Interior-point methods for symmetric optimization based on a class of non-coercive kernel functions

In this paper, we generalize polynomial-time primal–dual interior-point methods for symmetric optimization based on a class of kernel functions, which is not coercive. The corresponding barrier functions have a finite value at the boundary of the feasible region. They are not exponentially convex and also not strongly convex like many usual barrier functions. Moreover, we analyse the accuracy of the algorithm for this class of functions and we obtain an upper bound for the accuracy which depends on a parameter of the class.     

Parametric modeling of microwave passive components using combined neural networks and transfer functions in the time and frequency

A novel parametric modeling technique is proposed to develop combined neural network and transfer function models for both time and frequency (TF) domain applications of passive components, where the neural network is trained to map geometrical variables to the coefficients of transfer functions. Built on our previous work, a new order‐changing module is developed to enforce stability of transfer functions and simultaneously guarantee continuity of coefficients. A constrained optimization strategy is introduced to enforce passivity of transfer functions through a neural network training process. A general equivalent circuit for two‐port passive components is generated directly from coefficients of arbitrary‐order transfer functions. Once trained, the parametric model can provide accurate and fast prediction of the electromagnetic behavior of passive components with geometrical parameters as variables. Compared to our previous work, the proposed method enables models to work well in the time domain providing good accuracy in challenging modeling applications. Two parametric modeling examples of spiral inductors and interdigital capacitors, and their application in both time and frequency domain simulations of a power amplifier are examined to demonstrate the validity of the proposed technique. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2013.     

Understanding post‐adoption IS usage stages: an empirical assessment of self‐service information systems

Organisations have invested in self‐service information systems (IS) to provide a direct interface for service delivery. Enriching the usage of these systems can provide organisations with immense benefits. However, limited research has been directed towards understanding post‐adoption IS usage behaviour in general and specifically in the context of self‐service IS. This study proposes post‐adoption IS usage behaviour as a broader concept constituting feature level usage of IS, integration of IS in the work system and exploration of new uses of IS. We evaluate how the new conceptualisation can be used to classify users at different stages of self‐service IS usage. Further, we examine user perceptions that differentiate among the users situated at different self‐service IS usage stages. Data were collected in the context of a self‐service Web‐based IS to validate the post‐adoption IS usage constructs and to examine the proposed thesis. The newly developed conceptual structure and measures for post‐adoption IS usage behaviour exhibit strong psychometric properties. The analysis shows three distinct post‐adoption IS usage stages and highlights that usefulness, user‐initiated learning, ease of use, satisfaction and voluntariness of use differentiate users at the different stages of post‐adoption IS usage. The results show that these variables aggregate into value confirmation and learning orientation as two higher‐level concepts. Further, we evaluate the predictive efficacy of the research model in classifying users into different post‐adoption self‐service IS usage stages. Implications are drawn for future research.     

Adaptive Decentralized NN Control of Nonlinear Interconnected Time‐Delay Systems with Input Saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large‐scale uncertain nonlinear time‐delay systems with input saturation. Radial basis function (RBF) neural networks (NNs) are used to tackle unknown nonlinear functions. Then, the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique, along with the minimal‐learning‐parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constraints are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all of the signals in the closed‐loop large‐scale system, while the tracking errors converge to a small neighborhood around the origin. An advantage of the proposed control scheme lies in the number of adaptive parameters of the whole system being reduced to one and in the solution of the three problems of “computational explosion,” “dimension curse,” and “controller singularity”. Finally, simulation results along with comparisons are presented to demonstrate the advantages, effectiveness, and performance of the proposed scheme.     

自适应模糊支持向量机算法研究

一个有效的核方法通常取决于选择一个合适的核函数。目前研究核方法的热点是从数据中自动地进行核学习。提出基于最优分类标准的核学习方法,这个标准类似于线性鉴别分析和核Fisher判别式。并把此算法应用于模糊支持向量机多类分类器设计上,在ORL人脸数据集和Iris数据集上的实验验证了该算法的可行性。     

基于图像矩阵判别局部保持投影的人脸识别

提出一种基于图像矩阵判别局部保持投影的人脸识别方法。图像矩阵判别局部保持投影是在局部保持投影基础上进行了扩展,考虑了类标签信息并在其目标函数中增加类间散度约束,使得求解的特征更具判别性。另外,图像矩阵判别局部保持投影是直接处理图像矩阵而不需要将矩阵转化为向量,保留了像素间的空间位置关系,避免了奇异性问题。实验结果表明该方法是有效的。     

C逆编译系统的中间语言的一种优化表示方法

数据类型的重新定义、控制流恢复和自定义函数或结构体的识别是结构化语言逆编译过程中的难点,以往研究多采用改进汇编语言的数据类型表示和增加自定义函数或结构体的特征量的方式来优化逆编译结果。使用中间语言抽象表示逆编译后得到的汇编语言的语言形式,并设计了一种新的逆编译模式,一定程度上提升了逆编译结果的识别率、可读性和完整性。     

基于Matlab与VC的弱视视功能检查系统的设计与实现

弱视是一种发育紊乱性疾病,我国弱视患者检出率高达2.8%。对弱视视功能进行检查可以帮助医生更准确判定病情,从而制定有效的个性化治疗方案。针对弱视患者存在的视功能缺损模式,采用心理物理学的阈限算法,设计了弱视视功能检查系统。系统软件部分利用VC中的MFC以及Matlab的Psychophysics Toolbox实现视功能检查模块,通过SQL Server建立后台数据库记录检查数据。同时,为了达到心理物理学上对视功能检查的特殊显示要求,硬件部分加入了Vedio Switcher进行输出视频信号的转换。系统经临床实际试用,表明系统稳定性好,能满足弱视视功能检查的特殊临床需要。     

论区间B样条曲线的升阶与割角的关系

为了解决区间B样条曲线的升阶理论问题,提出区间控制多边形概念,利用双次B样条基函数证明了区间B样条曲线具有升阶性质;并阐明了区间B样条曲线的升阶就是对其控制多边形的割角过程.最后证明了当升阶次数趋于无穷时,区间B样条曲线的控制多边形收敛到该曲线.     

一种基于双向2DMSD的人脸识别方法

提出一种基于双向二维最大散度差线性判别分析(Bidirectional 2DMSD)的人脸识别方法.该方法通过在水平和垂直2个方向上顺序执行2次二维最大散度差线性判别分析(2DMSD)运算,将判别特征信息压缩到图像的左上角,大大减少了图像特征的维数;选用二维最小近邻分类法进行分类,计算识别率.在ORL和Yale人脸数据库上的实验结果表明,该方法不仅在识别率上优于最大散度差线性判别分析(MSD),而且在与2DMSD具有相同识别率的情况下,特征维数比2DMSD大大减小,降低了计算复杂度,减少了识别时间,提高了人脸识别效率.