小垂直发散角大光腔980nm单模InGaAs/GaAs/AlGaAs量子阱激光器

通过采用经过优化的新型大光腔结构,脊形波导980nm单模InGaAs/GaAs/AlGaAs多量子阱半导体激光器在保持大功率光输出的同时获得了较小的垂直发散角.结果表明波导中的光功率密度可以降低,获得了大于400mW、斜率效率0.89W/A的输出光功率,垂直方向远场发散角也降低到23°.     

Sensitivity analysis of the scattering parameters of microwave filters using the adjoint network method

A novel approach is presented to calculate the sensitivities of the scattering parameters of microwave filters obtained with the full‐wave mode‐matching (MM) technique. Using only the MM simulation of the original network, the sensitivities of the scattering parameters with respect to all designable parameters are obtained. The adjoint network method (ANM) is applied to the generalized scattering matrices of the different filter components. This guarantees good accuracy of the calculated sensitivities. The implementation details are discussed for N‐resonator ridge waveguide filters. Excellent agreement is obtained between the sensitivities calculated using ANM and those obtained using the expensive central differences. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

基于软测量的化工精馏过程推断控制策略

针对化工精馏过程产品成分无法在线检测及其用温度间接控制产品成分的常规控制策略存在着控制精度低的问题,提出基于软测量的精馏过程成分非线性串级推断控制策略。该控制策略首先提出核岭回归的实时软测量方法,即利用满足Mercer条件的核函数改进线性岭回归算法,实现精馏过程产品成分的在线检测;然后在此基础上,提出一种新的非线性串级推断控制策略,即副环采用常规的温度间接控制,主环采用基于核岭回归软测量的推断控制策略。通过Mejedell等建立的精馏塔动态模型分别对单端和双端成分非线性串级推断控制策略性能进行分析,仿真结果表明,与传统控制方案比较,新控制策略的控制质量有了较大提高,控制结构简单,易于实施。     

隐目标回归算法设计研究

对常用的回归方法进行研究.此类方法虽然几何解释明确、易于求解,但均须事先确定(或假定)变量间的因果关系,再考虑建模,在实际应用中,对于很难确定变量的因果关系的问题,如物联网数据分析,上述方法就会失效.为此,提出一种无需假定因变量的隐目标回归方法.该方法易于核化,可以推广到非线性回归问题.通过人工数据和国际标准数据集上的实验验证了所提算法的有效性.     

基于贪心核特征提取方法的中期峰值负荷预测

针对中期电力负荷预测, 提出基于贪心核主元回归(GKPCR)、贪心核岭回归(GKRR) 的特征提取建模方法. 通过对核矩阵的稀疏逼近, GKPCR和GKRR两种贪心核特征提取方法旨在寻找特征空间中数据的低维表示, 计算需求低, 适用于大数据集的在线学习. 将所提出的方法应用于不同地区的电力负荷中期峰值预测, 并与现有预测方法进行了比较. 实验结果表明, 在同等条件下, 所提出的方法能有效地改进预测精度, 而且性能更好, 显示了其有效性和应用潜力.

     

一种指纹特征点信息的表示方法

指纹图像经细化后并提取特征点,把特征点的信息构成特征向量作为指纹特征模板。本文提出了一种有效的指纹特征信息的表示方法,把中心点作为参照点,利用中心点和特征点之间的距离和纹线数与图像的旋转和平移无关的特性来标记特征点。通过结果分析,此方法占用的空间相对要小。     

我国寿险需求的结构分析

在既有研究成果的基础上,结合我国实际情况,综合考虑了影响我国寿险需求的经济因素、政策因素、人口因素、科技因素,并引入虚拟变量来反映1995年我国《保险法》和一系列配套行政法规及部门规章制度制定以后对保险业的影响。以1990~2008年的相关数据为基础,运用多元线性回归、岭回归等统计方法,借助SPSS13.0统计软件,通过建立岭回归模型对上述因素进行定量分析,对寿险需求进行结构分析。结果表明：居民收入是寿险需求的决定性影响因素,而GDP、社会保障支出、科技投入、人口因素对寿险需求都有正的效应,而通货膨胀对寿险需求有抑制作用。     

实时洪水预报中基于岭估计的AR修正模型研究

为提高实时洪水预报精度,经常将水文模型与误差修正模型相结合,AR模型因其结构简单广泛应用于实时洪水预报误差修正。然而,实际应用显示,AR模型时常出现修正结果不稳定现象,表现为流量修正幅度过大,甚至出现“震荡”现象,严重影响修正效果。鉴于此,本文从矩阵特征值角度解释了AR模型出现不稳定现象的原因,并引入岭估计方法选择性利用流量信息更新自回归系数,使其更满足真实流量的涨落过程,增强该模型的稳健性。将新方法应用于蔺河口流域,结果显示岭估计方法显著提高了AR模型的稳健性,从而改善了模型修正效果,进一步提高了洪水预报精度。     

A joint introduction to Gaussian Processes and Relevance Vector Machines with connections to Kalman filtering and other kernel smoothers

The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of modern application domains, providing both power and interpretability via uncertainty analysis. This article introduces and discusses two methods which straddle the areas of probabilistic Bayesian schemes and kernel methods for regression: Gaussian Processes and Relevance Vector Machines. Our focus is on developing a common framework with which to view these methods, via intermediate methods a probabilistic version of the well-known kernel ridge regression, and drawing connections among them, via dual formulations, and discussion of their application in the context of major tasks: regression, smoothing, interpolation, and filtering. Overall, we provide understanding of the mathematical concepts behind these models, and we summarize and discuss in depth different interpretations and highlight the relationship to other methods, such as linear kernel smoothers, Kalman filtering and Fourier approximations. Throughout, we provide numerous figures to promote understanding, and we make numerous recommendations to practitioners. Benefits and drawbacks of the different techniques are highlighted. To our knowledge, this is the most in-depth study of its kind to date focused on these two methods, and will be relevant to theoretical understanding and practitioners throughout the domains of data-science, signal processing, machine learning, and artificial intelligence in general.     

Modal Identification of Linear Time-varying Systems Using Continuous Wavelet Transform

A wavelet based identification method for linear time-varying systems is presented,and the ridge and skeleton of the continuous wavelet transform of free response is used to extract time-varying parameters. The stiffness and damping coefficients of single-degree-of—freedom systems,frequencies and damping ratios of multi-degree-of-freedom systems are estimated without any prior information of systems. The proposed method is applied to linear time-varying systems with both abrupt and smooth variation parameters. Gaussian white noise is added to the response to test the anti-noise performance of the algorithm. The simulation results show that the proposed method is capable of accurately tracking the variation of the systems.