An algorithm is proposed to detect community structure in social network. The algorithm begins with a community division based on prior knowledge of the degrees of the nodes, and then combines the communities until a clear partition is obtained. In applications such as a computer-generated network, Ucinet networks, andChinese rural-urban migrants' social networks, the algorithm can achieve higher modularity and greater speed than others in the recent literature.

In this paper, a novel multi-physics parametric modeling approach using artificial neural networks (ANNs) for microwave passive components is proposed. In the proposed approach, the ANN is used to learn the nonlinear relationships between electromagnetic (EM) behaviors and multiphysics design variables. The trained model can accurately represent the EM responses of the passive components with respect to the multi-physics input parameters. Therefore, the proposed model can provide accurate and fast prediction of EM responses using low computational cost and little time for multi-physics design. The advantage of the proposed model is demonstrated by two microwave examples: the proposed model can save about 98% computational cost compared with the EM model, and the CPU time of the proposed model is less than 0.1 s while that of the EM model needs many minutes.

This paper presents a novel modulation and digital controller based on a bi-directional voltage mode high frequency link inverter with active clamp to achieve fast dynamic response and high static performance. The inverter consists of a full-bridge converter, a bi-directional active rectifier, an active clamp branch and a Pulse Width Modulation (SPWM) inverter bridge. A novel modulation strategy is applied by modulating the triangle carrier wave and two inverse sinusoidal modulation waves, it is no need of checking the pole of the output voltage to switch the modulated signals, and it is able to weaken the distortion of the output waveform on zero passage. Double closed-loop digital control with load current feed-forward is applied to achieve good dynamic response. The modulation and control strategy is realized by using the TMS320LF2407A DSP and EPM7128 CPLD. Theoretical analysis and experimental results referring to a laboratory prototype (3KVA) indicate that the proposed modulation and control scheme are promising for the applications of DC/AC power supply.

This paper presents a novel Neurospace Mapping (Neuro-SM) method for packaged transistor modeling. A new structure consisting of the input package module, the nonlinear module, the output package module, and the S-Matrix calculation module is proposed for the first time. The proposed method can develop the model only using the terminal signals, instead of the internal and physical structure information of the transistors. An advanced training method utilizing the different parameters to adjust the different characteristics of the packaged transistors is developed to make the proposed model match the device data efficiently and accurately. Measured data of radio frequency (RF) power laterally diffused metal-oxide semiconductor (LDMOS) transistor are used to verify the capability of the proposed Neuro-SM method. The results demonstrate that the novel Neuro-SM model is more accurate and efficient than existing device models.