pollution on water sources is considered as one of the important problems of urban society,

especially in Bangkok. Since 2015 to 2019 in Bangkok, the amount of accumulated garbage reached 387,261
tons. Due to the large amount of garbage dumped into the water source, the water is polluted, which adversely
damages the ecosystem in the water source. [1] Additionally, this problem also makes the scenery of the city
look unattractive. Although there are many garbage collector machines at present day, collecting garbage on
various water sources is still mostly done by manual labor that spends much time to collect it, and the amount
of garbage that can be collected per time is quite few. Consequently, we came up the idea to create a water
garbage collector that can collect garbage without manual labor and can collect large amount of garbage.

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After researching academic papers, we found most of garbage collector generally used the belt-
conveyor system to collect garbage from water surface and often use waterwheels for movement. In addition,
we also found the interesting idea to build water garbage collector with a water bike, but garbage collecting
by this machine was still done by manual labor. [2] Therefore, we combine all ideas together and come up
with idea to build a garbage collecting water bike that collect garbage automatically after the user starts
cycling.

However, Because of the COVID-19 pandemic, the time for doing the practical work was shortened.
As the consequence, we had to create a scaled-down prototype model instead build an actual water bike for
use to test the functionality and efficiency of the designed water bike model.

Methodology
The designs were divided into 4 parts as follows,

Part 1: Design of the model’s frame.
Because the water bike must be floatable on water, consequently, we used the fluid statics principle,

such as the Centre of gravity and buoyancy. For this reason, the model must have light weight, large air pocket
and the weight does not transfer too much to one side. In production, the model’s frame is made from the ABS
plastic material by 3D-printer.

Part 2: Design of the belt-conveyor system for collecting garbage.
Design Concepts
The model of garbage collecting water bike is a model built with the purpose for collecting garbage

on the water as much as possible. The first thing of in the design was how to make the model can collect
garbage as much as possible. We brainstormed several possible ways, including the clamping method, the
scooping method, and the sliding method, then we discussed which method seemed to collect the most amount
of garbage and consume the least energy resources. As a result, we chose the garbage collection with the

sliding method, because it can collect garbage automatically after the model
started moving. In the other side, the model had to stop moving or employ people
to collect garbage when we used the clamping and scooping methods. In
addition, the sliding method also can collect large amounts of garbage according
to the width. Therefore, the concept of a belt for collecting garbage was coming
Figure 1: The model’s draft out, as shown in Figure 1.
Design description
Belts of the water bike were made of ABS plastic material. The distinguishing features of this
material are light weight and good flexibility. In addition to the material, there is
also the matter of the contact surface of the belt, because the belts are stable for
moving the garbage. For this reason, the belts should have the ability to hold the
waste. Then we designed the belt to have some degree of roughness.

Figure 2: The model’s front side

The garbage collecting belt-conveyor system must be driven by a motor. For this reason, the motor
must be located slightly behind the belt in a part of the garbage collection area, because those spaces are wide

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enough to contain two motors. Then we designed a pedestal to raise the two motors up from the bottom of the
boat, in this way it does not get in the way while the belt is moving and are collecting garbage.

Part 3: Design of the movement part.
Design Concept
As for the movement of the model, we were inspired by the radio-

controlled boat model, which has two propellers driven at the back of the model. Figure 3: The model’s back side
Design descriptions
We use two motors for driving the model. However, we can only use the rotation simulation feature

in the SOLIDWORKS 2020, due to the motor type has not yet been determined. For the components that
covered the motor, we use ABS plastic material, which is same as the garbage collector belts.

Part 4: Simulation
Due to the COVID-19 pandemic, we cannot create an actual model because of limitation of time.

Consequently, it had to be simulated the working simulation by the SOLIDWORKS 2020 instead. Then we
will form all the components of the model first. After the components start building, we will assemble all the
components to build the model of our garbage water bike by the SOLIDWORKS 2020.

Results
The recording of results is divided into two main parts:

Part 1: Model Balance SOLIDWORKS 2020 includes the Centre of mass feature on a defined position related
to an object or system of objects. Centre of mass is the average position of all the parts of the system, which
weighted according to its mass [3] that indicates if the model is balanced and shows the location of the model’s
Centre of mass The point in Figure 4 is the point where all an object’s weight may be concentrated and still
have the same external effect on the body. In this chapter we will learn to locate this point. [4]

Figure 4: The model’s Center of mass

This shows some side that the model is well-balanced.
Another issue is density of this model. According to the principle of buoyancy, if the model's density

value is less than the water density value, the model will float. This is calculated based on the scales of the
model and the air pockets inside the model. We will calculate the density of the empty model first. After that,
we will calculate density of the model when the garbage is inserted. We found that the weight of the garbage
is as much as the model can carry, it shows that the model's density is still less than the density of water at 2
kg. If more garbage is added, the model might be sink. [5]

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Part 2: Simulate Working in SOLIDWORKS 2020

We simulated by motor feature in SOLIDWORKS 2020, where we put

the motors in 4 points, two at the belt and two at the propeller, as shown

in Figure 3. The result is that the belt will rotate towards the model. As

the propeller rotates, the model moves forward to push the garbage to

be moved along the belt.

Figure 5: Four points of motors in the model

Conclusion

At this moment, the garbage water bike model is designed and simulated by SOLIDWORKS 2020,

but we could not really build it due to the current situation of COVID-19. After we designed and simulated

the model, we found our model has well-balanced, not submerged, and capable for simulating the model

rotation by SOLIDWORKS 2020. In the future, we expect that we will have the opportunity to build it the

actual model soon when we can work on-site together for more accurate results.

Acknowledgements
This project was supported by Science Classroom in University Affiliated School (SCiUS) under

King Mongkut's University of Technology Thonburi and Darunsikkhalai School for Innovative Learning. The
funding of SCiUS is provided by Ministry of Higher Education, Science, Research, and Innovation. This
extended abstract is not for citation.

References
1. กลมุ่ งานตดิ ตามประเมินสถานการณ์ กตป, 2562, 13 ธนั วาคม 2562 พบขยะในคคู ลองกทม. สะสม 5 ปี 387,261 ตนั [Online], Available:
https://www.onep.go.th/13-ธนั วาคม-2562-พบขยะในคคู ลองก/ [19 กรกฎาคม 2564].
2. Ristiawan, I., Naim, M. and Jumadil, A. A., 2019, “Design and Manufacturing of a Water Bike to Pick Up
Garbage on Matano Lake”, International Conference on Natural and Social Sciences (ICONSS)
Proceeding [Electronic], Vol.1, No.1, pp.121-127, Available: ICONSS Proceeding Series [2021, June 24].
3. Khan Academy, what is center of mass? [Online], Available: https://www.khanacademy.org/science/
physics/linear-momentum/center-of-mass/a/what-is-center-of-mass [2021, 6 September].
4. Baker, D. W., Haynes, W., 7.2 Center of Gravity [Online], Available: https://engineeringstatics.org/
Chapter_07-center-of-gravity.html [2021, 10 September].
5. Cengel, Y. A., and Cimbala, J. M., 2014, “Pressure and fluid statics”, Fluid Mechanics: Fundamentals
and applications, Third edition, McGraw-Hill, New York, pp.75-103

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Title : Design and Development of Quadcopter for OS2_02_0
Forest fire observation and suspression

Field : Stem and Innovation
:Author Chanachon Chanchote

School : Punyapat Takam
Kritmongkol Rattanathaworn

Demonstration school university of Phayao

:Advisor Acting Sub Lt. Thitinun Gas-Osoth (Division of Physics, School of Science, University of

Phayao, Phayao 56000, Thailand )

Mr. Sarit Promthep (Division of Information Communication and Technology, School of
Computer Science,University of Phayao, Phayao 56000, Thailand )

Abstract

This project proposes 1. develop quad copter use to observe forest fire and invasion,
extinguishedfire with extinguisher ball. 2. To solve forest fire problem in Phayao province. We design and
develop a Quad copter which is able to carry a fire extinguisher ball and equip First Person View (FPV)
camera to help the fire fighters to observe the cause of forest fire. The front FPV camera is able to send real-
time video image to ground control monitoring. The quad copter consists of carbon fiber base with 4 poles
at each end, flight controller, gyro sensor to balance the quad copter and a blushless motor at each pole
which is connected to DJI III phantom propeller, controlled by Electronic Speed Controller (ESC). The
mechanical servo arm, installed in the bottom of the quad copter, utilize drop extinguisher ball, controlled
by Arduino microcontroller with the NRF24L01 radio transceiver to respond commandsignal to drop the
extinguisher ball. This paper introduces the basic development of Quad copter prototype with approximately
17 minutes per flight, 400-meter length and carry 1 kg payload. The Quad copter is able to relieve the forest
fire problem and protect the community.

:Keywords Remote, Drone, Quad copter, First Person View, Forest Fire

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Introduction
Forest fire is the natural disaster occur in the hard-to-reach area in deep forest which if spread fast

enough can be dangerouse for both living thing and forest itself.
for the past ten-year forest fire make problem that hit on people escpilly people living in northern regioen
Thailand tiny PM2.5 pollution particles caused an estimated 32,000 premature deaths in Thailand in 2019,
according to the State of Global Air 2020 report.We take it apon ourselves to develop quadcopter to be able to
relieve forest fire problem by develop it to be fire fighther quadcopter with can carry up to 1 kg payload and
can drop fire extinguisher ball to help stall nor stop the fire spread.

In this development we separate in project into 4 part 1. quadcopter design 2. extinguished holder
3. extinguished ball 4. experiment with the quadcopter. In the first part we decuss on the position and how
many pole we will use and settle on 2 poles crossed. After that we order quadcopter part and aseemble to do
normal flight test. In the second part we put a lot of time in it because it the main key to make our project
difference we design a couple of samples the prototype and settle on the easy but make impact one. Third part
is also important but easy enough to make.Then the final part we need to test flight the modify quadcopter.

Methodology
The project were devided into 4 parts as follows,

Part 1 : Quadcopter design process.

