전이학습 기반 특징융합을 이용한 누출판별 기법 연구
A Study on Leakage Detection Technique Using Transfer Learning-Based Feature Fusion
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초록
시간 및 주파수 영역에서 각각 학습한 모델 간에 성능 차이가 발생할 경우, 앙상블을 수행하더라도 개별 모델 간의 성능 불균형으로 인하여앙상블의 성능이 오히려 저하되는 현상을 확인할 수 있었다. 따라서, 본 논문은 시간 영역과 주파수 영역에서 특징을 추출하고, 이들을 융합한단계적 학습 방법을 통해 파이프라인 누출 감지의 정확성을 높이기 위한 누출판별 기법을 제안한다. 이 방법은 두 단계의 학습 과정으로 이루어지며,먼저, 단계 1에서는 시간 영역과 주파수 영역에서 독립적으로 모델 학습을 수행하여 도메인별로 주어진 데이터로부터 중요한 특징들을 효과적으로추출하도록 하였다. 단계 2에서는 사전학습 완료된 각 모델로부터 해당 분류기를 제거한 후, 두 도메인의 특징들을 서로 융합하고 새로운 분류기를추가하여 재학습을 수행하였다. 본 논문에서 제안하는 전이학습 기반 특징융합 기법은 시간 및 주파수 영역에서 추출된 특징들을 융합하여 모델학습을 수행함으로써, 두 영역의 특징이 상호 보완적으로 작용하여 모델이 다양한 정보를 활용함으로 인해 99.88%의 높은 정확도를 달성하여파이프 누수 감지에 있어 우수한 성능을 입증하였다.
Abstract
When there were disparities in performance between models trained in the time and frequency domains, even after conducting anensemble, 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-wiselearning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-steplearning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucialfeatures 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 transferlearning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequencydomains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverseinformation. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.
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