基于全散度的C-V模型阈值法

为了提高传统C-V模型的收敛速度并降低其对噪声的敏感性,提出基于全散度的C-V模型及其快速阈值分割算法。将Bregman散度与全散度统一获得新的全散度,并将其引入C-V模型的拟合偏差项,提高图像灰度值与分割区域平均灰度偏差值计算的鲁棒性。同时,采用变分水平集理论获得基于直方图的快速阈值计算方法。实验结果表明,该方法分割效果及收敛速度得到提高,且具有较好的鲁棒性和抗噪性。     

Closed-loop model set validation under a stochastic framework

This paper deals with probabilistic model set validation. It is assumed that the dynamics of a multi-input multi-output (MIMO) plant is described by a model set with unstructured uncertainties, and identification experiments are performed in closed loop. A necessary and sufficient condition has been derived for the consistency of the model set with both the stabilizing controller and closed-loop frequency domain experimental data (FDED). In this condition, only the Euclidean norm of a complex vector is involved, and this complex vector depends linearly on both the disturbances and the measurement errors. Based on this condition, an analytic formula has been derived for the sample unfalsified probability (SUP) of the model set. Some of the asymptotic statistical properties of the SUP have also been briefly discussed. A numerical example is included to illustrate the efficiency of the suggested method in model set quality evaluation.     

Transfer latent variable model based on divergence analysis

Latent variable models are powerful dimensionality reduction approaches in machine learning and pattern recognition. However, this kind of methods only works well under a necessary and strict assumption that the training samples and testing samples are independent and identically distributed. When the samples come from different domains, the distribution of the testing dataset will not be identical with the training dataset. Therefore, the performance of latent variable models will be degraded for the reason that the parameters of the training model do not suit for the testing dataset. This case limits the generalization and application of the traditional latent variable models. To handle this issue, a transfer learning framework for latent variable model is proposed which can utilize the distance (or divergence) of the two datasets to modify the parameters of the obtained latent variable model. So we do not need to rebuild the model and only adjust the parameters according to the divergence, which will adopt different datasets. Experimental results on several real datasets demonstrate the advantages of the proposed framework.     

基于KL散度的DCT系数分布模型研究

利用分布拟合优度检验方法,对DCT系数分布模型进行了研究.通过理论分析和实验,验证了KL散度更适合作为DCT系数分布拟合优度度量;针对单个位置和不同范围的DCT系数,系统地比较了当前应用最广泛的4种DCT系数分布模型.实验结果表明,图像中低频交流系数服从对称α稳态分布而高频交流系数服从广义高斯分布.     

基于数据迭代的FOG误差参数闭环标定方法

针对光纤陀螺(FOG)误差参数是影响系统导航精度的主要因素,且传统开环标定方法对陀螺零偏、标度因数和安装误差角标定精度不高而制约系统精度进一步提高的现状,提出一种FOG迭代闭环标定方法。该方法分别在捷联惯导系统罗经对准和导航过程中完成对FOG零偏、标度因数、安装误差的标定。利用Matlab建立仿真环境,将传统分立式标定与闭环标定方法得到的仿真结果进行对比,结果表明:提出的闭环标定方法可有效地提高光纤陀螺标定精度。     

一种基于树结构的证书验证方法

分析了严格层次信任模型及其证书路径的特点和性质,在此基础上设计了一个基于树结构的证书验证模型。该模型利用证书路径的复用技术,并通过证书路径有效期和证书路径状态的概念,简化了证书验证的处理过程。该模型还给出了证书路径的扩展方法。     

Localization-based sensor validation using the Kullback-Leibler divergence

A sensor validation criteria based on the sensor's object localization accuracy is proposed. Assuming that the true probability distribution of an object or event in space f(x) is known and a spatial likelihood function (SLF) psi(x) for the same object or event in space is obtained from a sensor, then the expected value of the SLF E[psi(x)] is proposed as a suitable validity metric for the sensor, where the expectation is performed over the distribution f(x). It is shown that for the class of increasing linear log likelihood SLFs, the proposed validity metric is equivalent to the Kullback-Leibler distance between f(x) and the unknown sensor-based distribution g(x) where the SLF psi(x) is an observable increasing function of the unobservable g(x). The proposed technique is illustrated through several simulated and experimental examples.     

基于辅助变量的闭环系统子空间辨识

提出一种基于辅助变量的子空间辨识方法,适用于控制器信息未知以及参考输入已知的闭环系统参数辨识.通过将输入-输出数据块正交投影到辅助变量的行空间,直接得到扩展观测矩阵垂空间的估计.由此可从闭环系统中提取出对象模型信息,同时由SVD分解得到扩展观测矩阵与下三角Toeplitz矩阵的估计.给出了系统参数矩阵、噪声矩阵的计算方法.将所提出的子空间辨识方法应用于闭环动态的系统参数估计,其结果表明了该方法的有效性.     

虚拟维修模型的校验方法研究

介绍了虚拟雏修与建模仿真的关系,分析了虚拟维修模型校验的特点,研究了虚拟维修模型校验的方法,分别应用置信区间法、假设检验法、Bayes法、TIC法、频谱分析法从静态性能和动态性能两个方面对模型实施校验,从而为虚拟维修模型准确性、实时性提供了较为可靠的验证方法.为虚拟维修模型的校验提供一套系统的理论和方法,有利于虚拟维修模型得到更好应用.     

