Kamsing, Patcharin

Preferred name

Kamsing, Patcharin

Main Affiliation

International Academy of Aviation Industry

patcharin.ka@kmitl.ac.th

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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
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Kamsing, Patcharin
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Torteeka, Peerapong
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Manuthasna, Shariff
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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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Environmental Remote Sensing in Flooding Areas: A Case Study of Ayutthaya, Thailand
(2020-01-01)
Cao, Chunxiang
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Xu, Min
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Kamsing, Patcharin
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Boonprong, Sornkitja
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Yomwan, Peera
This book introduces flood inundation area and flood risks assessment based on a comprehensive monitoring system using remote sensing and geographic information system technologies. Taking the 2011 flood disaster of Ayutthaya in Thailand as an example, it presents a flood intrusion zone identification method based on remote sensing technology, spatial information technology and geographic information system for flood disaster monitoring and early warning system. It introduces the study area and data, vegetation index, improved support vector machine and flood intrusion zone identification method. It also analyzes the flood remote sensing parameters and waterborne diseases, method of risk assessment of waterborne disease outbreak, waterborne disease outbreak risk monitoring based on backpropagation neural network and its expert system. It not only promotes a new interdisciplinary approach both in public health and space information technology, but also greatly supports decision makers in disaster reduction.
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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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Cooperative Motion Planning for Multiple UAVs via the Bézier Curve Guided Line of Sight Techniques
(2022-01-01)
Hematulin, Warunyu
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Kamsing, Patcharin
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Torteeka, Peerapong
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Somjit, Thanaporn
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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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Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery
(2023-01-01)
Boonpook, Wuttichai
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Tan, Yumin
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Nardkulpat, Attawut
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Torsri, Kritanai
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Torteeka, Peerapong
Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use dataset is proposed for automatic land use classification using Landsat 8 imagery. The experimental results illustrate that deep learning semantic segmentation using the baseline network (SegNet, U-Net) outperforms pixel-based machine learning algorithms (MLE, SVM, RF) for land use classification. Furthermore, the LoopNet network, which comprises a convolutional loop and convolutional block, is superior to other baseline networks (SegNet, U-Net, PSPnet) and improvement networks (ResU-Net, DeeplabV3+, U-Net++), with 89.84% overall accuracy and good segmentation results. The evaluation of multispectral bands in the land use dataset demonstrates that Band 5 has good performance in terms of extraction accuracy, with 83.91% overall accuracy. Furthermore, the combination of different spectral bands (Band 1–Band 7) achieved the highest accuracy result (89.84%) compared to individual bands. These results indicate the effectiveness of LoopNet and multispectral bands for land use classification using Landsat 8 imagery.
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An enhanced learning algorithm with a particle filter-based gradient descent optimizer method
(2020-08-01)
Kamsing, Patcharin
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Torteeka, Peerapong
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Yooyen, Soemsak
This experiment integrates a particle filter concept with a gradient descent optimizer to reduce loss during iteration and obtains a particle filter-based gradient descent (PF-GD) optimizer that can determine the global minimum with excellent performance. Four functions are applied to test optimizer deployment to verify the PF-GD method. Additionally, the Modified National Institute of Standards and Technology (MNIST) database is used to test the PF-GD method by implementing a logistic regression learning algorithm. The experimental results obtained with the four functions illustrate that the PF-GD method performs much better than the conventional gradient descent optimizer, although it has some parameters that must be set before modeling. The results of implementing the MNIST dataset demonstrate that the cross-entropy of the PF-GD method exhibits a smaller decrease than that of the conventional gradient descent optimizer, resulting in higher accuracy of the PF-GD method. The PF-GD method provides the best accuracy for the training model, 97.00%, and the accuracy of evaluating the model with the test dataset is 90.37%, which is higher than the accuracy of 90.08% obtained with the conventional gradient descent optimizer.
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Trajectory Planning for Multiple UAVs and Hierarchical Collision Avoidance Based on Nonlinear Kalman Filters
(2023-02-01)
Hematulin, Warunyu
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Kamsing, Patcharin
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Torteeka, Peerapong
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Somjit, Thanaporn
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Phisannupawong, Thaweerath
Fully autonomous trajectory planning for multiple unmanned aerial vehicles (UAVs) is significant for building the next generation of the logistics industry without human control. This paper presents a method to enable multiple UAVs to fly in the same trajectory without collision. It benefits several applications, such as smart cities and transfer goods, during the COVID-19 pandemic. Different types of nonlinear state estimation are deployed to test the position estimation of drones by treating the information from AirSim as offline dynamic data. The obtained global positioning system sensor data and magnetometer sensor data are determined as the measurement model. The experiment in the simulation is separated into (1) the localization state, (2) the rendezvous state, in which the proposed rendezvous strategy is presented by using the relation between velocity and displacement through the setting area, and (3) the full mission state, which combines both the localization and rendezvous states. The localization state results show the best RMSE in the case of full GPS available at 0.21477 m and 0.25842 m in the case of a GPS outage during a period of time by implementing the ensemble Kalman filter. Similarly, the ensemble Kalman filter performs well with an RMSE of 0.5112414 m in the rendezvous state and demonstrates exceptional performance in the full mission state. Moreover, the experiment is implemented in a real-world situation with some basic drone kits as proof that the proposed rendezvous strategy can truly operate.
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Signal Calibration and Energy Resolution Optimization of a Double-Sided Silicon Strip Detector for Lunar-Based Particle Detection
(2025-01-01)
Panyalert, Thanayuth
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Manuthasna, Shariff
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Torteeka, Peerapong
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He, Xu
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Zhang, Ning
This letter presents a signal calibration and energy resolution analysis of a double-sided silicon strip detector (DSSD) developed for charged particle detection in a lunar-based space environment. The detector is part of the Moon-Aiming Thai-Chinese Hodoscope, i.e., a proposed scientific payload for the Chang'E-7 lunar orbiter, aimed at monitoring space weather and lunar-surface particle interactions. To evaluate the DSSD's performance under vacuum conditions, alpha sources (Am-241 and Pu-239) were used to generate energy spectra, which were processed through baseline correction and histogram generation. Four peak models, i.e., Gaussian, Gaussian + Exponential Tail, exponentially modified Gaussian (EMG), and Hyper-EMG, were compared using nonlinear least squares. Results show that the Hyper-EMG model yields superior fits, especially for Am-241, achieving an average reduced chi-squared of 1.64 ± 4.44 and energy resolution of 3.09% ± 0.45%, with 22 out of 32 Akaike Information Criterion (AIC) wins. In contrast, Gaussian fits showed higher fitting errors (e.g., x<sup>2</sup>/DoF up to 10.5) and the poorest resolution. AIC selection further confirms Hyper-EMG's robustness, while Gaussian fits were consistently inadequate. These findings support the use of tail-aware models, such as Hyper-EMG, for accurate energy reconstruction in spaceborne silicon detectors.
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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
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Torteeka, Peerapong
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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.
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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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Kamsing, Patcharin

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