Kamsing, Patcharin

Preferred name

Kamsing, Patcharin

Main Affiliation

International Academy of Aviation Industry

patcharin.ka@kmitl.ac.th

Now showing 1 - 9 of 9

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Practical Parallel of Autonomous Unmanned Aerial Vehicle by Mission Planner
(2022-01-01)
Suparnunt, Chairat
;
Boonvongsobhon, Chon
;
Baig, Farhaad Eounes
;
Leelalerthpat, Prachaya
;
Hematulin, Warunyu
Satellite imaging has always been a challenging task and at the current time being there are very few alternatives for capturing vertical images of Earth's vast landscape. Aerial photography by using multiple unmanned aerial vehicles (UAV) might be one of the solutions. This research aims to study, improve and demonstrate autonomy for UAVs by using specialized software and apparatus. The mission can be utilized to allow the UAVs to fly sequentially in the same pathway without colliding and creating the highest amount of coverage, which it is important for some remote sensing applications. Certain benefits can be obtained from using UAV rather than satellite imaging such as lower cost of overall equipment than a space satellite and higher resolution pictures due to flexibility in altitude operations. The results of implement the autonomous UAV has compare between path of actual flight and the setting way-points and it express exceptional results by presenting less of Root Mean Square Error.
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Aircraft trajectory recognition via statistical analysis clustering for Suvarnabhumi International Airport
(2020-02-01)
Kamsing, Patcharin
;
Torteeka, Peerapong
;
Yooyen, Soemsak
;
Yenpiem, Siriporn
;
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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Cooperative motion planning for multiple uavs via the bezier curve guided line of sight techniques
(2021-02-07)
Hematulin, Warunyu
;
Kamsing, Patcharin
;
Torteeka, Peerapong
;
Somjit, Thanaporn
;
Phisannupawong, Thaweerath
Multiple motion planning plays an essential role in several vehicle motions. This paper proposes a cooperative method between the Line of Sight techniques and the Bezier curve, applying this to motion planning for unmanned aerial vehicles. The experiment is implemented using the AirSim plugin on Unreal Engine 4. The results of the proposed method are compared with those for the conventional Line of Sight techniques to control multiple unmanned aerial vehicles. The results illustrate that the proposed method takes more time to process than the conventional one. However, the proposed method can reach a higher performance by addressing the target unmanned aerial vehicles and the pre-defining path more than the conventional method does, which is shown in all three simulation cases.
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Hardware-in-the-Loop Simulation Testbed for Three-Axis Earth's Magnetic Field Generation Based on 2.4- Meter Square Helmholtz Coils
(2023-01-01)
Chaisakulsurin, Jormpon
;
Manuthasna, Shariff
;
Masri, Tanawish
;
Panyalert, Thanayuth
;
Palee, Kritsada
This paper proposes a Hardware-in-the-Loop sim-ulation (HiLs) tested via square Helmholtz coils as a relative magnetic field generator. In technical terms, the HiLs is an indispensable tool for engineering design during rapid proto-typing of attitude determination and control algorithms for the turning parameters that control the attitude of the satellite, since most of the satellite's mission relies on its attitude, making this system one of the most essential to the satellite's operation. More-over, performing controlled experiments with the parameters for developing adaptive control algorithms improves the overall efficiency of the satellite's kinematic system. The conceptual design of a proposed system architecture can be composed of the electrical currents of 2.4-meter square Helmholtz coils produced by a low-level microcontroller equipped with a DC-motor driver by a pulse-width modulation (PWM) signal through real-time connection to an orbit propagator using a high-level computer. This research focuses on the attitude dynamic of satellites through the interaction between the Earth's magnetic field (EMF) and the magnetotorque in the satellite. To apply this phenomenon, the intensity and the direction of the magnetic field must be identified through Biot-Savart's law. Along with the EMF, the reference position is calculated using the standard general perturbations satellite orbit model (SGP4), and the intensity is modeled based on coefficients from the 13th edition of the International Geo-magnetic Reference Field (IGRF). Therefore, this paper presents a detailed development of a HiLs testbed for distributed attitude determination and control systems (hardware and software co-design, protocol, and control theory). Furthermore, it discusses a classic cooperative control case for the output of magnetic field intensity and direction, which was undertaken to explain the integrated simulation process and validate the effectiveness of the co-simulation tested to be a primary experiment.
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Wi-Fi Received Signal Strength-based Indoor Localization System Using K-Nearest Neighbors fingerprint integrated D∗algorithm