1.1 Study of quadcopter structure.The first step to building a quadcopter is to understand the components
that it uses to hovering mid air.A quadcopter consists of the following essential parts; Frame, motor,
Electronic speed controller (ESC), propeller, battery, flight controller, RC receive and for our quadcopter
we decided to add First Person View (FPV) camera, Video transmitter (VTX).

1.2 In this part our team design a 2 poles quadcopter in 3D program to see the general idea of what we will
need to order, After we receive all the part we ordered we star to assemble its together we run a bit of
test flight to check is it normally fly.
Part 2 : Fire Extinguisher Ball holder and camera arm.
Second part is fire extinguisher ball holder we design that it
can be able to hold weight approximately 1 kg. and can be
switch to release the ball. We have 3 design which represent
the difference way to hold the ball

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2.1This first design is the box which can attach to the lower part of
the quadcopter next to the battery contain content of servo motor able
to drop fire extinguisher ball and NRT24L01 which receive signal to
active servo motor to release the fire extinguisher ball. But this design
has a lot of problem because its weight and inefficiency of ball drop.

2.2This design are made for smaller attach to the bottom
of the quadcopter has low weight so it’s not gonna disturb
the balance of quadcopter while flying.We eventually 3D
print this design and attach to the quadcopter and it’s not
working because electric capacity problem due to the
design that require large motor to active

2.3The Final design is basic and easy to make because it only stick ,
glue and SG 90 the problem from two design above lead us to narrow
down to the easiest way which is to create fire extinguisher ball holder
form one single micro servo motor. This design we put together SG90
and 3D printed stick and fire extinguisher ball tie with a small rope
hang on the stick which rotate when switch and drop to the below.

2.4 camera holder is simple just a FPV camera on the stick which
attach to another servo able to move up and down to check the
situation in front to help pilot flight and below the quadcopter to check if it above the target.

Part 3 : In this part our team research in fire extinguisher
ball material, the content that its contain mainly are
monoamonia phosphate and gunpowder. Fire extinguisher
ball will explode after heating enough gunpower will
explode and spread ammonia phosphate to extinguish the
fire around it. We adapt this to create our own by put fine-
ash and firework inside balloon to replicate fire
extinguisher ball but lighter.

Part 4 : collect data
After modify quadcopter we did the test to confirm that quadcopter can be use in real situation by simply fly
with payload and use real time video from the quadcopter to drop fire extinguisher ball.

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Results

After the modify and the flight training with the normal condition clear sky , normal wind and fully

charge battery

Payload Flight #1 Fight #2 Flight #3 Flight #4 Avg. Flight

0 kg. 17.40 min 17.32 min 18.10 min 17.45 min 17.56 min

0.5 kg. 12.02 min 11.35 min 11.23 min 11.10 min 11.42 min

0.6 kg. 10.43 min 10.21 min 9.56 min 10.14 min 10.08 min

0.7 kg. 9.42 min 9.16 min 10.12 min 9.38 min 9.37 min

Forest fire is one of the most destructive and uncontrollable but with quadcopter we can detect and control the fire
before start to spreading.
Conclusion
A fire fighter quadcopter can be one of the special forces to help control the forest fire by observation and help
suspression in hard-to-reach area. This prototype might not be able to operate alone in real situation, but this is a sign

.that we are in the right path to make tools to help human control natural disaster more efficiency

Acknowledgements
This project was supported by Science Classroom in University Affiliated School (SCiUS). The

funding of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This
extended abstract is not for citation.

References
1. Muhammad Fadhil Abdullah,Position estimation and fire detection based on digital video color space for
autonomous quadcopter using odroid XU4,School of Electrical Engineering, Telkom University, Bandung,
Indonesia,2017
2. Sourav Barua,Design and Implementation of Fire Extinguishing Ball Thrower Quadcopter,European
University of Bangladesh, Dhaka, Bangladesh,2020
3. WorradornPhairuang,Faculty of Environmental Management, Prince of Songkla University, Hat Yai,
Songkhla, 90112, Thailand,2019

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Title : Masked driver drowsiness OS2_08_02
detection system with IoT
Field :
Author : Stem and Innovation

School : Mr.Nattagan Samit
Advisor :
Mr.Chonnawee Prajan

Lukhamhanwarinchamrab School, Ubon Ratchathani University

Dr.Kriengsak Treeprapin

:Abstract

In Thailand, road accidents caused by drowsiness or falling asleep while driving accounted for

7.2 percent of all accidents. According to Thailand's Ministry of Transport's Situational Analysis of Road
Accidents Report 2019. In order to prevent drowsiness or falling asleep while driving, conventional research
proposed an eye-based sleep-notification system using the information from the eye and mouth of the driver
for drowsiness detection. However, since the coronavirus outbreak, the driver, especially the public bus
driver must wear the mask while driving in order to prevent the risk of infection from the passengers. Thus,
the conventional drowsiness detection system is inaccurate, due to lacking information from the mouth. In
this paper, we proposed a new drowsiness detection system for the masked driver using machine learning on
TensorFlow and IoT (Internet of Thing) technology. The proposed system detects the driver's face with a
camera connected to the Raspberry pi device, analyzes the driver's facial behavior from the captured image
in order to detect drowsiness, and sends an alert to the notification system to warn the driver. The researchers
are developing models by training from datasets in order to improve the precision of detection and to make
the proposed system more affordable.

:Keywords Drowsiness, TensorFlow, IoT, Raspberry pi

Introduction
Nowadays, the popularity of private and public vehicles is on the rise. This makes it more

important to focus on road safety. According to the Thailand Ministry of Transport's Road Accidents
Situation Analysis Report 2019, Thailand has private car accidents up to 30% of all road traffic accidents in
Thailand. According to the report, 7.02% of the accidents were caused by fatigue or drowsiness while
driving, among other causes. This problem led to the development of a drowsiness detection system. which
can be detected in various ways, whether yawn detection by detecting the width of the mouth, Fatigue
detection by detecting the blink rate of eyes, Nodding detection with Head Gesture Detection.

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Methodology

Related work
Some researches include vibrating motor modules, GSP modules, and button switch modules.

Which serves to warn the driver inside the car and send a signal to the Blynk and Line applications to warn
the driver's family with the driver's location and wait for a response from the driver. When the driver
responds The system sends a signal to the Blynk and Line applications to inform the driver's family that the
driver is responding and conscious again. Data is saved to firebase in real-time to collect data and display it
on the website.
Material
Raspberry Pi 4

Fig. 1. Raspberry Pi 4 Model B

The Raspberry Pi has the ability to interact with the outside world and has been used in a wide array
of digital maker projects, from music machines and parent detectors to weather stations and tweeting bird
houses with infra-red cameras.

- Webcam Camera module

Fig. 2. Webcam Camera module

-Buzzer module

Fig. 3. Buzzer module

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Overview
In doing this project, the authors have begun to collect
preliminary data on statistics of road accidents caused by drowsiness.
Then analyze data related to this project. A related study examines
drowsiness by detecting deep eyes, with an accuracy of up to 70%
along with detecting head posture. Then develop the system, test
the system and improve the system.

Fig. 3. Method overview

System initialization is initiated from Fig. 4. Drowsiness Detection System
the driver's face input by capturing the image
from the webcam module, then the drowsiness
detection system is initiated using the Raspberry
pi to process the data. The data to be processed
comes from detecting deep eyes and detecting
the driver's head posture while driving.
overview.When the drowsiness is not detected,

it will restart the system. But when it detects
drowsiness, it will send an alarm by using a buzzer module.

We detect face and mask using image classification to
classify objects. The information obtained will show both fps
and accuracy.

We trained the drowsiness model, considering whether the
eyes were closed or not. and from yawning to analyze
drowsiness and detection of wearing a mask while driving a car.

Fig. 5. Image Classification

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Results, Discussion and Conclusion
From the experimental results, the authors are currently testing to develop a more efficient

model. by training from data sets The data used is obtained from data that contains images from various
situations, for example, Data sets that rapidly change the illumination and the reflections coming from
eyeglasses and contact lenses and motion blur artifacts, reflections and low contrast between the pupil and
dark regions around it. With these data sets, we can develop models with precision and efficiency. where
similar research samples were able to develop a model with an accuracy of 87% with less than 5 pixel error.

For future work, We will optimize the architecture and increase the precision to work more
efficiently. We also have plans to improve the system to be able to trade at more affordable prices. To achieve
this goal, the use of more data sets and improvements to the materials used to build the system will be
another factor that will be implemented in future versions of the Masked Driver Drowsiness Detection
System.

Acknowledgements
This project was supported by Science Classroom in University Affiliated School (SCiUS).

The funding of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This
extended abstract is not for citation.

References
1. Teerawut P, Wutthichai R. Car accident monitoring and prevention system using driver’s drowsiness
detection. Ubon Ratchathani: Ubon Ratchathani University; 2020.
2. Vera-Olmos, F. J., Pardo, E., Melero, H., Malpica, N. DeepEye: Deep convolutional network for
pupil detection in real environments. Integrated Computer-Aided Engineering, 26(1), 85-95.
3. Kati Blake. Everything You Need to Know About Drowsiness [Internet]. 2019 [cited 2022 Feb 13].
Available from: https://www.healthline.com/health/drowsiness
4. Wikipedia. Machine learning [Internet]. 2022 [cited 2022 Feb 13]. Available from:
https://en.wikipedia.org/wiki/Machine_learning
5. Ritwek K. Drowsiness Detection Model Tensorflow [Internet]. 2021 [cited 2022 May 7]. Available from:
https://www.kaggle.com/code/vanvalkenberg/drowsiness-detection-model-tensorflow?fbclid=IwAR18lwclT
mRKUTrP0VBMUj_ijL19oUiyb6ljg8-sYoC9PLZazWQW5AGrVh4

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Titel : Design of the temperature control system OS2_03_06

in the attic for energy saving of air conditioners.
Field : Stem and Innovation
Author : Mister Tanakith Huyheng

Mister Buntheppitak Daungthip
School : Naresuan University Secondary Demonstration School, Naresuan University
Advisor : Asst. Prof. Sitphan Kanla (Mechanical Engineering, Naresuan University)

Abstract
The objective of this research is to prove that temperature control system is better

than normal air ventilation, and help saving energy to reduce global warming. In addition, we
also expected the temperature control system in the attic would operate without problems and
the operation of the temperature control system would be highly efficient. In Methodology, the
experiments were divided into 2 parts making the temperature control system and model house
for test efficiency by measuring the temperature and recording the results. The results made us
know that the temperature control system could actually be used in real life house because the
temperature in the model house that had system lower than another house that had no system
at all times and in all point that we had measured the temperature even if it was lower than just
a degree Celsius but only this can be used for real life house and can save the energy of the air
conditioner. To make it works better, increasing the number of systems and increasing the fan
size will make it more efficient.

