SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79 . 1.45 . 1.18-competitive separately for traditional timeout PM . adaptive predictive PM and stochastic PM.

The structure and function of neurons with variable nonlinear transfer function

One novel neuron with variable nonlinear transfer function is firstly proposed, It could also be called as subsection transfer function neuron. With different transfer function components, by virtue of multi-thresholded, the variable transfer function neuron switch on among different nonlinear excitated state. And the comparison of output's transfer characteristics between it and single-thresholded neuron will be illustrated, with some practical application experiments on Bi-level logic operation, at last the simple comparison with conventional BP, RBF, and even DBF NN is taken to expect the development foreground on the variable neuron.. The novel nonlinear transfer function neuron could implement the random nonlinear mapping relationship between input layer and output layer, which could make variable transfer function neuron have one much wider applications on lots of reseach realm such as function approximation pattern recognition data compress and so on.

基于安全状态域的网络评估模型

将基于攻击图的评估与依赖标准的评估相结合,提出了一种基于安全状态域(security state region,简称SSR)的网络安全评估模型(security-state-region-based evaluation model,简称SSREM).该模型将攻击的影响分为攻击能力改变和环境改变,通过两者之间的因果关系建立数学模型,提出了安全状态域趋向指数的概念,借助Matlab进行攻击趋势的曲面拟合,进而进行安全状态域的划分和网络的安全性评估.实验结果表明,依据SSREM进行的评估能够通过安全状态城和安全状态域趋向指数反映网络进入不同状态的难易程度,对网络安全性量化评估具有借鉴意义.

内循环流化床生物质气化过程的神经网络模型

基于BP人工神经网络原理，利用MATLAB神经网络工具箱，以实验得到的57组气化实验数据作为样本，建立了一个以加料量和送风量为输入变量，以燃气热值、产气率、碳转化率和气化效率为输出变量，用于描述连续稳定气化过程的内循环流化床生物质气化模型。对模型的隐层节点数和训练周期改变对模拟结果的影响进行了分析，发现当隐层节点数为20，训练步骤为50步，模型的4个输出变量的模拟结果与实验结果相关系数均超过0．95；同时对该模型的预测能力进行了考察，模型预测结果与实验结果吻合良好，证明了该模型具有较强的泛化能力，为生物质内循环流化床气化系统的优化设计和自动控制提供新思路。

喷灌分布均匀系数研究

为突出局部灌溉不足或灌溉过量对均匀性的影响程度,提出了基于几何平均数分布均匀系数的概念,将其定义为部分测点水深几何平均值与所有测点算术平均值的比值。并根据部分测点水深数据的提取方法不同,分为1/4低值、1/4高值、1/2低值和1/2高值分布均匀系数。用MATLAB和VC~++语言编制了可以实现上述分布均匀系数计算的软件"SIUEW1.0"。结果初步证明:基于几何平均数的乘法模型要比基于算术平均数的加法模型更加突出了部分低(或高)于平均值的测点水深数据对均匀系数的影响程度,因此更适用于时局部灌溉不足或过量灌溉有严格控制要求地块的灌溉均匀性评价;无论高值和低值,取点数越少,均匀性的评价结果越差。

覆盖框架与主方向神经网络

分析了BP、RBF和ARTMAP等人工神经网络在实现非线性映射方面的共同之处，基于RBF等网络对于人脑功能方面的模拟和仿生模式识别的思想，总结出一种处理这类问题的基本框架。该框架的特点是将问题分解为样本覆盖问题和基于模型的映射拟合问题。在利用该框架研究某个函数集在连续函数空间中的稠密性的基础上，提出了一种新的人工神经网络模型——主方向神经网络（PDNN）。通过与BP网络和RBF网络在函数拟合和混沌时间序列预测方面的对比实验，发现PDNN具有非常良好的逼近性能和鲁棒性能。

