Coordinated nonlinear robust control of TCSC and excitation for multi-machine systems

An advanced nonlinear robust control scheme is proposed for multi-machine power systems equipped with thyristor-controlled series compensation (TCSC). First, a decentralized nonlinear robust control approach based on the feedback linearization and H∞ theory is introduced to eliminate the nonlinearities and interconnections of the studied system, and to attenuate the exogenous disturbances that enter the system. Then, a system model is built up, which has considered all the generators‘ and TCSC‘s dynamics, and the effects of uncertainties such as disturbances. Next, a decentralized nonlinear robust coordinated control law is developed based on this model. Simulation results on a six-machine power system show that the transient stability of the power system is obviously improved and the power transfer capacity of long distance transmission lines is enhanced regardless of fault locations and system operation points. In addition, the control law has engineering practicality since all the variables in the expression of he control strategy can be measured locally.     

The ES for Estimating Capital Requirement

In this paper,an expert system for estimating capital requirement is briefly illustrated.including the essential construction of the simulation model.Then various factors which have impact on the manager‘s decision making,the datails of the program using the GURU environment,and the major roles of the human experts and the knowledge engineer are described.     

未知相对度非线性系统的一阶D型迭代学习控制

The classical D-type iterative learning control law depends crucially on the relative degree of the controlled system, high order differential iterative learning law must be taken for systems with high order relative degree. It is very difficult to ascertain the relative degree of the controlled system for uncertain nonlinear systems. A first-order D-type iterative learning control design method is presented for a class of nonlinear systems with unknown relative degree based on dummy model in this paper. A dummy model with relative degree 1 is constructed for a class of nonlinear systems with unknown relative degree. A first-order D-type iterative learning control law is designed based on the dummy model, so that the dummy model can track the desired trajectory perfectly, and the controlled system can track the desired trajectory within a certain error. The simulation example demonstrates the feasibility and effectiveness of the presented method.     

Advanced learning resource management system with ontology-based hierarchy semantic model

The requirement for advanced learning resource management system is a hot topic in e-learning, which should support sharing, reusability and interoperability of learning resource. The development of semantic technology has provided opportunities to solve the problems. In e-learning area, there have been many research efforts in this direction. However, such kinds of existing systems have many weaknesses on resources usability and system availability because of various requirements on learning resources sharing, and the complexity of semantic technology itself. This paper proposes a feasible architecture for constructing learning resource sharing system with high availability through adopting semantic technology, and ontology-based hierarchy semantic model and method to construct course ontology are proposed. The approach has been validated with prototype development.     

Cooperative learning with joint state value approximation for multi-agent systems

This paper relieves the ‘curse of dimensionality’ problem, which becomes intractable when scaling reinforcement learning to multi-agent systems. This problem is aggravated exponentially as the number of agents increases, resulting in large memory requirement and slowness in learning speed. For cooperative systems which widely exist in multi-agent systems, this paper proposes a new multi-agent Q-learning algorithm based on decomposing the joint state and joint action learning into two learning processes, which are learning individual action and the maximum value of the joint state approximately. The latter process considers others’ actions to insure that the joint action is optimal and supports the updating of the former one. The simulation results illustrate that the proposed algorithm can learn the optimal joint behavior with smaller memory and faster learning speed compared with friend-Q learning and independent learning.     

Notes on the design of an integrated object-oriented DBMS family

This paper summarizes the design and implementation notes of the CodDdBase system,which is a combinative distributed database management system family based on an integrated object model.CoDdBase is implemented in CLUSTER 86,a general purposed object-oriented programming language designed by the authors.The CoDdBase environment is actually a part of the CLUSTER 86 programming environment,which offers various object-based models,supplies miscellaneous building blocks for constructing different distributed DBMS‘s,and provides integrated programming tools to develop new ways and new models in response to changing application needs.     

An Environment for Rapid Prototyping of Interactive Systems

This paper shows an environment which supports the development of multi-thread dialogue interactive systems.The environment includes several tools and run-time support programs for the design and implementation of the user interface of an interactive system.First,methods of user interface specification with Elementary Nets are discussed.Then,the syntax of a user interface specification language based on Elementary Nets and the pre-compiler for the language as well as a graphic editor for Elementary Nets construction are described.Finally,an example is given to illustrate the design process of a user interface.     

基于弹性图匹配的实时视频流人脸识别

This paper deals with the problem of face recognition from video streams based on Elastic Graph Matching (EGM)method. First, instead of manually selecting the feature points as in previous methods, they are automatically selected through feature selection and feature ordering algorithm and correspondingly weighted. Comparing the auto selected feature points with those manually selected from experiences, traditional empirical understanding for feature point selection is corrected. Second, in order to enhance the robustness of the system, the common behavior of the system under uneven illumination, occlusion or remarkable local distortion situation is discussed, based on which a novel graph similarity function that deals with the three situations uniformly is defined, in which failure points give no contribution to similarity score so that effectively enlarges the between class distance and results in enhanced robustness of face recognition. Finally we replace EGM with AdaBoost and Simple DAM in face location and feature alignment stage together with reduced feature points resulted from feature selection based on the characteristics of video streams to speed up the system significantly. The experiment on a video database of 50 persons shows its feasibility.     

