Zhu Yuelong

A Novel Approach to the Similarity Analysis of Multivariate Time Series and Its Application in Hydrological Data Mining

There has been large amount of hydrological data collected by various sensors during the last years and how to discover the hidden knowledge among these data has caused more and more attention from diverse fields, such as hydrologist and researchers from data mining. This paper deals with similarity mining from hydrological time series and concentrates itself on the similarity analysis of multivariate time series (MTS). A novel similarity measure has been put forward, which is based on the well-known BORDA count in multiple classifier system. Firstly, dimension reduction is adaptively conducted according to the target data complexity; then the similarity of single time series is computed and lastly, the overall similarity of the MTS is obtained by synthesizing each of the single similarity based on BORDA count. Experiments on the similarity analysis of water level data of TAIHU Lake and historical flood data from YIFENG Basin have shown the feasibility and effectiveness of the proposed method.

Study on Hydrology Time Series Prediction Based on Wavelet-neural Networks

To improve the performance of the model of wavelet-neural network for complex time series, a novel multi-factor prediction model is proposed, which inherits the advantage of previous wavelet-neural network and makes full use of the characteristics of different time series on different time scales. In addition, this paper also put forwards a criterion for the selection of wavelet functions, which is based on the weighted sum of the linear correlation coefficients of different time series with the target peer series. At last, the proposed model is applied to the prediction of daily discharge of WANGJIABA station at HUAIHE River and the experimental results show that higher accuracy is achieved, compared with previous models.

A new proxy signature scheme with message recovery using self-certified public key

A proxy signature scheme with message recovery using self-certified public key is proposed, which withstands public key substitution attacks, active attacks, and forgery attacks. The proposed scheme accomplishes the tasks of public key verification, proxy signature verification, and message recovery in a logically single step. In addition, the proposed scheme satisfies all properties of strong proxy signature and does not use secure channel in the communication between the original signer and the proxy signature signer.

Hybrid color image segmentation based fully automatic chroma-keying system with cluttered background

In this paper, we present a fully automatic digital chroma-keying system, which is based on the integration of color image segmentation algorithm and improved alpha estimation technique. Chroma-keying is a critical technology in virtual studio system. When used with cluttered background, it calls for much intelligence. According to the characteristics of frame images in the target application, a hybrid color image segmentation algorithm is put forward, which makes good use of both chromatic and luminance information. Then, refinement measures are further taken to deal with the color distribution in the neighborhood of the boundary through modified Ruzon-Tomasi alpha estimation algorithm. In contrast to the previously reported methods, our system needs no human interaction in the whole procedure. Experimental results on China sports lottery TV programs show that the proposed fully automatic keying system is viable and can be applied to the real program post production process of TV stations.

一种环境因素敏感的Web Service QoS监控方法

面向服务系统的执行能力依赖第三方提供的服务，在复杂多变的网络环境中，这种依赖会带来服务质量（QoS）的不确定性.而QoS是衡量第三方服务质量的重要标准，因此，有效监控QoS是对Web服务实现质量控制的必要过程.现有监控方法都未考虑环境因素的影响，比如服务器位置、用户使用服务的位置和使用时间段负载等，而这些影响在实际监控中是存在的，忽略环境因素会导致监控结果与实际结果有悖.针对这一问题，提出了一种基于加权朴素贝叶斯算法wBSRM（weightednaive Bayes running monitoring）的Web Service QoS监控方法.受机器学习分类方法的启发，通过TF-IDF（term frequency-inverse document frequency）算法计算环境因素的影响，通过对部分样本进行学习，构建加权朴素贝叶斯分类器.将监控结果分类，满足QoS标准为c0，不满足QoS标准为c1，监控时调用分类器得到c0和c1的后验概率之比，对比值进行分析，可得监控结果满足QoS属性标准、不满足QoS属性标准和不能判断这3种情况.在网络开源数据以及随机数据集上的实验结果表明：利用TF-IDF算法能够准确地估算环境因子权值，通过加权朴素贝叶斯分类器，能够更好地监控QoS，效率显著优于现有方法.