VPDN在虚拟校园网的应用

本文主要介绍利用虚拟专用拨号网(VPDN)实现虚拟校园网的技术方案,并讨论了虚拟校园网系统的安全性。     

酶的固载技术及纳米电化学酶生物传感器的研究进展

该文较详细地阐述了酶的固定化方法和各自的优缺点,以及综述了近年来各种类型的纳米材料在电化学酶生物传感器中的应用,并对今后的工作进行了展望.     

关联规则挖掘在教师成长中的应用

本文在对数据挖掘相关技术、关联规则挖掘算法进行深入研究的基础上,归纳总结了基于粗糙集理论的关联规则挖掘模型和属性约简算法,并将其应用于高校教师的成长中。     

基于优化算法的核函数参数选择的研究

尽管支持向量机在许多问题上有着良好的表现,但是其参数和核函数的参数选取问题依然亟待解决。以往多采用优化算法进行参数选取,但也需要预先经验地获得核函数的参数的选取范围。在介绍结构风险最小化原则及支持向量机算法的基础上,给出了基于优化算法的支持向量机参数选取的一般性算法。由于径向基核函数（RBF）的参数取值大小的不同,可导致其性质和作用不同,为此提出了一种分段函数对RBF的参数进行选择的方法,该方法使得RBF的参数取大值和小值的概率均等。由此可不必预先经验地指定RBF的参数的选取范围,依然可以优化获得最优的参数。通过对头部组织电导率估算问题进行对比研究,取得了良好的效果,验证了该方法的有效性。     

基于壳聚糖-硅溶胶复合膜固载甲苯胺蓝及纳米金的新型电流型过氧化氢生物传感器的研究

该文基于有机-无机复合膜和纳米技术研制了一种新型的高灵敏度的电流型过氧化氢(H2O2)生物传感器.首先将壳聚糖(CS)和氨丙基三乙氧基硅烷(APTES)交联制得复合膜(CSHMs),并以该膜固载甲苯胺蓝(TB)和纳米金(GNPs),然后将HRP与CSHMs-TB-GNPs混合滴涂在玻碳电极的表面,最后在其表面吸附一层Nafion保护膜,制得Nafion/CSHMs-TB-GNPs-HRP/GCE修饰电极.Nafion膜可以减少HRP的泄漏,同时增强了传感器的抗干扰能力.用紫外吸收光谱法分析了修饰膜成分的组成,用循环伏安法对修饰电极进行了表征,并用计时电流法对H2O2传感器的性能进行了研究.实验结果表明,在最佳实验条件下,H2O2浓度在7.0×10-7～2.3×10-3mol/L范围内与其还原峰电流呈现良好的线性关系,检测下限为2.4×10-7mol/L(信噪比3).     

Windows操作系统EFS加密方式应用及解析

本文介绍了Windows操作系统中EFS加密方式的优点、操作步骤、加密原理及解密方法,并详细列举了5个重点注意事项,为全面理解Windows操作系统中使用的EFS加密手段提供了帮助。     

An improved constraint satisfaction adaptive neural network for job-shop scheduling

This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.     

Using jump-diffusion modeling for valuing real options in infrastructure projects

In infrastructure financed projects, in order to attract private investors, host governments often provide some guarantees. This paper develops a value model of minimum revenue guarantee with multiple-exercise real options under the impact of the emergency incident. The model is applied to infrastructure financed projects using the minimum revenue guarantee under simulation. The simulation results indicate that, before quantifying the value of the minimum revenue guarantee, it is necessary to forecast the jump degree and intensity of the emergency incident, as well as prevent and control risks arising from such emergencies. Otherwise, underestimation of the guarantee value will occur and the government will have to bear huge debt in this condition. We also analyze the dependence of the guaranteed value on the minimum guaranteed revenue level, initial revenue and number of exercise rights. For various conditions, the diagrams of the guaranteed value are also presented.     

Entropy Optimization Filtering for Fault Isolation of Nonlinear Non-Gaussian Stochastic Systems

In this paper, the fault isolation (FI) problem is investigated for nonlinear non-Gaussian systems with multiple faults(or abrupt changes of system parameters) in the presence of noises. By constructing a filter to estimate the states, the FI problem can be reduced to an entropy optimization problem subjected to the non-Gaussian estimation error systems. The design objective for the FI purpose is that the entropy of the estimation error is maximized in the presence of diagnosed fault and is minimized in the presence of the nuisance faults or noises. It is shown that the error dynamics is represented by a nonlinear non-Gaussian stochastic system, for which new relationships are applied to formulate the probability density functions (PDFs) of the stochastic error in terms of the PDFs of the noises and the faults. The Renyi's entropy has been used to simplify the computations in the filtering for the recursive design algorithms. It is noted that the output can be supposed to be immeasurable (but with known stochastic distributions), which is different from the existing results where the output is always measurable for feedback. Finally, simulations are given to demonstrate the effectiveness of the proposed data-driven FI filtering algorithms.