制冷仿真实训系统

针对目前制冷企业技能培训的教学手段落后与制冷企业自动化管理水平普遍较低的现象。开发了制冷仿真软件1．0。该文首先阐明了软件的功能定位，并对开发工具MCGS的特点与运行机理作了讨论；通过制冷压缩机仿真模型的建立。重点分析了制冷仿真系统的建模方法——以各个设备为对象、对于不同的蒸发系统以热平衡和质平衡为主线，建立系统数学模型；对各个对象的传质与传热基理，在不违背制冷工艺变化规律的前提下，作恰当地较大简化，在此基础上使系统能动态地仿真其变化规律；最后对软件的各部分组成与功能作了介绍。     

飞行仿真系统中三维大气紊流模型仿真研究

为提高飞机飞行动力学系统的仿真逼真度,提出1种新的大气紊流模型,并研究出在实时飞行仿真系统中的实现技术。改变了以前基于Dryden模型的建模方法,对不能直接用于仿真模拟的Von．Karman大气紊流模型进行有理化处理,得出逼近于Von．Karman模型的新的紊流模型,确定新的滤波器参数,从而在时域内实时生成大气紊流。通过频域上的分析比较,结果表明该模型生成的紊流比以往的Dryden模型更好地符合了大气数理统计特性,并解决了Von．Karman的仿真实现问题。最后,将提出的数值模拟算法直接应用于某型飞机飞行仿真系统中,从而使飞机在风场中的模拟更加真实,对模拟器的研制具有实际意义。     

基于日志的网络背景流量模拟仿真

入侵检测系统（IDS）的开发与评估需要一个仿真的网络环境，网络流量模拟仿真技术是其中关键技术之一．在详细分析了网络流量的模拟仿真技术及其相关软件基础上，设计并实现了一种基于日志的网络背景流量模拟仿真软件，解决了入侵检测系统测试中的攻击类型定义和背景流量问题，并使用谊软件模拟真实的网络环境对入侵检测系统进行测试分析，实验结果表明，基于日志的网络背景流量仿真软件能够在日志信息的基础上以不同速度动态回放网络流量仿真数据，并能够对日志数据进行修改．增加了对入侵检测系统测试的灵活性．     

建筑安防设备实时控制虚拟仿真系统研究

虚拟现实技术在建筑中的运用目前还只局限于单纯的场景再现,对于建筑内突发事件的模拟以及安防设备应对突发事件时的交互和响应的仿真模拟程度还远远不够。针对这种现状,建立了能动态生成建筑内安防设备模型且能实时控制模型的虚拟仿真系统,且在系统中模拟出各种突发事件协助反映设备及控制人员应对建筑内突发事件的能力．提出通过虚拟设备模型控制真实设备的虚拟与真实相结合的模式。     

软开关Boost变换器的精确数学建模与混沌仿真

该文详细分析了电压反馈型零电流开关Boost变换器的工作过程，建立了变换器工作在不连续运行模式(DCM)下的精确离散映射，对此数学模型进行了稳定性分析，确定了变换器中元件参数与其分岔稳定性的关系，在此基础上准确计算出了系统稳定运行的典型参数范围，利用Natlab6．5软件中的电力系统仿真模块(SimPowerSystems)对电路进行仿真分析，针对ZCS Boost变换器的特点建立仿真模型，并在Matlab／Simulink软件包环境下对该模型进行模拟仿真，得到了满意的结果，与理论分析取得一致。     

基于虚拟DPU的生物质锅炉仿真研究

该文以国内某生物质电厂锅炉为研究对象，基于虚拟DPU技术开发一套生物质电厂锅炉仿真模型系统。介绍了生物质的锅炉特性，以燃烧系统为例，建立了燃烧系统的数学模型和仿真模型。应用某自动化集团的软件设计界面和虚拟DPU控制器内的组态，通过Pssim仿真系统搭建生物质电厂锅炉仿真模型，利用Group测点表和OPC技术实现虚拟DPU控制器和仿真模型间的通讯。此类技术仿真系统不仅在人员操作上更具有真实性，在热工自动控制方面也极为直观和真实，并可实现SIS系统和现场数据的接入仿真，监视与模拟效果更佳出色。     

