A weight recognition method for movable objects in sealed cavity based on supervised learning

Lv, Bingze; Wang, Guotao; Li, Shuo; Wu, Yaoyang; Wang, Guoqiang

doi:10.1016/j.measurement.2022.112149

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…Adding random noise to important features will have a great impact on the precision of the algorithm during RF training [43]. Out-of-bag data were selected in each decision tree and the error is calculated, and record it as: err 1 OOB [44]. Randomly add noise interference to all the sample features X obtained from the out-of-bag data, and calculate the out-of-bag data error again, recorded as: err 2 OOB [45].…”

Section: Variable Selectionmentioning

confidence: 99%

Long-Term Power Load Forecasting Using LSTM-Informer with Ensemble Learning

Wang

Zhang

et al. 2023

Electronics

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Accurate power load forecasting can facilitate effective distribution of power and avoid wasting power so as to reduce costs. Power load is affected by many factors, so accurate forecasting is more difficult, and the current methods are mostly aimed at short-term power load forecasting problems. There is no good method for long-term power load forecasting problems. Aiming at this problem, this paper proposes an LSTM-Informer model based on ensemble learning to solve the long-term load forecasting problem. The bottom layer of the model uses the long short-term memory network (LSTM) model as a learner to capture the short-term time correlation of power load, and the top layer uses the Informer model to solve the long-term dependence problem of power load forecasting. In this way, the LSTM-Informer model can not only capture short-term time correlation but can also accurately predict long-term power load. In this paper, a one-year dataset of the distribution network in the city of Tetouan in northern Morocco was used for experiments, and the mean square error (MSE) and mean absolute error (MAE) were used as evaluation criteria. The long-term prediction of this model is 0.58 and 0.38 higher than that of the lstm model based on MSE and MAE. The experimental results show that the LSTM-Informer model based on ensemble learning has more advantages in long-term power load forecasting than the advanced baseline method.

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Section: Variable Selectionmentioning

confidence: 99%

Long-Term Power Load Forecasting Using LSTM-Informer with Ensemble Learning

Wang

Zhang

et al. 2023

Electronics

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Fused machine learning based on Boosting algorithm for noise recognition of moving parts in sealed cavity

Gao

Sun

et al. 2023

Measurement

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Recognition Method for Electronic Component Signals Based on LR-SMOTE and Improved Random Forest Algorithm

Lv¹,

Wang²,

Li³

et al. 2023

SAE Int. J. Aerosp.

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<div>Loose particles are a major problem affecting the performance and safety of aerospace electronic components. The current particle impact noise detection (PIND) method used in these components suffers from two main issues: data collection imbalance and unstable machine-learning-based recognition models that lead to redundant signal misclassification and reduced detection accuracy. To address these issues, we propose a signal identification method using the limited random synthetic minority oversampling technique (LR-SMOTE) for unbalanced data processing and an optimized random forest (RF) algorithm to detect loose particles. LR-SMOTE expands the generation space beyond the original SMOTE oversampling algorithm, generating more representative data for underrepresented classes. We then use an RF optimization algorithm based on the correlation measure to identify loose particle signals in balanced data. Our experimental results demonstrate that the LR-SMOTE algorithm has a better data balancing effect than SMOTE, and our optimized RF algorithm achieves an accuracy of over 96% for identifying loose particle signals. The proposed method can also be popularized in the field of loose particle detection for large-scale sealing equipment and other various areas of fault diagnosis based on sound signals.</div>

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A weight recognition method for movable objects in sealed cavity based on supervised learning

Cited by 3 publications

References 40 publications

Long-Term Power Load Forecasting Using LSTM-Informer with Ensemble Learning

Long-Term Power Load Forecasting Using LSTM-Informer with Ensemble Learning

Fused machine learning based on Boosting algorithm for noise recognition of moving parts in sealed cavity

Recognition Method for Electronic Component Signals Based on LR-SMOTE and Improved Random Forest Algorithm

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