Myint, Lin Min
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Myint, Lin Min
Alternative Name
Myint, L. M.
Myint, L.
Myint, Lin Min Min
Myint, Lin M.
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linminmin.my@kmitl.ac.th
19 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) ;Thu, Phyo C.; ; ; 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, Equatorial spread-F forecasting model with local factors using the long short-term memory network(2023-12-01) ;Thammavongsy, Phimmasone; ; ;Hozumi, KornyanatLakanchanh, DonekeoThe predictability of the nighttime equatorial spread-F (ESF) occurrences is essential to the ionospheric disturbance warning system. In this work, we propose ESF forecasting models using two deep learning techniques: artificial neural network (ANN) and long short-term memory (LSTM). The ANN and LSTM models are trained with the ionogram data from equinoctial months in 2008 to 2018 at Chumphon station (CPN), Thailand near the magnetic equator, where the ESF onset typically occurs, and they are tested with the ionogram data from 2019. These models are trained especially with new local input parameters such as vertical drift velocity of the F-layer height (Vd) and atmospheric gravity waves (AGW) collected at CPN station together with global parameters of solar and geomagnetic activity. We analyze the ESF forecasting models in terms of monthly probability, daily probability and occurrence, and diurnal predictions. The proposed LSTM model can achieve the 85.4% accuracy when the local parameters: Vd and AGW are utilized. The LSTM model outperforms the ANN, particularly in February, March, April, and October. The results show that the AGW parameter plays a significant role in improvements of the LSTM model during post-midnight. When compared to the IRI-2016 model, the proposed LSTM model can provide lower discrepancies from observational data. Graphical Abstract: [Figure not available: see fulltext.]. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ionospheric Scintillation Prediction Using Decision Tree and Rainforest Techniques(2024-01-01) ;Trachuentong, Sirasake; ; 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, Equatorial Plasma Bubble Detection by Support Vector Machine at Chumphon Station, Thailand(2022-01-01); ; ; Hozumi, KornyanatEquatorial Plasma Bubble (EPB) is a phenomenon in which depletion of plasma density occurs in the ionosphere particularly in the equatorial region. It can degrade the performances of the navigation system and satellite communication. In this work, we analyze EPB based on the very-high frequency (VHF) radar images at Chumphon station, Thailand. Then an EPB detection system using the support vector machine (SVM) technique is developed, and the accuracies of the systems using different kernels: linear kernel, the polynomial kernel, the radial basic functions kernel (RBF), and the sigmoid kernel are compared. Among the different kernels, we find that the RBF kernel gives the highest accuracy in prediction at 86.67 percent. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of Equatorial Ionospheric Irregularities Using Unsupervised Machine Learning Based on Spatiotemporal ROTI Keograms(2025-01-01) ;Mutasov, Gleb; ; ;Tongkasem, NapatNishioka, MichiEquatorial ionospheric irregularities, particularly those associated with equatorial plasma bubbles (EPB), can significantly disrupt satellite navigation and communication systems. As the demand for reliable Global Navigation Satellite System (GNSS) and communication services grows, the prediction of ionospheric irregularities becomes critical. A key step in the prediction process is to identify distinct spatiotemporal patterns of irregularities, including day-to-day, longitudinal, and seasonal variations. However, with large datasets, manually classification or identification of these irregularities is a complex and challenging task. In this work, we propose unsupervised machine learning techniques to recognize and group irregularity patterns in large, unlabeled Rate of Total Electron Content (TEC) Index (ROTI) keograms. Specifically, two machine learning models: Gaussian Mixture Model and k-means clustering are employed. The ROTI keograms are constructed using GNSS data from two low-latitude receiver stations in Thailand. To reduce redundancy in the keogram images, three feature extraction techniques are applied before the clustering process. A comparative analysis is performed to determine the optimal number of clusters using these models. Based on the results, the optimal combination of feature extraction and clustering technique is determined for the proposed clustering model. The resulting k-means model with contour extractor classifies five distinct patterns of ionospheric irregularity patterns, providing valuable insights for enhancing EPB prediction models and deepening our understanding of ionospheric dynamics. Furthermore, these five irregularity patterns are analyzed in relation to space weather parameters such as the solar radio flux index (F10.7), and the geomagnetic index (Kp). The findings contribute to the development of robust prediction models, improving the reliability of satellite-based communication and navigation systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An early termination technique of polar codes for IR-HARQ scheme(2020-10-08) ;Mueadkhunthod, Krittiyaporn; ; In this work, we focus on polar codes in mobile communication systems where a retransmission strategy, namely, the incremental redundancy hybrid automatic repeat request (IR-HARQ) scheme, is performed. We propose an early termination (ET) of polar codes using the interleaved cyclic redundancy check (CRC) codes and the parity check (PC) codes. The parity-check equations of CRC and PC codes are used to detect the incorrectly decoded bits during the polar decoding. The simulation results verify that the proposed ET techniques can detect the erroneous bits in initial transmission and retransmission. The proposed technique provides the ET rate about 4-90%, and the block error rate (BLER) performance degradation is less than 2 dB. