서울대학교 초자율화 인공지능 연구실

2023 Frequency-learning Generative Network (FLGN) to Generate Vibration Signals of Variable Lengths

본문

Journal: Expert Systems with Applications

Author: Jin Uk Ko, Jinwook Lee, Taehun Kim, Yong Chae Kim, and Byeng D. Youn*

Date: 2023-10

Citation Index: SCIE (IF: 7.5, Rank: 5.2%)

Vol./ Page: Vol. 227, pp. 120255

Year: 2023

Link: http://doi.org/10.1016/j.eswa.2023.120255 256회 연결

Abstract

This paper proposes a new generative model to produce signals of variable lengths. The proposed frequency-learning generative network (FLGN), which is designed and trained based on signal processing knowledge, can generate signals in a desired time range. The frequency information of the training data can be directly learned by the proposed method. A frequency is assumed to be decomposed to include deterministic and stochastic frequency parts. In the proposed approach, the deterministic frequency is learned in the form of a trainable parameter and the stochastic frequency is determined by the output of a frequency extractor. First, a phase extractor outputs a feature that corresponds to the phase of each frequency component. Then, a sine-basis is defined using the phase feature and the final frequency which is the summation of the deterministic and stochastic frequencies. Next, a magnitude extractor produces the magnitude feature from the sine-basis. Finally, the final output becomes the dot product of the sine-basis and the magnitude features. In the work described here, the proposed method is evaluated both quantitatively and qualitatively using three datasets: one simulation dataset and two experimental testbed datasets. The validation results indicate that the generated signal is similar to the true signal, when comparing them in the time-domain and frequency-domain. The results of the quantitative evaluation show that the signal generated by the proposed method has statistical characteristics that are similar to the true signal. Finally, the evaluation shows that the proposed model focuses on the characteristic frequencies while learning the frequency components.