利用SVM进行车型识别

为了提高车辆图像的识别率,提出了利用支持向量机（SVM）理论进行轿车车型识别方法.SVM能够解决线性及非线性分类问题,以较少的支持向量确定分类面,对样本数量及维数不敏感.基于颜色直方图及惯性比确定的图像特征具有平移、旋转和尺度不变性,可以用来确定SVM的最优分类面,并由此识别车型.     

Closed-loop subspace identification algorithm based on correlation function estimates

A novel subspace identification method based on correlation function which estimates a state-space system dynamics of unknown plant operating in closed-loop experimental condition is proposed in this paper.It is shown that the cross-correlation function of the output and external input signals are equal to the crosscorrelation function of the input and external signals filtered through the system dynamics since noise signal has no correlation with the external input.The proposed algorithm is developed to obtain unbiased estimates of system matrices based on time-shifted invariance of the correlation function estimates.Later the algorithm is compared to other popular subspace methods in the simulation study and the results show the effectiveness of our method in the presence of colored noise and low signal-to-noise ratios.     

Cluster validity index based on Jeffrey divergence

Cluster validity indexes are very important tools designed for two purposes: comparing the performance of clustering algorithms and determining the number of clusters that best fits the data. These indexes are in general constructed by combining a measure of compactness and a measure of separation. A classical measure of compactness is the variance. As for separation, the distance between cluster centers is used. However, such a distance does not always reflect the quality of the partition between clusters and sometimes gives misleading results. In this paper, we propose a new cluster validity index for which Jeffrey divergence is used to measure separation between clusters. Experimental results are conducted using different types of data and comparison with widely used cluster validity indexes demonstrates the outperformance of the proposed index.     

Automatic transfer functions based on informational divergence

In this paper we present a framework to define transfer functions from a target distribution provided by the user. A target distribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is based on a communication channel between a set of viewpoints and a set of bins of a volume data set, and it supports 1D as well as 2D transfer functions including the gradient information. The transfer functions are obtained by minimizing the informational divergence or Kullback-Leibler distance between the visibility distribution captured by the viewpoints and a target distribution selected by the user. The use of the derivative of the informational divergence allows for a fast optimization process. Different target distributions for 1D and 2D transfer functions are analyzed together with importance-driven and view-based techniques.     

基于云模型的网络计划建模方法

项目建设过程中的不确定性因素对预测项目完工时间有较大影响,项目中的不确定性主要是随机性和模糊性.将云模型以及云运算引入到网络计划建模方法中,提出基于云模型的网络计划建模方法,可以将随机性和模糊性结合起来处理;这种基于云模型的网络计划建模方法,比传统的方法更真实地反映了项目的不确定性,能够提供更多有价值的信息.使用基于云模型的网络计划建模方法来计算整个项目完工的时间,充分考虑了项目建设过程中的不确定性,更加符合工程实际.     

灰色模型的交通图像编码

针对交通实时路况图像提出了一种基于灰色模型的图像编码方法。该方法通过对二维图像数据的一维转换、新数据的二维DCT变换和灰色模型GM(1,1)的系数保存等3个步骤来实现对图像数据的压缩。该法简单方便、快速高效,并已针对小幅复杂交通路况在中低信噪比取得了好的结果。对像素为256×256的复杂交通图像,误差图像正负误差值之和绝大部分都在150以内。     

基于高斯混合模型的脚步声身份识别方法

针对脚步声身份识别数据少、识别准确率低的问题,提出一种以相邻两个脚步声信号作为基本单元,基于高斯混合模型(GMM)实现脚步声身份识别的方法.首先,使用滤波和谱减法相结合的方法对原始脚步声信号进行降噪处理;其次,根据脚步声压级信号和自适应阈值对自然行走状态下连续脚步声信号的起始点进行检测,并将相邻两个脚步声信号作为一个脚...     

基于融合模型的人体行为识别方法

可穿戴设备的人体行为识别研究通常是提取传感器数据的特征值,然后结合分类算法识别人体行为动作.针对特征提取与分类器问题,提出一种融合模型的人体行为识别方法(HBRM).首先将加速度传感器采集的数据转换为二维张量格式,然后结合卷积神经网络(CNN)提取张量的特征,同时考虑到人体行为动作在时间序列上前后具有较强的关联性,提出...     

基于数据处理模型的软件测试方法

针对传统测试方法运用于数据处理软件测试时的不足,提出了一种基于数据处理模型的软件测试方法;为有效避免测试数据选取时的盲目性,提出了基于图解化的测试数据选取方法;将上述方法运用于坐标转换软件测试中,结果表明所提出的方法是一种可行且高效的数据处理软件测试方法.     

基于时域有限差分法的IBIS模型修正

针对高速电路设计中IBIS模型仿真精度较差问题,将时域有限差分算法与IBIS模型相结合。通过实验对高速PCB设计中常见的结构(完整接地面、狭缝和过孔)进行了信号完整性分析,结合时域有限差分法改进的波形、speed2000仿真波形与实际测量的波形三者之间的比较,结果表明该修正算法可以显著提高仿真模型的准确度,达到提高设计成功率,缩短研发周期,降低成本的功效。