The time series of blood glucose concentration in diabetics are time-varying, nonlinear and non-stationary. To improve the accuracy of blood glucose prediction, a short-term blood glucose prediction model (VMD-IPSO-LSTM) combining variational modal decomposition (VDM) and improved Particle swarm optimization optimizing Long short-term memory network (IPSO-LSTM) was proposed. Firstly, the time series of blood glucose concentration of patients was decomposed by using VMD method to obtain the intrinsic modal functions (IMF) of blood glucose components in different frequency bands, so as to reduce the non-stationarity of blood glucose time series. Then a prediction model was established for each blood glucose component IMF by using the long and short time memory network. Since the number of neurons, learning rate and time window length of LSTM are difficult to determine, the improved PSO algorithm is used to optimize these parameters. The optimized LSTM network was used to predict each IMF, and finally the predicted subsequence was superimposed to obtain the final prediction result. The data of 56 patients were selected as experimental data from 451 patients with diabetes mellitus. The experimental results showed that the proposed VMD-IPSO-LSTM model could achieve high prediction accuracy at 30min, 45min and 60min in advance. When predicted 60 minutes in advance, compared with the LSTM, VMD-LSTM and VMD-PSO-LSTM methods, the RMSE of proposed method decreased by 15.565,3.402,1.215 and the MAPE of proposed method decreased by 11.284%,2.024%, 0.834%, and the percentage of predicted values falling into the A zone increased by 23.5%,6.1% and 2.8% in the Clarke error grid, respectively. The improved accuracy of blood glucose prediction and longer prediction time can provide sufficient time for physicians and patients to control blood glucose concentrations and improve the effectiveness of diabetes treatment.

Accurate prediction the remaining useful life (RUL) and estimation the state of health (SOH) are critical to the management of lithium-ion batteries. In this paper, a lithium battery capacity prediction method based on cuckoo search optimization variational mode decomposition (CS-VMD) and gated recurrent unit (GRU) is proposed. Firstly, the VMD algorithm is used to divide the capacity into some intrinsic mode functions (IMFs) to reduce the impact of capacity regeneration and other situations. The number of decomposition layers and the quadratic penalty factor of VMD are optimized by the CS algorithm. Then, the GRU network is introduced to capture small changes in the capacity degradation process and perform the capacity prediction of decomposed sequence. Finally, some prediction results are integrated effectively. Based on two publicly available lithium-ion battery datasets, the model proposed in this paper can significantly reduce the complexity of the sequence and have high prediction accuracy, which is better than other prediction models. The root mean square error (RMSE) is controlled within 2%, and the maximum mean absolute error (MAE) does not exceed 2%.

Motor imagery brain–computer interface (MI-BCI) systems hold the potential to restore motor function and offer the opportunity for sustainable autonomous living for individuals with a range of motor and sensory impairments. The feature extraction and classification of motor imagery EEG signals related to motor imagery brain–computer interface systems has become a research hotspot. To address the challenges of difficulty in feature extraction and low recognition rates of motor imagery EEG signals caused by individual variations in EEG signals, a classification algorithm for EEG signals based on multi-feature fusion and the SVM-AdaBoost algorithm was proposed to improve the recognition accuracy of motor imagery EEG signals. Initially, the electroencephalography (EEG) signals are preprocessed using Finite Impulse Response (FIR) filters, and a multi-wavelet framework is constructed based on the Morlet wavelet and the Haar wavelet. Subsequently, the preprocessed signals undergo multi-wavelet decomposition to extract energy features, Common Spatial Patterns (CSP) features, Autoregressive (AR) features, and Power Spectral Density (PSD) features. The extracted features are then fused, and the fused feature vector is normalized. Following that, classification is implemented within the SVM-AdaBoost algorithm. To enhance the adaptability of SVM-AdaBoost, the Grid Search method is employed to optimize the penalty parameter and kernel function parameter of the SVM. Concurrently, the Whale Optimization Algorithm is utilized to optimize the learning rate and number of weak learners within the AdaBoost ensemble, thereby refining the overall performance. In addition, the classification performance of the algorithm is validated using a brain-computer interface (BCI) dataset. In this study, it was found that the classification accuracy reached 95.37%. Via the analysis of motor imagery electroencephalography (EEG) signals, the activation patterns in different regions of the brain can be detected and identified, enabling the inference of user intentions and facilitating communication and control between the human brain and external devices.

Aiming at the problem of denoising chaotic signals with low signal-to-noise ratio and unknown dynamic system parameters, a new chaotic signal denoising algorithm is proposed, which combines adjustable Q-factor wavelet transform (TQWT) and adaptive singular value decomposition (ASVD). This method uses TQWT to decompose the noisy chaotic signal. According to the maximum wavelet entropy theory and energy threshold rule, the subband of TQWT is accurately divided into signal subband and noise subband. For noise subbands, adaptive SVD is used to denoise them, to achieve preliminary denoising. In ASVD, the standard deviation of the singular value subset is used to determine the effective reconstruction order to improve the noise suppression effect. To further remove noise in the signal subband, TQWT reconstruction is performed on the preliminarily denoised signal, and ASVD is used to denoise the reconstructed signal again to obtain the chaotic signal after secondary denoising. Chua's simulated signal and four kinds of underwater radiated noise measured by TQWT-ASVD were denoised, and compared with the SVD denoising method, TQWT denoising method, complete ensemble empirical mode decomposition with adaptive noise and threshold denoising method (CEEMDAN-WT) and modified ensemble empirical mode decomposition combined with least squares denoising method (MEEMD-LMS), The experimental results show that the TQWT-ASVD method can reduce the noise of chaotic signals more effectively. Compared with SVD, TQWT, CEEMDAN-WT, MEEMD-LMS, and Chua's signal denoising method, the signal-to-noise ratio (SNR) of this method increased by 23.22%, 26.46%, 18.79%, 16.11% the root mean square error (RMSE) decreased by 32.53%,39.48%, 30.96%, 27.94%, and the row entropy (PE) decreased by 40.44%, 41.96%, 22.78%, 20.59%; After reducing the radiation noise of cargo ships, the PE value of this method is reduced by 13.91%, 10.18%, 10.88%, 8.68% respectively, and the FE value is reduced by 33.66%, 31.42%, 26.98%, 21.32% respectively.