Review and perspective on encrypted traffic identification research

Considering the importance of encrypted traffic identification technology and existing research work,first,the type of encrypted traffic identification according to the demand of traffic analysis were introduced,such as protocols,applications and services.Second,the encrypted traffic identification technology was summarized,and identification technology was compared from multiple views.Third,the deficiencies and the affecting factors of the existing encrypted traffic identification technologies were induced,such as tunneling,traffic camouflage technology,new protocols of HTTP/2.0 and QUIC.Finally,prospect trends and directions of future research on encrypted traffic identification were discussed.     

基于物联网无线通信的智能感知校园服务平台

以物联网技术为核心,整合目前校内分离的各个信息化系统,建立校内资源共享的综合信息门户。运用RFID技术、传感网技术、云计算等多种先进技术建设基于物联网理念的感知校园服务平台。     

The application of Bayesian network in Uncertainty management

Uncertainty management is playing an important role in many fields,especially in AI(artificial intelligence).This paper introduces the concept of probability and the Bayesian Network which are widely used in uncertainty management field.Additionally,two examples are completed and analyzed in the paper.     

k-Edge Failure Resilient Network Design

We design a network that supports a feasible multicommodity flow even after the failures of any k edges. We present a mixed-integer linear program (MILP), a cutting plane algorithm, and a column-and-cut algorithm. The algorithms add constraints to repair vulnerabilities in partial network designs. Empirical studies on previously unsolved instances of SNDlib demonstrate their effectiveness.     

Large scale automatic image annotation based on convolutional neural network

Automatic image annotation is one of the most important challenges in computer vision, which is critical to many real-world researches and applications. In this paper, we focus on the issue of large scale image annotation with deep learning. Firstly, considering the existing image data, especially the network images, most of the labels of themselves are inaccurate or imprecise. We propose a Multitask Voting (MV) method, which can improve the accuracy of original annotation to a certain extent, thereby enhancing the training effect of the model. Secondly, the MV method can also achieve the adaptive label, whereas most existing methods pre-specify the number of tags to be selected. Additionally, based on convolutional neural network, a large scale image annotation model MVAIACNN is constructed. Finally, we evaluate the performance with experiments on the MIRFlickr25K and NUS-WIDE datasets, and compare with other methods, demonstrating the effectiveness of the MVAIACNN.     

Ultrasonic treatment of water contaminated with various pollutants onto copper nanowires loaded on activated carbon using response surface methodology and artificial intelligent

In this study, the potential application of copper nanowires loaded on activated carbon for simultaneous removal of Disulfine blue (DB), Crystal violet (CV) and Sunset yellow (SY) has been described. The relation between adsorption properties with variables such as solution pH, adsorbent value, contact time and initial dyes concentration was investigated and optimized. A three‐layer artificial neural network (ANN) model was utilized to predict dyes removal (%) by adsorbent following conduction of experiments. The training of network at above mention experimental data confirms its ability to forecast the removal performance with a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm and tangent sigmoid transfer function (tansig) with 16 neurons at the hidden layer was applied. Parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) and desirability function. The accuracy of ANN was judged according to both MSE and AAD% at optimal conditions and results indicate its superiority to RSM model in term of higher R² and lower AAD% values. This observation was also corroborated by the parity plots between the predicted and experimental values. The ANN model was better in both data fitting and prediction capability in comparison to RSM model.     

PCB行业实施清洁生产取得的成效（2）——以汕头超声印制板（二厂）有限公司为例

汕头超声印制板（二厂）公司藉“节能减排”的契机，打破经验思维的束缚，把汕头超声印制板公司的成果和理念成功运用到本公司的清洁生产工作中，创造了良好的经济效益和环境效益。     

基于贝叶斯理论的神经网络算法研究

本文研究了基于贝叶斯理论的神经网络算法,采用贝叶斯方法来确定超参数,使得神经网络在训练过程中能自适应地调节超参数的大小,得出目标函数的最优化参数,从而达到提高神经网络泛化能力的目的.还编制仿真软件,验证了该算法的可行性.     

WDM-PON光接入网技术研究

介绍了WDM-PON系统的基本原理及技术优势,重点分析和探讨了其中所涉及到的关键技术,最后分析了WDM-PON的应用前景。