In order to solve interoperability of the distribution automation devices and Plug and Play issues, propose intelligent power distribution model based on IEC61850, Stratifie according to the method of information: Master level,feeder layer,the terminal layer,device layer, layer monitoring. Use the face of the object model of the distribution automation IED, at the same time, use abstract communication service independent of the specific communication technology model ACSI, Establish distribution network automation system communication system Follow the IEC61850 standard.
Theoretical analysis and practice experience have shown that, the vibration signal on power transformer surface can be used to analyze and judge the work state of the winding and iron core. For vibration analysis, to extract a precise feature of the vibration signal is a basic work. Based on wavelet multiresolution analysis, three-dimensional surface vibration signal of the running power transformer is analyzed. Based on the Parseval theorem, the feature of frequency bands-energy in X, Y, Z directions is computed and compared. It provides some reference for the power transformer vibration analysis.
Low anterior rectal resection is an effective way to treat rectal cancer at present, but it is easy to cause low anterior resection syndrome after surgery; so, a comprehensive diagnosis of defecation and pelvic floor function must be carried out. There are few studies on the classification of diagnoses in the field of intestinal diseases. In response to these outstanding problems, this research will focus on the design of the intestinal function diagnosis system and the image processing and classification algorithm of the intestinal wall to verify an efficient fusion method, which can be used to diagnose the intestinal diseases in clinical medicine. The diagnostic system designed in this paper makes up for the singleness of clinical monitoring methods. At the same time, the Res-SVDNet neural network model is used to solve the problems of small intestinal image samples and network overfitting, and achieve efficient fusion diagnosis of intestinal diseases in patients. Different models were used to compare experiments on the constructed datasets to verify the applicability of the Res-SVDNet model in intestinal image classification. The accuracy of the model was 99.54%, which is several percentage points higher than other algorithm models.
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