Magento Block设计分析

Magento中的Block是一个很重要的组件.它在Magento中充当非常重要的角色.本文主要讨论Magento中Block的设计.     

Globally Exponential Stability for Delayed Neural Networks Under Impulsive Control

In this paper, the dynamic behaviors of a class of neural networks with time-varying delays are investigated. Some less weak sufficient conditions based on p-norm and ∞-norm are obtained to guarantee the existence, uniqueness of the equilibrium point for the addressed neural networks without impulsive control by applying homeomorphism theory. And then, by utilizing inequality technique, Lyapunov functional method and the analysis method, some new and useful criteria of the globally exponential stability with respect to the equilibrium point under impulsive control we assumed are derived based on p-norm and ∞-norm, respectively. Finally, an example with simulation is given to show the effectiveness of the obtained results.     

Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method

Contaminant intrusion in a water distribution network is a complex but a commonly observed phenomenon, which depends on three elements – a pathway, a driving force and a contamination source. However, the data on these elements are generally incomplete, non-specific and uncertain. In an earlier work, Sadiq, Kleiner, and Rajani (2006) have successfully applied traditional Dempster–Shafer theory (DST) to estimate the “risk” of contaminant intrusion in a water distribution network based on limited uncertain information. However, the method used for generating basic probability assignment (BPA) was not very flexible, and did not handle and process uncertain information effectively. In this paper, a more pragmatic method is proposed that utilizes “soft” computing flexibility to generate BPAs from uncertain information. This paper compares these two methods through numerical examples, and demonstrates the efficiency and effectiveness of modified method.     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

Performance modeling,real-time dispatching and simulation of wafer fabrication systems using timed extended object-oriented Petri nets

Semiconductor wafer fabrication system (SWFS) is the most complex and capital-intensive phase in the entire semiconductor manufacturing cycle. With characteristics of re-entrant processing routes, equipment uncertainty, product diversity and improving technologies, great challenges are presented in SWFS’s modeling, scheduling and simulation. To implement efficient production control, this paper provides a timed extended object-oriented Petri nets (TEOPNs) approach to performance modeling, real-timed dispatching and simulation of SWFSs. The TEOPNs models are constructed in a hierarchy to accord with the real-world SWFS’s organization, and a new type of signal place is added into the TEOPNs to respond the dynamic states of all processing facilities. A novel autonomy and coordination-based real-time dispatching mechanism (A&C-RDM) is developed in this paper, which executes under the support of the TEOPNs-based hybrid real-time dispatching control system (HRDCS). Owning to the ability of gathering dynamic real-time information of all production facilities and WIP products, the HRDCS can make adaptive dispatching decisions according to the local and global real-time processing status. Two sets of key elements of real-time dispatching, i.e. the state thresholds and dispatching rules, are defined in the HRDCS so that the A&C-RDM can integrate different types of dispatching rules. A set of simulation experiments prove the efficiency of the proposed modeling and dispatching algorithm. In summary, the proposed TEOPNs, HRDCS and A&C-RDM form the cornerstones of a real-time dispatching simulation prototype of SWFS, and the work described in this paper carries out an advanced integrated “modeling–dispatching–simulation” methodology.     

Finite-time weighted average consensus with respect to a monotonic function and its application

The consensus state is an important and fundamental quantity for consensus problems of multi-agent systems, which indicates where all the dynamical agents reach. In this paper, weighted average consensus with respect to a monotonic function, which means that the trajectories of the monotonic function along the state of each agent reach the weighted average of their initial values, is studied for a group of kinematic agents with time-varying topology. By constructing a continuous nonlinear distributed protocol, such a consensus problem can be solved in finite time even though the time-varying topology involves unconnected graphs. Then the distributed protocol is employed to compute the maximum-likelihood estimation of unknown parameters over sensor networks. Compared with the existing results, the estimate scheme proposed here may reduce the costs of data communication, storage memory, book-keeping and computational overheads.     

Sweeping Points

Given a set of points in the plane, and a sweep-line as a tool, what is best way to move the points to a target point using a sequence of sweeps? In a sweep, the sweep-line is placed at a start position somewhere in the plane, then moved orthogonally and continuously to another parallel end position, and then lifted from the plane. The cost of a sequence of sweeps is the total length of the sweeps. Another parameter of interest is the number of sweeps. Four variants are discussed, depending on whether the target is a hole or a pile, and whether the target is specified or freely selected by the algorithm. Here we present a ratio 4/π≈1.27 approximation algorithm in the length measure, which performs at most four sweeps. We also prove that, for the two constrained variants, there are sets of n points for which any sequence of minimum cost requires 3n/2?O(1) sweeps.     

OMT在企业物流分析中的应用

文章介绍了面向对象的模型化技术（ＯＭＴ）的基本概念,分析了它的实现过程,即对象模型、动态模型、和过程模型的建立,并以一个具体的实例说明其在企业物流分析中的应用。     

一种简化的人体步行神经网络模型

本文针对复杂的人体步行的神经 -肌肉 -骨胳系统模型 ,分析了步行中的主要关系 ,对神经网络模型作了较大的简化和修改 ,提出了一个简单的神经网络模型 .该模型在步行状态、神经耦合、感觉信号反馈等方面都作了简化 .计算机仿真表明 ,该模型能产生稳定的步行运动