基于IP的存储网技术

文章分析存储网技术的现状,比较几个基于IP的存储网技术方案(FCIP、iFCP和iSCSI),并探讨目前基于IP的存储网技术所面临的问题。     

虚拟专用网的体系结构及实现

提出VPN接入设备的体系结构 ,包括系统的应用模型、层次划分和内部详细结构 ,阐述实现VPN的关键技术。     

无线局域网技术及应用

无线局域网技术是目前发展迅速的技术，相对于有线局域网有许多优点。随着无线网络产品性能的提高和价格的不断下降，无线局域网技术有着广泛的应用前景。据此对无线局域网的基本概念、特点及其技术作了简单的介绍，提出了无线局域网面向应用的几种解决方案，并对发展前景作了预测。     

Value management and common accounting performance measures for corporations

Management is aware that the value of firms is the ultimate measure of company performance. However, management has been using common accounting measures as an operating guide because the linkage between operational planning and value is vague and complex and, therefore, difficult to apply. Managers need to have clear targets and performance measures to track progress. This paper examines if a set of common accounting performance measures are linked tightly to the overall value of the firm. An abductive learning network (ALN) approach, an artificial intelligence technique, is used in this research because an ALN approach is non-parametric and can capture a subtle dynamic relationship between input and output variables, which is not obvious in linear statistical analysis. Empirical results show that an ALN model is very effective in synthesizing the value of the firm using six common accounting variables. The results also provide four strategic variables that can be used for devising strategic and operating plans to maximize the value of firms.     

构造性神经网络在测井岩性识别中的应用

测井岩性识别是石油勘探中十分重要的基础工作,准确的岩性识别结果可以为勘探和开发提供可靠的依据。人工神经网络方法可对复杂的高维数据进行非线性映射,在模式识别领域得到越来越广泛的应用。针对传统的BP神经网络算法存在收敛速度慢、隐层数以及隐层节点数难以确定等缺点,研究了一种构造性神经网络学习算法---级联算法（Cascade Correlation Algorithm,简称CC算法）及其在石油工程中的应用。采用该算法对甘肃镇原-泾川地区进行岩性识别研究,通过与BP神经网络的识别结果进行比较,体现出构造性神经网络的优越性。     

Inference and learning in hybrid probabilistic network

This paper proposed a novel hybrid probabilistic network, which is a good tradeoff between the model complexity and learnability in practice. It relaxes the conditional independence assumptions of Naive Bayes while still permitting efficient inference and learning. Experimental studies on a set of natural domains prove its clear advantages with respect to the generalization ability.     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

Sensitivity of generation reserve requirements in deregulated power systems

Since demand for power exhibits great variability the amount of firm energy to be purchased to meet real time demand based on forecasts is usually different from the realized demand for that period. The role of generation reserves is to meet the real time fluctuations of power demand. The predictable part of the demand is met through purchases of firm energy. In this paper a model is presented to determine optimal quantities of firm energy and generation reserves to meet random demands. The model is then parameterized introducing a set of factors to perform a sensitivity study. A full factorial experiment is designed to study the impact of five factors on the response variable (i.e., proportion of generation reserves on the total purchased quantity). An example consisting of 640 simulations corresponding to 2⁵ treatment combinations evaluated over 20 randomly generated mean demands is used to identify significant factors on the response variable. Results from the experiment suggest that generation reserve requirements should be adjusted considering changes in significant factors and in the mean demand over the dispatch interval.     

Research and Design of Network Management Construction Based on Mobile AGENT

The technical characters of mobile agent （MA） originated in the distributional artificial intelligence domain is introduced. A network management construction based on agent （NMCA） is then proposed. The NMCA structure features are elaborated in detail. A prototype design of NMCA is given by using the jKQML programming. The establishment of NMCA platform will be helpful to reduce the correspondence load of network management and improves the efficiency and the expansion ability of network management systems.