Ferroelectricity, Piezoelectricity, and Dielectricity of 0.06PMnN-0.94PZT(45/55) Thin Film on Silicon Substrate

The high piezoelectricity and high quality factor ferroelectric thin films are important for electromechanical applications especially the micro electromechanical system (MEMS). The ternary compound ferroelectric thin films 0.06Pb(Mn1/3, Nb2/3)O3 + 0.94Pb(Zr0.45, Ti0.55)O3 (0.06PMnN-0.94PZT(45/55)) were deposited on silicon(100) substrates by RF magnetron sputtering method considering that Mn and Nb doping will improve PZT properties in this research. For comparison, nondoped PZT(45/55) films were also deposited. The results show that both of thin films show polycrystal structures with the main (111) and (101) orientations. The transverse piezoelectric coefficients are e31,eff=−4.03 C/m2 and e31,eff=-3.5 C/m2, respectively. These thin films exhibit classical ferroelectricity, in which the coercive electric field intensities are 2Ec=147.31 kV/cm and 2Ec=135.44 kV/cm, and the saturation polarization Ps=30.86 μC/cm2 and Ps=17.74 μC/cm2, and the remnant polarization Pr=20.44 μC/cm2 and Pr=9.87 μC/cm2, respectively. Moreover, the dielectric constants and loss are εr=681 and D=5% and εr=537 and D=4.3%, respectively. In conclusion, 0.06PMnN-0.94PZT(45/55) thin films act better than nondoped films, even though their dielectric constants are higher. Their excellent ferroelectricity, piezoelectricity, and high power and energy storage property, especially the easy fabrication, integration realizable, and potentially high quality factor, make this kind of thin films available for the realistic applications

Comparative Constructions in Zhoutun from a Language Contact Perspective

The paper describes comparative constructions in Zhoutun, a Chinese variety that was heavily influenced by Amdo Tibetan and spoken in Guide County, Qinghai Province. There are five comparative constructions (Cxn), based on the type of comparative marker, in Zhoutun, namely (1) the xa-Cxn; (2) the pi-Cxn; (3) the ‘look’-Cxn; (4) the ‘and’-Cxn; and (5) the hybrid Cxn. The five constructions illustrate features from both Chinese and Amdo Tibetan, and their co-existence demonstrates the mixed nature of the comparative constructions, as well as the grammar system of Zhoutun due to language contact. This paper also argues that the “comparative subject” should be further subcategorized into “comparative subject” and “attributive subject”, and that the “comparative result” should be divided into “abstract measurement” and “concrete measurement” in the typological study of comparative constructions

Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network

Speech anti-spoofing is a crucial aspect of speaker recognition systems and has received a great deal of attention in recent years. Deep neural networks have achieved satisfactory results in datasets with similar training and testing data distributions, but their generalization ability is limited in datasets with different distributions. In this paper, we proposed a novel dual-input hierarchical fusion network (HFN) to improve the generalization ability of our model. The network had two inputs (the original speech signal and the time-reversed signal), which increased the volume and diversity of the training data. The hierarchical fusion model (HFM) enabled more thorough fusion of information from different input levels and improved model performance by fusing the two inputs after speech feature extraction. We finally evaluated the results using the ASVspoof 2021 PA (Physical Access) dataset, and the proposed system achieved an Equal Error Rate (EER) of 24.46% and a minimum tandem Detection Cost Function (min t-DCF) of 0.6708 in the test set. Compared with the four baseline systems in the ASVspoof 2021 competition, the proposed system min t-DCF values were decreased by 28.9%, 31.0%, 32.6%, and 32.9%, and the EERs were decreased by 35.7%, 38.1%, 45.4%, and 49.7%, respectively

Mechanical Fault Diagnosis of a Disconnector Operating Mechanism Based on Vibration and the Motor Current

The mechanical fault diagnosis of a disconnector operating mechanism using a single signal is not sufficiently accurate and reliable. To address this problem, this paper proposes a new fault diagnosis method based on the vibration signal and the motor current signal. First, based on the analysis of the motor stator current signal envelope, segmented envelope RMS values are extracted. Then, the vibration signal of the operating mechanism is processed with VMD (Variational Mode Decomposition). In this paper, the number of modal decompositions K is selected according to the envelope entropy. Second, the effective value of the current segment envelope is fused with the energy entropy value of each IMF component to construct the feature parameters for fault identification. Finally, a fusion weighting algorithm using AdaBoost is proposed to train an SVM as a strong classifier to improve the correct fault diagnosis rate. In this paper, the proposed new diagnosis method is applied to a 220 kV disconnector operating mechanism. The algorithm can effectively identify three operating states of a disconnector operating mechanism

Mechanical Fault Diagnosis of a Disconnector Operating Mechanism Based on Vibration and the Motor Current

The mechanical fault diagnosis of a disconnector operating mechanism using a single signal is not sufficiently accurate and reliable. To address this problem, this paper proposes a new fault diagnosis method based on the vibration signal and the motor current signal. First, based on the analysis of the motor stator current signal envelope, segmented envelope RMS values are extracted. Then, the vibration signal of the operating mechanism is processed with VMD (Variational Mode Decomposition). In this paper, the number of modal decompositions K is selected according to the envelope entropy. Second, the effective value of the current segment envelope is fused with the energy entropy value of each IMF component to construct the feature parameters for fault identification. Finally, a fusion weighting algorithm using AdaBoost is proposed to train an SVM as a strong classifier to improve the correct fault diagnosis rate. In this paper, the proposed new diagnosis method is applied to a 220 kV disconnector operating mechanism. The algorithm can effectively identify three operating states of a disconnector operating mechanism