A speaker recognition system based on joint factor analysis (JFA) is proposed to improve whispering speakers’ recognition rate under channel mismatch. The system estimated separately the eigenvoice and the eigenchannel before calculating the corresponding speaker and the channel factors. Finally, a channel-free speaker model was built to describe accurately a speaker using model compensation. The test results from the whispered speech databases obtained under eight different channels showed that the correct recognition rate of a recognition system based on JFA was higher than that of the Gaussian Mixture Model-Universal Background Model. In particular, the recognition rate in cellphone channel tests increased significantly.

With the increasing penetration rate of grid-connected renewable energy generation, the problem of grid voltage excursion becomes an important issue that needs to be solved urgently. As a new type of voltage regulation control method, electric spring (ES) can alleviate the fluctuations of renewable energy output effectively. In this paper, the background and basic principle of the electric spring are introduced firstly. Then, considering the influence of an electric spring on noncritical load voltage, noncritical loads are classified reasonably, and based on the electric spring phasor diagram, the control method to meet the noncritical load voltage constraint is proposed. This control method can meet the requirements of voltage excursions of different kinds of noncritical load, increase the connection capacity of the noncritical load and improve the voltage stabilization capacity of the electric spring. Finally, through the simulation case, the feasibility and validity of electric spring theory and the proposed control method are verified.

The normal modes cannot be extracted even in the Pekeris waveguide when the source-receiver distance is very close. This paper introduces a normal mode extraction method based on a dedispersion transform (DDT) to solve this problem. The method presented here takes advantage of DDT, which is based on the waveguide invariant such that the dispersion associated with all of the normal modes is removed at the same time. After performing DDT on a signal received in the Pekeris waveguide, the waveform of resulting normal modes is very close to the source signal, each with different position and amplitude. Each normal mode can be extracted by determining its position and amplitude parameters by applying particle swarm optimization (PSO). The waveform of the extracted normal mode is simply the waveform of the source signal; the real waveform of the received normal mode can then be recovered by applying dispersion compensation to the source signal. The method presented needs only one receiver and is verified with experimental data