KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 10 of 20

Some of the metrics are blocked by your
consent settings
Ground software architecture for a lunar particle detector: Implementation with a double-sided silicon strip detector
(2026-04-15)
Panyalert, Thanayuth
;
Manuthasna, Shariff
;
Torteeka, Peerapong
;
He, Xu
;
Zhang, Ning
The Moon-Aiming Thai-Chinese Hodoscope (MATCH) is a particle detector developed for the Chang’E-7 mission, designed to support space weather monitoring and cosmic radiation studies within the Sun-Earth-Moon system. The primary observational objectives of MATCH include space weather science and alerts, as well as the detection of cosmic radiation, particularly Jovian and Galactic Cosmic Ray (GCR) electrons. Additionally, MATCH can detect lunar albedo ions (alpha particles and protons) and contribute to understanding cosmic-ray interactions with the lunar surface, including high-energy particle backscattering mechanisms. These studies are essential for improving our knowledge of cosmic ray propagation and energy distribution in the lunar environment. MATCH integrates a double-sided silicon strip detector (DSSD) for precise position tracking of incoming particles, along with a bismuth germanate (BGO) scintillator stack for accurate energy measurements. This study presents the development and validation of a scalable ground software architecture that supports event detection, signal processing, and data calibration, optimized for constrained onboard resources. The system has been validated through hardware-in-the-loop (HIL) testing using alpha-emitting sources under mission-equivalent conditions, demonstrating high accuracy and resource efficiency for on-orbit data acquisition modes. Once deployed, MATCH is expected to provide the first continuous MeV-range cosmic electron measurements from lunar orbit, enabling new insights into Jovian and Galactic cosmic ray propagation, space weather variability, and lunar albedo ion generation. The software architecture developed here plays a critical role in enabling astrophysical investigations during the upcoming Chang’E-7 mission.
Some of the metrics are blocked by your
consent settings
Multi-UAV Standoff Tracking in Unknown Complex Environments Using a Modulated Adaptive Guiding Vector Field
(2026-03-01)
Chen, Guodong
;
Yuan, Shuai
;
Liu, Jingzong
;
Zhang, Zexu
;
Kamsing, Patcharin
This paper proposes a novel trajectory planning method for multiple UAVs (Unmanned Aerial Vehicle, UAV) collaborative standoff tracking using a consistency GVF (Guiding Vector Field, GVF) with an adaptive gain (AG-GVF), which is modulated by a modulation matrix for collision-free navigation in unknown complex environments. Firstly, the AG-GVF is established based on the desired path, with an adaptive gain and an additional virtual coordinate introduced to elevate dimensionality. The adaptive gain aims at reducing steady-state error and eliminating oscillations. This virtual coordinate is not only used for eliminating singular points but also utilized as a state variable for consistency control, ensuring uniform phase distribution among multiple UAVs during standoff tracking. Subsequently, in environments with obstacles, a modulation matrix is proposed to adjust the original GVF motion by estimating the normals of unknown obstacles using point clouds and constructing a modulation matrix to modify the AG-GVF direction for effective obstacle avoidance. Finally, the obtained desired path is optimized to generate flight trajectories that satisfy the kinematic constraints of fixed-wing UAVs. Simulation results demonstrate that the proposed method enables multiple UAVs to achieve collaborative standoff tracking with collision-free navigation in unknown complex environments.
Some of the metrics are blocked by your
consent settings
Day–Night All-Sky Scene Classification with an Attention-Enhanced EfficientNet
(2026-02-01)
Boonpook, Wuttichai
;
Torteeka, Peerapong
;
Torsri, Kritanai
;
Kamthonkiat, Daroonwan
;
Tan, Yumin
All-sky cameras provide continuous hemispherical observations essential for atmospheric monitoring and observatory operations; however, automated classification of sky conditions in tropical environments remains challenging due to strong illumination variability, atmospheric scattering, and overlapping thin-cloud structures. This study proposes EfficientNet-Attention-SPP Multi-scale Network (EASMNet), a physics-aware deep learning framework for robust all-sky scene classification using hemispherical imagery acquired at the Thai National Observatory. The proposed architecture integrates Squeeze-and-Excitation (SE) blocks for radiometric channel stabilization, the Convolutional Block Attention Module (CBAM) for spatial–semantic refinement, and Spatial Pyramid Pooling (SPP) for hemispherical multi-scale context aggregation within a fully fine-tuned EfficientNetB7 backbone, forming a domain-aware atmospheric representation framework. A large-scale dataset comprising 122,660 RGB images across 13 day–night sky-scene categories was curated, capturing diverse tropical atmospheric conditions including humidity, haze, illumination transitions, and sensor noise. Extensive experimental evaluations demonstrate that the EASMNet achieves 93% overall accuracy, outperforming representative convolutional (VGG16, ResNet50, DenseNet121) and transformer-based architectures (Swin Transformer, Vision Transformer). Ablation analyses confirm the complementary contributions of hierarchical attention and multi-scale aggregation, while class-wise evaluation yields F1-scores exceeding 0.95 for visually distinctive categories such as Day Humid, Night Clear Sky, and Night Noise. Residual errors are primarily confined to physically transitional and low-contrast atmospheric regimes. These results validate the EASMNet as a reliable, interpretable, and computationally feasible framework for real-time observatory dome automation, astronomical scheduling, and continuous atmospheric monitoring, and provide a scalable foundation for autonomous sky-observation systems deployable across diverse climatic regions.
