KMITL

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

Browse

Settings

Author: search.filters.author.Phakphisut, WatidSubject: search.filters.subject.Neural Network

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Reference Signal Received Power Prediction Using Convolutional Neural Network with Residual Loss
    (2023-01-01)
    Ngenjaroendee, Thearrawit
    ;
    Phakphisut, Watid
    ;
    Wijitpornchai, Thongchai
    ;
    Areeprayoonkij, Poonlarp
    ;
    Jaruvitayakovit, Tanun
    In this paper, LTE measurement reports collected from user equipments are used to generate the residual loss, which can represent the loss value of each grid. The residual loss and geospatial data are used in the learning process of convolutional neural network (CNN). We also use the site configuration and three-dimensional antenna pattern. Thus, the neural network and convolutional neural network are proposed to construct deep learning to predict the reference signal received power (RSRP) in Bangkok, Thailand. The results show that residual loss can improve the efficiency of prediction.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A Preliminary Neural Network Model for Range Spread-F Events at Chumphon Station, Thailand
    (2018-12-24)
    Thammavongsy, Phimmasone
    ;
    Phakphisut, Watid
    ;
    Supnithi, Pornchai
    In this work, we develop a preliminary neural network model for range-Type spread-F events over Chumphon station (10.7N latitude, 99.4E longitude), Thailand. The spread-F neural network model is designed with the input parameters including seasonal variations, diurnal variations, window-Averaged magnetic activity (Ap index) and window-Averaged solar activity (F10.7 index). The model is based on the ionogram data during the 24 <sup>th</sup> solar cycle from 2013 to 2016. As a result, the proposed model can provide the predicted results and the network performance of 97.8% for correct classification.