OMAPL138和FPGA的合并单元设计方案

提出了一种基于ARM9+DSP双核处理器OMAPL-138和FPGA的合并单元设计方案。给出了该方案的原理框图,以及具体的程序流程图,并在CCS开发环境下完成了软件编程,实现了合并单元按照IEC61850标准规定的格式进行数字量以太网传输。实验结果证明了该设计方案的可行性与正确性,为合并单元的设计提供了一种新的思路。     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

Dynamics and genetic fuzzy neural network vibration control design of a smart flexible four-bar linkage mechanism

In this paper, a dynamic modeling method and an active vibration control scheme for a smart flexible four-bar linkage mechanism featuring piezoelectric actuators and strain gauge sensors are presented. The dynamics of this smart mechanism is described by the Discrete Time Transfer Matrix Method of Multibody System (MS-DTTMM). Then a nonlinear fuzzy neural network control is employed to suppress the vibration of this smart mechanism. For improving the dynamic performance of the fuzzy neural network, a genetic algorithm based on the MS-DTTMM is designed offline to tune the initial parameters of the fuzzy neural network. The MS-DTTMM avoids the global dynamics equations of the system, which results in the matrices involved are always very small, so the computational efficiency of the dynamic analysis and control system optimization can be greatly improved. Formulations of the method as well as a numerical simulation are given to demonstrate the proposed dynamic method and control scheme.     

Optimal Performance in Tracking Stochastic Signal Under Disturbance Rejection

The analysis method of optimal tracking performance is proposed for multiple‐input multiple‐output (MIMO) linear time‐invariant (LTI) systems under disturbance rejection. An H₂ criterion of the error signal between the output of the plant and the reference signal is used as a measure for the tracking performance. Spectral factorization is applied to obtain the optimal solution of the system tracking error. The explicit expressions are derived for this minimal tracking error with respect to random reference signals under disturbance rejection. It is shown that the nonminimum phase zeros, the zero direction, the unstable poles, the pole direction of a given plant, statistical characteristics of the reference input signal, and disturbance signal have a negative effect on a feedback system's ability to reduce the system error with disturbance rejection. The results show that the optimal tracking performance will further be damaged because of disturbance rejection. Some typical examples are given to illustrate the theoretical results.     

变风量空调系统优化控制策略研究

变风量空调系统是多变量,大滞后、非线性和不确定性的系统,普通的模糊神经网络控制已难以满足其多变量动态控制的要求,为改善变风量空调系统控制性能,本文提出了一种小波模糊神经网络预测控制方法,实现变风量空调的温湿度有效控制.通过小波神经网络预测器在线建立被控对象的数学模型,并用模糊RBF神经网络控制器对所得到的信息在线修正,优化控制器参数,从而改善系统的控制效果.仿真结果表明,小波模糊神经网络预测控制具有很强的鲁棒性和自适应能力,控制精度高,控制效果好,安全可靠等优点,具有广泛的应用价值.     

基于内容的静态语义概念视频检索方法研究

本文针对互联网视频检索技术的发展,阐述了目前主流视频搜索引擎的技术现状,分析了互联网视频检索的关键技术,特别是对于视频特征的提取技术。本文的创新点是提出了一种通用的基于内容的静态语义视频检索方法,该方法可以弥补基于文本视频检索的有关不足,并且在TRECVID的视频概念检索数据的静态语义概念中得到验证,运行稳定。     

项目驱动式教学法在计算机基础课程中的应用

项目驱动式教学做为一种新兴的教学模式越来越多的应用于各门学科的教学应用当中。计算机基础这门课程由于其理论知识更新速度快,实践操作要求高的特点,将之与项目驱动式教学法相结合可以起到很好的教学效果。本文将传统教学方法和项目驱动式教学法进行对比,针对当前教学过程中存在的问题提出相应的解决方法。     

基于Web数据挖掘的B2C推荐系统的设计

电子商务相对传统贸易具有成本上面的优势。随着互联网的普及,电子商务的一种重要分支,企业对个人的电子商务（BUSINESSTOCUSTOMER,B2C）被越来越多人们接受和使用。但是互联网上的信息量非常巨大,普通消费者往往不知道如何才能买到自己心仪的商品。利用WEB数据挖掘的相关技术,设计了一个可以在B2C贸易中推荐优惠商品给用户的系统。     

嫦娥三号着陆控制研究与软件仿真

嫦娥工程分为3个阶段,目前已经进行到第二阶段,即发射可在月球表面软着陆的登陆器,这便是嫦娥三号的主要任务。为了保证登月探测器在月球表面平稳地降落并且能够有效应对外太空环境下各种因素造成的干扰,需要对控制方案进行深入的研究和认真的设计。选择非线性变结构控制与状态反馈相结合的办法设计控制方案,设定关于系统高度、速度以及加速度等状态变量的性能指标,通过对相应的哈密尔顿函数采用极小值原理来得到变结构控制的相关输入,从而完成控制方案的数学模型。使用Simulink软件对所得到的数学模型分别在理想的情况以及存在干扰的情况下进行仿真,并且得到了满意的仿真结果。所设计的控制方案有一定的可行性,但因为大多数工作只是理论层次的分析,因此还需要进一步的研究。     

TN系统低压电动机的几种配电方案对比及选择

针对TN系统低压电动机配电设计中保护电器与电缆选择不当造成保护拒动作的问题,从保护灵敏度等方面进行阐述,探讨在面对不同配电距离时的合理配电方案.