Coercivity mechanism in permanent magnets has been debated for many years.In this paper, various models of the coercivity mechanism are classified and re-examined by the comparison and contrast.Coherent rotation and c...Coercivity mechanism in permanent magnets has been debated for many years.In this paper, various models of the coercivity mechanism are classified and re-examined by the comparison and contrast.Coherent rotation and curling models can reveal the underlying reversal mechanism clearly based on isolated grains with elliptic shapes.By contrast, the numerical methods consider inter-grain interactions while simulating the evolution of the spins and hysteresis loops with complicated shapes.However, an exact simulation of magnetic reversal in permanent nanomagnets requires many meshes to mimic the thin domain wall well.Nucleation and pinning are the two main coercivity mechanisms in permanent magnets.The former signifies the beginning of the magnetic reversal, whilst the latter completes it.Recently, it is proposed that the large difference between the intrinsic magnetic properties of the nucleation centers and those of the main phase can result in a large pinning field(self-pinning), which has the attributes of both traditional nucleation and pinning.Such a pinning explains the experimental data of permanent magnets very well, including the enhancement of the coercivity by the grain boundary pinning.展开更多
Regarding the performance of traditional endpoint detection algorithms degrades as the environment noise level increases, a recursive calculating algorithm for higher-order cu- mulants over a sliding window is propose...Regarding the performance of traditional endpoint detection algorithms degrades as the environment noise level increases, a recursive calculating algorithm for higher-order cu- mulants over a sliding window is proposed. Then it is applied to the speech endpoint detection. Furthermore, endpoint detection is carried out with the feature of energy. Experimental results show that both the computational efficiency and the robustness against noise of the proposed algorithm are improved remarkably compared with traditional algorithm. The average prob- ability of correct point detection （Pc-point） of the proposed voice activity detection （VAD） is 6.07% higher than that of G.729b VAD in different noisy at different signal-noise ratios （SNRs） environments.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11074179,51771127,51571126,and 51772004)the Scientific Research Fund of Sichuan Provincial Education Department,China(Grant Nos.18TD0010 and 16CZ0006)
文摘Coercivity mechanism in permanent magnets has been debated for many years.In this paper, various models of the coercivity mechanism are classified and re-examined by the comparison and contrast.Coherent rotation and curling models can reveal the underlying reversal mechanism clearly based on isolated grains with elliptic shapes.By contrast, the numerical methods consider inter-grain interactions while simulating the evolution of the spins and hysteresis loops with complicated shapes.However, an exact simulation of magnetic reversal in permanent nanomagnets requires many meshes to mimic the thin domain wall well.Nucleation and pinning are the two main coercivity mechanisms in permanent magnets.The former signifies the beginning of the magnetic reversal, whilst the latter completes it.Recently, it is proposed that the large difference between the intrinsic magnetic properties of the nucleation centers and those of the main phase can result in a large pinning field(self-pinning), which has the attributes of both traditional nucleation and pinning.Such a pinning explains the experimental data of permanent magnets very well, including the enhancement of the coercivity by the grain boundary pinning.
基金supported by the National Natural Science Eoundation of China(61271352)
文摘Regarding the performance of traditional endpoint detection algorithms degrades as the environment noise level increases, a recursive calculating algorithm for higher-order cu- mulants over a sliding window is proposed. Then it is applied to the speech endpoint detection. Furthermore, endpoint detection is carried out with the feature of energy. Experimental results show that both the computational efficiency and the robustness against noise of the proposed algorithm are improved remarkably compared with traditional algorithm. The average prob- ability of correct point detection （Pc-point） of the proposed voice activity detection （VAD） is 6.07% higher than that of G.729b VAD in different noisy at different signal-noise ratios （SNRs） environments.
