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    A GUI based Self-learning Tool for Polar Codes using Successive Cancellation and List Decoders
    (2020-11-04)
    Ahsan, Rafee Al
    ;
    Khichar, Sunita
    ;
    Phakphisut, Watid
    ;
    Wuttisittikulkij, Lunchakorn
    ;
    Ngamjob, Audsadang
    Recently, the importance of channel coding has become prominent for next-generation 5G wireless communication networks. Channel coding is becoming an active area of research and scholars are aiming to improve their knowledge about channel coding schemes which will be playing a crucial part in high-performance 5G networks. polar codes are the class of channel coding techniques that have been standardized in 5G. Moreover, it is a fact that activity tool-based learning raises the learning efficiency and generates intrinsic motivation for learning. Therefore, the main motivation for designing this paper is to develop a Graphical User Interface (GUI) based learning tool to help students easily learn channel coding techniques. In our teaching curriculum, we have used figure-based interactive representations to teach the basic concepts of polar codes efficiently. This learning tool provides users the option to learn about the encoding technique and two different decoding techniques named as Successive Cancellation (SC) and Successive Cancellation List (SCL) for different lengths of user-defined data bits. This complete model has been designed in Python.
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    The Study and Analysis of Oil-immersed Power Transformer by Using Artificial Neural Network for Designing Program Apply in the Industry of Testing Oil-immersed Transformers
    (2020-10-25)
    Boonsaner, Nutthaphan
    ;
    Chancharoensook, Phop
    ;
    Bunnag, Chisanucha
    ;
    Suwantaweesuk, Achirawit
    ;
    Vongphanich, Kiattisak
    This research presents the study and analysis of faults in the oil-immersed transformer. Abnormalities can be analyzed from the Dissolved Gas Analysis (DGA) test. The oil has a variety of compounds such as hydrocarbons, oxygen, nitrogen and hydrogen. The amount of gas in oil has distinctive characteristics that can indicate abnormalities in different types. This program is developed based on according to standard IEEE Std C57.104TM - 2019. This program is designed to detect for defects in oil-immersed transformers by using artificial neural networks (Artificial Neural Network: ANN). The program using MATLAB programs is prepared for maintenance planning and supports applications from basic learning to industrial sector.
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    Development of Angle Control System Application Using Python
    (2019-11-01)
    Phutthanukun, Warinda
    ;
    Chayratsami, Pornpimon
    This paper presents an educational tool for feedback control system class. An application is created using Python for simulating the DC motor angle control. The control method used in the application is Proportional-Integral-Derivative (PID) controller. Inputs of the application consist of motor parameters obtained by theoretically modeling the system, the PID control constant gains, and a desired angle. Outputs are the displays of a root locus plot, a step response plot, and an animation of the motor movement. The results of using the application in the feedback control class indicate that the application helps enhancing student's understanding in the PID control concept and also helps students to visualize the system behavior practically.
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    Intelligent UV-Vis Spectrometry for Water and Environment Monitoring using GUI Software and Neural Networks
    (1996-01-01)
    Benjathapanun, N.
    ;
    Boyle, W. J.O.
    ;
    Grattan, K. T.V.
    This paper describes an Intelligent UV-Vis Spectrometry system using GUI software and neural network techniques for the identification and estimation of UV absorbing chemical species in water.