焦炉加热吸力控制模型及应用

依据焦炉正常生产条件下的系统压力分布,利用焦炉内气流流动方程,建立了燃烧系统参数与各压力控制点的模型。通过计算机仿真与实际结果比较,该模型能够较好的反映焦炉内气体流动规律,对吸力控制具有较好的理论指导意义。     

汛限水位动态控制在石门水库主汛期调度中的应用研究

在汛限水位静态控制的运行方式下常导致水库汛期不敢蓄水,汛后库水位却常常达不到正常蓄水位,极大地限制了水库兴利效益,造成水资源浪费。以石门水库为例,针对其静态汛限水位运行的现状,分析该运行方式下对水库兴利效益的影响,提出采用汛限水位动态控制的方法来提高水资源利用率。实际调度中,结合石门水库天气、水文等预报信息,采用预蓄预泄方法,分析汛限水位动态控制的上下限,给出了动态控制的调度策略,并以"20110728"洪水对比了两种调度方式。结果表明:当预报降雨或洪水偏小时,可抬高汛限水位最高至617.5 m,拦蓄洪尾,有效增加水库的蓄水量;当预报降雨或洪水偏大时,可降低汛限水位最低至610.0 m,腾空库容,迎接洪水。     

电极布置方式对CFRP电阻-应变特性的影响

用端部四电极布置方法和中间四电极布置方法对单向和正交CFRP材料在循环加载情况下的应变敏感性系数（GF）值进行了测试；结果表明：单向CFRP的GF随着电极布置方式的不同而反向，采用端部四电极布置方法其测试值为3．06、中间四电极布置方法为-4．76；研究了机理；为保证测得的电阻应变系数尽可能真实，应采用端部四电极布置方法。     

乐昌峡水利枢纽电站新型五防系统简介

乐昌峡水利枢纽新型五防系统,以"防误闭锁"为核心,在一般的五防基础上,整合了锁具管理系统、智能钥匙管理机、地线管理系统以及无线网络通信技术。该文介绍了系统的组成及功能,以及新型五防系统的优越性。     

低温固化双马来酰亚胶树脂基体研究──引发剂的作用

本文以典型的ＸＵ２９２双马体系作为研究对象，选用引发剂加速树脂的固化反应，以期得到低温固化耐热的双马树脂体系。研究结果表明，引发剂能很有效地降低双马树脂的固化温度，低温固化阶段比高温固化阶段对树脂耐热性的影响更大。经１２０℃／８ｈ＋１４０℃／２ｈ＋１６０℃／２ｈ固化树脂的热变形温度为２６６℃，再经１８０℃／８ｈ的后处理，热变形温度达到３００℃。     

土体固结粉在软土地基加固中的应用研究

针对上海地区软土地基特点,利用工业废渣为主要原料配制成新型土体固结材料——土体固结粉。通过室内的无侧限抗压强度及XRD试验得出其固化土的强度性能及加固机理;再通过室外原位搅拌桩施工及芯样试验进行分析,研究了土体固结粉在软土地基加固实际工程中的适用性。     

新型高压密封装置的优化设计

以最低制造成本为优化目标，建立了新型高压密封装置优化设计的数学模型，并给出设计实例。结果表明，采用优化设计可降低制造成本１４．７％。     

Finite-time consensus of multiple second-order dynamic agents without velocity measurements

This article considers the finite-time consensus of multiple second-order dynamic agents without velocity measurements. A feasible protocol under which each agent can only obtain the measurements of its position relative to its neighbours is proposed. By applying the graph theory, Lyapunov theory and the homogeneous domination method, some sufficient conditions for finite-time consensus of second-order multi-agent systems are established under the different kinds of communication topologies. Some examples are presented to illustrate the effectiveness of the theoretical results.     

Sliding mode control for Markov jump linear uncertain systems with partly unknown transition rates

In this paper, based on sliding mode control approach, the robust stabilisation problem for a class of continuous-time Markovian jump linear uncertain systems with partly unknown transition rates is investigated. The transition rate matrix under consideration covers completely known, boundary known and completely unknown elements. By making use of linear matrix inequalities technique, sufficient conditions are presented to derive the linear switching surface and guarantee the stochastic stability of sliding mode dynamics. Then a sliding mode control law is designed to drive the state trajectory of the closed-loop system to the specified linear switching surface in finite time in spite of the existing uncertainties and unknown transition rates. Finally, an example is given to verify the validity of the theoretical results.     

Fisher discrimination based low rank matrix recovery for face recognition

In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results.