A Self-Attention Integrated Learning Model for Landing Gear Performance Prediction.

Lin Lin, Changsheng Tong, Feng Guo, Song Fu, Yancheng Lv, Wenhui He

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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5 · Who and what money

Authors and funding

6 authors.

Lin LinSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.

Changsheng TongSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.

Feng GuoSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.

Song FuSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.

Yancheng LvSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.

Wenhui HeSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.

Funding

the Fellowship of China Postdoctoral Science Foundation 2022M720955the Fellowship of Heilongjiang Province Postdoctoral Science Foundation LBH-Z22187the National Natural Science Foundation of China Key Support Project U2133202

6 · The paper itself

Abstract

The landing gear structure suffers from large loads during aircraft takeoff and landing, and an accurate prediction of landing gear performance is beneficial to ensure flight safety. Nevertheless, the landing gear performance prediction method based on machine learning has a strong reliance on the dataset, in which the feature dimension and data distribution will have a great impact on the prediction accuracy. To address these issues, a novel MCA-MLPSA is developed. First, an MCA (multiple correlation analysis) method is proposed to select key features. Second, a heterogeneous multilearner integration framework is proposed, which makes use of different base learners. Third, an MLPSA (multilayer perceptron with self-attention) model is proposed to adaptively capture the data distribution and adjust the weights of each base learner. Finally, the excellent prediction performance of the proposed MCA-MLPSA is validated by a series of experiments on the landing gear data.

Indexed as

AircraftMachine LearningNeural Networks, Computerdata distributionfeature selectionintegrated learningperformance predictionself-attention

Identifiers

PMID37448071

DOI10.3390/s23136219

PMCPMC10346243

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.