Prediction Method for Sugarcane Syrup Brix Based on Improved Support Vector Regression

Hu, Songjie; Meng, Yanmei; Zhang, Hongjie

doi:10.3390/electronics12071535

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…SVR is a machine learning method that is used to solve regression problems [20]. It seeks a hyperplane to fit data by mapping the original data onto a high-dimensional characteristic space, aiming to minimize the distance from the data points to the hyperplane [21].…”

Section: Svr Modelmentioning

confidence: 99%

Non-Destructive Testing of the Internal Quality of Korla Fragrant Pears Based on Dielectric Properties

Tang,

Zhang,

Liang

et al. 2024

Horticulturae

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This study provides a method for the rapid, non-destructive testing of the internal quality of Korla fragrant pears. The dielectric constant (ε′) and dielectric loss factor (ε″) of pear samples were tested at 100 frequency points (range = 0.1–26.5 GHz) using a vector network analyzer and coaxial probe. The variations in the dielectric parameters of fragrant pears were analyzed. The linear relationships between the dielectric parameters and internal quality were explored. Internal quality prediction models for Korla fragrant pears were built using partial least squares regression (PLSR), support vector regression (SVR) and particle swarm optimization–least squares support vector regression (PSO-LSSVR). The optimal model was then determined. There was a weak correlation between the dielectric parameters and soluble solid content (SSC) under a single frequency. The model based on PLSR and using ε′ as a variable predicted hardness the best, while the model based on PLSR using ε″ as a variable predicted SSC the best. Its R and MSE values were 0.77 and 0.073 in hardness prediction, respectively, and 0.91 and 0.087 in SSC prediction. This study provides a new method for the non-destructive online testing of the internal quality of Korla fragrant pears.

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Section: Svr Modelmentioning

confidence: 99%

Non-Destructive Testing of the Internal Quality of Korla Fragrant Pears Based on Dielectric Properties

Tang,

Zhang,

Liang

et al. 2024

Horticulturae

View full text Add to dashboard Cite

show abstract

“…The results showed that the new model applied to the long-term compensation of pressure sensors affected by measured drift. Hu et al [23] used the penalty coefficient and radial basis kernel function of the particle swarm optimization algorithm SVR for optimization. By measuring the actual samples, it was proved that the optimized calculation model had better prediction performance.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Sun,

Dai,

et al. 2024

Electronics

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The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry.

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Prediction Method for Sugarcane Syrup Brix Based on Improved Support Vector Regression

Cited by 2 publications

References 20 publications

Non-Destructive Testing of the Internal Quality of Korla Fragrant Pears Based on Dielectric Properties

Non-Destructive Testing of the Internal Quality of Korla Fragrant Pears Based on Dielectric Properties

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

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