Keywords : air ventilation, energy saving, global warming, attic

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Introduction
Nowadays, global warming is becoming more severe every day due to the lives of

people, especially the use of electrical appliances such as air conditioners. So, to cool the
room temperature as needed. Most people use air conditioners more. The hotter the room
temperature gets, When the air conditioner is turned on, it only works harder, and more
importantly, turning the air conditioner on more will increase global warming even further.
We also need to make the air conditioning work harder as the temperature rises caused by
global warming.

Therefore, the researchers see a way to reduce the global warming problem by
reducing the room temperature so that it helps air conditioners work less. According to the
study, the main problem is that room temperature rises due to the high rise and accumulation
of temperature inside the attic. Therefore, the researchers wanted to design a temperature
control system.

Methodology
The experiments were divided into 2 parts as follows,

Part 1: Methodology of the temperature control system and model house for test
1.1 Write a program code for the temperature control system.
1.2 Connect the system device.

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1.3 Perform the model house for test.

Part 2: Do the experiment and record the results.
2.1 Prepare to test and install the system for one model house.

2.2 Measure the temperature and record the results.
Results

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Conclusion

The results made us know that the temperature control system could actually be
used in real life house because the temperature in the model house that had system lower than
another house that had no system at all times in all point we had measured the temperature
even if it was lower than just a degree Celsius but only this can be used for real life house and
can save the energy of the air conditioner.

Acknowledgements
This project was supported by Science Classroom in University Affiliated School

(SCiUS). The funding of SCiUS is provided by Ministry of Higher Education, Science,
Research and Innovation. This extended abstract is not for citation.

References
1. Danny Jost. (2019). What is a temperature sensor?. Retrieved 19 September 2021, From

https://www.fierceelectronics.com/sensors/what-a-temperature-sensor
2. Administrator. (2015). Microcontroller Types and Applications. Retrieved 19 September

2021, from https://www.electronicshub.org/microcontrollers

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Title : Shoes for health assessment by using IOT system OS2_03_02
Field : STEM and Innovation

Author : Mr. Thitikorn Sumsub

School : Mr. Kanpong Asawathamacoop
Advisor : Naresuan University Secondary Demonstration School, Naresuan University
Assoc. Prof. Suchart Yammen, Naresuan University

(เวน้ ระยะห่าง 1 บรรทดั )

Abstract
Shoes for health assessment by using IOT system project to create a shoe that can assess the health

of walking. Due to the current attention to health assessment methods. In the same way, there are many

instrumental health measurements. Which is a difficult and costly method but there is a way to predict or

predict walking health. This method called 6MWT, is a test to assess performance of the respiratory system

Cardiovascular system, Blood system, Nervous system and mind and the musculoskeletal system. The
organizers have the idea to bring an accelerometer in a micro: bit board attached to a walking shoe. Come to

collect the number of steps in walking and then take it to calculate the 6MWD value, which will change

according to the person who tested the walk. Which relies on writing code from BBC's Makecode program

and Arduino program to control the operation of the board. There will be a transmitter attached to the tester's

shoe. And a receiver connected to the computer to record the output acceleration value and store it as a CVS.

To find the 6MWD value from the number of steps and take the 6MWD value that differs from each tester.

VO2 max was calculated from the relationship formula of 6MWD and VO2 max and compared with the VO2

max table to determine the test subjects, stamina, fitness and health. After testing the shoe's performance, we

found it had a 96.42% step count accuracy. In conclusion, the shoe for health assessment system with the

IOT system can actually assess the fitness, stamina and health of testers.

(เวน้ ระยะห่าง 1 บรรทดั )
Keywords : IOT system, 6MWT,6MWD, microbit, VO2 max

(เวน้ ระยะห่าง 1 บรรทดั )

(Introduction อธิบายโดยยอ่ เก6ียวกบั หลกั การและความเป็นมาของการทดลอง

At present, there is attention to various methods of health assessment. In the same way, there are
many instrumental health measurements, which is a difficult and costly method but there is a way to predict
or predict walking health. This method called 6MWT, is a test to assess performance of the respiratory
system Cardiovascular system, Blood system, Nervous system and mind and the musculoskeletal system or
as a test to assess the patient's ability to perform activities which is a test from real activities by having the
patient walk in the designated area Walking is timed for brisk walking but not running in 6 minutes. Walk the
10 m. round-trip distance until the timer is complete, and steps are counted. Walking style, speed, number of
turns that can be walked and the distance walked within 6 minutes is referred to as 6MWD.

Before and after the test, fatigue and vital signs were assessed. To see the changes, the 6MWT is
an easy test method. It not complicated and uses only minimal testing sites and equipment, and the 6MWT
method is also used to monitor patient recovery before and after surgery able to predict and assess health.

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The creator therefore came up with the idea to bring Microbit board to the 6MWT test in the form of a shoe.

The organizer's project is the creation of an IOT system using a Microbit board that will store

data in the form of steps. and then record the relevant values throughout the experiment, which will use

program makecode and Arduino IDE to control Board Microbit to be used as controller of IOT system,

process values and output as results of the experiment from doing 6MWT to bring the number of steps to

calculate 6MWD to be used To calculate VO2 max, it is compared to a table to estimate fitness and the

endurance of the teste.

(เวน้ ระยะห่าง 1 บรรทดั )

Methodology (อธิบายรายละเอียดของวสั ดุ อุปกรณ์และวธิ ีการทดลอง)

The experiments were divided into 4 parts as follows,

Part 1 : Design and manufacture of IOT system, shoes for health

1.1 Study the documents and research or information related to the IOT system, shoes for health.

For design and develop IOT system, shoes for health which uses a Micro:bit board to process and store data

in the form of steps to find 6MWD for health assessment. Provide relevant equipment which will have to

order a set of related equipment

1.2 Create an IOT system for healthy shoes by bringing the design and materials to the assembly
and coding into the board through makecode program and Arduino IDE
Part 2 : Performance testing of the IOT system, healthy shoes

2.1 Testing the IOT system for healthy shoes will be taken to test by walking in a designated place
and setting up a place By using the step counter board compared to the actual count in order to find out how
much of an error the number of steps was taken.

2.2 Collect experimental results, calculate the resulting discrepancy and fix coding in Burgundy to
make output less errors and fewer errors.
Part 3 : Step count test from multiple samples

3.1 Choose different subjects to do the 6MWT test and wear shoe for health using IOT system on
the place that has been set and collect experimental results
Part 4 : Summary and Comparison

4.1 The data obtained from the collected results were taken to calculate 6MWD from the number
of steps taken.Calculate the VO2 max from the formula for the relationship between 6MWD and VO2 max.
And last obtained VO2 max values were compared with the VO2 max table, which would help to determine
fitness, stamina and health

(เวน้ ระยะห่าง 1 บรรทดั )

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Results and Discussion

It was found from the experiments part 2 and part 3.
From part 2 : we testing the IOT system for healthy shoes will be taken to test by walking in a designated
place and setting up a place By using the step counter board compared to the actual count in order to find out
how much of an error the number of steps, as shown in Table 1 and Table 2. There tables are collect the test
results and calculate the resulting discrepancy and average of accuracy.

Table 1 : 200 step counting test with Microbit

Testing performance of step counting code by compare between actual step and step counting by
using microbit and Test walk 200 steps 5 times. Average of accuracy is 96.6%
Table 2 : 500 step counting test with Microbit

Testing performance of step counting code by compare between actual step and step counting by using
microbit and Test walk 500 steps 5 times. Average of accuracy is 96.24%
From part 3 : we choose different subjects to do the 6MWT test and wear shoe for health using IoT system on
the place that has been set, as shown in Table 3 and Table 4. There tables are collect the test results and
calculate the resulting discrepancy and VO2 max.

Table 3 : The result of 6MWT testing to callculate VO2 max from 1st tester

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Table 4 : The result of 6MWT testing to callculate VO2 max from 2nd tester

The distance obtained from the first tester walk distance 500.64 meters, which a normal person
could walk an average distance of 536-560 meters, and when calculating the VO2 max, it was 50.50
ml/kg/min. VO2 max and age can be said to be in good condition, which can tell about health stamina and
fitness. The second tester walk distance 389.48 meters, where people aged 45 and over were able to walk is
475 meters for males and 406 meters females respectively. And when it comes to calculating VO2 max, its
33.99 ml/kg/min, which is excellent compared to the VO2 max and age criteria. in real health assessment.

(เวน้ ระยะห่าง 1 บรรทดั )
Conclusion

Able to create and design healthy shoes that actually work with step counting and high step
count accuracy as 96.42% and then the number of steps from shoes for health by using IOT system can
actually be used to assess the health, stamina and fitness of those who took the 6-minute walk test (6MWT).

(เวน้ ระยะห่าง 1 บรรทดั )
Acknowledgements

This project was supported by Science Classroom in University Affiliated School (SCiUS)
under Naresuan University and Naresuan University Secondary Demonstration School. The funding of
SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation, which is highly
appreciated. This extended abstract is not for citation.

