GENERALIZED INVERSE GROUP OF SIGNAL AND ITS IMPLEMENTATION WITH NEURAL NETWORKS

A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.     

光学全交叉网络与SW榕树（F＝S＝2）网络拓扑等价的多样性

本文对有关互连网络拓扑等价的分析方法作了简单评述。并采用互连网络拓扑等价的图分析法分析了自由空间光学互连全交叉网络与ＳＷ榕树网络的拓扑等价及其多样性，拓展了全交叉网络在数字光计算、光子交换及多处理机系统的潜在应用。     

Energy minimization and design for testability

The problem of fault detection in general combinational circuits is NP-complete. The only previous result on identifying easily testable circuits is due to Fujiwara who gave a polynomial time algorithm for detecting any single stuck fault inK-bounded circuits. Such circuits may only contain logic blocks with no more thanK input lines and the blocks are so connected that there is no reconvergent fanout among them. We introduce a new class of combinational circuits called the (k, K)-circuits and present a polynomial time algorithm to detect any single or multiple stuck fault in such circuits. We represent the circuit as an undirected graphG with a vertex for each gate and an edge between a pair of vertices whenever the corresponding gates have a connection. For a (k, K)-circuit,G is a subgraph of ak-tree, which, by definition, cannot have a clique of size greater thank+1. Basically, this is a restriction on gate interconnections rather than on the function of gates comprising the circuit. The (k, K)-circuits are a generalization of Fujiwara'sK-bounded circuits. Using the bidirectional neural network model of the circuit and the energy function minimization formulation of the fault detection problem, we present a test generation algorithm for single and multiple faults in (k, K)-circuits. This polynomial time aggorithm minimizes the energy function by recursively eliminating the variables.     

BP网络过拟合问题研究及应用

通过类比信息传递过程中的一般测不准关系式,研究BP网络出现过拟合现象时,代表网络学习能力的权值改变量与反映网络推广能力的未参与训练的检验样本的辨识误差之间的关系.在该关系式中引入一个过拟合参数P,通过对岷江流域紫坪埔站的流量预报,说明在判断网络是否出现过拟合现象时,计算过拟合参数P是一种有研究意义的参考.     

Analytical general results for handoff dwell time distribution in wireless networks

1 Introduction The calling dwell time characteristic is critical for the user network planning and deployment, e.g., global system for mobile communications (GSM), as well as the next generation wireless multimedia networks, such as, the currently standar…     

Bioprocess applications of model-based estimation techniques

The last decade has seen the development of a number of approaches for estimating those variables which are difficult to measure on-line in industrial process situations. Whilst a range of techniques is available, a common element is the use of process knowledge in the form of a system model. In the case of bioprocess systems, although a large range of models has been presented in the literature, their use in estimation schemes on an industrial scale has been limited. A number of reasons can be identified for their low level of utilisation. Of particular significance is the uncertainty which exists in quantifying system performance and the process-model mismatch which inevitably results. The level of ‘pre-defined model’ uncertainty, together with the knowledge gained during the course of the fermentation, serves to dictate estimator structure. The paper considers a range of estimation strategies and contrasts, through industrial applications, their performance characteristics and utility.     

中小企业创业资源的形成机制研究

创业资源是创业的基础,创业过程的规律性和市场配置资源的基础作用影响和决定中小企业创业资源的内在功能和形成方式。对中小企业资源的形成机制进行了分析探讨,并分析了它们之间的内在关系。     

Dynamic Survivability in WDM Mesh Networks Under Dynamic Traffic

Network survivability is a crucial requirement in WDM mesh networks. In this paper, we systematically consider the problem of dynamic survivability with dynamic single link failure in WDM networks under dynamic traffic demands. Specifically, we investigate various protection schemes, such as dedicated path protection (DPP), shared path protection (SPP), dedicated link protection (DLP), shared link protection (SLP), and two restoration schemes, path restoration (PR) and link restoration (LR). Moreover, two new shared protection methods are proposed, i.e., SRLG-based shared link protection (SRLG-SLP) and SRLG-based shared path protection (SRLG-SPP). The SRLG (shared risk link group) constraint defines the availability of protection resources to a working path, which requires that any two working paths sharing the same risk of failure (or in the same SRLG) cannot share the same protection resources. Furthermore, in our study, we consider a more practical dynamic single-link failure model, in which the link-failure-interarrival time and link-failure-holding time are considered as two independent parameters. Based on this link-failure model, extensive simulations are done to analyze and compare the dynamic survivable performance of various protection and restoration schemes. Resource utilization, protection efficiency, restoration efficiency, and service disruption ratio are employed as survivable performance metrics versus traffic load, link-failure frequency, and link-failure reparation time to evaluate the survivable performance. Many meaningful results are given. In addition, we show that the developed SRLG-SLP and SRLG-SPP protection schemes perform very well in terms of protection efficiency and service disruption ratio, while sacrificing some performance in terms of resource utilization.