Some of the metrics are blocked by your
consent settings
Sustainable Urban Healthcare Accessibility: Voronoi Screening and Travel-Time Coverage in Bangkok
(2025-12-01)
Boonprong, Sornkitja
;
Punturasan, Nathapat
;
Kamsing, Patcharin
;
Torteeka, Peerapong
;
Cao, Chunxiang
This study presents an integrated and reproducible framework for within-tier screening of potential healthcare accessibility in Bangkok. Facilities in three service tiers (primary 294 units, regular 75, referral 29) are analyzed using point-pattern diagnostics, Voronoi geometric partitions, population-weighted allocation from subdistrict controls, and cumulative network travel-time isochrones. Spatial diagnostics indicate clustering among primary care units, a near-random configuration for regular units, and modest dispersion for referral hospitals, summarized by observed-to-expected nearest-neighbor ratios of approximately 0.77, 1.05, and 1.19, respectively. Voronoi partitions translate these distributions into geometric units that enlarge with increasing inter-facility spacing, while population-weighted assignments reveal higher population-per-partition-area burdens in the outer east and southwest. Isochrone maps (5–60 min rings) show central corridors with short travel times and peripheral areas where potential access declines. Interpreted against statutory planning intent, the maps indicate broad consistency of siting with high-intensity zones, alongside residual gaps at residential fringes. Framed as repeatable indicators of access and coverage, the workflow contributes to measuring and monitoring urban health sustainability under universal health coverage and routine planning cycles. The framework yields transparent indicators that support monitoring, priority setting, and incremental adjustments within each tier. Limitations include planar proximity assumptions, uniform areal weighting, single-mode modeled travel times without temporal variation, and the absence of capacity measures, motivating future work on capacity-weighted partitions, minimal dasymetric refinements, and time-dependent multimodal scenarios.
Some of the metrics are blocked by your
consent settings
Generating Large-Scale Datasets for Spacecraft Pose Estimation via a High-Resolution Synthetic Image Renderer
(2025-04-01)
Hematulin, Warunyu
;
Kamsing, Patcharin
;
Phisannupawong, Thaweerath
;
Panyalert, Thanayuth
;
Manuthasna, Shariff
The trend toward conducting vision-based spacecraft pose estimation using deep neural networks, which necessitates accurately labeled datasets for training, is addressed in this paper. A method for generating an image regression-labeled dataset for spacecraft pose estimation through simulations involving Unreal Engine 5 is proposed herein. This work provides detailed algorithms for pose sampling and image generation, making it easy to reproduce the employed dataset. The dataset consists of images obtained under harsh lighting conditions and high-resolution backgrounds, featuring spacecraft models including Dragon, Soyuz, Tianzhou, and the ascent vehicle of Chang’E-6. The dataset comprises 40,000 high-resolution images, which are evenly distributed, with 10,000 images for each spacecraft model in scenes with both the Earth and the Moon. Each image is labeled with multivariate pose vectors that represent the relative position and attitude of the corresponding spacecraft with respect to the camera. This work emphasizes the critical role of realistic simulations in creating cost-effective synthetic datasets for training neural network-based pose estimators and publicly available for further study.
Some of the metrics are blocked by your
consent settings
Signal Calibration and Energy Resolution Optimization of a Double-Sided Silicon Strip Detector for Lunar-Based Particle Detection
(2025-01-01)
Panyalert, Thanayuth
;
Manuthasna, Shariff
;
Torteeka, Peerapong
;
He, Xu
;
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.
