Candidate point selection using a self-attention mechanism for generating a smooth volatility surface under the SABR model

Kim, Hyeonuk; Park, Kyunghyun; Jeon, Junkee; Song, Changhoon; Bae, Jungwoo; Kim, Yong-Sik; Kang, Myungjoo

doi:10.1016/j.eswa.2021.114640

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Then, the similarity of queries and key is computed, which is scaled by its dimension d k to adjust results distribution. 35 Besides, the Softmax function is utilized to obtain the weight. Finally, the self-attention mechanism is implemented as equation (4).…”

Section: Self-attention Mechanismmentioning

confidence: 99%

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Song

Shen

et al. 2022

Structural Health Monitoring

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Nowadays, acoustic and vibration signals have been widely used to detect crack and its degree on compressor blades for non-destructive evaluation and structural health monitoring. Due to complex working conditions and interference of strong noise, single signal-based methods reach unsatisfactory accuracy. To improve the reliability and accuracy of crack detection, the two-level fusion method based on Mahalanobis distance (MD) and self-attention mechanism is proposed based on multi-acoustic, vibration, and acoustic emission information. First, the MD is calculated to measure the similarity between raw samples and group, which can fuse the same type of samples in data level. Then, the processed raw and data-level fusion samples are inputted to one-dimensional convolutional neural network, and the preliminary decisions are obtained. Finally, the decision-level fusion based on self-attention mechanism and credit assignment is proposed to modify and correct the preliminary decisions. The compressor experiments are implemented to test the proposed method under single and mixed working conditions with strong noise intensity. Further, the results illustrate that the proposed method can accurately detect crack for compressor blades. By comparing with other data fusion approaches, the advantage of the proposed method is validated under complex conditions.

show abstract

Section: Self-attention Mechanismmentioning

confidence: 99%

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Song

Shen

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

Vision-based interaction force estimation for robot grip motion without tactile/force sensor

Lee

et al. 2023

Expert Systems with Applications

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Candidate point selection using a self-attention mechanism for generating a smooth volatility surface under the SABR model

Cited by 2 publications

References 18 publications

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism

Vision-based interaction force estimation for robot grip motion without tactile/force sensor

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