This paper discusses how the coupling capacitor of transmission line in high speed circuit is optimized. Take the 400G bit error tester as an example, the characteristic impedance of the transmission line before and after adding the coupling capacitor is simulated. In order to reduce impact on signal integrity caused by impedance mismatch, the treatment method of voiding is obtained through the impedance calculation formula. By comparing several different voiding scheme proposed by predecessors, on the basis of the previous work, the hollowing treatment is further optimized to study the effect of hollowing size on signal integrity. Then HFSS software is used for modeling and simulation, and the insertion loss and return loss under several different schemes are calculated. It is found that the size of the hollowing process will have a certain impact on the integrity of the signal, and to a certain extent, the larger the hollowing size, the smaller the insertion loss and return loss.
Pattern knowledge plays an important role in text recognition. The classification and segmentation system under a large concept system needs to focus on the problem of pattern knowledge mining. The relationship between labels and concepts is divided into three types: equal, belong, and irrelevant. There is a need for a classification method to accurately classify the relationships between label and concept, and between concepts. In this paper, the classification algorithm proposed is SA-KNN-SVM, which can retain the advantages of fast K-nearest neighbor (KNN) model training time and good prediction effect while improving accuracy. The SA-KNN algorithm aims to continuously adjust the parameters and determine the number of the iterations through loop iterations, and then quickly find the accuracy rate corresponding to the different K values. SVM has many kinds of kernel functions, select the kernel function that makes the classification accuracy higher. And find the best parameters by looping an iterative grid search. The experimental results of the proposed algorithm show significant improvement in classification accuracy and processing time.
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