电子CAD框架系统的长事务处理和网络版本管理

用面向对象方法管理框架系统中的工程和设计数据已成为大多数人的共识．本文对该系统中的Ｓｅｒｖｅｒ／Ｃｌｉｅｎｔ结构和并发控制、长事务管理、网络上的对象版本管理等进行了论述，提出了想法和实现．该系统已应用到电子ＣＡＤ框架系统中．     

面向Agent的程序设计

本文针对所谓合作Agent应用问题阐述了一种面向Agent的程序设计AOP（agentori－entedprogramming）方法框架．其中提出了一种新的Agent编程语言（AOPL），设计并实现了其程序设计系统（AOPS），该系统支持AOPL到C的转换．同时，提出了一种新的Agent关系模型，讨论了该模型的组成及其在体现合作Agent应用系统的体系结构、指导Agent之间的协作行为和支持对系统结构特点的深层理解方面所发挥的重要作用．最后讨论了AOP在多功能感知系统中的应用．     

使用模型检测解决概率布尔网络优化控制

系统生物学期望对复杂生物系统建立一个真实的、可计算的模型,以便于以系统的角度去理解生物系统的演变过程。在系统生物学中,一个重要的主题是通过外部的干预控制发展关于基因调控网络的控制理论,以作为未来基因治疗技术。目前,布尔网络及其扩展的概率布尔网络已经被广泛用于对基因调控网络进行建模。在控制问题的研究中,概率布尔控制网络的状态迁移本质上构成一条有限状态空间的离散时间马尔科夫决策过程。依据马尔科夫决策过程的理论,通过概率模型检测方法解决网络中有限范围优化控制问题和无限范围优化控制问题。针对带有随机干扰且上下文相关的概率布尔控制网络,使用概率模型检测器PRISM对其进行形式化建模,然后将两类优化控制问题描述为相应的时序逻辑公式,最后通过模型检测寻找出最优解。实验结果表明,提出的方法可以有效地用于生物网络的分析和优化控制。     

公平转换系统规范及其应用*

本文讨论了用于并发系统规范的2种方法；时序逻辑方法和状态自动机方法．由此，本文提出了一种新的规范形式——公平转换系统规范FTSS(fair transition system specification)．此规范方法集成了状态自动机方法和时序逻辑方法的优点，改进了时序逻辑方法通常较复杂、不易理解，特别是它不能用于描述并发系统的局部性质等不足．进一步对FTSS中的每一部分进行了讨论，得到结论；FTSS是机器封闭的，规范过程是相容的且是完全的．一个有丢失传输协议的例子表明作者的方法具有简单、直观、易于理解和便于使用等特点．最后给出了FTSS的一些应用．它为程序验证和并发系统的逐步求精提供了一个统一的框架，已成功地应用于程序验证中．     

神经场整体性和增殖性研究与分析

信息几何是20世纪90年代初开始形成的理论，它将流形上的现代微分几何方法引入到神经计算科学中，为神经网络和信息论提供了十分有用的新的数学工具，也为大脑信息传输方式引入新的观念．以信息几何为工具，研究由全体神经网络组成的非线性空间神经场的整体不变性，分析和证明了神经场复杂结构的可分解性，提出了知识可增殖人工神经网络模型．其结果将有助于理解和解释人的感知系统的组织结构、定位机理和嵌入问题，提高神经网络复杂模型的研究水平和层次，挖掘认知科学在计算机模式上新的突破点；有助于增强神经网络的可理解性，为其提供了重要的理论基础．     

对数字经济框架的理论经济学整合

为了提高经济学的解释力，数字经济学需要在框架上进行重大改进。经济学需要进行新的综合，在理论经济学水平，将现有框架之外的新的解释变量纳入进来，完善解释系统。为此，要进行历史方法与逻辑方法的综合、古典经济学（政治经济学与制度经济学）与新古典经济学的综合。     