(เวน้ ระยะห่าง 1 บรรทดั )
References (เขียนในรูปแบบแวนคูเวอร์ (Vancouver Style) โดยเลือกเอกสารท6ีสาํ คญั ไม่เกิน 10 เอกสาร)
1. กมลทิพย์ หาญผดุงกิจ. 6-Minute Walk Test. บทความฟLื นฟูวชิ าการ, กรุงเทพฯ: คณะแพทยศาสตร์ศิริราชพยาบาล มหาวทิ ยาลยั มหิดล; 2561.
2. มนตรี ยาสุด, เบญจา ทรงแสงฤทธRิ, พจีมาศ กิตติปัญญางาม. ปัจจยั ที6มีผลต่อระยะทางท6ีไดจ้ ากการทดสอบสมรรถภาพทางกายดว้ ยการเดิน 6 นาทีในผูป้ ่ วยหลงั

ผา่ ตดั หวั ใจและหลอดเลือดหลงั ออกจากศูนยห์ วั ใจสิริกิตRิ ภาคตะวนั ออกเฉียงเหนือ. (ปริญญานิพนธ์ วศ.ม.). ขอนแก่น: คณะแพทยศาสตร์ มหาวทิ ยาลยั ขอนแก่น;
2561.
3. อโนมา ศรีแสง พย.บ. ,ชลนรรจ์ วงั แสง พย.บ.,บรรณาธิการ. การประเมินสมรรถภาพของหวั ใจและปอดดว้ ยการทดสอบการเดิน 6 นาที. งานการพยาบาล
ระบบหวั ใจและหลอดเลือด. กรุงเทพฯ: คณะแพทยศาสตร์ศิริราชพยาบาล มหาวทิ ยาลยั มหิดล; 2561

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Title: The design and prototyping of prosthetic fingers using OS2_18_01

Field: PETG plastic and 3D Printing Technology
Author:
Stem and Innovation
School:
Advisor: Mr. Panrawat Prapansri
Mr. Peeradon Angkarongrak
Mr. Phurin Tantimala

Surawiwat School, Suranaree University of Technology

Dr. Wiwat Nuansing
Dr. Sutipoj Promtapan
Ms. Anatvida Sukchanta

Abstract
The aim of this project is designed to solve the problem of people who have fingers injured or severed

caused by an accident from the mistake of a machine which is one of the major reasons why people have their
fingers amputated and handicapped from birth. To solve this problem, we would like to represent the design
and the invention of a practical prosthetic finger to replace amputated fingers. In the creation process, we used
3D printing to create a prosthetic finger and two synthetic ligaments to replace the human tendon in the finger
to flex and extend by the flexion of the knuckle of the finger which uses plastic as a material to create a model
of fingers. The analytic results enumerated that it can bend as a human finger when the lower part is pulled
tautly and it can stretch back when the upper ligament is tautened. It can measure the efficiency of the prosthetic
finger from the actual use of the finger of a handicapped person.

Keywords: Prosthetic finger, 3D printing, PETG, PIP, DIP

Introduction
The fingers are one of the most crucial organs used in daily life. The fingers are the densest part of

nerve in the body that extremely responsive. Fingers play vital roles in human everyday life over centuries
such as writing, typing, picking or holding, scratching, body languages, sign languages and used it their fingers
in order to created counting techniques and calculate useful numbers.

According to the 2017 survey results, the number of population with disabilities registered in Thailand
accounted for 2.75% of the total citizens which people with mobility or physical disabilities and 48.69% of the
total number of people with disabilities in the country people who have had accidents or congenital disabilities
they either lose their fingers or have to be amputated especially the index finger because it is a very active
finger, thus this finger has the highest risk of getting into an accident.

Most prosthetic fingers or prosthetic hands are solid which built from wearable silicone and can only
worn individually therefore unable to move as a normal finger and the prices are around 10,000 baht to 20,000
baht each. We invent and develop prosthetic fingers that can move smoothly and can replace the real finger.
Our prosthetic fingers are effective, strong, light weight and cheap. These fingers are adjustable in length to fit
every inch and any size, hence the handicap person with torn fingers will be able to access prosthetic fingers
by designing and using 3D Printing Technology.

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Methodology

The operation is divided into 5 main steps as follows.

Study the mechanism of the finger and 3D printing technology. Human fingers consisting of 3 joints:
Metacarpophalangeal Joint ( MCP) , Proximal Interphalangeal Joint ( PIP) , Distal Interphalangeal Joint ( DIP)
from the innermost joint to the tip of the finger have three bones: Proximal phalanx, a bone between the MCP
and PIP joints called the base of the finger, Middle phalanx is the bone between the PIP and DIP joints, Distal
phalanx is the bone that follows the DIP joint, which is called fingertips. The simple function of 3D printing
technology is to create a tangible shape as desired by relying on information in digital form which the printing
will be gradually layer by layer.

The design of prosthetic finger model. Prosthetic fingers design through AUTO DESK FUSHION 360
program, which is the free program for students and has a variety of functions that are easy to use. Our model
is designed to have four parts and there are four screws used to connect all four joints. We also use two
ligaments with different properties. The lower ligament is a non-flexible ligament that pulls the finger to bend
when bending the MCP joint, while the ligament at the top has a flexible feature to straighten the finger back.
The last part that we designed is the wrist part. It is designed to attach the lower ligament so it can bend the
finger. There are two pieces that can be joined together to make it scalable that use flexible materials to prevent
injury and use the Velcro as a wristband.

Picture 1 shows the designed model (finger part) Picture 2 shows the designed model (wrist part)

Print the model with a 3D printer and put all pieces together. Save 3D files (.stl) that obtained from AUTO
DESK FUSION 360 and slide the resulting file into layers using Ultimaker Cura. Save the sliced file (.gcode)
and print it in the 3D printer machine named Creality cr- 10s. The material we use to print the models and
screws is Polyethylene Terephthalate Glycol- Modified ( PETG) , which is the Number 1 material that used in

the production of plastic water bottles mixed with Glycol for use in 3D printers, it takes about 5 hours to print
and Thermoplastic polyurethane (TPU) is a high flexible plastic used for wrist model printing. Then each piece

is put together into a practical finger and covered with silicone to increase the friction of picking objects same
as human skin.

Test the properties for bugs. and modify the model to reprint

Test the movement of the prosthetic finger by starting with the flexion by pulling the lower tendon and test the

relief from the lower tendon pull and watch the prosthetic finger stretch back then test the following properties
1. Durability test
2. Measure the maximum angle of bending the prosthetic finger.

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3. Weigh the prosthetic finger.

Then find the defects of the prosthetic finger and modify the model in the program then reprint the finger with
a better one until there are no defects. In order to find the most effective finger to test it on handicap person.

Find a handicapped finger and try it out for real Finger disabilities tested must have only the MCP joint
left, but the proximal phalanx knuckle can be protruded. We met a handicapped, 70- year- old woman who
volunteered to be a prosthetic finger tester, she wears it for 1- hour trial period then we conduct an interview
and evaluated the use in three aspects: comfort, efficiency and satisfaction of the tester.

Results
Our challenge was to create a prosthetic finger that could be used as a replacement for a real finger.

As a result, the prosthetic finger can actually work and the amputees can control the flexion and extension of
the fingers in every joint easily ( image 3 and 4) , they can use prosthetic finger to hold and pick the objects as
shown in the image 5. Not only this but also the weight of prosthetic finger is only 32 grams.

The cost of each prosthetic finger is 230 Baht which it consisting of PETG (13 Baht), TPU (22 Baht),
Silicone ( 26 Baht) , Synthetic ligament ( 50 Baht) , Velcro ( 80 Baht) , Glue ( 39 Baht) and 0 Baht for printing
process because we use 3D printer in Suranaree University of Technology.

Picture 3 shows the finger flexion Picture 4 shows the finger extension picture 5 shows how the example of picking object

Table 1: The table enumerates the angle that prosthetic finger can bend: measured 5 times, average, minimum
and maximum.

Joints Bending angle that measured 5 times Average Min Max

DIP 73.62° 74.54° 72.32° 67.79° 63.29° 70.31° 0° 81.52°

PIP 76.79° 76.44° 76.27° 79.15° 74.54° 76.64° 0° 80.13 °

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Table 1 reveals that both DIP and PIP can be bent up to 81.52° and 80.13 ° respectively which are the maximum.
The minimum angle is 0° if the prosthetic finger is stretched back. On the other hand, average angle can be
calculated when the finger is in used by tester and we measure for five times.

Table 2: The table illustrates how many kilograms the joints can receive.

Joints 1 Kilogram 2 Kilograms 3 Kilograms 4 Kilograms 5 Kilograms

DIP No reaction No reaction No reaction No reaction Cracked

PIP No reaction No reaction No reaction Cracked Broken

The strength of DIP joint and PIP joint can be tested by hanging pendulum in 5 different weights (1-
5 Kilograms) on the finger tip of prosthetic finger which the results are shown in Table 2.

However, there are few mistakes that happen with our model. The tester cannot command the finger
with full potential because the size is too big and she feel a little hurt due to the hardness of material.

Conclusion and Discussion
From the experimental results, the prosthetic finger that invented can use in daily life, its can pick up

objects with natural gestures similar as human fingers. The model has low expenses, light weight, no injuries
and can encourage finger disable person in to obtain better quality of life. But there are some flaws in the size
making it difficult to use and the surface is still hard causing discomfort. In the future, we will pledge this
mistake to enhance and develop enabling handicapped to adjust the width of finger. We will also install a soft
material for convenience in using. If we continue to develop prosthetic fingers, we can create a model that fully
replace the real human finger and has an affordable price to support every disabled person to receive a
betterment in future

Acknowledgements

This project was supported by Science Classroom in University Affiliated School (SCiUS). The
funding of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This
extended abstract is not for citation.

References
1) Lim, D. ; Georgiou, T. ; Bhardwaj, A. ; O’ Connell, G. , D. ; Agogino, A. , M. , Customization of a 3D
Printed Prosthetic Finger Using Parametric Modeling. ASME 2018 International Design Engineering
Technical Conferences and Computers and Information in Engineering Conference, 1-9.
2) Choi, Y., K.; Bretl, T.; Akhtar, A.; A Compliant Four-bar Linkage Mechanism that Makes the Fingers
of a Prosthetic Hand More Impact Resistant. IEEE Int Conf Robot Autom,1-6.
3) Lee, J. , W. ; Measurement of finger joint angles and maximum finger forces during cylinder grip
activity. Journal of Biomedical Engineering, 1-11.

