KMITL

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

Browse

Search Results

Now showing 1 - 5 of 5

Some of the metrics are blocked by your
consent settings
An operational machine learning framework for calibrating COSMIC radio occultation TEC to ground-based GNSS-derived TEC
(2026-03-15)
Okoh, Daniel
;
Habarulema, John Bosco
;
Nava, Bruno
;
Cesaroni, Claudio
;
Baki, Paul
The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) provides global Radio Occultation (RO) measurements of ionospheric total electron content (TEC), but these values are systematically underestimated relative to ground-based Global Navigation Satellite System (GNSS)-derived TEC due to the exclusion of the plasmaspheric contribution. This study presents a machine learning calibration framework that transforms COSMIC TEC into GNSS-equivalent values. Using co-located COSMIC and GNSS observations from 2006 to 2025, we developed neural network models (ROTEC-A and ROTEC-B) trained on (19 and 22) input features respectively, including COSMIC profile parameters, spatiotemporal descriptors, and optionally, solar and geomagnetic activity indices. Results show that the calibration effectively mitigates systematic underestimation, reducing mean bias from 6.97 TECU (uncalibrated COSMIC) to near zero (0.02–0.03 TECU). The calibrated products also substantially reduce skewness in residuals, yielding nearly symmetric error distributions suitable for data assimilation. Across various latitudinal, local time, and seasonal sectors, mean absolute errors were reduced by 50–75%, with the best performance at mid-latitudes and slightly elevated errors in high-latitude and equatorial regions. Although, the inclusion of solar and geomagnetic indices yielded marginal improvements, statistical tests confirmed no significant advantage over the baseline model. The operationally oriented framework outputs calibrated GNSS-equivalent TEC in near real-time, providing enhanced ionospheric monitoring capability, especially over GNSS-sparse regions such as oceans and deserts. These results demonstrate the potential of COSMIC RO data, once calibrated, to serve as a reliable complement to GNSS observations for ionospheric research, space weather monitoring, and operational applications.
Some of the metrics are blocked by your
consent settings
Monitoring Pesticide Residue on Bok Choi using Convolution Neural Network with NIR spectral Data
(2024-12-29)
Lapcharoensuk, Ravipat
;
Fhaykamta, Chawisa
;
Anurak, Watcharaporn
;
Chadwut, Wasita
Deep learning (DL) has been applied in agriculture, especially quality control in agricultural processing. One key area of interest is the detection and monitoring of pesticide residues in crops. The most popular measurement tool for nondestructive monitoring of pesticide residues is near-infrared spectroscopy (NIRS). A combination of CNN model with NIR spectral data was developed for monitoring pesticide residue on bok choi. The NIR spectral of bok choi with and without pesticide residue (chlorpyrifos) was collected in wavelength range between 908 and 1676 nm. A simple structure of CNN was modified for a one-dimensional task and this deep learning architecture was trained for classification of the bok choi samples. The results showed prefect prediction with 100% accuracy, precision, recall and specificity. This study also found that deep learning for NIR spectroscopy data requires less processing than traditional machine learning while still achieving great results.
Some of the metrics are blocked by your
consent settings
ACCURATE STATE OF CHARGE ESTIMATION OF LITHIUM-ION BATTERY USING RECURRENT AND NON-RECURRENT NEURAL NETWORKS FOR WLTP DRIVING PROFILES
(2024-12-01)
Praisan, Akkarawat
;
Polmai, Sompob
;
Kittiratsatcha, Supat
Estimating the state of charge (SoC) of a battery is essential to maximize its performance and ensure reliable operation and battery life. Nowadays, many countries are increasingly adopting electric vehicles (EVs) with lithium-ion batteries due to their high specific energy and long service life. This paper presents a method for estimating the state of charge of lithium-ion batteries using artificial neural networks, specifically the Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), through a data-driven approach. The training and testing of the networks are conducted using recorded datasets of the battery, based on the WLTP driving profiles class 2 and class 3. These driving profiles are specifically designed for testing electric vehicles, thereby enhancing the realism of the state of charge estimation by the network. In terms of the analytical aspect, the FNN was able to train the network faster due to its simpler structure, requiring less computation. On the other hand, the LSTM demonstrated more accurate SoC estimation with fewer response oscillations, thanks to its ability to learn and adapt network parameters internally.
Some of the metrics are blocked by your
consent settings
DEVELOPMENT OF AN EMBEDDED EXERCISE POSTURE PREDICTION SYSTEM FOR OFFICE SYNDROME USING MACHINE LEARNING
(2024-12-01)
Sisuk, Noppadon
;
Onkhong, Tada
;
Kaewpoonsuk, Anucha
;
Wardkein, Paramote
;
Prompak, Kriangsak
Office syndrome is a condition with a high prevalence among people of working age and an upward tendency. In this study, a wearable device implant based on machine learning will be created for exercise of office syndrome. Developing a machine learning model that can distinguish 3 exercise postures for implantable devices was the objective of the study. The Edge Impulse online software was used for data collecting, preprocessing, training, and testing. After that, the model was successfully installed and tested on an Arduino Nano 33 BLE microcontroller. In addition to designing embedded systems, this work also developed applications through the App Inventor program for exercising properly. From testing the system, the model showed an average classification accuracy of 97.6% of exercise postures. The results of testing the embedded system in conjunction with the presented application on 10 subjects showed that the system was able to provide predictive accuracy for wrist exercises, shoulder exercises and stretch exercises with 95%, 97%, and 93%, respectively.
Some of the metrics are blocked by your
consent settings
Decentralized control of cooperative robotics system using neural networks for the purpose of an object balancing flat plate tasks
(2020-02-01)
Jaisumroum, Nattapon
;
Chotiprayanakul, Pholchai
;
Sriseubsai, Wipoo
In this article, the manipulation to handle the object on a plate using neural networks, design of 1-DOF robot arm under cooperative control, will be explained. The robot's system specifies the object position and velocity, an assignment oriented components for cooperative control. The novelty of this experiment is that under the decentralized control, the robot estimates the position and speed of the object for control end-effector of robot arm using the camera to track position and speed of the object according to training and assigned collaborative tasks which differ from other experiments that use sensors. The experiment includes three robot manipulators which were capable balancing the objects on flat plate with dataset to training and control servo motor assigned to the corresponding position and the end-effector in decentralized to control robotics. Overall, neural network method can be a training scheme using a cooperative robotics in a decentralized control.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results