Fluorescence line narrowed spectra of Zn and metal-free cytochrome c

Fluorescence line narrowing (FLN) spectroscopy was used to study the role of the polypeptide chain in influencing the spectrum of Zn-substituted cytochrome c (Zn cyt c) and metal-free cyt c (porphyrin cyt c). For both derivatives the spectra show characteristics of relaxed fluorescence from an inhomogeneously broadened sample. Zero phonon lines and phonon wings can be clearly distinguished, and vibrational frequencies of the ground and excited states were identified. The inhomogeneous distribution width for porphyrin cyt c is slightly wider than that of Zn cyt c and a second population of molecules was apparent in the porphyrin cyt c. The phonon coupling was greater for Zn cyt c than for porphyrin cyt c, which may be due to the extra coupling to the polypeptide chain by metal ligation.This work has been done in partial fulfillment of the requirements for the Doctor of Philosophy degree.     

电子直线加速器磁控管频率稳定的自适应线性神经元方法

 电子直线加速器电子束能量的稳定与否取决于功率源工作频率的稳定性,磁控管在短时间内的散谱和轻微跳谱造成稳频系统的控制精度下降,最后电子束的扫描均匀度下降。引入自适应线性神经元方法(ADALINE)和噪声对消技术以消除对工作频率长期稳定性的影响,从而保证了电子束的扫描均匀度。     

二维随机激光器的模式选择及阈值与饱和特性

基于将Maxwell方程与四能级原子系统速率方程相结合而建立起随机激光时域理论，并利用有限时域差分法，研究了二维随机介质中激光模式的输出特性与介质尺寸、外形及抽运速率等参数的关系.结果表明，与传统激光模式相似，随机激光模式的强度随抽运速率的变化不仅具有阈值特性，而且具有饱和特性.基于模式特性对介质及抽运参数的依赖关系，提出了二维随机激光器的选模方式，在很大程度上不同于传统激光器的选模方式. 关键词： 随机激光器 模式选择 无序介质中的光学特性     

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.     

基于模糊神经网络的自适应PID控制器

提出了一种应用于自适应PID控制器的神经网络与模糊控制相结合的算法,该算法可以有效地解决普通PID控制器依赖于对象的数学模型的缺点,可实现控制系统的在线自适应调整,可满足实时控制的要求。仿真结果表明,基于模糊神经网络整定的PID控制器具有较好的自学习和自适应性,具有较快的响应速度。     

Numerical differentiation from a viewpoint of regularization theory

In this paper, we discuss the classical ill-posed problem of numerical differentiation, assuming that the smoothness of the function to be differentiated is unknown. Using recent results on adaptive regularization of general ill-posed problems, we propose new rules for the choice of the stepsize in the finite-difference methods, and for the regularization parameter choice in numerical differentiation regularized by the iterated Tikhonov method. These methods are shown to be effective for the differentiation of noisy functions, and the order-optimal convergence results for them are proved.