Yooyen, Soemsak

Preferred name

Yooyen, Soemsak

Alternative Name

Yooyen, S.

Main Affiliation

International Academy of Aviation Industry

soemsak.yo@kmitl.ac.th

Now showing 1 - 10 of 15

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Predicting Fuel Burn with Neural Network to Adjust Contingency Fuel of Airplane
(2023-01-01)
Ounsrimoung, Pimolrat
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Nootyaskool, Supakit
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Atchariyachanvanich, Kanokwan
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Yooyen, Soemsak
The amount of fuel in an airplane tank is very important for flying. however, flying a short distance by adding a fuel-full tank is not energy efficient because spending a lot of tons for holding fuel weight. The flight planners who consider the amount of fuel to add to the tank by using historical data, use fuel burn calculating and adjust contingency fuel. This research presents the neural networks to predict fuel burn, which learn from historical airplane data. The experiment applied to local and international flight data and used both Airbus and Boeing. The predicted model was swapped and tested on the outbound and inbound replacements for confirmation capable of the predicted mode.
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Aircraft trajectory recognition via statistical analysis clustering for Suvarnabhumi International Airport
(2020-02-01)
Kamsing, Patcharin
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Torteeka, Peerapong
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Yooyen, Soemsak
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Yenpiem, Siriporn
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Delahaye, Daniel
Since Suvarnabhumi International Airport is considered to be the biggest airport in Thailand, a big travelling-hub of southeast Asia and plays a significant part to the economy of Thailand relying on the tourism industry, an aircraft trajectory recognition is essential to support the high traffic management system from around the world. The first and essential stage of airport capacity enhancement is descriptive-analytic in several sections of the airport, including flight trajectory behaviors in order to plan an improvement procedure in the future. This experiment deploys K-mean and Gaussian Mixture clustering to compare results by using available automatic dependent surveillance-broadcast (ADS-B) dataset provided by the bigdata system from various websites. The test varies the number of clustering from three to ten and measures how similar an object is to its cluster by using the Silhouette score. Gaussian Mixture clustering produces at least three unique flight trajectories when setting the number of clustering equal to four, giving the Silhouette score of 0.43. K-mean clustering with the number of clustering equal to ten gives the highest Silhouette score of 0.45. However, its routes are not clearly recognized when compared with the Gaussian Mixture clustering. Although the overall results are not clearly shown in the pattern, it is enough to describe the trajectory patterns of the aircrafts taking off or landing over Suvarnabhumi International Airport.
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A statistical model for estimating statistical contingency fuel
(2022-01-01)
Atchariyachanvanich, Kanokwan
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Kruaklai, Warune
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Chaipatchareekorn, Nattanan
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Sukteab, Nuttavadee
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Yooyen, Soemsak
Contingency fuel is the amount of fuel used to compensate for unexpected events. This amount of fuel is equal to 5% of the trip fuel or 3% of the trip fuel when it has been determined to have an alternate airport on the route according to the rules of the Thai Civil Aviation Authority. Currently, contingency fuel planning determines the minimum and maximum values of contingency fuel based on aviation industry experience. As a result, the fuel supply may be either too much or too little on some flights. In this research, we aim to create a statistical model that can estimate the fuel required in the event of an emergency and measure the efficiency of contingency fuel with a loss function. The model uses statistical methods to calculate the contingency fuel in the form of Statistical Contingency Fuel (SCF) and monitors the fuel deviation for the planned and actual trip. We used fuel preparation data from 2018 and 2019 that was sourced from Thai Airlines data for six routes with a total of 4,184 flights. The results show that the SCF of flight A was at confidence of level 95, while that of other flights was at a confidence level of 99. The results obtained from the model can be used to assist flight planners to make better decisions concerning the determination of contingency fuel.
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The Development of an Automated Technology for Unmanned Aircraft CNS/UTM in Compliance with Safety and Security Measures of the State
(2025-01-01)
Yenpiem, Siriporn
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Yooyen, Soemsak
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Banchong-Aksorn, Sasicha
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Yoneyama, Keito R.
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Jansri, Anurak
