Feature Selection using Fuzzy Support Vector Machines

The feature selection consists of obtaining a subset of these features to optimally realize the task without the irrelevant ones. Since it can provide faster and cost-effective learning machines and also improve the prediction performance of the predictors, it is a crucial step in machine learning. The feature selection methods using support machines have obtained satisfactory results, but the noises and outliers often reduce the performance. In this paper, we propose a feature selection approach using fuzzy support vector machines and compare it with the previous work, the results of experiments on the UCI data sets show that feature selection using fuzzy SVM obtains better results than using SVM.     

Electrochemical Immunoanalysis for Carcinoembryonic Antigen Based on Multilayer Architectures of Gold Nanoparticles and Polycation Biomimetic Interface on Glassy Carbon Electrode

This paper describes a layer‐by‐layer (LBL) self‐assembly process of chitosan (CTS) and gold nanoparticles (Au) on the pretreated negatively charged glassy carbon (GC) electrode to fabricate electrochemistry immunosensor with a nontoxic biomimetic interface, which provided an environment similar to a native system and allowed more freedom in orientation for immobilization of carcinoembryonic antibody (anti‐CEA) to monitor carcinoembryonic antigen (CEA). UV‐vis spectroscope, atomic force microscopy (AFM), and cyclic voltammetric (CV) measurements were used to follow the multilayer film formation. The performance of the biominetic interface and factors influencing the assay system were investigated in detail. The differential pulse voltammetry (DPV) current response is used for the CEA concentration assay. The dynamic range was from 0.50 to 80.00 ng mL^?1 with a detection limit of 0.27 ng mL^?1 at 3σ. In addition, the experiment results indicate that immobilization described in this proposed method exhibits a good sensitivity, selectivity, and stability.     

Automatic detection and identification of shocks in Gaussian state‐space models: a Bayesian approach

An automatic monitoring and intervention algorithm that permits the supervision of very general aspects in an univariate linear Gaussian state–space model is proposed. The algorithm makes use of a model comparison and selection approach within a Bayesian framework. In addition, this algorithm incorporates the possibility of eliminating earlier interventions when subsequent evidence against them comes to light. Finally, the procedure is illustrated with two empirical examples taken from the literature. Copyright © 2005 John Wiley & Sons, Ltd.     

分析布拉格光栅破坏的两纤芯不同的光纤耦合器

进一步推广了描述纤芯不同光纤耦合器的耦合模理论，用于解释被布拉格光栅破坏的两纤芯不同光纤耦合器的耦合现象，理论分析的结果与已报道的实验结果相符，与单独的布拉格光栅所起的反射作用不同,这里布拉格光栅所起的作用主要是引入了强烈的色散，在光栅的频谱范围内使耦合器不再同步，在被布拉格光栅破坏的光纤耦合器的选择性频谱中，其边缘部分出现非常弱的波动，而在其阻带内出现非常强的凹陷。     

分辨率为1pm的波长监测新方法

在亚毫米尺度金属包覆波导研究基础上，提出了一种波长监测的新方法.实验采用自由空间耦合技术，激发了波导中对波长变化十分灵敏的超高阶导模，并以此为探针，实现了1pm波长漂移的监测. 关键词： 波长监测 金属包覆波导 超高阶导模     

双面金属包覆介质波导模序数的判别方法

在双面金属包覆介质波导中 ,随着介质层厚度的变化 ,会出现TM0 模与TM1 模的分离和简并两种状态 ,从而影响衰减全反射 (ATR)谱中模序数的确认。通过对金属波导的理论分析和实验研究 ,提出了三种模序数确认方法 :本征角计算法、吸收峰全峰半宽比较法和吸收峰峰间距比较法 ,实现了两种状态下对模序数的准确判断     

Bayesian Object Identification: Variants

We present a Bayesian theory of object identification. Here, identifying an object means selecting a particular observation from a group of observations (variants), this observation (the regular variant) being characterized by a distributional model. In this sense, object identification means assigning a given model to one of several observations. Often, it is the statistical model of the regular variant, only, that is known. We study an estimator which relies essentially on this model and not on the characteristics of the “irregular” variants. In particular, we investigate under what conditions this variant selector is optimal. It turns out that there is a close relationship with exchangeability and Markovian reversibility. We finally apply our theory to the case of irregular variants generated from the regular variant by a Gaussian linear model.