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Title: Development of Atmospheric Pressure Plasma Needle Jet for OS2 _15_01

Sterilization Applications

Field: STEM and Innovation

Authors : Ms.Karinya Nakchuay

Ms.Theeranut Keereenart

School: PSU.Wittayanusorn School, Prince of Songkhla University
Advisor: ASSOC.PROF.Dr.Yutthana Tirawanichakul, Prince of Songkhla University

Abstract

The purpose of this study is to develop and apply appropriate disinfection. The first part is to study the
development of the second cycle of high voltage generation and then establish a model from simple materials. The
organizer chose PVC pipe, which is easy to find. After that, when the construction is completed. The efficiency test of
these two circuits is because it is simple to test and cheap. After the test, the test results of the two circuits are compared.
Second, easy-to-use materials can be easily disinfected. Starting from the design of the micron, the work of the micron.
When the microwave oven is carried out safely by the steam generator, then a built-in pipe will be installed

The experimental results show the efficiency of the first and second circuits. The efficiency of the second circuit is
higher than the experimental results of the second circuit. Therefore, the second circuit is used to connect to a new jet
engine. Experimental problems in uncontrollable initial dose Unstable tests cannot determine whether there is infection.

Keywords: Plasma

Introduction
The current situation is an epidemic of coronavirus disease 2019 (COVID-19), a contagious disease caused by the

newest kind of corona virus detected. The first case of infectious disease in Thailand was recorded on January 31, 2020,
according to news reports. Droplets from the nose or mouth can spread the disease from person to person. Inhaling the
aerosol can lead to infection. or from holding your hand over an aerosol-sprayed region and touching your face, as well
as your belongings, appliances, and other items

The author of the research is interested in learning more about plasma and its features. The state of plasma. The
plasma produced in this experiment disinfects the specimen while also increasing its surface energy. work, If the produced
plasma jet is used for sterilizing at atmospheric pressure, the following results will be obtained. Plasma has the advantage
of being inexpensive. Because there's no need to buy a vacuum system. It's also simple to move the workpiece in and out.

The project's founders want to learn more by connecting urgent situations to the project. So we're
attempting to figure out how to get rid of the infection. to keep illness at bay Using the atmospheric pressure

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plasma process to create a sterilizer. that can set the time and provide adequate power and are manufactured from
materials that are readily available in everyday life
Methodology
Part 1 : Development of Atmospheric Pressure Plasma Needle Jet for Sterilization Applications

1.1 Design a plasma jet tube and use the microwave to rotate the sample.

1.2 Build the machine as it was designed.
1.2.1 Drill holes in PVC pipe 4 mm. 10 holes and aluminum sheet 10 holes

1.2.2 Drill a center hole in the end cap and insert the stainless steel. Drill a hole in the center on the
opposite side. Size 1.5 cm in diameter and insert brass fitting

1.2.3 Attach the 3D Printer nozzle to the aluminum plate with the holes drilled in the PVC pipe.

1.2.4 pull out the microwave cover remove the inner circuit Drill a hole in the microwave to pass the
wires and install high voltage inside the microwave.

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1.2.5 Install the air pump, connect the electric circuit, anode with stainless steel cathode to aluminum
plate put the plasma tube inside the microwave and close the cover.

The picture shows the prototype of Atmospheric Pressure Plasma Needle Jet for Sterilization
Part 2 : The plasma jet sterilizer performance test

1.Send the sample to the lab for analysis.

Result and Discussion

1. Experimental results of High voltage circuit 1

Table 1: The test results of the 188 V electric potential difference at a distance of 3 and 5 cm.

Distance Time spent Not sterile Sterile
(minute) ( CFU/g) ( CFU/กรมั )
3 cm.
5 cm. 5 min 30 TNTC
5 min 40 70

Table 2: The test results of the 3.8 kV potential difference at a distance of 3 and 5 cm.

Distance Time spent Not sterile Sterile
(minute) ( CFU/g) ( CFU/กรมั )
3 cm.
5 cm. 5 min 90 330
5 min 700 1630

Table 1 and Table 2 show the number of pre-sterile germs. and sterilization Therefore, it can be seen
that after sterilization there is a large number of germs. The problem arises from not controlling the number of
infections. and source of infection The organizers have simulated a situation that makes the mask dirty.
Therefore, the number of germs found is not all over the mask.

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2.High voltage circuit test results 2

Table 3 : the test results of the High voltage 2 circuit at a distance of 5 cm.

Distance Time spent Non sterile Sterile
5 cm 7 min ( CFU/g) ( CFU/g)

1600 1550

Table 3 shows a decrease in the number of germs after sterilization. This shows that the High voltage 2
circuit is efficient and can be used for further sterilization tests.

Conclusion

Atmospheric pressure plasma jet systems for sterilization applications were studied. The experiment was
divided into two parts: Part of the study and development of High voltage circuits to be suitable for the project and
the other part is the fabrication of the machine to make it easier to sterilize with materials that are readily available
and easily found. As for the study and development of plasma jet systems, two types of studies were conducted:
one, when the house power is supplied to 220 V, the output voltage can be as high as 3.8 kV, which can be adjusted.
And the second type, when the house power is supplied to 220 V, the potential difference that comes out of the
circuit will be equal to 220 V, otherwise known as one-to-one. can be adjusted

Acknowledgements
This project was supported by Science Classroom in University Affiliated School (SCiUS). The funding

of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This extended abstract
is not for citation.

References
[1] Lotfy, K. Cold Plasma Jet Construction to Use in Medical, Biology and Polymer Applications. Journal of

Modern Physics, 8, 1901-1910.
[2] Marc J. Rogoff, Francois Screve, in Waste-to-Energy (Second Edition), 2011

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Title: Development of ventilation device for PPE suit using 12 SCiUS Forum
Thermoelectric Peltier
Field: STEM and Innovation OS2_12_01
Author: Ms. Ananya Aryucharoen
Mr. Jakkarin Preeprem
School: Mr. Siwamate Sooksa-ardvisit
Adviser: Princess Sirindhorn's College, Silpakorn University
Asst. Prof. Dr. Itsara Masiri, Silpakorn University

Abstract

Ventilation device for PPE suit was developed using Thermoelectric Peltier for generating cool air
from electricity. This help medical officers to work comfortably and operate longer in such hot environment.
The development process is divided into two parts: testing the performance of Thermoelectric and developing
prototypes with maximum efficiency.

Efficiency of various thermoelectric peltries was tested to find out the maximum efficiency. The
Peltier were able to cool down the ambient air with temperature from 24.5 C to 13.3 C. Furthermore, some
of the Peltier were able to decrease temperature from 24.0 C to 7.0 C. It was found that the material and
design of heat sink play an important role helping to decrease the temperature. It can be observed that if the
heat generated from the Peltier was not properly removed, the heat will escape into the device and reduce its
cooling capability. On the other hand, cooling capability control by the speed of the ventilation fan.

Keyword: Thermoelectric, PPE, Personal Protective Equipment, Peltier

Introduction

The coronavirus pandemic has increased in numbers prompting medical personnel and other
volunteers to work closely with patients required to wear PPE suit. But the suit lacks ventilation making users
fall unconscious. This can be solved using thermoelectric material. This trial aims to study different
thermoelectric and develop a ventilation device for a PPE suit. Thermoelectric require an effect that changes
from heat energy into electricity.

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Methodology
The Organizer designed and constructed the ventilation device based on the experiments divided

into 4 parts as follows.
Part 1: Thermoelectric performance test
(Type 1): Paint thermoelectric with silicon and place it on an aluminum pipe 10 cm long.
Place Centrifugal Fan that wind from aluminum can directly flow, connect with DC power, and
supply 5 A electric potential 12 V to the circuit. Record the temperature inside the pipe every 5
seconds and draw a graph.
(Type 2): Overheating is still a problem. So, organizer adds a Hi-speed fan to help cool it
down.
(Type 3): Organizer added Heat sinks to help cool it even more.
(Type 4): Organizer added plastic foams to help aluminum kept its temperature.
(Type 5): Organizer reduced voltage to 15 A and the electric potential to 9 V.
(Type 6): More voltage reduction to 5 A and the electric potential to 6 V.
Part 2: Design and assemble.
2.1 Cooling device (Type 1).
2.1.1 Designing.

Figure 2 Design cooling device type 1
2.2 Cooling device (Type 2)

2.2.1 Designing.

Figure 4 Design cooling device type 2
2.3 Cooling device (Type 3)

2.3.1 Designing.

Figure 6 Design cooling device type 3

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2.3.2 Assembling cooling device (Type 3).
Drilled the top of the plastic box at the position equal to the cross-section of the ventilation
fan and another for the centrifugal fan. Drill a hole at the bottom of the box and two more out of the
cooling pipe for heat dissipation. Cut the aluminum for length of 15.5 cm. Two thermo-electric
sheets were attached using silicone glue on the aluminum tubes.

Part 3: Testing the device

Connect the ventilation device to the DC power supply delivering power to the thermoelectric
plaiter and the centrifugal fan. Connect the to the heat sink fan. The measured temperature inside the
device and the air outside were collected and compared. Supplied voltages and currents of the 1st and 2nd
devices were 12 V, 5 A, and 10 V, 3 A, respectively. The supplied voltage and current of the 3rd device
were 10V and 3A, respectively.

Result

Experimental part 1, Devices were examined for its efficiency, an increase in temperatures of Type 1,
from 22.5 C to 30.4C within 120 s, Type 2 from 24.5 C to 13.3 C within 120 s, Type 3 from 24.5 C to
13.3 C within 400 s, type 4 from 26.0 C to 11.1 C within 400 s, Type 5 from 25.0 C to 12.2 C within 400 s,
Type 6 from 25.0 to 12.3 C within 400 s.