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Simultaneous equatorial plasma bubble observation using amplitude scintillations from GNSS and LEO satellites in low-latitude region(2023-12-01) ;Seechai, Khanitin; ;Hozumi, Kornyanat ;Nishioka, MichiSaito, SusumuThis study estimates the scale sizes of the plasma density irregularities and the longitudinal width associated with equatorial plasma bubbles (EPBs) in equatorial and low-latitude regions. By analyzing amplitude scintillation S<inf>4</inf> indices and total electron content (TEC) measured from low earth orbit (LEO) satellite’s beacon signals with 400 MHz and Global Navigation Satellite System (GNSS) L1/E1 signals with 1575.42 MHz, recorded by receivers at the KMITL station in Bangkok, Thailand (geographic; 13.73° N, 100.77°E, magnetic: 7.26°N), we investigate the characteristics of these irregularities. We collected data of 154 LEO satellite pass events during nighttime on 21 disturbed days in four equinoctial months in 2021. Based on the presence or absence of the scintillation effects on GNSS and LEO beacon signals, the events are categorized into four classes to estimate the scale size of the plasma density irregularities. The analysis suggests that events with both GNSS and LEO scintillations, as well as events with GNSS scintillation alone, occur predominantly before midnight assuming the presence of the small-scale size of the irregularities within EPB. However, events with only LEO scintillation occur throughout the whole night and some events are observed before the events with both GNSS and LEO scintillations. Post-sunset LEO scintillation alone may be attributed to the onset of EPBs developing at low altitude, while post-midnight LEO scintillation events near the magnetic equator, observed during periods of low GNSS Rate of TEC Index (ROTI) values, are associated with bottom-side ionospheric irregularities but are not linked with EPB. The findings are consistent with previous researches on the generation and decay of electron density irregularities within plasma bubbles. However, this study provides new insights by using specific data sets and analysis techniques, offering a more comprehensive understanding of the association of LEO scintillations with bottom-side ionospheric irregularities near the magnetic equator, not observed in the ROTI map. Graphical Abstract: [Figure not available: see fulltext.] - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Interleaved CRC codes for polar codes with partitioned list decoding(2021-05-19) ;Wongsa, Anusorn; ; In this work, we propose an interleaved cyclic redundancy check (CRC) code for the partitioned successive cancellation list (PSCL) decoder of polar codes. Since the CRC bits are interleaved into each partition of the PSCL decoder, the CRC bit can be used to eliminate the unreliable codewords. We examine that the performances of the PSCL decoder are influenced by the first-row weight of the parity-check matrix of interleaved CRC code. Therefore, the parity-check matrix of interleaved CRC code must be constructed by minimizing the first-row weight. The simulation results show that the interleaved CRC with a smaller weight can provide better error-correcting performance than the interleaved CRC with a larger weight. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Investigating the F2-layer peak height of IRI-2016 model at the equatorial station during the deepest solar activity for 70 years ago(2023-01-01); ;Taugtragoonpaisan, Udomsit; The paper investigates the F2-layer peak height (hmF2) of IRI-2016 model at Sao Luis on the magnetic Equator in 2019. Since the deepest solar activity occurred surprisingly in year 2019 for 70 years ago, hence, the IRI-2016 model prediction should be investigated to know the anomalous ionosphere and the deviations between the observation and IRI prediction. The hmF2 is selected to be studied in this work. Our studied results show that the observed hmF2 and the three hmF2 models of IRI-2016 prediction generally show the similar variations only about 60% during this deepest solar activity and the variations of the four kinds of studied hmF2 values show the similar trends for all seasons, except June solstice. The diurnal variation of hmF2_Giro show three peaks for December solstice months and the equinoctial months, while they show four peaks amazingly in June solstice. The hmF2_SHU is the best option that can agree reasonably well to the observed hmF2 at Sao Luis, excluding the pre-sunrise hmF2 peak for all seasons and a very deep trough in June solstice. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Corrigendum to “Spatio-temporal characteristics of ionospheric irregularities in low latitude regions during the peak of solar cycle 25” [Adv. Space Res. 76(1) (2025) 254–268, (S0273117725004168), (10.1016/j.asr.2025.04.062)](2025-09-01) ;Tongkasem, Napat; ;Thammavongsy, Phimmasone ;Nishioka, MichiPerwitasari, SeptiThe authors regret that the following was omitted from the acknowledgment section: This research project is also financially supported by National Research Council of Thailand (NRCT) under grant N41A640235. The authors would like to apologise for any inconvenience caused.
