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A Novel Adaptive Load Shedding Scheme for Data Stream Processing
Yunyi Zhang,Deyun Zhang,Wei Wei 보안공학연구지원센터 2008 International Journal of Software Engineering and Vol.2 No.2
In this paper, we present a novel feedback control-based load shedding scheme for data stream processing. Firstly we apply system identification to establish a dynamic model to describe data stream management system (DSMS), which enables us to analyze DSMS quantitatively. Then, based on the model, we use the Root Locus method to design the PI controller with proven performance guarantees. Theoretic analysis and experimental results demonstrate that our approach is robust even when system load changes frequently. Comparing to existing strategies, our approach also achieves significantly better performance.
Research on Hadoop Identity Authentication Based on Improved Kerberos Protocol
Daming Hu, Deyun Chen,Yuanxu Zhang,Shujun Pei 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.11
This paper researches the authentication mechanism of Kerberos protocol under HDFS, and points out the problems that identity authentication mechanism of Kerberos protocol faced in HDFS cluster environment: time synchronization, KDC security, dictionary attacks and denial mechanism. Aiming at these security problems, firstly, this paper provides an overview of the authentication process of the current Kerberos protocol under HDFS cluster environment; secondly, it modifies Kerberos protocol by using public key encryption and data signature mechanism; lastly, it provides the authentication process of improved Kerberos protocol in HDFS environment. Comprehensive analysis shows that both safety and time efficiency of the improved Kerberos protocol are improved compared with the existing identity authentication mechanism. It provides a more reliable and efficient identity authentication solution for HDFS cluster.
Improvement of SVM Image Reconstruction Algorithm in ECT System
Li Yan,Song Haifeng,Zhang Guangwu,Chen Deyun,Wang Zhao,Cui Peng 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.3
Due to the problem of low imaging accuracy and slow imaging speed when applying SVM image reconstruction algorithm in ECT system to dealing with a large amount of sample data set, the method of combining feature dimension reduction with SVM algorithm is proposed. This method classifies the sample data by using the way of clustering and extracts the feature parameter, finds out the connection between each sample and the feature, and deals the sample data with dimension reduction, thus finally getting the high-quality training sample. Then it trains the simplified sample data by applying SVM algorithm and obtains decision function, then the decision function is used to predict and image. The experimental results of image reconstruction show that this method greatly reduces the running time and improves the accuracy of imaging compared to using the SVM algorithm alone.
Optimal Scheme of Retinal Image Enhancement using Curvelet Transform and Quantum Genetic Algorithm
( Zhixiao Wang ),( Xuebin Xu ),( Wenyao Yan ),( Wei Wei ),( Junhuai Li ),( Deyun Zhang ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.11
A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.