光催化降解废气过程中紫外辐射照度模拟和优化

为在光催化设计中合理布置紫外灯,减少光在传输过程中的损失,概括总结径向辐射模型、多点源叠加近似辐射模型和离散坐标辐射模型等3种经典的紫外辐射物理模型;基于离散坐标辐射模型模拟某公司生产的光催化氧化废气处理装置的紫外辐射照度;通过调整灯管布置距离改变光强分布,并拟合光催化单元和光催化网表面的平均紫外辐射照度随灯管布置距离变化曲线.结果表明,距离参数在55～65 mm时,该光催化反应装置的光能利用率较好;紫外灯的合理布置可有效提高光能利用率.     

基于YCbCr肤色模型改进算法及区域特征的人脸检测研究

针对当前人脸检测的研究现状与难题,采用改进的YCbCr椭圆聚类肤色模型进行肤色区域提取,根据肤色在YCb’Cr’空间的分布,对于亮度小于80的非肤色像素点会误判为肤色点,则缩小椭圆聚类;对于亮度大于230的肤色像素点会误判为非肤色点,则扩大椭圆聚类,有效避免了在高亮度区域和亮度较低的区域中的肤色点误判问题。接着利用人脸的几何特征,对二值化图中的目标区域进行比例、大小结构的分析,排除不可能的人脸区域,并基于肤色和位置进行区域优化,将处理后的结果作为候选人脸区域输出。     

堆积体内部裂隙对降雨入渗的影响

降雨条件下滑坡体稳定性与裂隙诱发入渗密切相关。为了探究裂隙发育对堆积体降雨入渗的影响,以澜沧江某巨型堆积体坡表发育的裂隙现象为出发点,设计了均质堆积体和主-次裂隙堆积体2种模型。通过室内物理模拟试验及数值分析,并结合土体中的含水率、基质吸力、湿润锋迁移速度及深度的变化趋势可得出以下结论:裂隙型堆积体在降雨1 h和观测23 h的整个时间段内,其湿润锋迁移变化趋势可归纳为入渗加速→峰值→入渗减速→趋向于0;裂隙的存在为雨水入渗提供了有利的通道,雨水可到达土体深部,形成暂态饱和区,随降雨结束又逐渐消散;降雨在裂隙型发育的堆积体中的入渗过程可分为4个阶段,即前期完全入渗、裂隙下方强烈入渗、补偿加速入渗及水平侧渗。研究结果可为后续滑坡复活机理和稳定性评价提供参考。     

快速修复路面的混杂纤维再生混凝土基本力学性能研究

通过计算及试配得到快速修复路面的水泥混凝土配合比,在此基础上配制了不掺加纤维的普通再生混凝土和混掺体积分数为0.15％玄武岩纤维与0.11％聚丙烯腈纤维的再生混凝土,再生骨料取代率及混杂纤维对路面再生混凝土基本力学性能的影响进行实验研究.结果表明:混凝土的各项力学性能基本上随再生骨料取代率的提高而降低;玄武岩与聚丙烯腈混杂纤维可以有效增加再生混凝土的劈裂抗拉强度和抗折强度.     

基于人工神经网络的银行客户流失分析模型

针对目前中国建设银行存在的客户流失问题,利用BP人工神经网络网络稳定、学习能力强的特点,通过输入变量和输出变量之间的相关性分析,建立银行客户流失分析模型,以此获取即将流失的客户,以便银行做出经营决策,挽留有关用户,确保银行效益不受影响。实验证明,此模型能够很好的对银行客户流失进行预测分析。     

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.