35

30

25

Temperature (C) 20

15

10

5

0

0 50 100 150 200 250 300 350 400 450

Time (s)

Series1 Series2 Series3 Series4 Series5 Series6

Figure 10 Temperature-time relationship graph from thermoelectric test

After testing, the air inside the device is not cold enough due to the lack of heat dissipation. The aluminum
pipe absorbs heat from heat sinks into the cooling tube. Type 2, the current in the battery is not enough. Type 3,
temperature of the air inside of the cooling device while working is at 24.0°C and after 600 s it decreases to 7.0°C.

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Temperature (C) 12 SCiUS Forum

30
25 (0,24)
20
15
10 (600,7)

5
0

0 100 200 300 400 500 600 700
Time (s)

Figure 11 Temperature-time relationship graph from cooling device

Conclusion
Type 4 design gave the highest efficiency, which decreased the temperature from 26.0 °C to 11.1 °C, a

reduction of 14.9°C. Second experiment yielded acceptable results on the 1st and 2nd designs, which reduced the
temperature from 24.0 °C to 7.0°C, a reduction of 17.0°C.

Acknowledgement
This project was supported by Science Classroom in University Affiliated School (SCiUS). The

funding of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This
extended abstract is not for citation.

References
1. Wirat Kongsin, (2014) Study and design of a thermoelectric device for vaccine storage containers,
academic conference Sustainable Rural Development, 4th edition, 138-142.
2. Kitti Ninphung, (2015) Thermoelectric cooling box powered by solar cells, The 8th Symposium on
Renewable Energy to Community in Thailand, 260-263.
3. Thitipong Kutiphan, Pitchnaree Lalitaporn and Monton Thanuttamwong, (2016) Dew generation by the
thermoelectric chiller, Kasetsart University academic conference at 54, 415-421.
4. Jirayusawat Prasom, Sitthichok Suebthaetrakul and Thaweedej Sirithanapipat, (2019), Study on
Characteristics of Thermoelectric Cooling, The Mechanical Engineering Network of Thailand Symposium
33rd, 1-9.

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Oral presentation
STEM and Innovation Group 2

Sunday August 28, 2022

No. Code Title Author School
Miss Kunkawin Boonkird Rajsima Witthayalai
1 OS2_06_01 Classification of Heart Miss Jinjuta Chomputsa School
Sounds Disease Diagnosis Miss Siwanart Prueksrirat
using Mel-Frequency Engineering Science
Miss Pawanrat Chullaphol Classrooms
Cepstrum Coefficient and Miss Chayanis Ra-Ob (Darunsikkhalai School)
Artificial Neural Network Miss Chanistha Ra-Ob Paphayomphittayakom
Methods Mr. Jirapat Kongchuay School
2 OS2_09_01 Facility Device for Blinded Mr. Teetouch Srakhao
People Control via Hand Chiang Mai University
Gesture Recognition Mr. Panuwitch Yawila Demonstration School
Mr. Veerapat Sintupong Engineering Science
3 OS2_14_04 Automatic Medicine Miss Bhavida Phussadisophon Classrooms
Dispenser for Elderly by Mr. Singha Junchan (Darunsikkhalai School)
Face Recognition Miss Naphat Saereerak
Demonstration School,
4 OS2_01_01 Skillful Secretary Mr. Chotayakorn Duangkaew University of Phayao
Mr. Palaprut Weerayutthakarn
5 OS2_09_07 An Automatic Analysis and

Controlled Cat Feeder

System Monitored by Web

Application

6 OS2_02_01 The development of
transportation medical
supplies robot to use in
infected area in covid 19
situation

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Title: Classification of Heart Sounds Disease Diagnosis using Mel- Frequency Cepstrum Coefficient
and Artificial Neural Network Methods

Field: STEM and Innovation OS2_06_01

Authors: Jinjuta Chomputsa, Kunkawin Boonkird and Siwanart Prueksrirat

School: Ratchasima Witthayalai School, SCiUS-Suranaree University of Technology

Advisor: Assoc. Prof. Peerapong Uthansakul, Institute of Engineering, Suranaree University of Technology

Abstract:

According to statistics from the World Health Organization (WHO), cardiovascular disease is the number
one cause of death in the world, accounting for 31% of deaths worldwide. Any abnormality in the heart sound may
indicate some problems in the heart. Even the slightest malfunction can be life-threatening. Normally, accessing
cardiac treatments must take place at hospitals using the Electrocardiography (EKG) method by a medical doctor,
which diagnoses heart abnormalities with electric waves, but EKG can only detect serious heart disease, not minor
ones. Analyzing heart sounds related to heart symptoms may be another way to diagnose heart disease. As today's
technology has advanced and has been applied in more medical applications, the authors, therefore, aim to develop
an algorithm would be effective and can be used to classify normal and abnormal heart sounds (murmur and
extrasystole) using the Mel frequency cepstrum coefficient. In a previous study, which used 26 features in the
MATLAB toolbox, could only classify 2 types of heart sounds, which were normal and abnormal heart sounds. In
this study, we increased Mel-Frequency Cepstrum coefficient (MFCC) into 39 features and used artificial neural
network ( ANN) methods for distinguishing normal and abnormal heart sounds, which were murmur and
extrasystole. Three different algorithms in ANN, including Levenberg-Marquardt (LM), Bayesian Regularization
(BR), and Scaled Conjugate Gradient (SCG), were used. It was found that the LM algorithm gave the highest
accuracy, which was 87.18%. Therefore, the algorithm developed in this work can classify the heart abnormality
between murmur and extrasystole, which is beneficial for medical usage in accurately diagnosing heart disease.

Keywords: Machine Learning, Artificial Neural Network, Mel-Frequency Cepstrum Coefficient (MFCC), Heart
sound, Heart disease

Introduction

So far, Disease Diagnostic technology has been used to detect heart abnormalities in medical practice
because it can be used to aid in the diagnosis of disease from the regular physical examination of the patient [1].
The variety of data samples are used for the program to recognize and classify heart sounds patterns. This method
is called Machine Learning which is a simulation similar to the human nervous system, known as an Artificial
Neural Network (ANN). This ANN is a computational system that mimics the activity of the brain. The system can
give high accuracy when we put a large number of data to train the system. [2], [3]

There are three types of training algorithms, namely, Levenberg- Marquardt ( LM) , Scaled Conjugate
Gradient (SCG), and Bayesian Regularization (BR) backpropagation algorithms for a given multilayer perceptron
(MLP) feedforward neural network. The performance of each type can be evaluated based on several statistical
metrics, namely, mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error
(MAPE). For this purpose, MATLAB is chosen as an experiment environment to perform the required computations
to make decisions based on neural networks for the analysis of high-dimensional data, machine learning offers a
worthy approach for making classify and automatic algorithms. Principles to compare the extraction performance
of normal and abnormal heart sounds, including Murmur and extrasystole.

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Methodology

Part 1 : Signal in heart sound

We adopted the concept of Machine Learning to analyze heart sound. From the preliminary study, it had
been found that Machine Learning can well classify only the heart abnormalities as shown in Figure 1. However,
there are many different types of heart deformities and varying degrees of danger, such as murmur or extrasystole.

A heart murmur depends on the velocity of blood flowing
through the heart's ventricles. This can be used as a signal of
aortic stenosis and mitral. An extrasystole occurs when heart

contracts faster than the normal rhythm, which can lead to chest
pain or difficulty breathing. After the analysis, it is possible to
Figure 1: Normal and abnormal heart sounds
determine whether it is a normal or abnormal heart sound, but it
is not and requires further treatment with a medical professional.

Phonocardiogram vary by time, amplitude, intensity, homogeneity, content spectra. etc.

Part 2 : Proposed method

Starting by extracting the data in Mel-frequency cepstrum coefficient (MFCC) so the extracted data will
be able to analyze in Artificial Neural Network (ANN). During the process in ANN, the extracted data was tested
in 3 different algorithms in ANN including Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and
Bayesian Regularization (BR) were used in this study in order to find the most accurate method.

Part 3 : Features from heart sounds

Figure 2: Flow chart of working process Characterization extraction, called MFCC, is the extraction of
the characteristics of each phoneme that is different from each other.
Then, the system recognizes the characteristics of each phoneme. When
the signal comes later, the system can recognize which one is the same
or close to the distinctive feature of any phoneme. Also, it can reduce
the amount of data where large amounts of data are converted to smaller
datasets and retain the key features of the original data [4].

The explanation of MFCC flow chart is below

Figure 3: Flow chart of MFCC sound wave a. Fast Fourier transform (FFT) : To convert each frame of N samples
from the time domain into the frequency domain FFT is being used.
conversion Centre frequency.
b. Mel filter bank processing: The frequencies range in the FFT
spectrum is very wide and the voice signal does not follow the linear scale.
Each filter’ s magnitude frequency response is triangular in shape and

equal to unity at the Centre frequency and decreases linearly to zero at the

c. Discrete cosine transform: This is the process to convert the log Mel spectrum into the time domain
using DCT. The result of the conversion is called Mel Frequency Cepstrum Coefficient. The set of coefficients is
called acoustic vectors. Therefore, each input utterance is transformed into a sequence of an acoustic vector.

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From Figure 4, there are 39 heart sound features in total including Delta feature 12,

Double Delta, feature 12, Stat feature 12, 1 frame energy, 1 Delta frame energy, and 1
Delta Delta frame energy. Fs is the sampling frequency, which is 2000 Hz, Window
Length is 512 ms, NFFT is the number of FFT is 20 and the No Filter is 13. [5]

We selected 3 main algorithms from ANN into consideration consisting of
Levenburg- Marquardt ( LM) , Scaled Conjugate Gradient ( SCG) , and Bayesian
Regularization (BR). The algorithm we equipped was LM which reduced error function
Figure 4: Delta, Double and had efficient technique for minimizing a nonlinear function. Moreover, this
delta, and Stat features algorithm has stable convergence. The second algorithm we examined was SCG. This

is a modified version of the steepest descent algorithm while working in a direction to generate a faster
cstoantivseFtiricggaeulnrpcereo. b4Tl:ehmeDliaensltttaha,elgmoreiathnms owf ae well- modeled
examined was BR which changes nonlinear regression into a
ridge regression [6]
Double delta, and
Part 4S:tEatxpfeeartiumrenstation

In this study, the results of using ML in combination with the normal and abnormal heart sound datasets
were found. Heart sounds can be distinguished by selecting the MFCC feature from the sample data, referring to
the data set (https://physionet.org) of abnormal and normal heart sounds. PhysioNet is an open-source research
resource. And ( https: / / github. com/ vgadalov/ Heartbeat_CNN_kNN_SVM_Classification) of heart Murmur and
Extrasystole heart sound. In order to know the preliminary information, we compared different types of heart
sounds while dividing abnormal heart sounds into murmur and extrasystole. Within the heart sound data, 212 sound

were used for the validation of the model, including 100 normal heart sounds, 66 murmur heart sounds, and 46
extrasystole heart sounds were used for training and testing.

