Precision Measurement of the Proton Flux in Primary Cosmic Rays from Rigidity 1 GV to 1.8 TV with the Alpha Magnetic Spectrometer on the International Space Station

A precise measurement of the proton flux in primary cosmic rays with rigidity (momentum/charge) from 1 GV to 1.8 TV is presented based on 300 million events. Knowledge of the rigidity dependence of the proton flux is important in understanding the origin, acceleration, and propagation of cosmic rays. We present the detailed variation with rigidity of the flux spectral index for the first time. The spectral index progressively hardens at high rigidities.</p

An adaptive interval construction based GRU model for short-term wind speed interval prediction using two phase search strategy

The application of wind power is greatly restricted due to the volatility and intermittency of wind. It is a challenging task to quantify the uncertainty of wind speed prediction. To tackle such a challenge, an adaptive interval construction-based gated recurrent unit (GRU) model is proposed for directly generating short-term wind speed prediction intervals in this paper, using the two phase search strategy to search the model parameters. Different from the traditional interval prediction techniques, in the proposed model an adaptive interval construction method is designed, where the target values of wind speed are characterized by two interval width adjustment variables which are used to determine the lower and upper bounds of the interval of wind speed. A two phase search strategy is designed to optimize the parameters. In Phase I, the dynamic inertia weight particle swarm optimization algorithm is used to search the two interval width adjustment variables. In Phase II, the GRU networks are trained using the root mean square prop (RMSProp) algorithm (an effective gradient-based optimizer) to fit the upper and lower bounds of the constructed intervals, respectively. The two phases are executed alternately, so as to obtain optimal prediction intervals. The performance of the proposed method is compared with eight other machine learning and deep learning methods, and the experimental results show that the proposed method outperforms the compared methods. It indicates that the proposed method can generate satisfactory and better prediction intervals compared with other methods