In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.
Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%.
CDX2 has recently been identified as a prognostic marker for pancreatic adenocarcinoma. However, the role and mechanism of CDX2 in progression of pancreatic adenocarcinoma are still elusive. In this study, we observed that CDX2 expression was much lower in mouse pancreatic adenocarcinoma tissues and pancreatic cancer cells. A network integrated by ChIPBase platform hinted that miR-615-5p, a most newly discovered tumor suppressor, was probably bound by CDX2 in the promoter region. Chromatin immunoprecipitation (ChIP)-qPCR assay showed that CDX2 exhibited a high capacity of binding to miR-615-5p promoter region compared to the negative control. Real-time PCR and western blotting analyses revealed that CDX2 overexpression caused inflation of miR-615-5p and depression of insulin-like growth factor 2 (IGF2), a direct target of miR-615-5p. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and EdU approaches showed that CDX2 overexpression markedly suppressed pancreatic adenocarcinoma cell proliferation. CDX2 small interfering RNA (siRNA) transfection showed an opposite effect on gene expression and cell proliferation to that of CDX2 overexpression. Collectively, CDX2 inhibited pancreatic adenocarcinoma cell proliferation via promoting tumor suppressor miR-615-5p. Our findings suggested a potential molecular target for pancreatic adenocarcinoma therapy.
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