三网融合中的安全风险及防范技术研究

文章分析了三网融合中可能出现的网络和内容安全的隐患,提出了通过认证机制、防护机制、监控机制及信任机制等技术来应对。同时,文章还深入分析了三网融合下信息安全监管问题,提出继续加强融合法律法规建设,适时成立统一融合监管机构,建立融合统一监管标准体系,以及全面纳入信息安全保障体系等一系列三网融合信息安全监管的建议。     

互联网用户信息泄露事件探究

文章通过对当前互联网用户信息泄露的严峻形势及互联网用户信息情况进行介绍,分析了造成用户信息泄露的原因,阐述了用户信息泄露可能带来的危害,进而提出了对于中国互联网用户信息的立法、管理、安全意识及打击等方面的建议。     

矿用隔爆兼本质安全型不间断直流稳压电源的设计

基于本质安全型电源的原理及特点,提出了一种矿用隔爆兼本质安全型不间断直流稳压电源的设计方案,详细介绍了该电源主要电路的设计。该电源采用LM317可调稳压芯片实现稳压功能,具有使用方便、电路简单、变换效率高、成本低等优点。测试结果表明,该电源达到了本安标准,满足了安全型电气设备的需求。     

一种测量工业环境感应传导干扰的方法

针对采用干扰测量仪或频谱分析仪只能测量工业环境中干扰源的辐射干扰强度的问题,提出了一种测量工业环境感应传导干扰的方法。该方法采用HBYV非双绞2芯线作为干扰接收体,采用数字示波器记录干扰波形的最大幅值和单个脉冲的最大强度,通过与标准干扰发生器感应到HBYV非双绞2芯线上的干扰信号大小进行比较,确定干扰强度。以瞬变脉冲群干扰强度和浪涌干扰强度的测量为例,详细介绍了该方法的具体实现。     

基于FPGA的MEMS陀螺仪SAR150实时数据采集系统设计

针对传统MEMS陀螺仪输出信息为模拟量且易受干扰、噪声较大的问题,结合SAR150陀螺仪可以直接输出数字信号的特点,提出了一种新型的数字陀螺仪实时数据采集系统;该系统以FPGA为主控制器,通过SPI接口完成对MEMS陀螺仪SAR150的控制与数据的传输,实现了对陀螺仪输出信号的采集、实时传输及存储;通过将所研制的数据采集系统应用于SAR150陀螺仪测量角速率实验,并对测得数据进行误差分析,实验结果表明:数据采集系统采集输出的角速率值与理论值的绝对误差标准差均小于0.2°/s;且该系统的可靠性高、实时性好,为后续的陀螺仪测试及标定奠定了基础,有一定的工程应用价值。     

New results on robust output regulation of nonlinear systems with a nonlinear exosystem

The global robust output regulation problem for nonlinear plants subject to nonlinear exosystems has been a challenging problem and has not been well addressed. The main difficulty lies in finding a suitable internal model. The existing internal model for handling the nonlinear exosystem is not zero input globally asymptotically stable, and can only guarantee a local solution for the output regulation problem. In this paper, we first propose a new class of internal models, which is guaranteed to exist under the generalized immersion condition. An advantage of this internal model is that it is zero input globally asymptotically stable. This fact will greatly facilitate the global stabilization of the augmented system associated with the given plant and the internal model. Then we will further utilize this class of internal models to solve the global robust output regulation problem for output feedback systems with a nonlinear exosystem. Copyright © 2011 John Wiley & Sons, Ltd.     

Feature extraction using orthogonal discriminant local tangent space alignment

A novel algorithm called orthogonal discriminant local tangent space alignment (O-DLTSA) is proposed for supervised feature extraction. Derived from local tangent space alignment (LTSA), O-DLTSA not only inherits the advantages of LTSA which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. The experimental results of applying O-DLTSA to standard face databases demonstrate the effectiveness of the proposed method.     

Stochastic TCP friendliness: Expanding the design space of TCP-friendly traffic control protocols

While the current definition of TCP friendliness has enabled a wide variety of traffic control protocols other than TCP, it still considerably restricts the design space of TCP-friendly traffic control protocols. For example, some multimedia streaming applications prefer a smooth sending rate on a time scale of tens of seconds, however, a UDP flow maintaining such a long smoothness time scale is naturally not TCP friendly by the current definition. In this paper, we propose an innovative method to relatively compare the degrees of TCP friendliness of different traffic control protocols, and use it to define a new class of TCP friendliness definitions, called stochastic TCP friendliness (STF). STF greatly expands the design space of TCP-friendly traffic control protocols, while still effectively maintaining the desired fairness of the Internet. To demonstrate the expanded design space, we also develop a new congestion control protocol, called TCP-friendly CBR-Like Rate Control (TFCBR), for multimedia streaming applications which do not require a high sending rate but prefer a smooth sending rate on a time scale of tens of seconds. TFCBR is stochastically TCP friendly, however, it is not TCP friendly by the current definition.     

Financial distress prediction using support vector machines: Ensemble vs. individual

Financial distress prediction (FDP) is of great importance to both inner and outside parts of companies. Though lots of literatures have given comprehensive analysis on single classifier FDP method, ensemble method for FDP just emerged in recent years and needs to be further studied. Support vector machine (SVM) shows promising performance in FDP when compared with other single classifier methods. The contribution of this paper is to propose a new FDP method based on SVM ensemble, whose candidate single classifiers are trained by SVM algorithms with different kernel functions on different feature subsets of one initial dataset. SVM kernels such as linear, polynomial, RBF and sigmoid, and the filter feature selection/extraction methods of stepwise multi discriminant analysis (MDA), stepwise logistic regression (logit), and principal component analysis (PCA) are applied. The algorithm for selecting SVM ensemble's base classifiers from candidate ones is designed by considering both individual performance and diversity analysis. Weighted majority voting based on base classifiers’ cross validation accuracy on training dataset is used as the combination mechanism. Experimental results indicate that SVM ensemble is significantly superior to individual SVM classifier when the number of base classifiers in SVM ensemble is properly set. Besides, it also shows that RBF SVM based on features selected by stepwise MDA is a good choice for FDP when individual SVM classifier is applied.     

Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis

General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.