Speaker verification using attentive multi-scale convolutional recurrent network

In this paper, we propose a speaker verification method by an Attentive Multi-scale Convolutional Recurrent Network (AMCRN). The proposed AMCRN can acquire both local spatial information and global sequential information from the input speech recordings. In the proposed method, logarithm Mel spectrum is extracted from each speech recording and then fed to the proposed AMCRN for learning speaker embedding. Afterwards, the learned speaker embedding is fed to the back-end classifier (such as cosine similarity metric) for scoring in the testing stage. The proposed method is compared with state-of-the-art methods for speaker verification. Experimental data are three public datasets that are selected from two large-scale speech corpora (VoxCeleb1 and VoxCeleb2). Experimental results show that our method exceeds baseline methods in terms of equal error rate and minimal detection cost function, and has advantages over most of baseline methods in terms of computational complexity and memory requirement. In addition, our method generalizes well across truncated speech segments with different durations, and the speaker embedding learned by the proposed AMCRN has stronger generalization ability across two back-end classifiers.Comment: 21 pages, 6 figures, 8 tables. Accepted for publication in Applied Soft Computin

The Wavelet Identification of Thresholds and Time Delay of Threshold Autoregressive Models

Abstract: In this paper, we consider identification of the thresholds and time delay of threshold autoregressive models with p− dependence and an unknown number of thresholds. By checking p different empirical wavelets of the data to see which have significantly large absolute values, the time delay is identified first. By further checking the empirical wavelets corresponding to the time delay across the fine scale levels, the thresholds and their number are identified. All estimators are shown to be strongly consistent

Research on Online Moisture Detector in Grain Drying Process Based on V/F Conversion

An online resistance grain moisture detector is designed, based on the model of the relationship between measurement frequency and grain moisture and the nonlinear correction method of temperature. The detector consists of lower computer, the core function of which is the sensing of grain resistance values which is based on V/F conversion, and upper computer, the core function of which is the conversion of moisture and frequency and the nonlinear correction of temperature. The performance of the online moisture detector is tested in a self-designed experimental system; the test and analysis results indicate that the precision and stability of the detector can reach the level of the similar products, which can be still improved

A niche model to predict Microcystis bloom decline in Chaohu Lake, China

Cyanobacterial blooms occur frequently in lakes due to eutrophication. Although a number of models have been proposed to forecast algal blooms, a good and applicable method is still lacking. This study explored a simple and effective mathematical-ecological model to evaluate the growth status and predict the population dynamics of Microcystis blooms. In this study, phytoplankton were collected and identified from 8 sampling sites in Chaohu Lake every month from July to October, 2010. The niche breadth and niche overlap of common species were calculated using standard equations, and the potential relative growth rates of Microcystis were calculated as a weighted-value of niche overlap. In July, the potential relative growth rate was 2.79 (a.u., arbitrary units) but then rapidly declined in the following months to -3.99 a.u. in September. A significant correlation (R =0.998, P < 0.01) was found in the model between the net-increase in biomass of Microcystis in the field and the predicted values calculated by the niche model, we concluded that the niche model is suitable for forecasting the dynamics of Microcystis blooms. Redundancy analysis indicated that decreases in water temperature, dissolved oxygen and total dissolved phosphorus might be major factors underlying bloom decline. Based on the theory of community succession being caused by resource competition, the growth and decline of blooms can be predicted from a community structure. This may provide a basis for early warning and control of algal blooms.Cyanobacterial blooms occur frequently in lakes due to eutrophication. Although a number of models have been proposed to forecast algal blooms, a good and applicable method is still lacking. This study explored a simple and effective mathematical-ecological model to evaluate the growth status and predict the population dynamics of Microcystis blooms. In this study, phytoplankton were collected and identified from 8 sampling sites in Chaohu Lake every month from July to October, 2010. The niche breadth and niche overlap of common species were calculated using standard equations, and the potential relative growth rates of Microcystis were calculated as a weighted-value of niche overlap. In July, the potential relative growth rate was 2.79 (a.u., arbitrary units) but then rapidly declined in the following months to -3.99 a.u. in September. A significant correlation (R =0.998, P < 0.01) was found in the model between the net-increase in biomass of Microcystis in the field and the predicted values calculated by the niche model, we concluded that the niche model is suitable for forecasting the dynamics of Microcystis blooms. Redundancy analysis indicated that decreases in water temperature, dissolved oxygen and total dissolved phosphorus might be major factors underlying bloom decline. Based on the theory of community succession being caused by resource competition, the growth and decline of blooms can be predicted from a community structure. This may provide a basis for early warning and control of algal blooms

Standardized Volume Power Density Boost in Frequency-Up Converted Contact-Separation Mode Triboelectric Nanogenerators

The influence of a mechanical structure’s volume increment on the volume power density (VPD) of triboelectric nanogenerators (TENGs) is often neglected when considering surface charge density and surface power density. This paper aims to address this gap by introducing a standardized VPD metric for a more comprehensive evaluation of TENG performance. The study specifically focuses on 2 frequency-up mechanisms, namely, the integration of planetary gears (PG-TENG) and the implementation of a double-cantilever structure (DC-TENG), to investigate their impact on VPD. The study reveals that the PG-TENG achieves the highest volume average power density, measuring at 0.92 W/m3. This value surpasses the DC-TENG by 1.26 times and the counterpart TENG by a magnitude of 69.9 times. Additionally, the PG-TENG demonstrates superior average power output. These findings introduce a new approach for enhancing TENGs by incorporating frequency-up mechanisms, and highlight the importance of VPD as a key performance metric for evaluating TENGs