Tuning of multi-loop PI controllers based on gain and phase margin specifications

In this paper, a single-iteration strategy is proposed for the design of a multi-loop PI controller to achieve the desired gain and phase margins for two-input and two-output (TITO) processes. To handle loop interactions, a TITO system is converted into two equivalent single loops with uncertainties drawn from interactions. The maximum uncertainty is estimated for the initial controller design in one loop and single-input and single-output (SISO) controller design is applied. This controller is substituted to other equivalent loop for design, and finally, the first loop controller is refined on knowledge of other loop controller. For SISO controller tuning, a new method is presented to determine the achievable gain and phase margins as well as the relevant controller parameters. Examples are given for illustration and comparison.     

基于虚拟机的运行时入侵检测技术研究

入侵检测技术通常分为误用检测和异常检测两类,误用检测根据攻击模式库检测已知的攻击行为,但却难以防范未知的攻击行为;异常检测技术虽然可以预测偏离正常值阈区间的潜在攻击行为,但却存在较高的误报现象。在虚拟机监视器中对虚拟机操作系统的运行行为进行带外监控,避免了操作系统内监控模块被病毒感染的难题;通过监视虚拟机的运行时行为,对之作组合序列的合法性分析,扩展了误用检测防范长时间段攻击行为的能力,识别通过合法系统调用进行的恶意攻击。测试数据表明,该技术能够较好地检测出复杂组合攻击行为。     

一种认知无线电网络接入控制和功率控制联合设计

在underlay频谱共享模式下,提出一种认知无线电网络接入控制和功率控制的联合算法。该算法针对主用户干扰温度和次用户信号干扰噪声比(SINR)限制,通过引入冲突因子,实现网络自适应调整被拒绝参与频谱共享的次用户数目,通过迭代进行接入控制和功率分配实现频谱共享。仿真结果表明,该算法能够保证接入共享主网络频带的次用户数近似最优,而且缩短了接入控制的时间。     

Online deadline scheduling with preemption penalties

This paper presents a study of the problem of online deadline scheduling under the preemption penalty model of Zheng, Xu, and Zhang (2007). In that model, each preemption incurs a penalty of ρ times the weight of the preempted job, where ρ ? 0 is the preemption penalty parameter. The objective is to maximise the total weight of jobs completed on time minus the total penalty.     

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.     

An interactive concept map approach to supporting mobile learning activities for natural science courses

Mobile and wireless communication technologies not only enable anytime and anywhere learning, but also provide the opportunity to develop learning environments that combine real-world and digital-world resources. Nevertheless, researchers have indicated that, without effective tools for helping students organize their observations in the field, the mobile learning performance could be disappointing. To cope with this problem, this study proposes an interactive concept map-oriented approach for supporting mobile learning activities. An experiment has been conducted on an elementary school natural science course to evaluate the effectiveness of the proposed method. The experimental results show that the proposed approach not only enhances learning attitudes, but also improves the learning achievements of the students.     

Strong solutions to the incompressible magnetohydrodynamic equations with vacuum

We consider the incompressible magnetohydrodynamic (MHD) equations with the coefficients depending on the density and temperature. We prove the existence of unique local strong solutions for all initial data satisfying a natural compatibility condition. The initial density need not be positive and may vanish in an open set.     

Deterministic convergence of conjugate gradient method for feedforward neural networks

Conjugate gradient methods have many advantages in real numerical experiments, such as fast convergence and low memory requirements. This paper considers a class of conjugate gradient learning methods for backpropagation neural networks with three layers. We propose a new learning algorithm for almost cyclic learning of neural networks based on PRP conjugate gradient method. We then establish the deterministic convergence properties for three different learning modes, i.e., batch mode, cyclic and almost cyclic learning. The two deterministic convergence properties are weak and strong convergence that indicate that the gradient of the error function goes to zero and the weight sequence goes to a fixed point, respectively. It is shown that the deterministic convergence results are based on different learning modes and dependent on different selection strategies of learning rate. Illustrative numerical examples are given to support the theoretical analysis.     

Fabrication of metallic micromirror using electroplating technology

A nickel micromirror array was designed and successfully fabricated using a thick photoresist as a sacrificial layer and as a mold for nickel electroplating. It was composed of two address electrodes, two support posts and a nickel mirror plate. The mirror plate, which is supported by two nickel posts, is overhung about 10 μm from the silicon substrate. The nickel mirror plate is actuated by an electrostatic force generated by electrostatic potential difference applied between the mirror plate and the address electrode. Optimized fabrication processes have been developed to reduce residual stress in mirror plate and prevent contact between the mirror plate and the substrate, which ensure a reasonable flat and smooth micromirror for operation at low actuation voltage.