A Study on Leakage Detection Technique Using Transfer Learning-Based Feature Fusion 


Vol. 13,  No. 2, pp. 41-47, Feb.  2024
https://doi.org/10.3745/TKIPS.2024.13.2.41


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  Abstract

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

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[IEEE Style]

Y. Han, T. Park, J. Lee, J. Bae, "A Study on Leakage Detection Technique Using Transfer Learning-Based Feature Fusion," The Transactions of the Korea Information Processing Society, vol. 13, no. 2, pp. 41-47, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.2.41.

[ACM Style]

YuJin Han, Tae-Jin Park, Jonghyuk Lee, and Ji-Hoon Bae. 2024. A Study on Leakage Detection Technique Using Transfer Learning-Based Feature Fusion. The Transactions of the Korea Information Processing Society, 13, 2, (2024), 41-47. DOI: https://doi.org/10.3745/TKIPS.2024.13.2.41.

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