Bionic autonomous learning control of a two-wheeled self-balancing flexible robot

This paper presents an OCPA (operant conditioning probabilistic automaton) bionic autonomous learning system based on Skinner’s operant conditioning theory for solving the balance control problem of a two-wheeled flexible robot. The OCPA learning system consists of two stages: in the first stage, an operant action is selected stochastically from a set of operant actions and then used as the input of the control system; in the second stage, the learning system gathers the orientation information of the system and uses it for optimization until achieves control target. At the same time, the size of the operant action set can be automatically reduced during the learning process for avoiding little probability event. Theory analysis is made for the designed OCPA learning system in the paper, which theoretically proves the convergence of operant conditioning learning mechanism in OCPA learning system, namely the operant action entropy will converge to minimum with the learning process. And then OCPA learning system is applied to posture balanced control of two-wheeled flexible self-balanced robots. Robot does not have posutre balanced skill in initial state and the selecting probability of each operant in operant sets is equal. With the learning proceeding, the selected probabilities of optimal operant gradually tend to one and the operant action entropy gradually tends to minimum, and so robot gradually learned the posture balanced skill.     

Barrier-Certified Learning-Enabled Safe Control Design for Systems Operating in Uncertain Environments

This paper presents learning-enabled barriercertified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact.That is,safety constraints are imposed by not only the ego system’s own physical limitations but also other systems operating nearby.Since the model of the external agent is required to impose control barrier functions(CBFs)as safety constraints,a safety-aware loss function is defined and minimized to learn the uncertain and unknown behavior of external agents.More specifically,the loss function is defined based on barrier function error,instead of the system model error,and is minimized for both current samples as well as past samples stored in the memory to assure a fast and generalizable learning algorithm for approximating the safe set.The proposed model learning and CBF are then integrated together to form a learning-enabled zeroing CBF(L-ZCBF),which employs the approximated trajectory information of the external agents provided by the learned model but shrinks the safety boundary in case of an imminent safety violation using instantaneous sensory observations.It is shown that the proposed L-ZCBF assures the safety guarantees during learning and even in the face of inaccurate or simplified approximation of external agents,which is crucial in safety-critical applications in highly interactive environments.The efficacy of the proposed method is examined in a simulation of safe maneuver control of a vehicle in an urban area.     

基于ARM技术的电站仿真机接口系统的开发

为了改善基于串行总线技术的电站仿真机接口系统通信速率低、分布距离有限、扩展不方便以及调试困难,提出了基于ARM技术和以太网技术的分布式智能化输入输出接口系统.基于提出的分布式仿真机接口系统思想,分别从系统设计、系统软硬实现以及组态设计等进行了设计和实现.系统实际应用表明,基于ARM技术和以太网技术的接口系统通信速率高、分布距离远和扩展更方便,能够满足电站全范围仿真机输入输出接口的需要.     

基于用户属性的个性化教学智能辅助研究和设计

本文针对网络学习中如何个性化使用资源库的问题,从解决用户真实需求获取的瓶颈问题出发,通过分析此类应用环境下资源特性和用户特性,引入资源分布矩阵和偏好矢量概念来表征个性化特性,进而构建用户属性结构特征模型。然后采用用户属性结构和资源特性描述之间的精准检索,以及针对学习资源实施多重相关度排序和定位,设计验证了一个基于JADE平台的个性化资源检索系统。模拟实验证明,随着用户检索次数的增加,用户属性模型不断更新和完善,资料的个性化匹配度良好,能够较好地处理需求获取的瓶颈,达到实施个性化学习的目的。     

基于操作记录与回放技术的远程同步教学工具

随着国家教育信息化的发展,远程教育成为一种越来越重要的教学模式。目前以“中国教育在线”为代表的大多数远程教学平台只提供了文本课件、视频/音频等静态教学资源。这些资源缺乏交互性,难以向学生精细化再现其制作过程与教学场景,无法满足互动教学的要求。此外,这些教学资源数据量通常较大,不适于在网络上及时传输,难以保障远程教学的及时性和实效性。在分析远程教学需求以及所面临的挑战基础上,提出了一种基于记录/回放技术的操作远程同步方法,通过记录教师在Eclipse等教学平台上制作教学资源的操作序列,将其远程同步到学生端并进行回放,由此在学生端重现教学资源,以交互式地帮助学生掌握学习知识、提高学习效率。相比于传统的以文本、视频/音频等静态教学资源为主的远程教学方式,该方法可实现及时的知识展现,且由于传输的操作序列数据量相比视频/音频要少,因而更能适应复杂网络环境下远程教学的需要。在此基础上,实现了一个基于Eclipse教学平台的操作远程同步工具并进行了详尽的实例研究,证明了所提出方法的可行性和高效性。     

