Jaksukam, Komgrit

Loading...
Thumbnail Image
Preferred name
Jaksukam, Komgrit
Alternative Name
Jaksukam, K.
Main Affiliation
Email
komgrit.ja@kmitl.ac.th

Settings

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Analysis of multi-hop wireless sensor networks using probability propagation models
    (2019-01-01)
    Jaksukam, Komgrit
    ;
    Tongloy, Teerawat
    ;
    Chuwongin, Santad
    ;
    Boonsang, Siridech
    This paper presents a formula for estimating the probability of collecting a given amount of data from a propagation model and multi-hop wireless sensor networks (WSNs) based on Monte Carlo simulation with cluster-tree topology. The probabilistic model is based on an analytical model of the IEEE 802.15.4 MAC protocol. The probability of successful node transmission is extended to the probabilities of successful collection at the cluster P(X=k) and sink node P(X ≥ k). A numerical example has been provided for comparing the probabilities. We propose a model to calculate the probability from the ratio of the collection rate to the total number of nodes and therefore provide the likeliness of complete data collection. Finally, the results from our analysis provide an estimation of the probability of achieving successful transmission in WSNs.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Asynchronous deep reinforcement learning for the mobile robot navigation with supervised auxiliary tasks
    (2017-07-02)
    Tongloy, T.
    ;
    Chuwongin, S.
    ;
    Jaksukam, K.
    ;
    Chousangsuntorn, C.
    ;
    Boonsang, S.
    In this paper, we present the method based on asynchronous deep reinforcement learning adapted for the mobile robot navigation with supervised auxiliary tasks. We apply the hybrid Asynchronous Advantage Actor-Critic (A3C) algorithm CPU/GPU based on TensorFlow. The mobile robot is simulated as the navigation tasks on the OpenAI-Gym-Gazebo-based environment with the collaboration with ROS Multimaster. The supervised auxiliary tasks include the depth predictions and the robot position estimation. The simulated mobile robot shows the capability to learn to navigate only the input from raw RGB-image and also perform recognition of the place on the map. We also show that the combination of all possible auxiliary tasks leads to the different learning rate.