云存储安全增强系统的设计与实现

云存储是一种新型的网络存储形式,并逐步为大家所接受,企业和个人用户都开始使用云存储作为其网络存储媒介。目前很多著名的IT企业都推出了云存储服务,其中Amazon公司推出的SimpleStorageService（S3）就是商用云存储服务的典型代表。随着云存储的广泛使用,云存储中数据的安全问题,如数据泄漏和数据篡改,也成了用户广泛关注的问题。文章基于Amazons3的云存储服务,设计并实现了一款云存储安全增强系统,对用户上传至Amazons3的数据进行加密保护,使得文件以密文形式存储于Amazons3,可以有效防止数据在传输和存储过程中的泄漏;同时系统还对从s3下栽的文件进行完整性校验,检测其内容与上传时是否一致,以防止文件被篡改;最后,系统还提供了多用户访问控制支持,多个用户可以共享同一个S3账号,同时保证各自存储的内容互相隔离,禁止一个用户非授权访问其他用户存储的文件。     

基于WebGIS井下WSN定位数据分析与管理平台设计与开发

在煤矿井下WSN定位跟踪应用中,目标区域通常布置较多的传感器节点,这些节点布置以后,没有人为看管,且受环境影响大,易于出错;文中将针对WSN在煤矿井下的定位应用,利用WebGIS技术开发一种煤矿井下传感器节点分布、链路状态以及移动目标运动轨迹可视化显示的网络数据分析与管理平台;实验结果表明,通过该平台可以远程利用Web浏览器方便、快捷地管理和维护WSN,使WSN在煤矿井下发挥强大生命力。     

雾天车牌识别中的图像增强研究

通过对车牌图像的研究与分析,提出了一种针对雾天车牌识别系统图像增强预处理的方法。该方法是在车牌图像的预处理过程中,采用了先进的暗原色先验去雾算法,对采集到的雾天车牌图像有很好的增强效果。实验结果验证该算法对雾天的车牌图像的识别率有很大的提高。     

浅析《计算机应用基础》课程教学设计方案

通过对《计算机应用基础》公共基础课的课程分析,从课程性质和任务、教学内容的安排、课程目标的设计、课程教学媒体的设置、教学过程及学习建议这五个方面阐述了《计算机应用基础》课程教学的设计思路。     

Augmented Lyapunov functional approach for stability of neutral systems with mixed delays

This paper is concerned with the neutral‐delay‐dependent and discrete‐delay‐dependent stability for uncertain neutral systems with mixed delays and norm‐bounded uncertainties. Through constructing a new augmented Lyapunov‐Krasovskii functional and proving its positive definiteness, introducing some slack matrices and using integral inequality, the improved delay‐dependent stability criteria are derived in terms of linear matrix inequalities. Numerical examples are given to illustrate the significant improvement on the conservatism of the delay bound over some existing results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Feedback predictive control for constrained fuzzy systems with Markovian jumps

In this paper, a feedback model predictive control method is presented to tackle control problems with constrained multivariables for uncertain discrete‐time nonlinear Markovian jump systems. An uncertain Markovian jump fuzzy system (MJFS) is obtained by employing the Takagi‐Sugeno (T‐S) fuzzy model to represent a discrete‐time nonlinear system with norm bounded uncertainties and Markovain jump parameters. To achieve more generality, the transition probabilities of the Markov chain are assumed to be partly unknown and partly accessible. The predictive formulation adopts an on‐line optimization paradigm that utilizes the closed‐loop state feedback controller and is solved using the standard semi‐definite programming (SDP). To reduce the on‐line computational burden, a mode independent control move is calculated at every sampling time based on a stochastic fuzzy Lyapunov function (FLF) and a parallel distributed compensation (PDC) scheme. The robust mean square stability, performance minimization and constraint satisfaction properties are guaranteed under the control move for all admissible uncertainties. A numerical example is given to show the efficiency of the developed approach. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

RBF neural networks-based robust adaptive tracking control for switched uncertain nonlinear systems

In this paper, a robust adaptive H∞ control scheme is presented for a class of switched uncertain nonlinear systems. Radical basis function neural networks (RBF NNs) are employed to approximate unknown nonlinear functions and uncertain terms. A robust H∞ controller is designed to enhance robustness due to the existence of the compound disturbance which consists of approximation errors of the neural networks and external disturbance. Adaptive neural updated laws and switching signals are deducted from multiple Lyapunov function approach. It is proved that with the proposed control scheme, the resulting closed-loop switched system is robustly stable and uniformly ultimately bounded (UUB) such that good capabilities of tracking performance is attained and H∞ tracking error performance index is achieved. A practical example shows the effectiveness of the proposed control scheme.     

Improved stability conditions for systems with interval time-varying delay

The paper is concerned with the stability of linear systems with interval time-varying delay. Through constructing a new augmented Lyapunov-Krasovskii functional (LKF) which contains some quadruple-integral terms and estimating the time derivative of the LKF less conservatively, new stability criteria are derived without introducing any free matrices. Moreover, by proving the positive definiteness of the LKF with some integral inequalities, the constraints on some functional parameters are relaxed and the conservatism of the obtained results are further reduced. Numerical examples are also given to demonstrate the effectiveness and reduced conservatism of the obtained results.     

一种改进的基于多项式滤波的稳态检测方法

化工过程的数据中常常含有较多的随机误差和粗差干扰,导致传统的稳态检测方法无法得到准确的结果,从而降低系统故障诊断的可靠性。针对实际的工业过程数据,提出一种融合自适应平滑技术的稳态检测方法,该方法首先以基于导数分析的自适应平滑算法进行降噪处理,消除随机误差的影响,然后引入阈值拟合技术进一步抑制粗差干扰,以多项式滤波方法对数据进行稳态检测,根据测量信号的趋势特征确定过程是否处于稳态。仿真实验研究表明:融合自适应平滑技术的稳态检测方法能够克服传统稳态检测方法中随机误差和粗差干扰对检测结果的影响,进而显著提高稳态检测处理的准确性,检测结果明显优于传统的基于多项式滤波的检测方法。     

偏最小二乘回归在气温预测中的研究与应用

气温的预测要考虑多重气象因子,然而各气象因子间的多重相关性导致普通的多元回归模型失真,预测精度降低。为了解决以上问题,本研究采用偏最小二乘回归(Partial Least Squares Regression,简称PLS)建模,根据实际建模需要对算法进行改进。为了验证该模型,本研究以成都市10月份气温场为预报场,并结合成都市13个自动站气温数据,提取预报所需主要因子,进行PLS回归建模。实验表明该模型有较好的预测能力。