On the relationship between Gaussian stochastic blockmodels and label propagation algorithms

The problem of community detection receives great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimation of this model has the same objective function as general label propagation with node preference. The node preference of a specific vertex turns out to be a value proportional to the intra-community eigenvector centrality (the corresponding entry in principal eigenvector of the adjacency matrix of the subgraph inside that vertex's community) under maximum likelihood estimation. Additionally, the maximum likelihood estimation of a constrained version of our model is highly related to another extension of label propagation algorithm, namely, the label propagation algorithm under constraint. Experiments show that the proposed Gaussian stochastic blockmodel performs well on various benchmark networks.Comment: 22 pages, 17 figure

Simulation and experiment of algorithm and circuit design for UPQC

Power quality issues have become one of the most important issue for researchers to concern. In this paper, simulation and experiment of algorithm and circuit design of Unified Power Quality Conditioner (UPQC) are provided. Control algorithm and topology design of one UPQC which includes active power filter (APF) and dynamic voltage restorer (DVR) are introduced. Stability condition of the filter unit is deduced and proved by Routh stability criterion. Simulation for APF and DVR is carried out in PSCAD to show the proposed control strategy. Experiments such as current tracking, harmonic detection and compensation and voltage drop compensation are provided in details. Experimental results show that the proposed control method and the designed topology are effective and practical

Solving empty result problem in keyword search over relational databases

Master'sMASTER OF SCIENC

Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy

In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier