A Data-Driven Kernel Principal Component Analysis–Bagging–Gaussian Mixture Regression Framework for Pulverizer Soft Sensors Using Reduced Dimensions and Ensemble Learning

Jin, Shengxiang; Si, Fengqi; Dong, Yunshan; Ren, Shaojun

doi:10.3390/en16186671

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…Kernel principal component analysis (KPCA) is the use of the kernel function in principal component analysis, which can solve the nonlinear feature extraction problem [17]. It is first mapped to a high-dimensional space and then mapped to another low-dimensional space by linear dimensionality reduction, i.e., the original data sample η is transformed into a 2 × 2 symmetric matrix, also known as a two-dimensional covariance matrix by the kernel function:…”

Section: Kpca Principlesmentioning

confidence: 99%

Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

Ye,

Chen,

Liu

et al. 2024

Energies

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In order to improve the reliability and maintainability of rod control power cabinets in nuclear power plants, this paper uses insulated gate bipolar transistors (IGBTs), the key power device of rod control power cabinets, as the object of research on cross-working-condition fault prediction. An improved transfer learning (TL) model based on a temporal convolutional network (TCN) is proposed to solve the problem of low fault prediction accuracy across operating conditions. First, the peak emitter voltage of an IGBT aging dataset is selected as the source domain failure characteristic, and the TCN model is trained after the removal of outliers and noise reduction. Then, the time–frequency features are extracted according to the characteristics of the target domain data, and the target domain representation data are obtained using kernel principal component analysis (KPCA) for dimensionality reduction. Finally, the TCN model trained on the source domain is transferred; the model is fine-tuned according to the target domain data, and the learning rate, the number of hidden layer nodes, and the number of training times in the network model are optimized using the dung beetle optimization (DBO) algorithm to obtain the optimal network, making it more suitable for target sample fault prediction. The prediction results of this TCN model, the long short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the recursive neural network (RNN) model are compared and analyzed by selecting prediction performance evaluation indexes. The results show that the TCN model has a better predictive effect. Comparing the prediction results of the TCN-based optimized transfer learning model with those of the directly trained TCN model, the mean square error, root mean square error, and mean absolute error are reduced by a factor of two to three, which provides an effective solution for fault prediction across operating conditions.

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Section: Kpca Principlesmentioning

confidence: 99%

Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

Ye,

Chen,

Liu

et al. 2024

Energies

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Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals

Huang,

Xi,

Jiao

et al. 2024

MBE

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<p>It is important to classify electroencephalography (EEG) signals automatically for the diagnosis and treatment of epilepsy. Currently, the dominant single-modal feature extraction methods cannot cover the information of different modalities, resulting in poor classification performance of existing methods, especially the multi-classification problem. We proposed a multi-modal feature fusion (MMFF) method for epileptic EEG signals. First, the time domain features were extracted by kernel principal component analysis, the frequency domain features were extracted by short-time Fourier extracted transform, and the nonlinear dynamic features were extracted by calculating sample entropy. On this basis, the features of these three modalities were interactively learned through the multi-head self-attention mechanism, and the attention weights were trained simultaneously. The fused features were obtained by combining the value vectors of feature representations, while the time, frequency, and nonlinear dynamics information were retained to screen out more representative epileptic features and improve the accuracy of feature extraction. Finally, the feature fusion method was applied to epileptic EEG signal classifications. The experimental results demonstrated that the proposed method achieves a classification accuracy of 92.76 ± 1.64% across the five-category classification task for epileptic EEG signals. The multi-head self-attention mechanism promotes the fusion of multi-modal features and offers an efficient and novel approach for diagnosing and treating epilepsy.</p>

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A Data-Driven Kernel Principal Component Analysis–Bagging–Gaussian Mixture Regression Framework for Pulverizer Soft Sensors Using Reduced Dimensions and Ensemble Learning

Cited by 2 publications

References 33 publications

Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals

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