基于Flash的网络游戏传输模式

该文介绍了Flash开发游戏的优势,分析了基于Flash的网络游戏的几种网络传输模式,通过一个游戏在线用户列表实例,探讨了采用XML网络传输模式的建立方法。     

Simulating serious games: a discrete-time computational model based on cognitive flow theory

This paper presents a computational model for simulating how people learn from serious games. While avoiding the combinatorial explosion of a games micro-states, the model offers a meso-level pathfinding approach, which is guided by cognitive flow theory and various concepts from learning sciences. It extends a basic, existing model by exposing discrete-time evolution, allowing for failure, drop-out, and revisiting of activities, and accounting for efforts made and time spent on tasks, all of which are indispensable elements of gaming. Three extensive simulation studies are presented involving over 10,000 iterations across a wide range of game instances and player profiles for demonstrating model stability and empirical admissibility. The model can be used for investigating quantitative dependences between relevant game variables, gain deeper understanding of how people learn from games, and develop approaches to improving serious game design.     

P2P模型在校园网上的应用研究

针对目前流行的P2P网络模型的特点,结合非结构化的P2P网络Gnutella路由和结构化的P2P Pastry路由,提出了一种应用在校园网内的P2P结构模型方案,并给出了详细的P2P节点结构和路由,同时提出了设计该网络模型上的P2P网络应用软件的可行性.     

A new neural network model for the feedback stabilization of nonlinear systems

A new neural network model termed ＇standard neural network model＇ （SNNM） is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities （LMIs）, which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks （including the chaotic neural network） and nonlinear systems modeled by neural networks or Takagi and Sugeno （T-S） fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.     

基于网络平台的教学模式设计

本文通过对网络教学平台及教学方法的研究分析,设计了一套基于网络平台的教学模式,提出了弹性化、个性化、层次化的网络教学新途径。     

浅析网络游戏的经营模式

网络游戏作为一种新兴的产业,近几年发展特别迅速。网络游戏是文化、艺术和高技术的结合体,网络游戏产业具有重大的经济和文化价值,对传承和繁荣民族文化具有重要作用。     

基于Elman网络的企业盈利能力测评模型

为了克服传统的企业盈利能力测评方法的诸多缺陷,发挥神经网络能自学习、自组织,能在事先不知事物内部机理的情况下进行建模的优势,建立基于Elman网络的测评模型,对企业盈利能力进行测评,结果表明该模型的仿真结果可靠,测评精度高,具有很大的实用价值.     

基于找形模型研究微丝束对细胞骨架刚度的影响

研究目的：基于找形分析建立的细胞骨架力学模型研究微丝束对细胞骨架刚度的影响。创新要点：目前存在的细胞模型很少考虑微丝束对细胞力学特性的重要作用。本文基于细胞找形模型模拟了同时包含微丝和微丝束的细胞骨架网络结构,并且分析了细胞中微丝束的排列方向、微丝束的含量以及微丝波动对细胞刚度的影响。研究方法：基于找形模型,随机生成由微丝、微丝束（梁单元）以及交联蛋白（索单元）形成的细胞骨架网络结构,依靠非线性有限元计算和样本统计,计算出模型的弹性模量。通过分别改变模型中微丝束的排列方向、微丝束的含量以及模型初始最大位移等参数,得出细胞骨架模型的弹性模量随这些参数的变化趋势,以此来研究微丝束对细胞刚度的影响。重要结论：细胞骨架网络中微丝的波动会导致细胞刚度降低;与拉伸方向平行排列的微丝束可以显著地提高细胞的刚度,相比之下随机分布的微丝束对细胞刚度没有贡献;在微丝材料总量固定的情况下,细胞刚度随着平行排列微丝束含量的增加呈现出先升高后降低的趋势。     

Synchronization criteria based on a general complex dynamical network model

Many complex dynamical networks display synchronization phenomena. We introduce a general complex dynamical network model. The model is equivalent to a simple vector model of adopting the Kronecker product. Some synchronization criteria, including time-variant networks and time-varying networks, are deduced based on Lyapunov＇s stability theory, and they are proven on the condition of obtaining a certain synchronous solution of an isolated cell. In particular, the inner-coupling matrix directly determines the synchronization of the time-invariant network; while for a time-varying periodic dynamical network, the asymptotic stability of a synchronous solution is determined by a constant matrix which is related to the fundamental solution matrices of the linearization system. Finally, illustrative examples are given to validate the results.     

两自由度闭链机器人的神经网络自适应控制

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical use. This paper presents an adaptive proportional integral differential (PID) control algorithm based on radial basis function (RBF) neural network for trajectory tracking of a two-degree-of-freedom (2-DOF) closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.     

用CSocket实现网络文件的传输

文件在网络上的传输往往需要客户发送端与服务器接收端多次的交互才能完成，本文介绍一种用微软的基本类库(Microsoft Foundation Class，简称MFC)的套接字CSocket类来实现的文件在网络上传输的新方法。     

基于SLSD道路网络模型的最优路径算法

提出了基于单线单向（SLSD）道路网络的最优路径算法.不同于传统网络,在SLSD网络中,路元素被抽象成网络的节点,且都是单向单线的;而道路节点被抽象成网络的链接.该网络模型可以很好地表述拐弯限制、回路以及多条道路存在于2个路口等只有超图模型才能很好表示的真实路网情形.基于此网络模型,给出了相关的最优路径算法,并且证明了将超图转化为SLSD道路网络后,A＊及Diskstra算法可以不加修改直接用于计算任何真实路网的最优路径.最后,结合新加坡道路网络数据,给出了一个预先计算的两步法最优路径算法及其计算结果,验证了所提出的模型和算法.     

