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Wind turbines have been widely used for clean and renewable electricity generation. The maintenance costs of wind turbines constitute a significant portion of the total cost of the generated electricity. Thus, health management systems are increasingly needed to reduce the maintenance costs and improve the reliability of wind turbines. This paper proposes a novel framework for the quantitative evaluation...
Wind turbines have been widely used for clean and renewable electricity generation. The maintenance costs of wind turbines constitute a significant portion of the total cost of the generated electricity. Thus, health management systems are increasingly needed to reduce the maintenance costs and improve the reliability of wind turbines. This paper proposes a novel framework for quantitative evaluation...
This paper addresses the problem of developing an efficient compression scheme with high quality and low computational complexity for ECG signal compression. Taking into account the joint sparsity existing in ECG data and the temporal dependencies in ECG signal sequence, a novel scheme for JSM-2 based ECG compression is developed to exploit these characteristics. We first predict support information...
Compressed sensing (CS) is a technique that enables sparse signal reconstruction from much fewer samples. In this paper, we propose ECG compressed sensing methods based on distributed compressed sensing to exploit the joint sparsity for both single- and multi-lead ECG signals. We apply JSM-2 (joint sparse model type 2) for jointly sparse ECG signals and formulate how to establish a partially known...
This paper proposes a radial basis function (RBF) neural network-based model for short-term solar power prediction (SPP). Instead of predicting solar power directly, the model predicts transmissivity, which is then used to obtain solar power according to the extraterrestrial radiation. The proposed model uses a novel two-dimensional (2D) representation for hourly solar radiation and uses historical...
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