KMITL

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

Browse

Settings

Access type: UnknownAuthor: search.filters.author.Khichar, Sunita

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Proximal Policy Optimization for Crowd Evacuation in Complex Environments - A Metaverse Approach at Krung Thep Aphiwat Central Terminal, Thailand
    (2024-01-01)
    Chaudhary, Sushank
    ;
    Sinpan, Nitinun
    ;
    Sasithong, Pruk
    ;
    Khichar, Sunita
    ;
    La-Aiddee, Panithan
    Efficient crowd evacuation from railway platforms is critical for passenger safety during emergencies. This study introduces a novel dynamic emergency evacuation route generator using the Proximal Policy Optimization (PPO) algorithm within a custom-built 3D simulation environment developed in Unity. We independently created a detailed digital twin of Krung Thep Aphiwat Central Terminal, Thailand's largest train station, and implemented all elements of the simulation, including the Social Force Model, to accurately replicate crowd behaviors and interactions during evacuation scenarios. Through extensive training over 3,000,000 episodes, our PPO-based model achieved significant improvements in evacuation efficiency. The results indicate that in a major emergency scenario, increasing the number of agents in the station reduced the number of remaining passengers from 111 to just 6, highlighting the model's effectiveness. Similarly, in a minor emergency scenario, the average number of remaining passengers dropped from 38 to 1 with the addition of more agents. These findings confirm the model's ability to adapt to different emergency conditions, offering a practical and scalable solution for enhancing evacuation strategies in high-density environments. Furthermore, increasing the agents' sight range also improved evacuation efficiency, with a 20-meter sight range yielding the best results.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.