Development of a Tractor Hydrostatic Transmission Efficiency Prediction Model Using Novel Hybrid Deep Kernel Learning and Residual Radial Basis Function Interpolator Model Article Swipe
Jin Kam Park
,
Oleksandr Yuhai
,
Jin-Woong Lee
,
Yubin Cho
,
Joung Hwan Mun
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15222325
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15222325
This study proposes a data-efficient surrogate modeling approach for predicting hydrostatic transmission (HST) system efficiency in tractors using minimal data. Only 27 samples were selected from a dataset of 5092 measurements based on the minimum, mean, and maximum values of the input variables (input shaft speed, HST ratio, and load), which were used as the training data. A hybrid prediction model combining deep kernel learning and a residual radial basis function surrogate was developed with hyperparameters optimized via Bayesian optimization. For performance verification, the proposed model was compared with Neural Network (NN), Random Forest, XGBoost, Gaussian Process (GP), and Support Vector Regressor (SVR) models trained using 27 samples. As a result, the proposed model achieved the highest prediction accuracy (R2 = 0.93, MAPE = 5.94%, RMSE = 4.05). Process, SVM (Support Vector MA). These findings indicate that the proposed approach can be effectively used to predict the overall HST efficiency using minimal data, particularly in situations where experimental data collection is limited.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture15222325
- https://www.mdpi.com/2077-0472/15/22/2325/pdf?version=1762589203
- OA Status
- gold
- References
- 68
- OpenAlex ID
- https://openalex.org/W4416093173
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Raw OpenAlex JSON
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https://openalex.org/W4416093173Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/agriculture15222325Digital Object Identifier
- Title
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Development of a Tractor Hydrostatic Transmission Efficiency Prediction Model Using Novel Hybrid Deep Kernel Learning and Residual Radial Basis Function Interpolator ModelWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-11-08Full publication date if available
- Authors
-
Jin Kam Park, Oleksandr Yuhai, Jin-Woong Lee, Yubin Cho, Joung Hwan MunList of authors in order
- Landing page
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https://doi.org/10.3390/agriculture15222325Publisher landing page
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https://www.mdpi.com/2077-0472/15/22/2325/pdf?version=1762589203Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2077-0472/15/22/2325/pdf?version=1762589203Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
- References (count)
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68Number of works referenced by this work
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| abstract_inverted_index.effectively | 142 |
| abstract_inverted_index.hydrostatic | 10 |
| abstract_inverted_index.performance | 81 |
| abstract_inverted_index.experimental | 157 |
| abstract_inverted_index.measurements | 30 |
| abstract_inverted_index.particularly | 153 |
| abstract_inverted_index.transmission | 11 |
| abstract_inverted_index.optimization. | 79 |
| abstract_inverted_index.verification, | 82 |
| abstract_inverted_index.data-efficient | 4 |
| abstract_inverted_index.hyperparameters | 75 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5062061370 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I848706 |
| citation_normalized_percentile |