基于信息融合的入侵检测模型与方法

研究了入侵检测系统(IDS)研究现状，针对当前IDS系统误报率高和对时间及空间上分散的协同攻击无法有效检测的缺陷，引入信息融合和多传感器集成的观点，提出了一个多层次的IDS推理框架和原型系统．该原型系统采用贝叶斯网络作为多传感器融合的工具，用目标树的方法来分析协同攻击的攻击企图，并最终量化系统的受威胁程度．相比现有的IDS，该原型的结构更加完整，能够更容易发现协同攻击并有效的降低误报．     

现代化远程教育系统建设研究

通讯技术的迅猛发展和计算机网络的广泛应用，促进了远程网络教学的发展。现代化远程教育作为一种教学手段，在科学技术含量上是空前的。它采用了通讯卫星系统、互联网络及其他多媒体手段进行实况或非实况教学活动。远程教育突出的特点是具有非线性结构和交互性功能．即可在授课内容、授课时间及学习过程方面．打破传统的线性框架和模式．使教．学双方自由地．有机地结合．从而提高效率。     

基于Struts的网络管理系统设计与开发

为了缩短综合网络管理系统开发周期，提高软件质量，并使系统各模块间具有松耦合的特点，根据综合网管的需求设计了具有结构层次化和功能组件化的系统模型。在系统模型的实现方法上采用基于MVC模式的Struts框架。框架将对用户请求的控制、处理与反馈分别交由位于控制层、模型层和视图层的类组件来实现。最后重点描述了综合网络管理系统各层在Struts框架上所处的位置和对各个类进行的扩展。     

基于Servlet/JSP和XML技术的远程教学系统的研究与实现

本文研究了基于Web的远程教学系统，介绍其功能和结构，探讨了现代远程教学系统的实现技术。在本文中采用新的网络发布框架，提出了采用Java Servlet/JSP和XML技术的系统实现方案，并着重阐述了实现远程教学系统的关键技术。     

Challenges for intelligent systems in biology

Biological processes have produced the ultimate intelligent system (humans), and now we are trying to understand biology (and ourselves) by building intelligent systems. Intelligent systems research in biology strives to understand how living systems perform difficult tasks routinely (ranging from molecular phenomena such as protein-folding to organism-level phenomena such as cognition). The definition of intelligent systems in biology can lead to hours of debate. Some say that all high-performance systems that do something difficult with (or to) biological data should be considered intelligent systems. Others insist that the term intelligent system should be reserved for systems using the methods typically associated with modem AI.     

人工生命

人工生命是一类具有生命特征，以自适应、自组织为主要特征的一类复杂系统，其研究成果可广泛用于生物学及计算机科学等许多工程领域。文中介绍了人工生命的产生背景、定义、研究内容、研究方法、理论基础、研究意义及与人工智能等相关学科的区别，以便全面深入地理解它。     

Complex agent networks: An emerging approach for modeling complex systems

Complexity and complex systems are all around us: from molecular and cellular systems in biology up to economics and human societies. There is an urgent need for methods that can capture the multi-scale spatio-temporal characteristics of complex systems. Recent emphasis has centered on two methods in particular, those being complex networks and agent-based models. In this paper we look at the combination of these two methods and identify “Complex Agent Networks”, as a new emerging computational paradigm for complex system modeling. We argue that complex agent networks are able to capture both individual-level dynamics as well as global-level properties of a complex system, and as such may help to obtain a better understanding of the fundamentals of such systems.     

一种由代谢方程构建网络的方法及其实现

代谢网络的拓扑结构分析和可视化是系统生物学研究的热点。如何自动合理地由数量众多的代谢方程构建代谢网络是研究者首先面临的问题。基于二部图模型,采用节点唯一化和忽略高度节点策略,提出一种由代谢方程直接构建代谢网络的方法,并给出MATLAB实现步骤,最后使用生物网络模拟软件可视化生成的代谢网络,结果表明这种方法能用来构建复杂代谢网络,并具有较好展现网络模块和拓扑结构的特点,适用于由大量代谢反应方程自动构建复杂代谢网络的一般应用需求。该方法的MATLAB源码和说明文档可以通过http://bioinf.jiangnan.edu.cn/在线获得。     

合成生物医学应用领域态势分析

合成生物学是生命科学领域的一门新兴热门工程科学,其实质是在工程学思想指导下,按照特定目标理性设计、改造乃至从头重新合成生物体系,通过构造人工生物系统来研究生命科学中的基本问题或应对人类面临的重大挑战,其核心旨在通过研究以期理解生命本质(造物致知)和创造社会经济价值(造物致用).合成生物技术被多国评价为未来的颠覆性技术之...     

