Nowadays, the hate speech diffusion of texts and images in social network has become the mainstream compared with the diffusion of texts-only, raising the pressing needs of multimodal hate speech detection task. Current research on this task mainly focuses on the construction of multimodal models without considering the influence of the unbalanced and widely distributed samples for various attacks in hate speech. In this situation, introducing enhanced knowledge is necessary for understanding the attack category of hate speech comprehensively. Due to the high correlation between hate speech detection and sarcasm detection tasks, this paper makes an initial attempt of common knowledge transfer based on the above two tasks, where hate speech detection and sarcasm detection are defined as primary and auxiliary tasks, respectively. A scalable cross-domain knowledge transfer (CDKT) framework is proposed, where the mainstream vision-language transformer could be employed as backbone flexibly. Three modules are included, bridging the semantic, definition and domain gaps simultaneously between primary and auxiliary tasks. Specifically, semantic adaptation module formulates the irrelevant parts between image and text in primary and auxiliary tasks, and disentangles with the text representation to align the visual and word tokens. Definition adaptation module assigns different weights to the training samples of auxiliary task by measuring the correlation between samples of the auxiliary and primary task. Domain adaptation module minimizes the feature distribution gap of samples in two tasks. Extensive experiments show that the proposed CDKT provides a stable improvement compared with baselines and produces a competitive performance compared with some existing multimodal hate speech detection methods.

Full-bridge current doubler rectifier topology is used to restrict the ripple of output current and quicken the dynamic response. However, mass and power loss of filter composed of passive components are large. To optimize the output filter parameters, this paper adopts machine learning (ML) methods to train a support vector machine (SVM) model and an artificial neural network (ANN) model using data samples collected from simulation. SVM is used to judge the feasibility of filter design parameters, and the trained ANN serves as a dedicated surrogate model mapping from the design variables to the two optimization objectives (mass and power loss). After the ML aided filter optimization, the filter prototype based on the optimal design point is manufactured and tested on an experiment platform for the method validation.

Unbalanced three-phase voltages, harmonics and DC offset to the grid voltage will affect the speed and accuracy of a grid-connected inverter when estimating the frequency and phase angle of the grid voltage. Existing synchronization methods were difficult in accurately estimating the phase angle of the three-phase voltages. To solve these problems, this paper proposes a synchronous control method for three-phase grid-connected inverters using the energy operator. First, the cascade delay signal cancellation algorithm is used to eliminate harmonics and DC offset of the grid voltage, and the grid voltage is unitized to eliminate the adverse effects of unbalanced amplitudes. After that the energy operator is applied to obtain the frequency and the phase angle of the thee-phase input voltages. Therefore, the inverter and the grid voltage can be accurately synchronized even under unbalanced grid voltage conditions with harmonic distortion and DC offset. Finally, simulation results are used to verify the effectiveness of the synchronous control method proposed in this paper.

The weak grid with high line impedance and low short-circuit capacity leads to a large number of background harmonics and fluctuation of the grid voltage, and it is difficult for the inverter to synchronize with the weak grid. In order to overcome these problems, a novel fast and accurate frequency-locked loop(FLL) method is proposed. Firstly, an improved quadrature signal generator(QSG) is utilized to effectively eliminate the impact of the DC offset on the accuracy of frequency and phase detection. Secondly, low-pass filters(LPF) with specific harmonic filtering capabilities are used so that the system has good dynamic response speed and anti-interference ability. Finally, the grid frequency is calculated by the Teager energy operator(TEO) algorithm without any parameter adjustment, and use Park transform to obtain grid phase information. It can be known from the modeling that the FLL system is a type I system with an excellent dynamic response speed and stability margin. The simulation results verify the effectiveness of the proposed method when the grid voltage fluctuates or distorted.

High impedance characteristics of weak grid can easily lead to some problems such as poor stability of photovoltaic (PV) power generation system, high grid-connected current harmonics and so on. For solving these problems, this paper establishes a system stability criteria based on impedance matching for current-source PV power generation system, deduces the output impedance model of single-phase grid-connected PV inverter, and studies the impact of weak grid on inverter stability and grid-connected current harmonics. Then proposes a control method based on virtual inductance for increasing the output impedance of inverter to improve the system stability, and adopts a grid synchronization method based on second-order generalized integrator phase locked loop (SOGI-PLL) to improve the quality of the grid-connected current. Finally, simulation and experimental results verify that the proposed method can effectively improve the stability of PV grid power system and reduce the grid-connected current harmonics under weak grid.

This paper presents an autonomous control scheme for DC micro-grid system both in grid-connected and islanding conditions. The proposed DC micro-grid architecture consists of a wind turbine generation unit, a photovoltaic generation unit, two battery stack units, a grid-connected inverter/rectifier and loads. Corresponding control strategy of each unit is presented. DC droop control method is adopted into voltage control loop to suppress the circulating current when two or more voltage sources are connected to DC bus. The energy that flows through each unit and different operating modes of the system both in grid-connected and islanding conditions are analyzed. Power management method to balance the energy of the system and stable the DC bus voltage is presented. Simulation results verify the effectiveness of the proposed control scheme in various operating conditions.

Analog to digital converters (ADC) and cascade integrator-comb (CIC) filters are the basic modules in a digital intermediate frequency (IF) spectrum analyzer. The optimal output signal-to-noise ratio (SNR) of the digital IF spectrum analyzer with the Gaussian input signal is considered in this letter. The idea is to strike a trade-off between the saturation error and granular error when quantizing the Gaussian input signal. This letter firstly derives a relationship among the maximum allowed input signal amplitude, input signal power, ADC quantization bits and optimal quantization SNR. Besides, an optimal clipping strategy for the CIC decimation filter with variable decimation rates is proposed. Both numerical and simulation results are presented to demonstrate that the proposed clipping method is able to achieve significant SNR gain compared with the traditional rounding or truncation method.