机器人仿真与监控系统的研究

本文介绍了一种机器人仿真与监控系统，该系统能实时监视机器人的运动状态，本文对机器人的几何建模和运动学建模进行了研究．详细论述了机器人本体的建模方法．说明了机器人三维实时图形仿真系统的开发技术与开发方法。通过改变机器人的几何模型和运动学描述就能方便的对不同的机器人本体进行仿真与监控而不需要对源代码进行修改，最后，介绍了该系统分别应用于PT500和PT600不同机器人本体的实例。     

基于逻辑模型的过程仿真系统研究

企业的业务流程是非常复杂的,在为企业设计与流程相关的软件的失败率很高,原因在于所设计的软件处理的流程与企业实际运行的流程不相符合。针对该问题,本文采用一阶逻辑形式化的描述了一个过程仿真系统模型,并基于此模型设计过程仿真系统,以用于检验所仿真的流程是否与实际流程相符合。     

基于Agent的企业演化模型仿真研究

针对企业演化过程的动态性和不确定性,借鉴Holland教授的复杂适应系统理论的建模思想,提出运用神经网络的学习能力模拟智能体适应性的方法,建立基于复杂适应系统理论的企业演化模型.主要从企业行为的视角对处于动态竞争中的企业如何应对竞争实现企业演化的问题进行探讨,期望充实关于竞争和演化的理论.通过开发基于swarm平台的Ob-ject-c仿真程序,模拟了不确定环境下企业的规模分布和动态演化过程,并对仿真结果进行分析.     

传统PC市场正经历一次以外观与功能演变为先导的变革：PC当真要走了吗？

近几个月以来，关于一家或更多PC制造商可能退出过于饱和．已无利可图的PC制造业的传言越来越多。事实上这些传言并非空穴来风。因为2001年第三季度．IBM买出了约为30亿美元的打印机与PC给企业客户，却亏损7千万美元(亏损主要来自于计算机）康柏销售PC与掌上电脑所出现的亏损则更为严重。33亿美元的销售额．竟亏损2亿3．800万美元不过对于这种情况．华尔街分析师普遍认为．不管IBM或康柏．任谁都不会立即退出PC业。     

电力市场中发电企业竞价行为的RePast仿真

不完全信息,寡头垄断的电力市场中,发电企业竞价上网,面临的关键是如何采用最优的申报策略以获得最大的发电收益。基于多主体的建模思路和方法,将复杂适应系统理论与博弈理论相结合,利用RePast平台对发电企业的竞价过程进行了建模仿真研究。模型考虑了发电企业群体成员的自学习性及个体之间的交互行为。仿真过程中,各参与主体在各次竞价中积累知识,不断修正各自的预测函数,以达到最优收益。仿真结果验证了该方法的有效性。     

(Virtual) Agents for running electricity markets

This paper describes a multi-agent based simulation (MABS) framework to construct an artificial electric power market populated with learning agents. The artificial market, named TEMMAS (The Electricity Market Multi-Agent Simulator), explores the integration of two design constructs: (i) the specification of the environmental physical market properties and (ii) the specification of the decision-making (deliberative) and reactive agents. TEMMAS is materialized in an experimental setup involving distinct power generator companies that operate in the market and search for the trading strategies that best exploit their generating units’ resources. The experimental results show a coherent market behavior that emerges from the overall simulated environment.     

Implications from customer behavior for manufacturing

Due to increasing competition caused by globalization manufacturers have to reduce costs and at the same time provide better products to their customers’ individual needs. This can only be done, if the companies are able to understand the behavior of their customers and forecast the sales numbers for their individual products. One way to get a better prognosis of customer behavior patterns are observations on public market places. But the companies have to link together the observations with events influencing the decisions of customers. This can be done by using a decision support system which was developed for retailers in combination with a data warehouse. The experiences from this project can be transferred to manufacturing companies as well, helping them to achieve better planning data for the manufacturing process.     

Information of group-correlations in Korean financial market

We study two sides of the KOSPI, classified as an emerging market. First, the evolutionary property is examined in terms of overlapping matrix and survival ratios. To this end, we apply the random matrix theory (RMT) and the one-factor model to analyzing correlation matrix and finding business clusters. Second, we examine the relations between the market capitalization and the business. For the well-developed markets such as NYSE, the contribution of the firms to the second-largest eigenvector shows an exponential function of the market capitalizations while no clue is observed for the KOSPI. We confirm that the market capitalization is distributed in a power-law with the exponent 1.2 like a Pareto's distribution. Particulary, the KOSPI shows a different behavior compared to the mature market, that is, one or two companies lead a number of companies with the little money and big companies competed to win each other. The clusters also represent by largest eigenstates show a weak affiliation compared to smaller ones. These results imply that the KOSPI is the target for the short-positioned investors.     

