Early detection of arrhythmias has become critical due to the increased mortality from cardiovascular disease, and ECG is an effective tool for diagnosing cardiovascular disease and detecting arrhythmias. Classification based on ECG signal segments is more suitable for clinical application. An improved deep residual convolutional neural network is proposed to classify arrhythmias automatically. Firstly, the overlapping segmentation method is used to segment the ECG signals in the MIT-BIH database into segments of 5 seconds in length to overcome the imbalance between classes, and these segments of the ECG signals are re-labeled. Then the discrete wavelet transform (DWT) is used to denoise these segments and the improved deep residual convolutional neural network is used for arrhythmia classification. In addition, the focal loss function is used to overcome the imbalanced classification difficulty between classes. The proposed method gives 94.54% sensitivity, 93.33% positive predictivity, and 80.80% specificity for normal segments. For the supraventricular ectopic segment, the proposed method gives 35.22% sensitivity, 65.88% positive predictivity, and 98.83% specificity. For the ventricular ectopic segment, the proposed method gives 88.35% sensitivity, 79.86% positive predictivity, and 94.92% specificity. The results of this study indicate that the proposed improved deep residual convolutional neural network model trained by the training set obtained by using the overlapping segmentation method is comparable to a classical method and three state-of-art methods. In addition, the classification performance of the network model trained by focal loss as the loss function is further improved.

Wind energy is a clean energy source and is receiving widespread attention. Improving the operating efficiency and economic benefits of wind power generation systems depends on more accurate short-term wind speed predictions. In this study, a new hybrid model for short-term wind speed forecasting is proposed. The model combines variational modal decomposition (VMD), the proposed improved seagull optimization algorithm (ISOA) and the kernel extreme learning machine (KELM) network. The model adopts a hybrid modeling strategy: firstly, VMD decomposition is used to decompose the wind speed time series into several wind speed subseries. Secondly, KELM optimized by ISOA is used to predict each decomposed subseries. The ISOA technique is employed to accurately find the best parameters in each KELM network such that the predictability of a single KELM model can be enhanced. Finally, the prediction results of the wind speed sublayer are summarized to obtain the original wind speed. This hybrid model effectively characterizes the nonlinear and nonstationary characteristics of wind speed and greatly improves the forecasting performance. The experiment results demonstrate that: (1) the proposed VMD-ISOA-KELM model obtains the best performance for the application of three different prediction horizons compared with the other classic individual models, and (2) the proposed hybrid model combining the VMD technique and ISOA optimization algorithm performs better than models using other data preprocessing techniques.