Some of the metrics are blocked by your
consent settings
Characterization and verification of the optimal feedback gain of a satellite magnetorquer-based attitude control system
(2024-12-01)
Panyalert, Thanayuth
;
Manuthasna, Shariff
;
Chaisakulsurin, Jormpon
;
Masri, Tanawish
;
Palee, Kritsada
In spacecraft mission planning and operation, the attitude determination and control subsystem (ADCS) of a satellite provides information about the orientation of the satellite in the inertial reference frame. Furthermore, this subsystem produces the control actions required to adjust the orientation of the satellite, especially in the low-Earth orbit (LEO) regime. This paper focuses on the satellite's three-axis attitude control problem within the context of active and passive control, which includes detumbling control, pointing control, magnetic control, and attitude stabilization after solar panel wing deployment using magnetorquers as the primary actuators. The objective is to stabilize and reduce the angular rate while orienting the satellite to the desired attitude. The proposed satellite attitude control system (ACS) strategies are designed, developed, characterized, and verified. These strategies encompass the B-dot control algorithm for detumbling control along with pointing control and attitude stabilization after solar panel wing deployment. hardware-in-the-loop simulation (HiLs) tests are conducted to assess the performance of the satellite magnetorquer-based ACS in the presence of noise. These tests involve a relative Earth's magnetic field (EMF) generator in conjunction with SGP-4-based satellite orbital propagator high-level control software. Additionally, cascade proportional-integral-derivative (PID) and state-dependent Riccati equation (SDRE) controllers are implemented to generate sufficient torque using three-axis magnetorquers on a frictionless air-bearing platform. The platform is balanced to closely simulate the dynamic motion of a spacecraft in space. The testing includes a single initial condition and three inertia conditions for stabilization after solar panel wing deployment. Finally, the effectiveness of the cosimulation as a primary experiment through an integrated HiLs process is validated. This comprehensive approach confirms the control system's performance and its ability to meet mission requirements.
Some of the metrics are blocked by your
consent settings
Trajectory Planning for Multiple UAVs and Hierarchical Collision Avoidance Based on Nonlinear Kalman Filters
(2023-02-01)
Hematulin, Warunyu
;
Kamsing, Patcharin
;
Torteeka, Peerapong
;
Somjit, Thanaporn
;
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.
Some of the metrics are blocked by your
consent settings
PCL-PTD Net: Parallel Cross-Learning-Based Pixel Transferred Deconvolutional Network for Building Extraction in Dense Building Areas With Shadow
(2023-01-01)
Boonpook, Wuttichai
;
Tan, Yumin
;
Torsri, Kritanai
;
Kamsing, Patcharin
;
Torteeka, Peerapong
Urban building segmentation from remote sensed imageries is challenging because there usually exists a variety of building features. Furthermore, very high spatial resolution imagery can provide many details of the urban building, such as styles, small gaps among buildings, building shadows, etc. Hence, satisfactory accuracy in detecting and extracting urban features from highly detailed images still remains. Deep learning semantic segmentation using baseline networks works well on building extraction; however, their ability in building extraction in shadows area, unclear building feature, and narrow gaps among buildings in dense building zone is still limited. In this article, we propose parallel cross-learning-based pixel transferred deconvolutional network (PCL-PTD net), and then is used to segment urban buildings from aerial photographs. The proposed method is evaluated and intercompared with traditional baseline networks. In PCL-PTD net, it is composed of parallel network, cross-learning functions, residual unit in encoder part, and PTD in decoder part. The performance is applied to three datasets (Inria aerial dataset, international society for photogrammetry and remote sensing Potsdam dataset, and UAV building dataset), to evaluate its accuracy and robustness. As a result, we found that PCL-PTD net can improve learning capacities of the supervised learning model in differentiating buildings in dense area and extracting buildings covered by shadows. As compared to the baseline networks, we found that proposed network shows superior performance compared to all eight networks (SegNet, U-net, pyramid scene parsing network, PixelDCL, DeeplabV3+, U-Net++, context feature enhancement networ, and improved ResU-Net). The experiments on three datasets also demonstrate the ability of proposed framework and indicating its performance.
Some of the metrics are blocked by your
consent settings
Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery
(2023-01-01)
Boonpook, Wuttichai
;
Tan, Yumin
;
Nardkulpat, Attawut
;
Torsri, Kritanai
;
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.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results