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.     

恒温供水装置的自动控制

恒温水是能效标定的重要组成部分.针对目前国内外的研究现状,提出了一种新型的自动恒温供水系统.系统采用制冷半导体和电加热器实现对温度的调节,同时利用积分分离PID抑制系统超调,并应用固态继电器构建开关功放,最后通过ARM LPC2210控制器对系统进行了闭环自动控制.实验结果表明,系统出水温度精确、稳定、可靠;系统在暖水炉、空调等能效标定中将得到广泛应用.     

Image morphing with conformal welding

We address the issue of deforming an image of a source object to that of a target. Previous works including barycentric coordinates and functional maps can hardly enforce shape consistency, especially for the objects with complex nested shape components. We leverage the conformal welding theory that maps 2D shapes (planar contours) to the automorphisms of the unit circle, named shape signatures. Conformal welding enables us to apply the Laplacian constraint to deformations in the signature space (or unit circle domain), which renders efficiency and flexibility. Additionally, we are able to fully reconstruct complex shape contours from deformed signatures, and hence generate the morphed images for target shapes. The experiments on complex shape contours and facial images, where multiple components exist, validate the effectiveness of the proposed approach.     

Observer‐based fault estimators using iterative learning scheme for nonlinear time‐delay systems with intermittent faults

This paper deals with the intermittent fault estimation problem for a class of nonlinear time‐delay systems with measurement noise. The time delays are assumed to occur in state vector, nonlinear term as well as output vector, thus reflecting the time delays influence in reality more closely. The aim of the problem is to estimate the intermittent fault by using iterative learning scheme, with the property of index, hence attenuating the influence from measurement noise. Different from existing fault estimating schemes, the state error information and fault estimating information in the previous iteration are used in the current iteration to improve the estimating results. The stability and convergence of iterative learning observer and uniform boundedness of dynamic error system are achieved by using Lyapunov function and optimal function design. Simultaneously, an improved sufficient condition for the existence of such an estimator is established in terms of the linear matrix inequality by the Schur complements and Young relations. Furthermore, the results are both suited for the systems with time‐varying delay and the systems with constant delay. Finally, two numerical examples are proposed to illustrate the effectiveness of the proposed method, and a comparability example is presented to demonstrate its superiority. Copyright © 2017 John Wiley & Sons, Ltd.     

A fast chaos-based image encryption scheme with a novel plain image-related swapping block permutation and block diffusion

In the paper, a fast image encryption scheme with block permutation and block diffusion is introduced. Considering the increasing size of the image, encryption process is manipulated by block, and the encryption of every block image consists of block permutation and block diffusion. A novel plain image-related swapping block permutation strategy is presented, the swapping operation of the block image is controlled by the random chaotic sequences, and the initial value and parameter of the chaotic system are produced by SHA 256 hash value of the plain image, thus our algorithm is highly sensitive to changes of the plain image. Diffusion method within the block image (DMWBI) and diffusion method between two block images (DMBTBI) are presented to effectively eliminate the correlation of adjacent pixels in the plain image. The correlated chaos is employed to enhance the relationship of chaos, and take fully use of chaotic maps. One-dimensional Logistic-Sine System (LSS) is used to generate pseudo-random sequences during the whole encryption process. Experiment results and security analysis have proved the proposed image encryption algorithm is secure and effective.