Fuzzy local maximal marginal embedding for feature extraction

In graph-based linear dimensionality reduction algorithms, it is crucial to construct a neighbor graph that can correctly reflect the relationship between samples. This paper presents an improved algorithm called fuzzy local maximal marginal embedding (FLMME) for linear dimensionality reduction. Significantly differing from the existing graph-based algorithms is that two novel fuzzy gradual graphs are constructed in FLMME, which help to pull the near neighbor samples in same class nearer and nearer and repel the far neighbor samples of margin between different classes farther and farther when they are projected to feature subspace. Through the fuzzy gradual graphs, FLMME algorithm has lower sensitivities to the sample variations caused by varying illumination, expression, viewing conditions and shapes. The proposed FLMME algorithm is evaluated through experiments by using the WINE database, the Yale and ORL face image databases and the USPS handwriting digital databases. The results show that the FLMME outperforms PCA, LDA, LPP and local maximal marginal embedding.     

Generalized extreme learning machine acting on a metric space

Functional data learning is an extension of traditional data learning, that is, learning the data chosen from the Euclidean space ${\mathbb{R}^{n}}$ to a metric space. This paper focuses on the functional data learning with generalized single-hidden layer feedforward neural networks (GSLFNs) acting on some metric spaces. In addition, three learning algorithms, named Hilbert parallel overrelaxation backpropagation (H-PORBP) algorithm, ν-generalized support vector regression (ν-GSVR) and generalized extreme learning machine (G-ELM) are proposed to train the GSLFNs acting on some metric spaces. The experimental results on some metric spaces indicate that GELM with additive/RBF hidden-nodes has a faster learning speed, a better accuracy, and a better stability than HPORBP algorithm and ν-GSVR for training the functional data. The idea of GELM can be used to extend those improved extreme learning machines (ELMs) that act on the Euclidean space ${\mathbb{R}^{n}, }$ such as online sequential ELM, incremental ELM, pruning ELM and so on, to some metric spaces.     

Stability of a class of switched stochastic nonlinear systems under asynchronous switching

The stability of a class of switched stochastic nonlinear retarded systems with asynchronous switching controller is investigated. By constructing a virtual switching signal and using the average dwell time approach incorporated with Razumikhin-type theorem, the sufficient criteria for pth moment exponential stability and global asymptotic stability in probability are given. It is shown that the stability of the asynchronous stochastic systems can be guaranteed provided that the average dwell time is sufficiently large and the mismatched time between the controller and the systems is sufficiently small. This result is then applied to a class of switched stochastic nonlinear delay systems where the controller is designed with both state and switching delays. A numerical example illustrates the effectiveness of the obtained results.     

协调的分布式决策虚拟企业风险管理模型

虚拟企业要取得预期的利润,就必须成功规避运行中的风险.考虑虚拟企业的分布性以及联盟成员之间关系多样性的特点,根据非合作完全信息静态博弈理论,采用分布式决策的思想对虚拟企业风险进行管理.针对各盟员构成非团队关系,并且不存在机会主义行为的虚拟企业,探讨了其风险管理的特点,以及在风险管理问题上各盟员之间可能达成的协作;建立了协调的分布式虚拟企业风险管理模型,给出了模型的求解方法,同时设计了相应算法对模型进行求解,并对解的不同情况进行了分析;另外还比较了不同协调原则下联盟总利润以及各盟员利润的变化情况.仿真分析表明,所提出的模型和算法,能在虚拟企业各盟员风险管理中起到协调控制的作用,达到“双赢”的目的.     

A novel technique to measure the group velocity of sound in dispersive media

A technique is proposed for measuring the group velocity of sound by a frequency-modulated (FM) continuous wave (CW) technique. Detailed calculations are presented, showing that V(g)=(deltaomega(m)/deltaphi)d , where V(g), deltaomega(m), deltaphi, and d are, respectively, the group velocity, change of modulation frequency, phase change of the received signal detected by a biphase lock-in amplifier, and the distance traveled by the sound. The approach has the advantage of high resolution compared to the conventional time-of-flight measurement technique.     

经食道心房调搏的放射性核素心血管造影

本文应用反符合电路剔除调搏脉冲干扰的方法,制成经食道心房调搏心电心音门电路触发装置,用于经食道心房调搏的核素心血管造影。参考Tzivoni标准,当逐级增加调搏心率至140次/min或亚极量心率后发现:冠心病人有调搏后EF降低;急性心肌梗塞病人EF减低更为明显,并呈室壁反向运动。因此,心房调搏核素心血管造影有助于冠心病,特别是心肌梗塞的诊断。     

A self-adjusting algorithm for byzantine agreement

Summary Byzantine Agreement is important both in the theory and practice of distributed computing. However, protocols to reach Byzantine Agreement are usually expensive both in the time required as well as in the number of messages exchanged. In this paper, we present a self-adjusting approach to the problem. The Mostly Byzantine Agreement is proposed as a more restrictive agreement problem that requires that in the consecutive attempts to reach agreement, the number of disagreements (i.e., failures to reach Byzantine Agreement) is finite. Fort faulty processes, we give an algorithm that has at mostt disagreements for 4t or more processes. Another algorithm is given forn3t+1 processes with the number of disagreements belowt ²/2. Both algorithms useO(n ³) message bits for binary value agreement. Yi Zhao is currently working on his Ph.D. degree in Computer Science at University of Houston. His research interests include fault tolerance, distributed computing, parallel computation and neural networks. He obtained his M.S. from University of Houston in 1988 and B.S. from Beijing University of Aeronautics and Astronautics in 1984, both in computer science. Farokh B. Bastani received the B. Tech. degree in electrical engineering from the Indian Institute of Technology, Bombay, India, and the M.S. and Ph.D. degrees in electrical engineering and computer science from the University of California, Berkeley. He joined the University of Houston in 1980, where he is currently an Associate Professor of Computer Science. His research interests include software design and validation techniques, distributed systems, and fault-tolerant systems. He is a member of the ACM and the IEEE and is on the editorial board of theIEEE Transactions on Software Engineering.     

降膜蒸发过程的数值模拟和传热传质分析

利用CFD软件对逆流降膜蒸发过程进行了实验模拟研究,研究了速度边界层、热边界层和浓度边界层的变化对降膜蒸发传热传质特性的影响规律;通过建立一维逆流降膜蒸发的数学方程编程求解出了对流传热传质的Nu数和Sh数,利用Fluent软件模拟出的实验结果采用回归分析得出了气液流量比Raw、流道的长宽比αL、空气进口无量纲温度θai以及空气进口Re数与Nu数、Sh数之间的无量纲关系式,可为降膜蒸发换热器的设计提供参考。     

基于分形扫描的高灰度平板显示的硬件实现

本文将分形算法应用于2048级高灰度平板显示的扫描映射,使用平板显示系统灰度图像的最优扫描结构及分形模型,以解决平板显示系统扫描形成灰度图像过程中的时间冗余问题。由分形扫描核心模块,得到了相应的每个扫描时间对应的子空间码和位码序列进行扫描,然后通过检测模块验证了2048级高灰度分形扫描方法的准确性,最终由硬件系统验证了2048级高灰度分形扫描的可实现性。