LEARNING EVENTS FROM SIMULATION MODEL RUNS: FROM PROCEDURAL MODELSTO RELATIONAL ABSTRACTIONS

Simulation models are largely procedural and are proposed to be used to coordinate the-actions of an autonomous system. The problem, however, is the fact that procedural models lack a well-developed reasoning system. This paper suggests that relational representations, which have a relatively well-developed logical basis for deductive reasoning, be extracted from simulation models and be used as metaknowledge to reason about aspects of simulated behavior generated by procedural model simulation runs.     

Predicting published news effect in the Brazilian stock market

The Efficient Market Hypothesis states that the value of an asset is given by all information available in the present moment. However, there is no possibility that a single financial analyst be aware of all published news which refers to a collection of stocks in the moment they are published. Thus, a computer system that applies text mining techniques and the GARCH model for predicting the volatility of financial assets may helps analysts and simple investors classifying automatically the news which cause the higher impact on stock market behavior. This work has the goal of creating a method for analyzing Portuguese written news’s content about companies that have their stocks negotiated in a stock market and trying to predict what kind of effect these news will cause in the Brazilian stock market behavior. Also, it was demonstrated in this study that it is possible to find out whether certain news may cause a considerable impact on prices of a negotiated stock.     

Capacity sharing in a network of independent factories: A cooperative game theory approach

Market changes, uncertainties and unpredictability characterize the market environment nowadays. The possibility to cooperate in a network allows companies to stay competitive. This paper proposes a game theory coordination mechanism for a network of independent plants; the approach is based on the OWEN’s theorem properly simulated in order to investigate different performances in various environmental conditions. To support the network and face the capacity sharing issue, a Multi-Agent Architecture has been specifically developed. The simulation results show that the proposed mechanism could be considered a valid alternative to the more usual negotiation approaches in several market conditions. The plants gain considerable benefits by participating in a network supported by the cooperative mechanism proposed.     

SITUATED CELLULAR AGENTS APPROACH TO CROWD MODELING AND SIMULATION

The article describes a multi-agent approach to crowd modeling and simulation. After a brief introduction of the Situated Cellular Agents model, the guidelines to the crowd modeling approach is introduced as a way to support the communication among the different actors that are part of the simulation project team. The approach is then applied to describe a complex scenario providing a blend of competitive and cooperative behavior for pedestrian agents: an underground station. A module supporting the effective 3D visualization of simulated crowd dynamics is finally introduced, as an instrument for the communication of simulation results to decision makers and nonexperts in crowd phenomena.     

The Emergence of Knowledge Exchange: An Agent-Based Model of a Software Market

We investigate knowledge exchange among commercial organizations, the rationale behind it, and its effects on the market. Knowledge exchange is known to be beneficial for industry, but in order to explain it, authors have used high-level concepts like network effects, reputation, and trust. We attempt to formalize a plausible and elegant explanation of how and why companies adopt information exchange and why it benefits the market as a whole when this happens. This explanation is based on a multiagent model that simulates a market of software providers. Even though the model does not include any high-level concepts, information exchange naturally emerges during simulations as a successful profitable behavior. The conclusions reached by this agent-based analysis are twofold: 1) a straightforward set of assumptions is enough to give rise to exchange in a software market, and 2) knowledge exchange is shown to increase the efficiency of the market.      

Management decision support using long-term market simulation

In recent years Agent-based Computational Economics (ACE) has become an increasingly important method in market simulation. After liberalization of many former governmental owned or controlled industries the used operations research models are not longer sufficient to simulate market behavior due to individual action and increasing competition. Agent-based simulation appears to be an alternative approach considering also individual behavior and competition. Some short-term simulation approaches have shown promising results for the simulation in the domain of electricity markets. Picking up the desire for a long-term oriented simulation, this paper presents a basic agent-based model considering the investment decision within long-term planning of electricity markets. Additionally, regulatory agents are introduced as a third side in the market simulation to represent governmental decisions. This results in the definition of three types of agents representing electricity generating companies, consumers and governmental instances.