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다중 센서 데이터 분석을 위한 설명 가능한 합성곱신경망 모델
안건이(Gonie Ahn),유재형(Jae Hyeong Yoo),이상훈(Sang Hoon Lee),김성범(Seoung Bum Kim) 대한산업공학회 2019 대한산업공학회지 Vol.45 No.2
In this paper, we propose a new convolutional neural networks (CNNs) structure to overcome the interpretation problem posed by a series of the nonlinear transformation processes in CNNs. Convolutional neural networks (CNNs) have been successfully used in various applications including images, signals, and texts. Despite their superior performance in various tasks, the main limitation of CNNs is the lack of interpretability. Recently, deep neural network models including CNNs have been effectively used for the analysis of multi-sensor data. Although the original characteristics of deep neural network models offer high accuracy in classification for activity recognition, they are difficult to identify the critical sensors that play a significant role in discrimination of various equipment conditions. The proposed CNNs offers good performance and interpretability simultaneously. The effectiveness and applicability of the proposed method were demonstrated by the real sensor data collected from construction equipment for activity recognition.
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