Kaitwanidvilai, Somyot

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Kaitwanidvilai, Somyot
Alternative Name
Kaitwanidvilai, S.
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Email
somyot.ka@kmitl.ac.th

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    Item type:Publication,
    Dual-stage Classification Framework for Detecting Rare or Unseen Patterns Based on Novelty Detection and Supervised Learning
    (2024-01-01)
    Rungtalay, Natthakritta
    ;
    Kaitwanidvilai, Somyot
    In this article, we propose a dual-stage classification framework designed for identifying rare or unseen patterns in the hard disk drive (HDD) industrial test process. The proposed framework integrates novelty detection and supervised learning methodologies to effectively address the challenges associated with imbalanced datasets and the detection of infrequent or unseen patterns within continuously changing environments. By employing novelty detection as the first-stage classifier followed by supervised learning as the second-stage classifier, the proposed method demonstrates an increased capacity to adapt to fluctuating environments, consequently enhancing the overall accuracy of process classification in practical manufacturing settings. To strengthen the robustness of novelty detection methods, an ensemble model technique is employed. Notably, the accuracy of the novelty detection methods in the first stage can be further enhanced with the incorporation of supervised learning techniques, particularly when a sufficiently large number of labeled samples are amassed. The proposed method consistently maintains accuracy, even in the face of changing environments, as it demonstrates the ability to adapt to data drift without necessitating the acquisition of new labeled data in the initial stage. This adaptability makes it particularly well suited for managing imbalanced datasets, rendering it highly practical for industrial applications. In a comprehensive case study conducted within the HDD industry, the framework exhibits immediate adaptability to rapidly changing environments while preserving high accuracy. This highlights the practical effectiveness of the proposed dual-stage classification framework in addressing the unique challenges posed by industrial scenarios.
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    Item type:Publication,
    Energy Prediction of Cleanroom-type Differential Drive Mobile Robot Based on Recurrent Neural Network
    (2023-01-01)
    Yanyong, Sarucha
    ;
    Konghuayrob, Poom
    ;
    Chaisiri, Punyavee
    ;
    Kaitwanidvilai, Somyot
    The battery charger time is a major issue for mobile robots. The study of the power usage of each component is important for optimizing the overall power consumption. Additionally, knowing the total energy consumption before commanding a robot to execute a task is essential for effective queue management and determining which robots are ready to execute tasks or move to the charging station. In this paper, we propose an energy modeling system consisting of an energy sensing technique, logging, and a recurrent neural network prediction model. The model is configured to recognize the dynamic system of the drive unit with the support of the robot operating system. The proposed model has a prediction error of only 3.58%. The simulation and experimental results demonstrate the effectiveness of the proposed system.