垂直腔面发射激光器的TO封装工艺操作误差分析

采用FRESNEL光学软件和MATLAB编程，详细分析了垂直腔面发射激光器的TO封装工艺操作误差对耦合效率的影响．发现在芯片横向偏移、芯片倾斜和管帽倾斜这三种操作误差中，管帽倾斜对封装组件的耦合效率影响最大。

垂直腔面发射激光器的TO封装的耦合效率模拟分析

采用FRESNEL光学软件和MATLAB软件，详细分析了垂直腔面发射激光器的TO封装组件对耦合效率的影响．发现增加耦合透镜的折射率、减少管帽的高度和耦合透镜的尺寸可以提高耦合效率。

最小球覆盖几何算法及其在模式识别中的应用

通过对高维空间中超弦角（Hyper—Chord Angle）的定义，引出一种高维最小球覆盖的几何算法．结合RBF神经元和优先度排序网络，高维最小球覆盖算法可以有效解决模式识别中若干类样本的分类问题．超弦角的定义也为其他高维空间几何问题的研究提供新思路．

一种新型双权值人工神经元网络的数据拟合研究

在本文中提出了一种针对新型双权值神经元网络的数据拟合算法.采用这种新型网络结构和算法,可以克服传统的通用前馈网络中BP算法易陷入局部极小的问题.通过实验比较证明在相同的网络规模下,采用这种新型网络结构和算法可以取得比径向基(RBF)网络更高的拟合精度和更少的迭代次数.

一种CMOS折叠结构ADC中的失调抵消技术

CMOS折叠预放电路的失凋是限制CMOS折叠结构A／D转换器实现高分辨率应用的主要原因之一．文中提出差分对的动态匹配技术改善了折叠预放电路的失调，从而为研制CMOS工艺中的高分辨率折叠结构A／D转换器提供了一种可行方案，并给出了MATLAB和电路仿真的实验结果．

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

日光温室地下滴灌土壤水分运动数值模拟

依据土壤水分动力学理论和地下滴灌土壤水分运动特征，在测定地下滴灌滴头出流特性、土壤水分运动参数、地表蒸发强度的基础上，引入作物根系吸水模型，建立了地下滴灌土壤水分运动数值模型，用有限单元法中的Galerkin法对模型进行数值求解，田间验证结果表明模型具有较高的精度。 研究发现，地下滴灌滴头流速随供水压力的增大而增加，呈幂函数关系；不同埋深对滴头流速基本无影响；而土壤初始含水量只在灌水开始时对滴头流速有一定影响，最终滴头流速趋于相同且稳定；地下滴灌滴头流速主要受供水压力和滴头孔径的影响。 运用matlab数值分析软件对土壤持水曲线的van Genuchten模型进行求参，所求VG模型计算的不同水势下土壤含水量计算值与田间实测值之间偏差较小，曲线回归模型相关指数高达0.95，能很好地表征土壤水分特征曲线。 建立了地下滴灌土壤水分运动数值模型，确定地下滴灌条件下具体模型应用的边界和初始条件，利用HYDRUS-2D软件进行有限元求解。运行结果表明所建立的数学模型和所采用的数值方法能够较好地模拟地下滴灌条件下土壤水分运动过程。 采用量纲分析法推导了日光温室地下滴灌条件下的土壤湿润模式模型，建立了湿润宽度和湿润深度的经验公式，与实际监测结果对比，土壤湿润宽度和湿润深度模型估计值与实测之间偏差较小，能准确的模拟地下滴灌条件下土壤湿润体的宽度和深度。

金属材料超高周疲劳行为的Monte-Carlo模拟

<正>基于Tanaka-Mura的微观位错模型并结合结构可靠性及灵敏度的统计理论,采用MATLAB自编程序对金属材料S-N曲线的分散特性进行了Monte-Carlo模拟分析,并对比了SUJ2钢的旋转弯曲疲劳实验数据,最后根据结构可靠性灵敏度理论分析了超高周疲劳失效概率的各影响因素的敏感性。