Result, Discussion and Conclusion

Figure 5: Training and Testing by Figure 6: Training and Testing Figure 7: Training and Testing by
Levenberg Marquardt (LM) by Bayesian regularization (BR) Scaled Conjugate Gradient (SCG)

From Figure 5 to Figure 7, graphs illustrate the differences of heart sound analyzed by 3 methods. Normal
heart sounds are represented with 1, 0 identify the murmur heart sounds (abnormal) and -1 define extrasystole heart
sounds (abnormal). And all Figures show the results of putting data into training (a.), validation (b.), and testing (c.).
While all (d.) is the average of training, validation and testing.

Table 1 : illustrates the outcome of each algorithm after inputting heart sounds

Training is when we input heart sounds into the system to make the it classifies types of heart sounds.
After training, we ensured the accuracy with the process of Validation while applying the validation set can also

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forecast the percentage of precision. Lastly, Testing, we inputted the test set into the system and estimate the
precision.

Figure 8 was the result from LM. It was found that after inputting 100
normal heart sounds, the algorithm is able to predict 94 correct heart sounds.
After inputting 66 murmur, the algorithm is able to predict 61 correct heart
sounds. And after inputting 46 extrasystole, the algorithm is able to predict 32
correct heart sounds.

Figure 8 : Results after Figure 9 was a result of comparing the Murmur and Extrasystole heart
inputting types of heart sounds sounds classification algorithms using LM, BR and SCG algorithms. Out of the
three techniques, LM has the least MSE (8.73) and epochs (25) which is the
Figure 9 :Results of Testing three number of training times and it had good convergence speed. Hence it had the
algorithms which are LM, BR and most accuracy of 87.18%.

SCG The ANN method, Levenbergs- Marquardt algorithm was chosen
because it gave the data learning outcome of 87.72% and the data test result of
87. 18% . The method developed from ANN can be used to predict heart
abnormalities as murmur or extrasystole which is beneficial in medical usage for
accurately diagnosing heart disease from heart sound.

References

[1] S. Ari, K. Hembram, and G. Saha, “Detection of cardiac abnormality from pcg signal using lms based least
square svm classier,” Expert System Applications, 2010.

[2] P. Wang, C. Lim, S. Chauhan, J. Foo, and V. Anantharaman, “Phonocardiographic signal analysis method using
a modified hidden markov model,” Annual Biomedical Engineering, 2007.

[3] R. Saracoglu, “Hidden markov model-based classification of heart valve disease with pca for dimension
reduction,” Engineering Applications Artificial Intelligence, 2012.

[4] Bala, A., Kumar, A. and Birla, N., 2010. Voice command recognition system based on MFCC and DTW.
International Journal of Engineering Science and Technology, 2(12), pp.7335-7342.

[5] Cen, Ling, Fei Wu, Zhu Liang Yu, and Fengye Hu. "A real-time speech emotion recognition system and its
application in online learning." In Emotions, technology, design, and learning, pp. 27-46. Academic Press, 2016.

[6] R. Parmar, M. Shah, and M.G. Shah, “A Comparative study on different ANN techniques in wind speed
forecasting for generation of electricity,” IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE),
vol.12, no.1, pp.19-26, 2017.

[7] Boulares, Mehrez, Reem Alotaibi, Amal AlMansour, and Ahmed Barnawi. “Cardionvascular Disease
Recognition Based on Heartbeat Segmentation and Selection Process.” International Journal of Environment
Research and Public Health, vol.18, no.20, pp.1-27, 2021.

Acknowledgment: This project was supported by Science Classroom in University Affiliated School (SCiUS)
under the Suranaree University of Technology and Rajsima Wittayalai School. The funding of SCiUS provided
by the Ministry of Higher Education, Science, Research, and Innovation, is highly appreciated.

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Title : Facility Device for Blinded People Control via OS2_09_01
Field : Hand Gesture Recognition
Author :
STEM and Innovation
School :
Advisor : Ms.Chayanis Ra-ob

Ms.Chanistha Ra-ob

Ms.Pawanrat Chullapol

Darunsikkhalai School, King Mongkut's University of Technology Thonburi

Siam Charoenseang, Institute of Field Robotics (FIBO), King Mongkut's University of
Technology Thonburi

Nion Vinarukwong , Darunsikkhalai School, King Mongkut's University of Technology
Thonburi

Kitsada Doungjitjaroen , Darunsikkhalai School, King Mongkut's University of Technology
Thonburi

Abstract :

Blinded people refer to those who have visual impairment in both eyes, with visual abilities less than 1/10

of normal people. For this reason, it obstructs the way blinded people live. Therefore, Facility Device for Blinded

People Control via Hand Gesture Recognition had been developed in order to facilitate blinded people with hand

gesture recognition as a command system. The device was designed as a necklace consisted of web camera and

Raspberry pi 4. To begin with, realtime images were captured by the web camera and sent to Raspberry Pi 4 where

hand gesture recognition was processed. After that, the hand gesture was then checked, to see which conditions

were matched. Each conditions enabled different functions including home automation function which cooperated

with Sonoff Switch S31 that allowed electrical appliances to turn on and off automatically, and emergency function

which sent a notification to via Line notify. As a

result, the prototype of the device was built and was able to recognize hand gestures with an accuracy of 97.8%

and enable both functions in real time.

Keywords : Hand Gesture Recognition, Home Automation, Line Notify, Sonoff Switch

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Introduction

According to the recent statistical record of disabled people in Thailand, there are almost 200,000 people

who are visually impaired which is considered as a large number Most of

blinded people find movement limitations as their major problem, therefore remote controlling was used to solve
the aforementioned problem. Hand gesture recognition, one kind of a remote controlling, was chosen to be a main
system of the project because hand gesture is a universal language that everyone can understand. Additionally,
u convenience are two factors that must be concerned. For that reason, this device consists of 2
functions including home automation and emergency function which will start operating when conditions are
matched The idea had

Methodology

Before Raspberry Pi 4 can be coded, it needs to be prepared by these following procedures. Firstly,
Raspberry Pi OS (32-bit), an official operating system specially designed to run on Raspberry Pi 4, was installed
on an SD card. Next, an Integrated Development Environment (IDE) was needed as a tool for coding Python
programs, so Spyder 4 was subsequently installed. OpenCV and Mediapipe, libraries those were required for
programming the device, were lastly installed because they contain important commands used in camera
operations and hand recognition.

After Raspberry Pi 4 was prepared, programs had been coded. The programs were separated into 4 parts
for an easy review, including a hand detection program, a Line notify program, a home automation program, and
a main program. The main program contained code for hand recognition and was able to call functions from the
other programs when conditions were matched.

Focusing on hand detection and hand recognition programs, commands from OpenCV and Mediapipe
were coded to operate the camera and recognize hands in real time. The program was able to detect the space
between the fingers in order to process the hand gesture that the user showed. Therefore, the way how the hand
gesture is presented did not affect the result, the program only recognized the number of fingers that are raised.

Turning to a Line notify program, a free function from Line application, Line notify, was used to send an
for asking help in an emergency. In order to enable the

function, a line group which contains a line notify account and message receivers must be created. Subsequently,
an access token, which was required for sending messages to a specific group, can be generated from Line notify
website.

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Focusing more on a home automation program, Sonoff S31, a Wi-Fi smart plug, allowed us to toggle
s were flashed to Tasmota

which unlocked full control over the Sonoff devices, but more importantly allowed us to integrate them with the
python code. As a means to send commands from Raspberry Pi 4 to Sonoff S31, a mqtt broker called HiveMQ
was used.

Finally, the main program was coded by using basic conditions commands in python. If the hand
gesture was recognized as 1, 2, 3 or 4 fingers, functions from the home automation program would then be called
to toggle the switch number 1, 2, 3 and 4 respectively. In like manner, a function from the Line notify program
would be called if the hand gesture was recognized as 5 fingers.

Results and Discussion

Figure 1: Prototype of the device Figure 2: System scenario

A prototype of the Facility Device for Blinded People Control via Hand Gesture Recognition was shown
in figure 1 and the system scenario of the device that represent how the device works was shown in figure 2. The
device was able to recognize hand gestures and enable both functions in real time.

Table 1: System accuracy test

Hand gesture Accuracy According to the system performance test where accuracy
(Number of fingers) was used as a criterion, the Facility Device for Blinded People
95% Control via Hand Gesture Recognition was able to recognize each
1 100% hand gesture with a good accuracy ranged from 95% to 100% as
2 96% shown in the table. To summarize, the device has an accuracy of
3 98% 97.8% in average.
4 100%
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Table 2: User preferences 12th SCiUS Forum

Criteria Average Standard User preferences were also recorded. The
rating deviation (S.D.) questionnaires were done by 20 volunteers (with
normal eyesight) via Google form after a trial of
Convenient 3.56 0.04 the hand gesture recognition system. The user
preferences were rated in 3 criteria whether the
Practical 3.78 0.02 device is convenient, practical, and
understandable. The rating ranged from 1 to 5,
Understandable 4.89 0.003 from lowest to greatest, respectively and the
average ratings are represented in table 2.

Conclusion

According to the study, the Facility Device for Blinded People Control via Hand Gesture Recognition
has a high efficiency because the device has an accuracy over 95% and user preferences are satisfied.