(2022-01-01)
Jarawan, Tanatthep
;
Kamsing, Patcharin
;
Torteeka, Peerapong
;
Manuthasna, Shariff
;
Hematulin, Warunyu
The indoor localization system is essential since the Global Positioning System cannot give an accurate position indoors, especially when several floor plans are considered. WiFi received signal strength becomes an alternative indicator for indoor localization systems. The experiment proposed a localization system created by integrating and working between the K-Nearest Neighbors algorithm and the D∗algorithm. The result illustrates the optimal path from the start point to the target point by avoiding the obstacle performing exceptionally well. The K-Nearest Neighbors algorithm provide the result for localization with Root Mean Square Errors of displacement at 1.190 meters, 2.491 meters, and 1.363 meters of X-Axis Y-Axis, respectively. The proposed indoor localization system can have various applications considering different environmental factors in different applications, such as the size of unmanned aerial vehicles when applying indoor unmanned aerial vehicles.
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Vision-based attitude estimation for spacecraft docking operation through deep learning algorithm
(2020-02-01)
Phisannupawong, Thaweerath
;
Kamsing, Patcharin
;
Tortceka, Peerapong
;
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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Cooperative Motion Planning for Multiple UAVs via the Bézier Curve Guided Line of Sight Techniques
(2022-01-01)
Hematulin, Warunyu
;
Kamsing, Patcharin
;
Torteeka, Peerapong
;
Somjit, Thanaporn
;
Phisannupawong, Thaweerath
Multiple motion planning plays an essential role in several vehicle motions. This paper proposes a cooperative method between the Line of Sight techniques and the Bézier curve, applying this to motion planning for unmanned aerial vehicles. The experiment is implemented using the AirSim plugin on Unreal Engine 4. The results of the proposed method are compared with those for the conventional Line of Sight techniques to control multiple unmanned aerial vehicles. The results illustrate that the proposed method takes more time to process than the conventional one. However, the proposed method can reach a higher performance by addressing the target unmanned aerial vehicles and the pre-defining path more than the conventional method does, which is shown in all three simulation cases.
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Wi-Fi Received Signal Strength-based Indoor Localization System Using K-Nearest Neighbors fingerprint integrated D*algorithm
(2021-02-07)
Jarawan, Tanatthep
;
Kamsing, Patcharin
;
Tortceka, Peerapong
;
Manuthasna, Shariff
;
Hematulin, Warunyu
The indoor localization system is essential since the Global Positioning System cannot give an accurate position indoors, especially when several floor plans are considered. Wi-Fi received signal strength becomes an alternative indicator for indoor localization systems. The experiment proposed a localization system created by integrating and working between the K-Nearest Neighbors algorithm and the D*algorithm. The result illustrates the optimal path from the start point to the target point by avoiding the obstacle performing exceptionally well. The K-Nearest Neighbors algorithm provide the result for localization with Root Mean Square Errors of displacement at 1.190 meters, 2.491 meters, and 1.363 meters of X-Axis Y-Axis, respectively. The proposed indoor localization system can have various applications considering different environmental factors in different applications, such as the size of unmanned aerial vehicles when applying indoor unmanned aerial vehicles.
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Deep Convolutional Neural Networks for plane identification on Satellite imagery by exploiting transfer learning with a different optimizer
(2019-07-01)
Kamsing, Patcharin
;
Torteeka, Peerapong
;
Yooyen, Soemsak
Object identification is on an available problem. Automating plane identification on Satellite imagery can be applied for activity and traffic patterns to monitoring airports, and including defense intelligence issues. This paper implements Deep Convolutional Neural Networks(CNN) to classify a plane in the planesnet dataset. Pre-trained model and transfer learning are deployed to overcome a limitation of computation resources by adding new top layer consists of a fully-connected layer and softmax layer to identify the new classes and re-train it. Besides, the experimental designs for testing an implementation of a pretrained model with some kinds of the optimizer to comparing a result. There are four types of optimizer. The first two are well-known optimizer namely Stochastic Gradient Descent optimizer and Adam Optimizer, while others are PowerSign and AddSign optimizer. PowerSign and AddSign optimizer are methods to minimize cost, which discover by using Recurrent neural network(RNN) and Reinforcement Learning. A result demonstrates that a plane identification on Satellite imagery can be achieved by implementing the pre-trained model and obtains an exceptional result with Adam optimizer.

Kamsing, Patcharin

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