Torteeka, Peerapong

Preferred name

Torteeka, Peerapong

Now showing 1 - 8 of 8

Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
Optimization based telescope passive auto-focusing through image quality assessment for Thai National Space object Observation
(2022-01-01)
Torteeka, Peerapong
;
Anutarawiramkul, Rungrit
;
Butpan, Pathompong
;
Palee, Kritsada
;
Prasit, Pakawat
The coming years, the growth of orbital debris in the Resident of Space Objects (RSOs) could be a serious issue for operational spacecraft and manned spaceflight. To prevent debris-related risks in operational orbit, a ground-based passive optical telescope was used as a fundamental imaging sensor for Space Situation Awareness to periodically maintain the orbital parameters of a space object in RSOs through an astrometry engineering approach. In the case of a small and dim space object, it produces low contrast images and suffers from a lack of sharpness which are easily influenced by background interference. Normally, a manual adjustment of a preliminary focus range of the telescope is one of the tedious practices that astronomers constantly face for the high-contrast image capturing before beginning a tracking procedure. Therefore, the passive automatic focusing process was initially required for a robotic telescope as a function of image quality assessment. Technically, the auto-focusing is a combination of processes between software and motorized focusing hardware with position feedback. In this paper, we investigate a new approach and software for passive automatic focusing charge-coupled device based telescope systems for finding a focal-point of an imaging system that minimizes objectives while satisfying constraints. These systems are most appropriate for unobserved all-night telescope operations as an asteroid and/or orbital debris monitoring. Based on the image quality assessment, a function of Full-Width Half Maximum was applied, the auto-focusing algorithm of the telescope system was realized via the golden section optimizer through the least square method. The experimental results were obtained during the commissioning of an alt-azimuth mounting equipped with a 0.7-meter optical aperture telescope from two observation sites at the National Astronomical Research Institute of Thailand. Two state-of-the-art methods were used for demonstrating and presenting method efficiency. Final results denote that the presented proposed algorithm improves the quality of the image contrast and can provide clearer details and information.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
Experimental Verification of Control Strategies for Satellite Magnetic-Based Attitude Control System Under a Three-Axis Helmholtz Cage Environment
(2023-01-01)
Panyalert, Thanayuth
;
Manuthasna, Shariff
;
Chaisakulsurin, Jormpon
;
Masri, Tanawish
;
Palee, Kritsada
During satellite mission planning and operation, the main function of the satellite's attitude determination and control subsystem (ADCS) is to gather information about the satellite's orientation relative to the inertial reference frame. Additionally, this subsystem generates control actions that produce the required torques for adjusting the satellite's orientation, particularly in the context of the Low-Earth Orbit (LEO) regime. This paper focuses on the satellite three-axis attitude control problem for a de-tumbling mode of spacecraft using only magnetorquers as actuators under the presence of noise and investigates their performance through Hardware-in-the-Loop simulation (HiLs) tests, which consisted of a relative Earth's magnetic field generator along with the SGP-4-based satellite orbital propagator high-level control software. The design, development, and verification of proposed satellite attitude control system (ACS) strategies are presented. In detail, as an example of experimentation, the classical B-dot control algorithm is used for the de-tumbling mode to stabilize and reduce the angular rate, along with the pointing algorithm for orienting the satellite to the desired attitude. Then, a cascade Proportional-Integral-Derivative (PID) is implemented to generate enough torque through the three-axis magnetorquers on the frictionless air-bearing platform to verify the performance of the controller using an onboard computer. Finally, the effectiveness of the co-simulation tested as the primary experiment was confirmed through the integrated simulation process.
Some of the metrics are blocked by your
consent settings
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.

Torteeka, Peerapong

Filters

Settings

Sort By

Results per page