Supnithi, Pornchai
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Supnithi, Pornchai
Alternative Name
Supnithi, P.
Supnithi, Pomchai
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pornchai.su@kmitl.ac.th
77 results
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Item type:Publication, Effects of Equatorial Plasma Bubbles over Real-Time Kinematic Positioning in Low-Latitude Region(2023-01-01); ; ; Saito, SusumuEquatorial plasma bubbles (EPBs) refer to ionospheric irregularities in low-latitude regions, commonly observed after sunset. They originate at the magnetic equator and then potentially spread to mid-latitude region. As cm-level positioning techniques are increasingly important to various segments of society, the performance degradation of these systems due to EPB at low latitudes needs to be investigated. In this work, we analyze the EPB effects on the performances of real-time kinematic (RTK) positioning at the short, medium, and long baselines at low-latitude stations in Thailand. The low-latitudes local ionospheric disturbances such EPBs are shown to degrade the positioning accuracy of RTK in different seasons in 2022. It is found that the positioning errors are higher during the disturbance periods and more severe at the long baselines than the shorter ones, especially during the equinoctial months. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparative Analysis of Deep Learning Models for Daily Solar Indices Forecasting in Solar Cycle 25(2025-01-01); Accurate forecasting of solar activity indices, particularly the Sunspot Number (SSN) and the F10.7 solar radio flux index (F10.7), is essential for effective space weather monitoring, as severe solar and ionospheric disturbances can significantly impact satellite operations, radio communications, and navigation systems. This paper presents a comparative analysis of deep learning models - Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and encoder-only Transformer architectures - for daily forecasting of SSN and F10.7 up to 14 days ahead based on past 27 days. Considering relatively simple model structures, both single-step and multi-step prediction strategies are explored to evaluate the models' capability in handling short-and long-term dependencies in time series data. Daily solar activity data spanning seven solar cycles (Cycles 19-25), obtained from the GFZ Helmholtz Centre for Geosciences, are used for model training and evaluation. Experimental results show that LSTM consistently achieves the best performance across most forecast horizons, particularly in short-to medium-term predictions. The Transformer model delivers competitive and stable results, while TCN performs relatively less effectively, indicating the need for more complex architecture and optimization strategies. These findings highlight the strengths and limitations of each architecture for solar activity forecasting applications. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reliability Ratio-Based Serial Algorithm of LDPC Decoder for Turbo Equalization Schemes(2022-02-01); Serial decoding algorithms of low-density parity-check (LDPC) code converge efficiently with low errors. Previously, a serial decoding algorithm, named a shuffled belief-propagation (SBP), was applied in turbo equalization of bit-patterned magnetic recording (BPMR) systems. With the SBP algorithm, an LDPC decoder converged twice as fast as one using conventional BP algorithms. We further improved the convergence speed of SBP by updating the messages in an adaptive order, which played a flexible role throughout decoding. We proposed two adaptive-serial algorithms for LDPC codes in turbo equalization. One updated the messages using the extrinsic loglikelihood ratio (LLR) and the result of the parity-check equation checking. The second contained an additional rule that tracked the LLR sign changes in each iteration. Both algorithms converged faster and with lower bit error rates (BERs) than the SBP and previous adaptive-serial algorithms in a BPMR system with media noise. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Construction of punctured polar codes based on genetic algorithm(2021-05-19); In this work, we propose a new construction of punctured polar codes, which provides a good error-correcting performance at arbitrary code lengths and code rates. We exploit the density evolution to evaluate the error probability of punctured polar codes. Then, the puncturing pattern and frozen bit positions are selected by genetic algorithm to construct a punctured polar code with low error probability. The results show that the proposed technique provides superior block error rate performance than the quasi-uniform puncturing technique at a high code rate. At a low code rate, the proposed technique can show the better performance than the shortening technique. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Equatorial spread-F forecasting model with local factors using the long short-term memory network(2023-12-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improvement of Kalman Filter for GNSS/IMU Data Fusion with Measurement Bias Compensation(2020-07-01); ; The ASEAN IVO project currently supports the research related to GNSS and ionospheric data products for disaster prevention and aviation in low-latitude regions. In vehicle navigation, Real Time Kinematic (RTK) positioning distorted from the environment often contaminates the measurement vectors (such as position or speed of a rover). In this situation, the conventional Kalman filter with a linear motion model could not reduce positioning errors sufficiently due to existing bias. Hence, the measurement bias compensation method based on the mean of residual vectors is proposed. We modify the conventional Kalman filter by including this compensation in the estimation step. We test the algorithm in both of the simulations and actual experiments. From the results, the proposed method outperforms the baseline method in terms of positioning error by 60% and 12% for the simulation test and the field test respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performances of GAGAN Satellite-Based Augmentation System in Thailand Region(2020-07-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ionospheric Scintillation Prediction Using Decision Tree and Rainforest Techniques(2024-01-01); ; Saito, SusumuThe ionosphere contains electron density variation. When radio signals transmitted from global navigation satellite systems (GNSS) pass through such medium, additional delays are added. With ionospheric irregularity, fluctuation in GNSS signals known as scintillation are often observed resulting in reduced number of tracked satellites then degrade positioning performances. At present, scintillation is considered random, hence, the ability to detect or predict such phenomenon is crucial to efficient system operation. In this research, we design machine learning algorithm for scintillation prediction. Both Decision Tree (DT) and Random Decision Forest (RF), are implemented to predict daily ionospheric scintillation at King Mongkut's Institute of Technology Ladkrabang (KMITL) station in Thailand (13.73 ° E, 100.77° N). The rate of total electron content change index (ROTI) is also used. Modeling is carried out for four months in March (equinox), June (solstice), September (equinox), and December (solstice) in 2022, representing different seasons in space weather study. The prediction results are evaluated using the S 4 index observations at KMITL station and then compared between DT and RF methods. The designed model has a high potential for scintillation prediction. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Compatibility of Low-Cost GNSS Receivers for Total Electron Content (TEC) Analysis(2025-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identifying Geomagnetic Storms with Ionospheric Storm Scale for GNSS and Disaster Prevention(2020-03-01); ; ; ; Tangtrakunphaisan, UdomsitThis paper proposes an ionospheric storm scale (I-scale) for identifying the impact of geomagnetic or ionospheric storms in the Ionosphere for GNSS (global navigation satellite system) service and disaster prevention. The I-scale in this work is computed based on the observed foF2 at Chumphon station (10.72°N, 99.37°E) over equatorial latitude from January 2004 to July 2018. The results report that the severe geomagnetic storms, i.e., IP3 and IN3, seldom occur at Chumphon with the probabilities of 0.02% and 0.07%, respectively. The probability of quiet ionospheric condition is the maximum value of 70.73%. Meanwhile, the other I-scales sometimes occur and range from 0.60% to 13.97%. The benefits of the foF2-based I-scale are to indicate the violence level of geomagnetic storms and to announce the ionospheric irregularities in practice.