No abstract

No abstract

The importance of conducting potential analysis of load data and ensuring the effectiveness of feature selection cannot be overstated when it comes to enhancing the accuracy of short-term power load forecasting. Bisecting K-Means Algorithm is adopted for cluster analysis of the load data, the similarity data is categorized into the same cluster, and then the load data is decomposed into several Intrinsic Mode Functions (IMFs) by Ensemble Empirical Mode Decomposition (EEMD) in this study. Then the candidate features are selected by calculating Pearson correlation coefficient, and finally the forecasting input is constructed. A hybrid neural network forecasting model based on Deep Belief Network (DBN) and Bidirectional Recurrent Neural Network (Bi-RNN) is proposed. The method adopts unsupervised pre-training and supervised adjustment training methods and is verified on two different datasets. Compared with the forecasting results of other methods, it shows that the method can effectively improve the accuracy of load forecasting. INDEX TERMS Short-term power load forecasting, ensemble empirical mode decomposition, deep belief network, recurrent neural network.

No abstract

Auscultation is an important tool for diagnosing respiratory-related diseases. Unfortunately, the quality of auscultation is limited by the professional level of the doctor and the environment of the auscultation. Some studies have focused on automated auscultation techniques. However, existing approaches suffer from two challenges: (1) the models cannot learn from data distributed among multiple hospitals; and (2) the predictions of the models are difficult to interpret for physicians. To address this issue, this work proposes a novel explainable respiratory sound analysis framework with fuzzy decision tree regularization. This framework develops an ensemble knowledge distillation technique to learn distributed data and achieves good performance in terms of model efficiency and accuracy. Fuzzy decision trees are used to explain the predictions of the model and produce decision rules that can be well accepted by physicians. The effectiveness of this framework is thoroughly validated on Respiratory Sound Database and compared with other existing approaches.