基于LINUX的嵌入式Email系统的设计与实现

嵌入式Email系统是类PC嵌入式系统,是嵌入式操作系统及其应用软件的一个重要组成部分,但是现有PC上的嵌入式Email系统都不能适应类PC嵌入式系统的硬件环境要求。文中采用了一种新的构建嵌入式Email系统的方法,设计与实现了清华嵌入式Email系统,可根据各种类PC嵌入式系统的不同要求进行定制。首先,论述了类PC嵌入式系统的硬件平台对嵌入式Email系统的苛刻要求,其次,描述了清华嵌入式Email系统的体系结构,接着,讨论了清华嵌入式Email系统的实现机制与原理,以及系统模型,最后,给出了嵌入式Email系统的特点。由于采用了现在流行的一种开放源码的组件,使得整个系统功能完善,并能够完全符合类PC嵌入式系统的要求。嵌入式Email系统已经在自主研制的掌上电脑HPC和交互式网络电视上成功地运行。     

计算机支持的远程协作学习的理论、环境及技术

远程教育若想持续发展和生存 ,其关键是解决好教学的规模和质量问题 .目前的远程教学中主要依赖的信息共享 ,个体学习 ,有限交互的模式已经不能满足学习者对学习质量越来越高的期望 .本文认为 ,导致学习效果不佳的原因是由于网络环境下学习者之间相互隔离 ,缺少课堂学习的互动互助效果 .因此在远程学习系统中提供多种方式的交互手段 ,促进协作学习 ,是提高学习质量的有效途径 .为此 ,本文讨论了计算机支持的远程协作学习中有关的理论和环境要求 ,提出了基于信息资源重构 ,可操作浏览器以及 XML 统一数据类型协议等技术的解决方案     

基于SCORM标准的远程教育通信模型研究

学习管理系统（learning management system，LMS）的互操作性（interoperability）是当今远程教育研究的重要领域，实现LMS系统互操作性的关键是LMS系统和CBT（computer based training）课件之间的通信机制。在分析和研究基于SCORM标准的LMS／CBT远程教育通信模型的基础上，提出了基于SCORM标准实现学习资源标准化的解决方案。这一方案已在中国工商银行的远程教育实践中得到成功应用，对研究学习资源的标准化建设工作有一定的参考意义。     

基于黑板的自治代理协同入侵检测系统模型

人侵检测系统是针对网络攻击的重要检测机制。迅速发展的网络技术、互联网流量的迅速增长以及攻击的复杂性使得现存的人侵检测系统难以提供可靠的服务。这就需要考虑优化性能，提供一种具备学习和自适应能力的入侵检测系统。文章提出了一种入侵检测模型，它基于自治agent和黑板结构，具有更好的自学习能力，这种检测系统能够自动适应网络环境和攻击类型的不断变化提高自身对新型的网络攻击的检测能力。     

基于个性化学习的现代远程职业培训系统

现代远程职业培训之所以受到越来越多的人的瞩目，除了由于网络资源的日益丰富外，最突出的是能够根据个人的知识背景、学习目标，生成具有个性化的网络学习方案，并且实时跟踪学习过程，结合学习目标和个人的学习速度对学习方案灵活地进行动态调整，达到因材施教的目的。因此，建立基于个性化学习、动态的、兼具诱导作用的现代远程职业培训体系是实现职业培训的主要途径之一。     

基于OGSA的服务网格应用系统研究与设计

为提高资源的共享能力,在校园网环境下,借助于Globus平台搭建了校园服务网格应用系统.该系统将已有的计算资源和服务,利用网格环境中日臻成熟的中间件技术设计了满足特定需求的服务网格系统构架.提出了基于OGSA-GT3的校园服务网格应用系统的结构CSGA,讨论了CSGA的实现方案和执行流程.在开发的原型系统进行了计算服务的实现,并阐述了服务网格系统今后的研究方向.     

海水养殖环境决策支持系统的设计与实现

概述了目前国内海水养殖环境所面临的主要问题,在深入分析了分布式计算系统的主流解决方案———J2EE技术特点及体系结构的基础上,针对海水养殖环境决策支持系统的特点与要求,提出了基于J2EE规范的海水养殖环境决策支持系统的设计策略和体系结构。最后,使用Jbuilder9.0 Weblogic8.02集成开发环境设计开发了这一系统。