National University of Defense Technology

Hunan University

China North Nuclear Fuel Co., Ltd

Hang Zhang

Bin Liu

Zhaoyun Ding

Xianqiang Zhu

Cheng Zhu

Yi Liu

Weike Liu

Chao Jiang

Jian Liu

Yun Zhou

Songping Huang

Ning Chen

Deqi Cao

Rong Li

Zhaochuan Hu

Fei Wang

Yan Xiong

A method for detecting the thicknesses of coating layers of nuclear fuel particles, comprising: collecting a surface image of a sample to be tested under a first amplification factor (S); determining a testable particle in the surface image (S); collecting a cross section image of the testable particle under a second amplification factor, wherein the second amplification factor is greater than the first amplification factor (S); and determining the center of the testable particle in the cross section image and profile lines of all coating layers, and determining the thickness of each coating layer according to the center and the profile lines of each coating layer (S). Also provided is a device for detecting the thicknesses of coating layers of the nuclear fuel particles.

Method for detecting thicknesses of coating layers of nuclear fuel particles

The disclosure relates to a method and device for identifying unknown traffic data based on a dynamic network environment. The method includes following steps. The known traffic in the network data is classified by using the known network traffic classification model, then the preliminary determination is performed according to a classification prediction result, network data preliminarily determined as the unknown traffic data is classified by using the adaptive clustering method, and then respective classes are identified by using a similarity coefficient estimation method so as to identify the classes of the malicious traffic and the normal traffic, that is, to further identify and learn the unknown traffic data, and transform it into known traffic data, and then the known network traffic classification model is trained and updated again with the new known traffic data.

Method and device for identifying unknown traffic data based dynamic network environment

Disclosed are a network traffic analysis method and a device based on multi-source network traffic data. The method includes: deploying a pre-training classifier pool in a network stream data source; receiving multi-source network stream data at a current moment for each data source, classifying the multi-source network stream data through an online classifier, performing feature processing and transformation on data collected by each network stream data source at each preset time interval, and transmitting processed traffic data features and a feature transformation matrix to a traffic drift detection module. The traffic drift detection module contains historical concept data to detect a concept drift according to the traffic data features, the feature transformation matrix and the historical concept data; if the concept drift is detected, the online classifier deployed by multiple sources is reset. This method is used for continuous real-time and accurate analysis of the multi-source network traffic data.

Network traffic analysis method and device based on multi-source network traffic data

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Changsha, China

Hang Zhang