Validation of a Method of Determination of Apparent Diffusivity Versus Composition in Solids

A method is presented for computing the values of apparent diffusivity in solids with respect to the concentration of the diffusing substance (water or sodium chloride). This method does not require any assumption upon the mathematical relationship between diffusivity and concentration. It can be applied to experimental measurements of local concentration versus position within the solid (profiles) with relatively few measurements (circa 10) and a mathematical smoothing of the experimental data by using an artificial neural network model. The method was first validated on simulated data obtained by using a constant diffusivity value and on experimental profiles when the relation between diffusivity and concentration was given. It was then applied to original experimental moisture profiles obtained by putting gelatin gels with different initial moisture contents into contact for up to 14 days. The method was also successfully applied to five sets of experimental moisture and sodium chloride profiles taken from the literature and obtained from different food products. Apparent diffusivities calculated by our method were found in agreement with those obtained by authors using different numerical methods to compute the diffusivity values.     

智能化系统在新火车站的应用——福州火车站智能化系统设计总结

福州火车站智能化系统包括到发通告、旅客引导显示、客运广播、建筑设备自动控制等7个子系统和系统总集成。     

Mobility Influences on the Capacity of Wireless Cellular Networks

Capacity has always been a major concern in wireless networks. This letter studies the impact of mobility on the overall system capacity in wireless cellular networks. In this letter, we present a simple system model which we developed to capture the inherent relationships among system capacity, new call blocking probability, handoff dropping probability, call terminating probability, and bandwidth utilization rate. We investigate the complex relationship between mobility and capacity‐related parameters. Through simulation, we demonstrate that mobility has a significant impact on capacity and is reversely proportional to the bandwidth reserved for handoff traffic.     

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.