Evolving Genetic Networks for Synthetic Biology

The sibling disciplines, systems and synthetic biology, are engaged in unraveling the complexity of the biological networks. One is trying to understand the design principle of the existing networks while the other is trying to engineer artificial gene networks with predicted functions. The significant and important role that computational intelligence can play to steer the life engineering discipline towards its ultimate goal, has been acknowledged since its time of birth. However, as the field is facing many challenges in building complex modules/systems from the simpler parts/devices, whether from scratch or through redesign, the role of computational assistance becomes even more crucial. Evolutionary computation, falling under the broader domain of artificial intelligence, is well-acknowledged for its near optimal solution seeking capability for poorly known and partially understood problems. Since the post genome period, these natural-selection simulating algorithms are playing a noteworthy role in identifying, analyzing and optimizing different types of biological networks. This article calls attention to how evolutionary computation can help synthetic biologists in assembling larger network systems from the lego-like parts.     

Intelligent systems in biology: why the excitement?

Biology has rapidly become a data-rich, information-hungry science because of recent massive data generation technologies. Our biological colleagues are designing more clever and informative experiments because of recent advances in molecular science. These experiments and data hold the key to the deepest secrets of biology and medicine, but we cannot fully analyze this data due to the wealth and complexity of the information available. The result is a great need for intelligent systems in biology. There are many opportunities for intelligent systems to help produce knowledge in biology and medicine. Intelligent systems probably helped design the last drug your doctor prescribed, and they were probably involved in some aspect of the last medical care you received. Intelligent computational analysis of the human genome will drive medicine for at least the next half-century. Intelligent systems are working on gene expression data to help understand genetic regulation and ultimately the regulated control of all life processes including cancer, regeneration, and aging. Knowledge bases of metabolic pathways and other biological networks make inferences in systems biology that, for example, let a pharmaceutical program target a pathogen pathway that does not exist in humans, resulting in fewer side effects to patients. Modern intelligent analysis of biological sequences produces the most accurate picture of evolution ever achieved. Knowledge-based empirical approaches currently are the most successful method known for general protein structure prediction. Intelligent literature-access systems exploit a knowledge flow exceeding half a million biomedical articles per year. Machine learning systems exploit heterogenous online databases whose exponential growth mimics Moore's law.     

Perspectives on systems biology

Systems biology is a new field in biology that aims at system-level understanding of biological systems, such as cells and organisms. Molecular biology has already made remarkable contribution to our understanding of biological systems, and its current focus is on the identification of genes and the functions of their products; that is, on the components of systems. There is no doubt that molecular biology will progress even faster and finally identify all the components of biological systems. As such a moment approaches, major importance need to be placed on the establishment of methodologies and techniques that enable us to understand biological systems as systems. This paper overviews the field of systems biology. Hiroaki Kitano, Ph.D.: Hiroaki Kitano is a Senior Researcher at Sony Computer Science Laboratories, Inc., a Project Director of Kitano Symbiotic Systems Project, Japan Science and Technology Corporation and a visiting associate at California Institute of Technology. He received a B.A. in Physics from the International Christian University, Tokyo, and a Ph.D. in Computer Science from Kyoto University. Since 1988, he has been a visiting researcher at the Center for Machine Translation at Carnegie Mellon University. Kitano received Computers and Thought Award from the International Joint Conferences on Artificial Intelligence in 1993. His research interests include RoboCup, computational molecular biology, engineering use of the mophogenesis process, and evolutionary systems.