基于模糊观测数据的RBF神经网络回归模型

提出了一种基于模糊观测数据的RBF神经网络（FORBFNN）,用于解决一类输出不可精确测量但可用模糊隶属度来表征的非线性系统建模问题．神经网络模型中各隐层神经单元的权重系数采用一种新的模糊EM算法辨识获得;隐层神经单元的数量及径向基函数的中心和宽度基于一种数据驱动的方法自适应确定,即首先初始生成一个隐层单元,然后根据一定的规则逐步加入新的单元,该过程不断迭代直到模型满足预设要求．该方法同时考虑了模型的复杂度及预测精度．数值模拟实验结果表明该建模方法是有效的,且建立的模型具有较高的预测精度．     

Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model

In this paper, an application of a nonlinear predictive controller based on a self recurrent wavelet network （SRWN） model for a direct internal reforming solid oxide fuel cell （DIR-SOFC） is presented. As operating temperature and fuel utilization are two important parameters, the SOFC is identified using an SRWN with inlet fuel flow rate, inlet air flow rate and current as inputs, and temperature and fuel utilization as outputs. To improve the operating performance of the DIR-SOFC and guarantee proper operating conditions, the nonlinear predictive control is implemented using the off-line trained and on-line modified SRWN model, to manipulate the inlet flow rates to keep the temperature and the fuel utilization at desired levels. Simulation results show satisfactory predictive accuracy of the SRWN model, and demonstrate the excellence of the SRWN-based predictive controller for the DIR-SOFC.     

Stability analysis of discrete-time BAM neural networks based on standard neural network models

To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.     

以太无源光网络接收机的建模(英文)

建立了以太无源光网络接受机的行为模型,该模型包括光纤、光电探测器、跨阻放大器、限幅放大器及时钟和数据恢复电路.模型中各部分都是利用电路结构和行为特性来进行建模的,同时也对它们的噪声和抖动进行了建模,如散粒噪声、热噪声、确定性抖动和随机抖动.由于使用了行为模型,仿真速度比普通电路模型更快,同时比解析模型更准确.整个模型用C + +语言描述,在Microsoft Visual C + +6.0 中进行实现,并利用蒙特卡洛方法对系统仿真.最后建立了测试环境对模型进行验证,结果表明仿真结果与实验结果有较好的一致性.     

基于最小生成树模型的通信网络铺设方案构建

针对通信网络设计问题,通过有关数据分析,运用最小生成树模型并结合prim算法得出使通信网络的总铺设费用最省的铺设方案,分别考虑通信网络结点与链路的可靠性,对铺设方案进行进一步非线性规划,从而保证通信畅通的结点都能够达到90%。     

基于传染病扩散规律和聚类分析的动态疫苗配送模型（英文）

针对当传染病发生时医药物资的配送对于应急需求响应至关重要这一特点,以在一定区域内发生传染病疫情为研究背景,根据传染病扩散规律和疫区紧急救助程度的不同,研究了具有多个疫区和多个应急配送中心的疫苗配送最优方案.引入具有脉冲接种的SIQR模型来描述传染病的扩散规律和获得各区域疫苗需求量,在SIQR模型的基础上,利用SOM神经网络将各疫区进行聚类并量化结果.以实现运输成本和缺货量最小为目标,在聚类的基础上建立了动态的疫苗配送模型.以20个受感染区域和4个分布中心为例,给出了算例和仿真分析,结果表明所提出的方法有助于控制具有较高紧急等级区域的疫情,且与不使用聚类方法进行疫苗配送相比,利用聚类方法能更好地控制疫情扩散.     

对高职高专英语多媒体网络教学的思考

随着我国英语教学改革的实施,多媒体网络教学也在各高职院校试行和推广。在多媒体网络环境下,各种教学资源将得以更有效的应用,有利于创设一个智能化的教学环境,使学生从被动接受知识的模式中解放出来。然而,如果过多过滥地运用多媒体教学,违背了多媒体“辅助”教学的宗旨,就会使学生在课堂上走马观花,导致课堂教学流于形式。本文分析我国高职院校英语课程采取这种教学模式的必要性,以及存在的问题,提出多媒体教育应与传统教育相结合。     

Comparative study and error analysis of digital elevation model interpolations

Researchers in P. R. China commonly create triangulate irregular networks （TINs） from contours and then convert TINs into digital elevation models （DEMs）. However, the DEM produced by this method can not precisely describe and simulate key hydrological features such as rivers and drainage borders. Taking a hilly region in southwestern China as a research area and using ArcGISTM software, we analyzed the errors of different interpolations to obtain distributions of the errors and precisions of different algorithms and to provide references for DEM productions. The results show that different interpolation errors satisfy normal distributions, and large error exists near the structure line of the terrain. Furthermore, the results also show that the precision of a DEM interpolated with the Australian National University digital elevation model （ANUDEM） is higher than that interpolated with TIN. The DEM interpolated with TIN is acceptable for generating DEMs in the hilly region of southwestern China.