This research project supports the Senate's study and represents the government committee on promoting and developing the use of unmanned aircraft in low-altitude flights, with safety measures in accordance with the International Civil Aviation Organization's and Civil Aviation Authority of Thailand's minimum requirements. The main objective of this study is to create an innovative approach to enhance the country's competitiveness, which must also be accepted at the national and international level. This is based on the reasons for building infrastructure and networks for communications, navigation, and surveillance analysis to prevent aircraft from colliding and assessing the aircraft's behaviors in both segregated and current manned air traffic-controlled airspace. The study supports the utilization of air traffic management for the Unmanned Aircraft System Traffic Management (UTM) Platform, which is a necessary tool in facilitating the policy of using unmanned aircraft for commercial use. It can be extended for practical uses and further development in areas where there is an integration between manned and unmanned aircraft, both small and large for sustainable future development. It consists of basic theoretical analysis, system requirements for flight operations, and applications for low-altitude operations (within 500 feet and weighing less than 150 kilograms). The study methodology for constructing a quantitative and qualitative method includes: 1.Creating a system for requesting risk assessment levels for flight operation and program development 2.Creating systems for managing aviation mapping and geo-awareness information 3.Creating a system for requesting activities, flight and flight plan authorization, examining flight areas, and communication network identifications. Which will be used with tracking, advising, helping, recording, and monitoring aircraft behaviors. 4.Creating a system for examining other aircraft, airspace traffic information, obstacles during flight operations, and deconflicts. This research lays the foundation for future studies in the aviation sector, including the development of unmanned aircraft system traffic management and urban air mobility systems, and the integration of unmanned with manned aircraft, regardless of aircraft type. However, continuous national and international research will remain critical.
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The Development of a Prototype for Low Altitude Operations of Unmanned Aircraft Flight Plan Systems
(2025-09-01)
Yenpiem, Siriporn
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Yooyen, Soemsak
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Tungkasthan, Anucha
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Banchongaksorn, Sasicha
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Yoneyama, Keito R.
The use of Unmanned Aircraft has grown significantly in Thailand and worldwide, particularly for operations below 450 feet. However, unlike manned aviation, there remains a lack of integrated digital platforms to manage flight plans that align with regulatory and operational requirements specific to low altitude activity. This study employed both secondary research and expert interviews to gather technical and regulatory user requirements. The data were analyzed and validated using Structural Equation Modeling to identify key variables influencing safety operations. Based on these findings, a standardized low altitude flight plan format was developed and converted into a prototype web platform called GoFly. The system enables operators to register aircraft and pilot credentials and to submit flight plans digitally. This platform addresses the current fragmentation in Thailand’s flight planning process by centralizing operations and enhancing regulatory compliance. The study contributes to the foundational development of a digital Unmanned Aircraft Traffic Management system tailored for emerging airspace users in Thailand and demonstrates potential scalability to other international regulatory contexts.
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Structural Equation Modeling for Airspace Optimization: The Analysis of Causal Factors Influencing Aviation Safety
(2026-05-01)
Yenpiem, Siriporn
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Yooyen, Soemsak
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Delahaye, Daniel
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Yoneyama, Keito R.
Increased flight volumes necessitate urgent reforms in Airspace Management (ASM) to mitigate risks of fatalities and near-misses. In order to enhance aviation system safety, the International Civil Aviation Organization (ICAO) mandates that state parties must conduct the Universal Safety Oversight Audit Program (USOAP) to continuously monitor civil aviation. This research aims to identify critical factors influencing Thailand’s ASM by employing experimental design and Structural Equation Modeling (SEM) to analyze influences and relationships among communication, surveillance, navigation, Air Traffic Management (ATM), and ASM. The methodology includes stimulation and a questionnaire-based survey conducted with aviation professionals and mapping out their answers to find the influences, relationships, and importance of the different factors. The results were validated using various statistical tools. The findings indicate signi1ficant direct and indirect effects on ASM, emphasizing that effective communication and robust surveillance are essential for safety and operational efficiency. This study highlights the need to increase the ASM framework, providing actionable insights for optimizing air traffic control in response to the growing air traffic demand. Furthermore, SEM for Airspace optimization can be applied internationally to significantly reduce accidents and incidents in the future.
