一种适用于OFDM系统的自适应AGC改进方法

动态环境下,光纤陀螺的噪声更加难于处理并且是制约光纤捷联惯导精度的重要因素。针对光纤陀螺动态信号的复杂特性难以准确估计,提出一种利用广义交叉验证对信号的小波分解层分层确定阈值的方法。在信号先验统计特性未知的情况下,利用广义交叉验证原理,该方法能够识别信号和噪声所在的小波分解层,从而分层自适应给出合理阈值。对模拟数据和动态信号仿真算例的分析表明,该方法可有效解决在动态条件下抑制光纤陀螺噪声与保留高频信号的矛盾,提高光纤陀螺动态信号处理的精度。     

基于支持向量机和交叉验证的变压器故障诊断

为及时监测变压器潜伏性故障和准确诊断故障,提出基于优化惩罚因子C参数的支持向量机算法（C-SVC：C-support vector classification）和交叉验证算法相结合的变压器故障诊断方法。该方法利用变压器在故障时产生的氢气、甲烷、乙烷、乙烯、乙炔的体积分数数据建立训练集和测试集。在训练集中,该方法能自动优化出（寻找最佳）支持向量机的核函数的参数γ和惩罚因子C,利用优化的参数对训练集进行训练,可得到最佳的支持向量机模型,并用该模型对测试集进行分类,从而诊断出变压器的故障类型。变压器故障诊断实例分析结果证明,该方法可行,有效,且具有较高的故障诊断准确率。     

三种空间插值算法在地磁匹配基准图构建中的应用

针对地磁匹配导航对构建高密度、高精度的磁基准图的需求,结合目前应用较为广泛、效果较好的3种空间插值方法,用某地区的实测地磁数据进行了插值效果分析。对于插值效果的评价引人了交叉验证法,并借助误差最大值、最小值、平均误差、标准差和运算时间等6项评价指标对3种插值算法进行了综合评价。通过研究说明了3种插值算法各自的特点和应用范围。     

基于稀疏表示的SOM多失真图像质量评价方法

针对非线性回归下客观评分与主观评分一致性差的问题,本文提出一种基于稀疏表示的SOM多失真图像质量评价方法。首先,将参考图像及失真图像应用独立变量分析进行稀疏化表示,应用稀疏表示下的参考图像与失真图像间的结构相似度描述失真图像的质量,再使用SOM聚类算法和交叉验证方法提高非线性回归下的客观评分与主观评分之间的一致性。最后,在LIVE2,TID2013及IVC数据库中的实验结果显示,所提评价模型性能优越;3种数据库的平均结果说明,该文方法的总体性能高于现有的经典算法,表明该文方法能够很好地反映图像的视觉感知效果。通过对比时间效率,该方法基本能够满足实际要求,具有较高的适用性。     

基于特征脸空间的人脸图像恢复

本文提出一种基于特征脸空间新的正则化恢复方法,并用Cross-validation(交叉验证)法确定最优的正则化因子.特征脸空间包含的人脸信息可以补充不可辨识的模糊图像丢失的信息,交叉验证法能保证恢复结果忠实于原像.实验结果表明,用本文的方法与成熟的迭代法和正则化方法比较,不但以ISNR为标准,还是以人的主观感觉判断,本文的方法都要优于其他两种方法.而且,本文方法的恢复效果对图像的模糊程度依赖性较小,可以恢复严重模糊的图像.     

A region-segmentation combining surrogate model based on L-indicator and N-fold cross-validation technique

For complex engineering problems, for which the mathematical models may be linear, low-order nonlinear or even high-order nonlinear, surrogate models which have high adaptability and accuracy are required. This article develops a method for constructing a region-segmentation combining surrogate model. It is based on the idea that in the entire experimental domain, different local regions may present different characteristics (linearity, low-order nonlinearity and high-order nonlinearity), and the entire domain should be divided into several subregions to be approximated by different surrogates so as to achieve high prediction accuracy in local regions. The preferred models in each subregion then constitute a weight-average combining surrogate model. The investigations reveal that the new model not only is more adaptive to analytically unknown functions, but also gives more accurate predictions. The method has been applied to three benchmark problems and a practical engineering problem, and the results maintain validity.     

Material Degradation Modeling and Failure Prediction Using Microstructure Images

Degradation data, frequently along with low-dimensional covariate information such as scalar-type covariates, are widely used for asset reliability analysis. Recently, many high-dimensional covariates such as functional and image covariates have emerged with advances in sensor technology, containing richer information that can be used for degradation assessment. In this article, motivated by a physical effect that microstructures of dual-phase advanced high strength steel strongly influence steel degradation, we propose a two-stage material degradation model using the material microstructure image as a covariate. In Stage 1, we show that the microstructure image covariate can be reduced to a functional covariate while statistical properties of the image are preserved up to the second order. In Stage 2, a novel functional covariate degradation model is proposed, based on which the time-to-failure distribution in terms of degradation level passages is derived. A penalized least squares estimation method is developed to obtain the closed-form point estimator of model parameters. Analytical inferences on interval estimation of the model parameters, the mean degradation levels, and the distribution of the time-to-failure are also developed. Simulation studies are implemented to validate the developed methods. Physical experiments on dual-phase advanced high strength steel are designed and conducted to demonstrate the proposed model. The results show that a significant improvement is achieved for material failure prediction by using material microstructure images compared with multiple benchmark models.     

Choosing the smoothing parameter for unordered multinomial data

We consider the estimation of multinomial probabilities in the non-sparse univariate unordered case. We describe a number of explicit methods (mostly pre-existing) for the choice of the smoothing parameter in this context. In simulations, we compare these methods in terms of mean root mean squared error performance. Our recommendation is for the routine use of the simplest Bayesian estimation formula in which the probability in thek'th cell is estimated by (n _k +1)/(N+K) wheren _k is the count in thek'th cell,N is the sample size andK is the number of cells.     

基于纹理特征的糖网临床前期眼底自发荧光图像识别

及时发现和干预潜在的糖尿病视网膜病变患者，对帮助提升糖尿病患者的整体视觉质量和降低医疗成本具有十分积极的意义.由于糖尿病视网膜病变临床前期和正常人的眼底荧光图像在视觉感观基本上没有差别，为此本文通过目前应用较广的纹理特征算法和支持向量机对这两组图像进行了模式识别.通过将185张眼底荧光图片十折交叉检验发现，LBP算法对其具有很好的识别效果.等价模式下的59维LBP算子的十折交叉准确率达到了91.89%，同时在测试集和训练集以1：1随机划分的情况下，由训练集数据所生成的模型对测试集中92张眼底荧光图像的识别准确率达到了88.12%，AUC值为0.943.