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.     

Corrective classification: Learning from data imperfections with aggressive and diverse classifier ensembling

Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise. The essential goal is to reduce noise impact and eventually enhance the learners built from noise-corrupted data. In this paper, we propose a novel corrective classification (C2) design, which incorporates data cleansing, error correction, Bootstrap sampling and classifier ensembling for effective learning from noisy data sources. C2 differs from existing classifier ensembling or robust learning algorithms in two aspects. On one hand, a set of diverse base learners of C2 constituting the ensemble are constructed via a Bootstrap sampling process; on the other hand, C2 further improves each base learner by unifying error detection, correction and data cleansing to reduce noise impact. Being corrective, the classifier ensemble is built from data preprocessed/corrected by the data cleansing and correcting modules. Experimental comparisons demonstrate that C2 is not only more accurate than the learner built from original noisy sources, but also more reliable than Bagging [4] or aggressive classifier ensemble (ACE) [56], which are two degenerated components/variants of C2. The comparisons also indicate that C2 is more stable than Boosting and DECORATE, which are two state-of-the-art ensembling methods. For real-world imperfect information sources (i.e. noisy training and/or test data), C2 is able to deliver more accurate and reliable prediction models than its other peers can offer.     

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.     

A robust scheduling method based on a multi-objective immune algorithm

A robust scheduling method is proposed to solve uncertain scheduling problems. An uncertain scheduling problem is modeled by a set of workflow models, and then a scheduling scheme (solution) of the problem can be evaluated by workflow simulations executed with the workflow models in the set. A multi-objective immune algorithm is presented to find Pareto optimal robust scheduling schemes that have good performance for each model in the set. The two optimization objectives for scheduling schemes are the indices of the optimality and robustness of the scheduling results. An antibody represents a resource allocation scheme, and the methods of antibody coding and decoding are designed to deal with resource conflicts during workflow simulations. Experimental tests show that the proposed method can generate a robust scheduling scheme that is insensitive to uncertain scheduling environments.     

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.     

大纯滞后过程的采样控制及其工程实现中的若干问题

提出了采样控制机制的新观点,介绍控制器参数确定的新方法,分析了采样控制的质量,指出了在工程实施应考虑的若干问题。