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Ozonation effectiveness on the mitigation of airborne microorganisms and particulate matter for enhanced indoor air quality
(2026-01-01)
Yooyen, Soemsak
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Pohsa, Akrom
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Hayeema, Asmin
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Fuengfung, Jakkarin
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Sookpordee, Pilun
The efficacy of gaseous ozonation in eliminating indoor air pollutants has been investigated to improve indoor air quality. The ozone generator was operated until the air conditioning room attained ozone concentrations of 25, 50 and 75 ppm for 15 minutes. Rooms exposed to all ozone concentrations were efficacious in eradicating microorganisms and particulate matter (PM) within the air-conditioned room, with the level of elimination contingent upon the ozone concentration and duration of the test. Among these investigations, the 75 ppm ozone condition was the most efficacious in diminishing room microbe numbers. Ozone gas activity exerted a more pronounced effect on diminishing the number of PM. PMs within the 1.0–5.0 µm range were 75%–90% eliminated following the attainment of ozone levels of 75 ppm. This inquiry suggests that pre-treatment of the air with ozone can improve the indoor air quality and make it safer for occupants.
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Vision-based attitude estimation for spacecraft docking operation through deep learning algorithm
(2020-02-01)
Phisannupawong, Thaweerath
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Kamsing, Patcharin
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Tortceka, Peerapong
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Yooyen, Soemsak
On-orbital services, especially in docking operation and other space object interaction. The missions need accurate, reliable, and robust detection to be an accurate in joining any interaction concerned. Two spacecrafts with an unknown mathematical model to predict the position and orientation, a computer vision-based attitude estimation system to detect the poses of spacecraft via camera is the key option of the mission. In astronautics control, the position coordinates are normally represented as the cartesian coordinate system and used a quaternions coordinate system for orientation representation because quaternions can represent the orientation of spacecraft better than physical angle and can overcome the problem of singularity. This paper aims to construct a model for both position and orientation estimation with public data. The input images are the dataset of Soyuz in the resolution of 1280x960, which is simulated by Unreal Engine 4. The implementation of this paper use GoogLeNet for a convolutional neural network model with the mathematical model of loss subject to direct regression. The result shows that a position estimation is significantly accurate with having distance error smaller than 1 meter and trand to reduce when setting a proper scaling factor for loss function. The result demonstrates a high error for orientation estimation. However, the experiment expresses that both position and orientation estimation can be improved in case of selecting a suitable scaling factor of loss function.
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Vision-based spacecraft pose estimation via a deep convolutional neural network for noncooperative docking operations
(2020-09-01)
Phisannupawong, Thaweerath
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Kamsing, Patcharin
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Torteeka, Peerapong
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Channumsin, Sittiporn
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Sawangwit, Utane
The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.
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A Stress Tensor-based Failure Criterion for Ordinary State-based Peridynamic Models
(2022-01-01)
Dipasquale, Daniele
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Sarego, Giulia
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Prapamonthon, Prasert
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Yooyen, Soemsak
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Shojaei, Arman
Peridynamics is a recent nonlocal theory of continuum mechanics that is suitable to describe fracture problems in solid mechanics. In this paper, a new failure criterion based on the stress field is developed by adopting the damage correspondence model in the ordinary state-based peridynamic theory. The proposed stress tensor-based failure criterion is capable of predicting more accurately crack propagation in the mixed mode I-II fracture problems different from other failure criteria in peridynamics. The effectiveness of the proposed model is demonstrated by performing several examples of mixed-mode dynamic fracture in brittle materials.

Yooyen, Soemsak

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