Acknowledgements

This project was supported by Darunsikkhalai Science School (DSS), King Mongkut's University of
Technology Thonburi (KMUTT), and Science Classroom in University Affiliated School (SCiUS). The funding
of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This extended abstract
is not for citation.

References

: ;.

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Title : Automatic Medicine Dispenser for Elderly by OS2_14_04
Face Recognition
Field : STEM and innovation
Author : Mr. Jirapat Kongchuay
Mr. Teetouch Srakhao
School : Paphayompittayakom School, Thaksin University.

Advisor : Dr.Naphat Keawpibal, Thaksin Universiy

Co-Advisor : Asst.Prof. Dr.Visit Boonchom, Thaksin Universiy

Abstract
A drug resistance has been critical issues in past few years, which may lead to a delayed recovery

of the disease or may lead to complications. For indirect effects, such as patients with depression, taking the
drug indefinitely may have psychological effects such as self-harm or suicide. Therefore, an automatic drug
dispenser for the elderly with facial recognition using the Haar cascade algorithm was developed to help
facilitate the accurately dispensing of drugs. In this work, the efficiency and mechanism of operation of the
automatic drug dispensing system for elderly patients were studied by using facial recognition techniques to
analyze and dispense drugs accordingly. The automatic drug dispensing system is divided into 2 parts.
Hardware part controls the dispensing mechanism using the ESP32 board, Raspberry Pi, camera module, servo
motor, stepper motor, buzzer and LED indicator. Software part is the partial of a facial recognition system
using the Haar cascade algorithm written in Python and developing a web application for scheduling pills on
Raspberry Pi devices. The face dataset used for testing consists of 42 images per person. From the study of
drug dispensing efficacy of automatic drug dispensers found that the proposed drug dispenser provides an
efficiency of up to 87.5%, which can be developed into a comercial product that facilitates the daily life of
elderly patients.

Keywords: Automatic medicine dispenser, face recognition, elderly patients, Internet Of Things

Introduction
Recently, in Thai society, the rate of elderly patients in the country is increasing every year. Each

elderly patient has a different congenital disease, resulting in the need for elderly patients to eat. Medicines are
of different dosages and need to be administered regularly. Sometimes, some patients may forget to take their
medication or get confused with the dosage that may not be the same at each meal. It can cause a patient to
take a drug overdose, which can be fatal. Forgetting to take a drug or taking an overdose can directly or
indirectly affect health. Drug resistance, which may delay the treatment of the disease or cause complications.
As for the indirect effects, such as patients suffering from depression, when they do not take the full dosage
regimen, they may have psychological effects such as self-harm and suicide.

The increasing of technology development offers a new challenge to facilitate people in daily life.
According to our knowledge, many researches have been interested in the methods for dispensing medicine
with Internet of Things underlying sensor technology. In this paper, an automatic dispensing of medicines for

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the elderly with facial recognition is proposed by using Haar cascade algorithm to help facilitate accurate and
timely drug dispensing.

Methodology
The research project has been studied the drug habits of older people, types of medications that are

usually found in the diet of the elderly, and analyze problems caused by taking the wrong medications of elderly
people. Due to the problem of forgetting taking the medications of the elderly, this work has designed and
developed a prototype of an automatic dispenser for elderly patients using facial recognition techniques to help
dispense correctly according to the person recorded in the database. Moreover, Haar cascade algorithm is used
in this work in conjunction with openCV software to recognize the faces of the elderly. The architecture of the
proposed automatic dispenser system is shown in Fig 1.

Fig 1. The overview architecture of the proposed automatic drug dispenser
The proposed automatic dispenser system is divided into 2 parts. First, the hardware part is a control
of the dispensing mechanism using the following devices: ESP32 as a microcontroller, Raspberry Pi as a
microprocessor, camera module, servo motor, step motors, sound modules and LED lights. Fig 2 illustrates the
prototype of our proposed automatic drug dispenser, which consists of 6 dispensing channels for 2 patients.
Second, the software part consists of a section of facial recognition using the Haar cascade algorithm,
which is written in Python language and the web application section for scheduling dispensarie written in
HTML language. The image data used in facial recognition is 42 photos/person, 21 images for each, for the
automatic drug dispenser design and application. The example of web application is shown in Fig 3.

Fig 2. Prototype of automatic drug dispenser

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Fig 3. An example of a web application for scheduling medicines.

Results and Discussion
We conducted two experiments for evaluating the accuracy of facial recognition and the performance

of proposed drug dispenser. The accuracy results of the facial recognition system using the Haar cascade
classifier algorithm developed on Raspberry Pi and OpenCV was evaluated. The accuracy performance was
obtained for 20 times per person. It was found that the accuracy of the facial recognition system achieved in
high accuracy. For the first person the facial recognition system gives 17 times out of 20 times, which is
accuracy of 85%. For the second person, the facial recognition gives 18 times out of 20 times, yields 90%
accuracy. Therefore, overall facial recognition performance was calculated as accuracy of 87.5%.

Face recognition accuracy

1st person

2nd person Pass
Not pass

Fig. 4. Face Recognition Accuracy Performance Graph

Next, when the proposed dispenser receives a dispensary command from the Raspberry Pi, which has

a facial recognition module, it appears that the dispenser can dispense the drug correctly according to the

required channels. The results of drug dispensing is show in Table 1.

Table 1: Results of drug dispensing efficacy test

Pill dispenser 1 Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Pill dispenser 2 ✓ ✓ ✓ ✓ ✓ ✓ ✓
Pill dispenser 3 ✓ ✓ ✓ ✓ ✓ ✓ ✓
Pill dispenser 4 ✓ ✓ ✓ ✓ ✓ ✓ ✓
Pill dispenser 5 ✓ ✓ ✓ ✓ ✓ ✓ ✓
Pill dispenser 6 ✓ ✓ ✓ ✓ ✓ ✓ ✓
✓ ✓ ✓ ✓ ✓ ✓ ✓

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According to study of the dispensary eligibility of automatic dispensers, it found that when an
automatic dispenser with the face recognition of the elderly can be used with up to 87.5% of the accuracy. This
can be further developed into a daily amenity product for elderly patients.
Conclusion

According to a study of the dispensary rights of automatic dispensers, it was found that when an
automatic dispenser is used, the face of the elderly is recognized. It can be used with up to 87.5% efficiency,
which can be developed into a product. The daily amenities of elderly patients can be facilitated.
Acknowledgements

This project was supported by Science Classroom in University Affiliated School (SCiUS). The
funding of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This
extended abstract is not for citation.
References
Chompoonut Phromphak. (2013). Elderly people. Retrieved March 5

2021, from https://www.parliament.go.th/ewtadmin/ewt/elaw_par cy/ewt_dl_lin
Wirot Kittiwornpreeda. (2013). Documents for teaching introductory microcontrollers. Retrieved March 1

2021, from http://www.sbt.ac.th/new/sites/default/files/TNP_Unit_1.pdf

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Title : Skillful Secretary OS2_01_01
Field :
Author : STEM
Mr. Panuwitch Yawila
School : Mr. Veerapat Sintupong
Advisor : Chiang Mai University Demonstration School
Asst. Prof. Dr. Karn Patanukhom
Department of Computer Engineering, Faculty of Engineering, Chiang Mai University

Abstract
Due to busy life, people sometimes forget to do what needs to be done. For example, there may be a

special meeting coming up or urgent tasks that need to be done. The purpose of this project is to develop a
mobile application that reminds the user of appointments or tasks that need to be done. This mobile application
uses speech recognition [1] to receive a voice command from the user and convert it into a text. Then, the
mobile application sends the text to Dialogflow [2], a chatbot and natural language processing platform that
helps computers understand and interpret the meaning of text. Dialogflow sends the extracted information
back to the application. The application adds information about activities or appointments to a database and
creates events in a calendar. When it's time for the activities soon, the application notifies the user. We also
make this application to work with the Northern Thai dialect as voice commands.

Keyword : Mobile Application, Speech recognition, Dialogflow, Natural language processing.

Introduction
Nowadays people have many appointments, things-to-do or deadlines each day. Most of the activities

that we've to do are very important and we don’t want to miss those activities. In the past, people had to note
their appointments, things-to-do or deadlines on notebooks or calendars or used sticky notes and stuck them
on a wall or a table. Sometimes they don't have notebooks, sticky notes or calendars with them everywhere,
then they can forget about those important activities. Nowadays, most people have smartphones with them at
all times. So they can put the appointments or deadlines in calendar applications such as Google Calendar and
make to-do-lists by using to-do-list applications such as Microsoft To Do. In this project, we would like to
develop a Skillful Secretary application which is an application that can help users to remember any activities
and notify them when the time has come. Since most calendar and to-do list applications are based on tapping
and touching the screen, the Skillful Secretary application allows for more flexible and natural usage by
allowing users to control the application by speaking naturally. As a novelty, this application can also receive
voice commands in Northern Thai dialects (Kham Mueang).

The principle of operation of the application is that the user enters voice commands into the program.
The program converts the voice message into text and sends messages to the server. The server analyzes the
messages, adds them to the calendar and sends replies to users.

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Methodology

Tools :
1. Flutter[3]: Flutter is an open source framework for building applications.
2. Visual Studio Code[4]: Visual studio code is a code editor. We use Visual studio code to write Flutter
program.
3. Dialogflow: Dialogflow is a platform for developing chatbots. We use Dialogflow as a backend for
designing and integrating a conversational user interface into a mobile application.
4. Firebase[5]: Firebase is a platform used to create backend tools for mobile app development. There are
many tools, but we use Cloud Firestone to create databases.
5. Speech-to-Text: Speech-to-Text It is a software used to convert audio data into text. We implemented it to
obtain information about user commands, which are then converted into appointment time.
6. Google Form[6]: We use Google forms to collect introductory sentence samples to train in Dialogflow.

The experimentation is divided into two parts :
Part 1. On the server side, the chatbot was trained via Dialogflow to understand the meaning of the commands
and interact in the most natural way. And a database was created via Firebase to store activities so they could
be displayed in the calendar.
Part 2. On the client side, we create an application via Flutter and design the user interface, create a calendar
to display appointment information, and create a model to express the sentiments of received sentences .

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